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text:
G2A.COM G2A News Features Games With Female Protagonists
Games With Female Protagonists
If you are bored with games glorifying male heroes and would like to see some brave women fighting for their goals, then this list is right for you. Those ladies won't stop till they drop.
The Best Games With Female Protagonists
Developer: Eidos-Montréal
Release year: 2018 (PC, PS4, X One), 2019 (Stadia)
Lara Croft is one of the most iconic female characters in video games. The intrepid archaeologist/treasure hunter has been traipsing around the world for decades, and has survived several reboots.
The most recent one is trilogy developed by Eidos Montréal, and it tells something of a Lara Croft origins story, culminating in Shadow of the Tomb Raider. This time she ventures to South America, trying to prevent the apocalypse she has accidentally set off while doing her thing. Thankfully she's now more experienced and hardened than she has ever been during this reboot trilogy.
You visit a legendary city of Paititi
The culmination of a great trilogy
Lara vs. Apocalypse
Great hub-based map
Genre: TPS, Action-adventure
Developer: Remedy Entertainment
Remedy Entertainment's Control was a surprise hit. It's inspired by the writing from the SCP Foundation, it's set in a fundamentally WEIRD office building that's bigger on the inside, and features a great leading character in Jesse Faden, who discovers the bizarre supernatural hidden world at the same time the players do.
As the acting director of an agency committed to containing and studying paranormal phenomena, she's going to deal with a lot of odd stuff during her first day of work. Equipped with newly discovered psionic powers and a very versatile gun, she has to face a problem that destabilised the entire agency. That's a harsh first day on the job, but it's very fun to watch her adapt and potentially thrive.
Inspired by the SCP Foundation brand of creepy stories
Has ties to Alan Wake
Jesse Faden is a great protagonist
You get a cool gun and cool powers, and a building full of stuff to try them out on
Music by Poets of the Fall
Genre: Graphic adventure
Life Is Strange is a graphic adventure game, whose story is divided into 5 chapters, released separately.
The story focus on the life of 18-year-old Max Caulfield. She's a photography student and learns that she is able to rewind time at her will. Her choices affect the future and thanks to her visions of a destructive storm going to destroy her town, she knows that she needs to do everything to prevent it.
The game is about the plot. Characters' personalities and problems are in the foreground here.
Your choices matter in Life Is Strange – Max is able to enact the butterfly effect by making decisions and interacting with the environment.
You are able to travel in time, so use it wisely to prevent the cataclysm.
Collect items, interact with various objects, and engage in dialogues with NPCs to learn (and shape) the story.
The game has got numerous nominations and won most of the awards it was nominated to. Most concerned the narrative, the sound, and the character design.
Developer: PlatinumGames
Release year: 2017 (PC, PS4), 2018 (X One)
The war between aliens and humans fought by machines and androids with a female android as the protagonist, plus a lot of action – are you in?
Take control over 2B who is supported by 9S and A2. All of them are androids from the YoRHa series – man-made android forces, commanded from a base orbiting above Earth, and tasked with the mission to stand against the alien units, controlled by Adam and Eve.
The dark and grim setting of the game is based on that of Nier. The story of Nier: Automata takes place thousands of years after the fourth ending of the previous instalment.
In total, the players get three playable characters, each of which has their own experience points and skills.
Except for fighting and action the game also features some puzzle-solving moments.
The proxy war between humans and alien is fierce and there is no time and place for emotions.
You will need to finish the whole game more than once as there are around 20 different endings.
Genre: Platform RPG
Developer: Ubisoft Montreal
This side-scrolling platformer with RPG elements takes you to a fictional land of Lemuria. You take control over a girl named Aurora, accompanied by a friendly firefly Igniculus.
On her mystical journey through the magical realm of Lemuria, Aurora needs to find her way home and then defeat Umbra who poses a threat to the whole kingdom. However, Princess Aurora is not alone as various characters gradually join her on her important quest.
Help Aurora and her companions bring peace to the kingdom and stop evil Umbra.
Heart-warming story and fairy-tale-like visuals emphasise the atmosphere of the game.
Use the help of Igniculus who can get to places Aurora would not be able to reach.
Progress through the story and form your party in a Final Fantasy manner to face your ultimate enemy.
The developers have also put an emphasis on the game soundtrack – composing nearly 20 tracks was part of the plan to make the game a "playable poem".
Genre: Action RPG, Hack'n'slash
Developer: Gunfire Games
The third instalment of the series puts you in the control of Fury, War and Death's sister. The game is a mixture of the former Darksiders and the mechanics from the FromSoftware games.
Fury is described as the most unpredictable from the Fourth Horsemen of Apocalypse. She can take different elemental form to enhance her attacks and grant her access to new weapons. Besides the novelties and changes, the combat still plays the main role in the game.
The story of the Fourth Horsemen of Apocalypse continues – take control of Fury who is tasked with finding Seven Deadly Sins.
Darksiders III take place simultaneously with the events presented in the previous instalment.
Fury is armed with a wide selection of various weapons, such as whips, blades, swords, and many more.
Implementing some Dark Souls mechanics resulted in a slight changes to the combat system – the straightforward counters were removed completely and some enemies in the game may pose a real challenge.
As it would not be enough hard, the game level is growing simultaneously with the player's strength – the leveling system lets you develop Fury's health, damage and arcane abilities.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: RxJS cacheError((error:any) cannot accept any object type as parameter I used RxJS like below:
  catchError((error: any) => {
    if (error instanceof HttpErrorResponse) {
      .......
      if (error.status === 401) {
          .....
          return;
        }}

But cacheError((error:any) usage gives below error:
Argument of type (error: any) => Observable<never> | undefined is not assignable to parameter of type (err: any, caught: Observable<HttpEvent<any>>) => ObservableInput<never>.
Type Observable<never> | undefined is not assignable to type ObservableInput<never>.
Type undefined is not assignable to type ObservableInput<never>
How can I solve it? I am using rxjs 6.6.3 version

A: According to RxJS docs / learn

Remember to return an observable from the catchError function!

So to get rid of the error, you could return empty observable of():
catchError((error: any) => {
    if (error instanceof HttpErrorResponse) {
      .......
      if (error.status === 401) {
          .....
          return of();
        }}

redpajama_set_name: RedPajamaStackExchange


Image | This entry was posted in Government, Politics, Uncategorized. Bookmark the permalink.
Put your foot on their neck President Trump, and don't let them up.
Give em the works, Pres Trump, we deplorables will ride your bomb to victory with you.
He's a past master at picken his battles.
Only two things that bother me are: (a) it's DoJ investigating DoJ, and (b) they've had months to dispose of papers and disappear digital data.
Everything leaves a trace. Which is completely traceable these days.
We could make disks, hard drives, any discrete storage device disappear completely. In the event that someone with access permissions to where/whatever data were to have made a personal copy there might well be a footprint leading to them but these guys are lawyers who also look for these traces on other folk's stuff so they should have a leg up.
Yeah, the DOJ is corrupt and I'm not convinced there are any that can be trusted.
that the Department of Justice look into whether or not the FBI/DOJ infiltrated or surveilled the Trump Campaign for Political Purposes (Loretta Lynch and James Comey I'm looking at you 😎).
Once you get Trump you can't un-get him 🙂 !
Well well well look what my little eye spied on Thomas Wictor's time line!
Why did he wait so long?
Because President Trump is cleaning up corruption that has its particular DC roots as deep as 1992, because people who cross the Clintons end up dead, because Team Obama is so arrogant and has such power that they are floating Eric Holder for 2020.
I have believed for a long tome now that there are certain tells within President Trumps personal history, remember when Ivanka was 6 her father tried to bite his tongue when she made a Trump tower with Legos but finally after 3 days he said "You know your Lego Trump Tower only had 5 of this (I can't remember what) the actual tower has 6." On a whim Trump decided to offer a company millions of dollars less for a golf course and he got his price.
Remember Lindsey Grahm's phone number, I really do believe that was important. It sent a message as did all the other nuanced things that candidate Trump did during the campaign.
As for Rosenstein life is reaches critical mass for a lot of folks. President Trump painted him into a corner with his tweets.
Replace Godzilla with President Trump and Tokyo with DC elite and you get a good picture of what is happening in the United States.
Oh no! There goes the dominos.


Henry's love for the food business began as a teenager working for a local deli in his hometown of Queens, New York. Despite this love of food, Henry decided to enroll in Hofstra University in 1986. Upon completing his degree in Finance, he immediately decided to pursue his true love, food and in 1990 he enrolled in the Culinary Institute of America. After graduating with High Honors in 1992, Henry took on various cooking jobs, eventually rising to the position of Executive Chef. Soon afterwards, Henry decided to open PRIME Fine Catering with his longtime friend Marc Giudice. With their new business in hand, Henry and Marc thrived on bringing restaurant quality food to people's homes and various venues. Now, with a solid catering business built, the idea of catering to those with a passion for a healthy lifestyle began to take shape. Members of a local Cross Fit gym began to dine at PRIME Fine Catering 2 and 3 times a day. With this encouragement, our "Healthy Cuisine" began to take shape. As we increased our healthy menu items, our client base began to increase. More and more gyms began "dine at PRIME". The word was quickly getting out; PRIME Fine Catering was the place to go for healthy "Paleo Style" food. With his love and knowledge of food, his desire to cater to his ever growing clientele, and his passion for healthy foods, it was natural for PRIME Healthy Cuisine to be born.


The Underwood Pinot Noir has aromas of ripe cherries, plum and bacon. The palate is filled with sweet cherry, raspberries, and chocolate. This accessible wine is yummy with fine tannins that come together in a fruit laden finish.


Be sure to Like/Fav the video and sub to the artist! Designed by //www.youtube.com/babifoot Find even more emblems our website!
+Brad Orcutt Thank you for watching!
That would be on the PC version.
I had to restart my system but Activision has an online chat service if that doesn't work.
It's basically the same thing cause it has the same icons except for the gun icons "because it's a new game of course"
There is one on this channel in fact. I believe it was Babi who made it as well.
+ztlc19 I bought this on Amazon a couple years back. Looks like the vendor doesn't sell them anymore, but I'm sure you can find someone else that makes them. Look for: "batman brake light vinyl decal"
V-ray car paint, caustics, GI, low irradiance map, optical flares.
The acrylic sign was already made. It was made by a CNC milll with a conical bit.
Thanks. I think I should try making a sign like that.
Yes, the batmant logo is engraved fainrly deep into the acrylic (3mm or so).


text:
Ping Ma1 , Hongli Zhang2 , Wenhui Fan3 , Cong Wang4
1, 2, 4College of Electrical Engineering, Xinjiang University, Urumqi, China
3Department of Automation, Tsinghua University, Beijing, China
Journal of Vibroengineering, Vol. 21, Issue 7, 2019, p. 1859-1874. https://doi.org/10.21595/jve.2019.20132
Received 10 August 2018; received in revised form 14 January 2019; accepted 22 January 2019; published 15 November 2019
Copyright © 2019 Ping Ma, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Ma Ping, Zhang Hongli, Fan Wenhui, Wang Cong Fault diagnosis using an improved fusion feature based on manifold learning for wind turbine transmission system. Journal of Vibroengineering, Vol. 21, Issue 7, 2019, p. 1859-1874. https://doi.org/10.21595/jve.2019.20132
WoS Core Citations 2
In this paper, a novel fault diagnosis method based on vibration signal analysis is proposed for fault diagnosis of bearings and gears. Firstly, the ensemble empirical mode decomposition (EEMD) is used to decompose the vibration signal into several subsequences, and a multi-entropy (ME) is proposed to make up the fusion features of the vibration signal. Secondly, an improved manifold learning algorithm, local and global preserving embedding (LGPE), is applied to compress the high-dimensional fusion feature set into a two-dimension feature set. Finally, according to the clustering accuracy of different feature set, the fault classification and diagnosis can be performed in the reduced two-dimension space. The performance of the proposed technique is tested on the fault of wind turbine transmission system. The application results indicate that the proposed method can achieve high accuracy of fault diagnosis.
Keywords: fault detection and diagnosis, ensemble empirical mode decomposition, multi-entropy, local and global preserving embedding.
Wind energy as a new clean energy source is the fastest-growing energy source in the world, this trend should endure for some time. However, wind turbines are long-term operated in harsher environments, have relatively higher failure rates. Fig. 1 shows the percentage of downtime with different failure per year at the Dutch wind farm during 3 years operation [1]. It can be seen that the primary causes of failure are damage to the gearbox, generator and faults with the control and pitch systems. The bearings and gears are important parts of those systems, which faults will inevitably cause a long downtimes and increase operating costs. Therefore, proposing a timely and accurate diagnosis method to detect those faults are extremely available, which will reduces energy losses, improves productively and increases safety of such systems [2, 3].
The techniques based on vibration signal analysis are the most popular and useful approaches applied in fault diagnosis of rotating machinery [4-6]. In general, the processing of fault diagnosis can be divided into three steps: data collection, feature extraction and classification. Feature extraction is a key step, which can extract more useful information of the vibration signals from their original measured space for fault diagnosis. Some self-adaptive time-frequency analysis methods have been proposed for fault diagnosis. Empirical mode decomposition (EMD) [7], which were carried out to decompose vibration signals into the sum of intrinsic mode functions (IMF) given different frequencies, and some IMFs containing fault feature frequencies are selected to reconstruct a new signal for outstanding fault feature for further analysis [8, 9]. But the decomposed process of EMD is sensitive to noise and has exist mode mixing problem. Ensemble empirical mode decomposition (EEMD) is a newly improved version of EMD, which can eliminate the mode mixing problem of EMD automatically [10]. This approach has been applied in signal analysis of rotating machinery [11-14]. In conclusion, the self-adaptive time-frequency analysis method EEMD can extract sensitive features and improve the fault diagnosis accuracy.
Fig. 1. Downtimes for the Dutch wind farm Egmond aan Zee
The vibration signals collected from wind turbine transmission systems are complex and non-stationary, which are submerged in noise under the variable speeds conditions. Therefore, the fault features from the original signal cannot be comprehensively extracted using single or single-domain processing methods [15, 16]. Therefore, the mix-domain features fusion is proposed to construct high-dimensional feature set. However, the high-dimensional feature set of mixed-domain inevitably contains redundant and disturbed information, and the accuracy of fault diagnosis can be reduced using the high dimensional feature set for pattern recognition. Using an appropriate dimensionality reduction method to extract the main eigenvectors with low dimension, high sensitivity and good clustering characteristics from the high-dimensional feature set for fault recognition is necessary.
Manifold learning [17-20], an effective nonlinear dimension reduction method has gained attention in various research fields. The idea of manifold learning is to project the original high dimensional data into a lower dimension feature space with the local neighborhood structure preserved. This technique including several mainstream algorithms such as locally linear embedding (LLE) [18], locality preserving projection (LPP) and kernel locality preserving projection (KLPP) [20]. Manifold learning is widely used in the filed of image recognition and data classification [21, 22], and there are few researches in fault diagnosis [23, 24]. Due to the advantages of the time-frequency analysis and manifold learning techniques in dealing with non-linear signal, some research works combined fault diagnosis models with time-frequency analysis techniques and dimensionality reduction techniques. Su et al. [25] combined EMD, incremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier to build a new method for gearbox fault diagnosis. Ding et al. [26] presented a fusion feature extraction method on rolling bearings based on wavelet packet transform and LPP. The classification result shows that this method can enhance the discrimination between all fault classes for fault classification. Huang et al. [27] used a new technique for dimensionality reduction called the discriminate diffusion maps analysis, compared with other manifold learning algorithms, which has high classification accuracy of the bearings faults.
In the above literatures, at the process of feature extraction, the dimensionality reduction methods of manifold learning, which embed the original high-dimensional data into a lower dimension feature space with considering the local nonlinear characteristics preserved. But undeniably, in the process of reducing dimensionality, in order to extract more abundant information, it is necessary to preserve the intrinsic structure and extract the structure of the high-dimensional set.
In this paper, a novel fault diagnosis model based on fusion feature and manifold learning is proposed. Firstly, EEMD is used to decompose the vibration signals into a set of intrinsic mode functions (IMFs) with different characteristics scales, and several IMFs that containing more sensitive information are selected. Secondly, envelope entropy, permutation entropy and energy entropy of those selected IMFs are extracted as representative fusion features. Thirdly, local and global preserving embedding (LGPE), is applied to extract the main eigenvectors from the high-dimensional fusion feature set. This method considers the local nonlinear characteristics and global external structure of the fusion feature set. The feature space dimension is optimally reduce to a low dimensional space by using LGPE, achieving the classification and identification of the fault. The proposed method is verified on the fault diagnosis of the bearing's and gearbox's signals.
The main contributions of the proposed method are presented as follows:
1) A fusion feature set is established based on EEMD and Mutli-entropy, in which the fault features can be comprehensively extracted from mixed time-domain signals.
2) A LGPE method is proposed to extract fault features from the high-dimensional feature set, which makes the fault features in low dimensional space has high sensitivity and good clustering characteristics.
3) A novel fault diagnosis model based on EEMD-ME and LGPE is performed to diagnose the faults of bearings and gears, which accurately achieves the identification and classification of different faults.
The organization of the rest of the paper is as follows. In Section 2, the EEMD, ME and LGPE are presented. Section 3 describes the proposed model for fault diagnosis. In Section 4, two fault diagnosis experiments are carried out to verify our proposed method, and the experimental result is discussed. The conclusions are appeared in Section 5.
2. The proposed fault diagnosis model
Fig. 2 shows the flow chart of the proposed fault diagnosis method. The relative methods including EEMD, ME and LGPE are presented in this section.
Fig. 2. The fault diagnosis process of the proposed method
2.1. Ensemble empirical mode decomposition (EEMD)
EEMD is a non-linear multi-resolution self-adaptive decomposition technique, which can adaptively decompose a complex signal into a set of IMF. The vibration signal of the rotating machinery is nonlinear, non-stationary and submerged in heavy noise, which makes it difficult to detect faults using the original signal. Therefore, EEMD is proposed to decompose the vibration signal into several IMFs with different frequencies for further analysis.
The principle of the EEMD is simple: in the process of decomposition, the frequency scales are natural separated by adding white noise in the whole time-frequency space uniformly, which can reduce the occurrence of mode mixing. The decomposition steps of EEMD is as follows:
Step 1: Add a white noise series nit with zero mean and equal variance to the original vibration signal xt:
x i t = x t + n i t ,
where nit represents the ith added white noise series, and xit denotes the noise-added signal of the ith trial, while i= 1, 2, …, M, M is the number of ensemble.
Step 2: Each noise-added signal xit is decomposes into several IMFs using EMD:
x i ( t ) = ∑ s = 1 S c i s t + r i S t ,
where S represents the number of IMF, s= 1, 2, …, S. cist denotes the IMFs ci1,ci2,…,ciS with different frequency bands, and riSt is the residue of xit.
Step 3: Repeat step 1 and step 2 with M times, the different white noise series is added to the signal xt to obtain an ensemble of IMFs [{c1s(t),c2s(t),…,cMs(t)}].
Step 4: Obtain the ensemble means of the corresponding IMFs as the final IMFs:
c s ( t ) = 1 M ∑ i = 1 M c i s t ,
where cst represents the ith IMF decomposed by EEMD, while s= 1, 2, …, S.
In order select the IMF that containing useful feature information for further analysis, the cross-correlation coefficient ρ and energy index E are introduced to eliminate illusive IMF. Cross-correlation coefficient a is common used in signals analysis and is no longer discussed in here. For a IMF obtained by EEMD, the energy index Es is defined as follows:
E s = ∑ t = 1 N c s t 2 ∑ t = 1 N x t 2 ,
where N is the length of the signal, cst denotes the sth IMF, and Es represents the index of energy between the sth IMF cst and the original signal xt.
2.2. Mutil-entropy
The entropy-based methods, such as envelope entropy, energy entropy and permutation entropy [28-30], have been applied in fault diagnosis. In this section, envelope entropy, energy entropy and permutation entropy of the selected IMFs obtained from section 2.1 are extracted as the fusion features of fault signal.
Entropy can identify nonlinear parameters and present the information of the signal, and envelope entropy can reflect the sparseness of the original signal. The envelope entropy H1(s) of a IMF cst (t=1,2,…,N) can be expressed as:
H 1 s = - ∑ t = 1 N p t l g p t , p t = a t ∑ t = 1 N a t ,
where at is the envelope signal that obtained by the Hilbert demodulation of signal cs(t), pt is the normalized form of signal cs(t).
In the same way, the energy entropy of the IMF cs(t), which can be defined as:
H 2 s = - ∑ s = 1 S p s l o g p s ,
where ps=E(s)/E is the proportion of the energy of the sth IMF in the whole signal energies.
Permutation entropy (PE) is used to analyze the data complexity. For a IMF cs(t), constructing an embedded d-dimensional delay embedding matrix Cd(t)=xt+σ,xt+2σ,⋅⋅⋅,xt+dσ, and arranging each vector of Cd to an increasing order Ci(t)=ci+j1≤ci+j2≤⋅⋅⋅≤ci+jd. Letting πi=j1,j2,…,jd, which is one of the d! permutations of d distinct symbols. Then the permutation entropy of IMF cs(t), with the probability distribution function pπi is defined as:
H 3 s = - ∑ i n - σ d + 1 p π i ln  p π i .
2.3. Local and global preserving embedding (LGPE) algorithm
The generic reduction problem of feature space dimension is described as follows: let a n-dimension points X=[x1,x2,…,xn]∈Rn, which can be transformed into an d-dimension Y=[y1,y2,…,yd]∈Rd, (d<n), where yi=WTxi, and W is an transformation matrix. In this section, a novel dimensionality reduction method, LGPE is used to project a manifold in high-dimensional space Rn to a low-dimensional space Rd while preserving the local neighborhood and global structure of the dataset.
The objective function of the LGPE can be divided into two parts: the local nonlinear characteristics preservation objective function and the global variance maximum objective function. The local nonlinear characteristics preservation objective function makes the low-dimension feature space has the similar neighborhood structure with high-dimensional feature space. The global variance maximum objective function extracts the maximize variance of the data during the dimensionality reduction process.
2.3.1. The local nonlinear characteristics preservation objective function
Given a set of data X=[x1,x2,…,xn]∈Rn, xi is a n-dimensional feature vector. Firstly, we use a possibly nonlinear function ϕ to map the data into a high-dimension feature space H: ϕ(X)=[ϕ(x1),ϕ(x2),…,ϕ(xn)]. Inspired by the idea of kernel locality preserving projection (KLPP), we seek a projecting transformation W, which can preserve the local nonlinear structure of the data ϕ(x) by minimizing the sum of the weighted distance of samples. The minimization problem of local nonlinear characteristics preservation objective function can be expressed as:
J l o c a l W = m i n W ∑ i , j = 1 N y i - y j 2 S i , j ,
where yi=(ϕ(x))TW is the low-dimension projection of ϕ(x) onto W. S represents a weight matrix, which is constructed through the nearest-neighbor graph. It is defined as follows:
S i , j = e x p - ϕ x i - ϕ x j 2 t ϕ x i , ϕ ( x j ) as neighbors , 0 , other ,
where t is a suitable constant. Si,j denotes the relationship of ϕ(xi) and ϕ(xj). The objective Eq. (7) can be transformed as:
J l o c a l ( W ) = m i n W ∑ i , j = 1 N y i - y j 2 S i , j = m i n W ∑ i , j = 1 N ( ϕ ( x i ) ) T W - ( ϕ ( x j ) ) T W 2 S i , j = m i n W { W T ϕ X ) T D - S ϕ X W ,
where Dii=∑jSi,j is a diagonal matrix. Because the projecting transformation W must be in the span of ϕx1,ϕx2,…,ϕ(xn), there have a coefficient vector A=(a1,a2,…,an)T to satisfy the equation W=∑j=1najϕ(xj)=ϕ(X)TA. Then the local nonlinear characteristics preservation objective function can be expressed as:
J l o c a l ( A ) = m i n A { A T ϕ ( X ) ( ϕ ( X ) ) T ( D - S ) ϕ ( X ) ( ϕ ( X ) ) T A } = m i n A { A T ϕ ( X ) ( ϕ X ) T L ϕ X ϕ X ) T A ,
where L=D-S is a Laplacian matrix. In order to solve this nonlinear problem, a positive definite and symmetric kernel matrix K(i,j)=ϕ(xi)⋅ϕ(xj)T is introduced to the Eq. (11). The objective Eq. (11) can be calculated as:
J l o c a l A = m i n A A K L K A = m i n α A T L l A , L 1 = K L K .
The local nonlinear structure of the high-dimension dataset is preserved by keeping the nearest neighbor relation of the dataset in the kernel space. It is obvious that, the solution of local nonlinear characteristics preservation objective function will keep the local structural characteristics of dataset in the process of dimensionality reduction.
2.3.2. The global variance maximum objective function
Given a set of data X=[x1,x2,…,xn]∈Rn, and using a possibly nonlinear function ϕ to map the data into a high-dimension feature space H: ϕ(X)=[ϕ(x1),ϕ(x2),…,ϕ(xn)]. Inspired by the idea of kernel principal component analysis (KPCA), the global variance maximum objective function Jglobal(W) is defined as: seeking a projecting transformation W, which makes the matrix yi=(ϕ(xi))TW after projection preserves the maximum variance information for the high-dimension data ϕ(x). Then the optimization task can be expressed as:
J g l o b a l W = m a x W ∑ i = 1 n y i 2 = m a x W ∑ i = 1 n ( ( ϕ ( x i ) ) T W ) 2 , s . t . W T W = 1 .
It is well known that exist a coefficient vector A=(a1,a2,…,an)Tto satisfy the equation W=∑j=1najϕ(xj)=ϕ(X)TA. Substituting this equation into Eq. (13), the optimization problem transformed into:
J g l o b a l ( A ) = m a x W ∑ i = 1 n ( ϕ ( x i ) ) T ∑ j = 1 n a j ϕ ( x j ) 2 , s . t . ∑ j = 1 n a j ϕ ( x j ) T ∑ j = 1 n a j ϕ x j = 1 ,
with the introduction of the kernel function K(i,j)=ϕ(xi)⋅ϕ(xj)T, the Eq. (14) can be expressed as:
J g l o b a l A = m a x W ∑ i = 1 n ∑ j = 1 n a j K i , j 2 , s . t . ∑ i = 1 n ∑ j = 1 n a i a j K ( i , j ) = 1 ,
we can further express Eq. (15) as:
J g l o b a l A = m a x W A T K K A = m a x W A T C A , s . t . A T K A = 1 .
2.3.3. The objective function of LGPE
In order to preserve the local nonlinear structure between neighboring data points and extract the variance of the maximal high-dimension data, the objective function of the LGPE is to minimize ATLlA (for local structure preserving) and maximize ATKKA (for global variance extracting). The objective function of LGPE can be transformed to the following optimization problem:
J A = m a x A J g l o b a l A - J l o c a l A = m a x A A T C A - A T L 1 A = m a x A A T C - L 1 A , s . t . A T K A = 1 .
In order to eliminate the influence of noise in the process of dimensionality reduction, an orthogonal constraint is introduced. The derivation process is inspired by literature [31]:
α 1 T α k = α 2 T α k = ⋅ ⋅ ⋅ = α k - 1 T α k = 0 .
In order to obtain the k orthogonal basis vector αk, the following objective function need to be minimized:
J α = m a x α α n T C - L 1 α n , s . t . α 1 T α k = α 2 T α k = ⋅ ⋅ ⋅ = α k - 1 T α k = 0 , α 1 T K α 1 = α 2 T K α 2 = ⋅ ⋅ ⋅ = α k T K α k = 1 .
In order to compute the nth discriminant vector, the lagrange multipliers in introduced to transform the Jα. criterion including all the constraints:
J α = α k T C - L 1 α k - λ α k T K α k - 1 - ∑ i = 1 k - 1 μ i α k T α i .
The optimization is performed by setting the partial derivative of Jα with respect to αk equal to zero:
∂ L k ∂ α k = 0 ⇒ 2 ( C - L 1 ) α k - 2 λ K α k - ∑ i = 1 k - 1 μ i α i = 0 .
Multiplying the left side of Eq. (21) by αkT obtained:
2 α k T C - L 1 α k - 2 λ α k T K α k = 0 ⇒ λ = α k T C - L 1 α k α k T K α k .
Multiplying the left side of Eq. (21) successively by α1TK-1, α2TK-1, …, αk-1TK-1, and obtaining a set of k-1 expressions:
μ 1 α 1 T K - 1 α 1 + ⋅ ⋅ ⋅ + μ k - 1 α 1 T K - 1 α k - 1 = 2 α 1 T K - 1 C - L 1 α k , μ 1 α 2 T K - 1 α 1 + ⋅ ⋅ ⋅ + μ k - 1 α 2 T K - 1 α k - 1 = 2 α 2 T K - 1 C - L 1 α k , ⋮ μ 1 α k - 1 T K - 1 α 1 + ⋅ ⋅ ⋅ + μ k - 1 α 1 T K - 1 α k - 1 = 2 α k - 1 T K - 1 C - L 1 α k .
Define the matrix notation:
U k - 1 = [ μ 1 , μ 2 , … , μ k - 1 ] T , α = ( α 1 , α 2 , … , α k - 1 ) T , M k - 1 = M i j k - 1 , M i j k - 1 = α i T K - 1 α j .
Then the equations can be transformed in a single matrix relationship:
M k - 1 U k - 1 = 2 α k - 1 T K - 1 C - L 1 α k .
Or in another form:
U k - 1 = 2 ( M k - 1 ) - 1 α k - 1 T K - 1 C - L 1 α k .
Multiply the left side of Eq. (21) by K-1:
2 K - 1 ( C - L 1 ) α k - 2 λ α k - K - 1 α k - 1 U k - 1 = 0 .
Including Eq. (26), we can obtained:
K - 1 ( C - L 1 ) α k - K - 1 α k - 1 ( M k - 1 ) - 1 α k - 1 T K - 1 ( C - L 1 ) α k = λ α k .
I - K - 1 α k - 1 M k - 1 - 1 α k - 1 T K - 1 C - L 1 α k = λ α k .
We need to maximized the criterion λ of Eq. (28):
R k = I - K - 1 α k - 1 M k - 1 - 1 α k - 1 T K - 1 C - L 1 .
Thus, the required the k orthogonal basis vector αk is the eigenvector corresponding to the maximum eigenvalue of Rk. It is easy to verify that α1 is the eigenvector corresponding to the minimum eigenvalue of the generalized eigenvalue equation (C-L1)=λK.
3. Fault diagnosis process
In this study, a novel fault diagnosis model based on EEMD-ME and LGPE is proposed. The detail frameworks are shown in Fig. 3, the procedures of this model is described in successive steps:
Step 1: For a test signal xi=[x1,x2,…,xN], EEMD is used to decompose the signal into several IMFs. Then calculate cross-correlation coefficient and energy index of each IMF, and select the first s IMFs to further analysis.
Step 2: The envelope entropy, permutation entropy and energy entropy of each selected IMFs are combined into an 3s-dimension fusion features xi=[xi1,xi2,…,xi3s]T.
Step 3: The 3s-dimension fusion feature set of test samples is input into LPGE to reduce the high-dimension feature space to two-dimension feature set Y=[y1,y2]. The dimensionality reduction steps are shown as follows:
1) For a high-dimension feature dataset X=[x1,x2,…,x3s]∈R3s, we can construct the local nonlinear characteristics preservation objective function Jlocal(A)by using Eq. (12).
2) The global variance maximum objective function Jglobal(A) can be obtained by Eq. (16)
3) The objective function of proposed LGPE is defined at Eq. (19), and calculate the k orthogonal basis vector αkof projection matrix α=(α1,αk,…,αd) by iteration αk is the eigenvector corresponding to the maximum eigenvalue of Eq. (29).
4) According to the equation yi=(ϕ(xi))TW, the low-dimension feature set Y=[y1,y2] after projection space transformation can be obtained.
Step 5: Classify and recognize the different faults by the degree of clustering in two-dimensional space.
Fig. 3. The details framework of the proposed model
4. Result and discussion
To investigate the effectiveness of the proposed technology for fault diagnosis, two experimental cases are considered. They including the bearing data obtained from the Case Western Reserve University (CWRU) Bearing Data Center and the gear data produced by QPZZ-II system.
4.1. The fault diagnosis of bearings
The bearing data obtained from CWRU Bearing Data Center has become a standard preference at the filed of fault diagnosis in bearings. A ball bearing as shown in Fig. 4, which was installed in a motor driven mechanical system. Vibration data is collected using accelerometers, which are attached to the housing with magnetic bases. In total four sets of data are obtained form the experiment systems, which include under normal conditions, with inner race fault, with ball fault and with outer race fault. The sampling frequency is 12 kHz for drive end bearing experiments. In this paper, the 0.007 inches fault diameter is selected for fault diagnosis. The vibration signal obtained from four different conditions are divided into 100 segments of 1024 sample each, as shown in Fig. 5.
Fig. 4. Experimental system
Fig. 5. Segments of the vibration signals collected from four different conditions of bearing
Each segment of vibration signal is decomposed into 10 IMFs by EEMD. For a segment, the cross-correlation coefficients ρ and energy index E of the former 4 IMF are presented in Table 1. It can be noticed that the first 4 IMFs have higher cross-correlation coefficients and energy index, which are chosen as the sensitive components for further analysis. The envelope entropy, permutation entropy and energy entropy of the first 4 IMFs in each segment are extracted as fusion features. In total 12 features are extracted for each segments as a point xi=(xi1,xi2,…,xi12) in the feature space with the dimension of 12. And these points make up a fusion feature set X=[x1,x2,…,x100] in a high-dimension space. Fig. 6(a), (b) and (c) show the envelope entropy, permutation entropy and energy entropy of the IMF1, respectively. In Fig. 6, we can see that the extracted feature have a certain potential for the fault detection at some extent but it also not for a fault diagnosis.
Since none of the extracted feature of 12 respective features is completely suitable for fault diagnosis, it is necessary to extract the features that can achieve a better classification ability between different classes of bearing condition. The idea of LGPE is to find a feature space whose dimension can be reduced without any loss information, and makes the classification process become simple. In this paper, we use the LGPE to solve the classification problem.
Table 1. Cross-correlation coefficients and energy index of each IMF
IMFs
Outer race fault
Ball fault
Inner race fault
Fig. 6. a) Envelope entropy, b) permutation entropy, c) energy entropy of the IMF1, outer race fault (red), ball fault (green), inner race fault (blue) and normal (magenta)
In this experiment, the reduce dimensionality of LPGE is set to d= 2, and the number of neighborhood points k is 10. using the mixed-domain feature fusion method EEMD-ME and dimensionality reduction method LPGE, the distribution of the low-dimensional feature set after dimension reduction as shown in Fig. 7(a). As is can be seen in Fig. 7(a), all these four types of samples separated from each other in d-dimension space. The low dimensional feature set of the three fault sample cluster well, which are far away from the normal state. Thus, the fault diagnosis is performed.
The dimension reduction effect of LPGE is compared with other three mainstream dimension reduction algorithms KLPP, KPCA and LPP. The dimensionality reduction parameters of these algorithms are also set to d= 2, k= 10. The distributions of the low-dimensional feature sets after dimension reduction of KLPP, KPCA, LPP are shown in Fig. 7(b)-(d). Both Fig. 7(b), (c) and (d) indicate that KLPP, KPCA and LPP cannot separate the four different type of bearing effectively. As it can be notice that while using KLPP and KPCA, there are some overlaps between the outer race fault and ball fault, and outer race fault is large mixed with the inner race fault while using KPCA. The result proves that LPGE has more excellent clustering and classifying performance than KLPP, KPCA and LPP.
Fig. 7. The distribution of the low-dimensional feature sets of bearing after dimensionality reduction of LPGE, KLPP, KPCA and LPP
a) Dimensionality reduction with LPGE
b) Dimensionality reduction with KLPP
c) Dimensionality reduction with KPCA
b) Dimensionality reduction with LPP
4.2. The fault diagnosis of gear
In this experiment, the QPZZ-II test rig is designed to perform the fault test of gears. In total three types of data are obtained from the experiment systems, which include under normal conditions, with wear fault and with broken tooth fault. The sampling frequency is 5120 Hz. Each vibration signal is divided into 50 segments of 1024 sample, as shown in Fig. 8.
Fig. 8. Vibration signals collected from three different conditions of the gear
In the same way, each vibration segment of gear signal is decomposed into 10 IMFs by EEMD. Taking one segment as an example, the cross-correlation coefficients ρ and energy index E of each IMF of a segment are presented in Table 2. From it we can see that the first 3 IMFs have higher cross-correlation coefficients and energy index of the original vibrant signal. We select these 3 IMFs as the sensitive components for further analysis. Calculating the envelope entropy, permutation entropy and energy entropy of the first 3 IMFs in each segment. In total 9 features are extracted for each segments as a point xi=(xi1,xi2,…,xi9) in the feature space with the dimension of 9. And these points make up a feature dataset X=[x1,x2,…,x100] in a high-dimension space.
For the gear vibration signal, using EEMD-ME and LPGE, the distribution of the low-dimensional feature set is shown in Fig. 9(a). The proposed method can separate three different gear conditions from each other. At the low dimensional space, the normal sample and the wear fault sample cluster well, while the broken tooth fault sample which are also has a certain degree of dispersion. In general, these samples after dimensionality reduction can effectively achieve the different conditions classification of gears.
Fig. 9. The distribution of the low-dimensional feature sets of gear after dimensionality reduction of LPGE, KLPP, KPCA and LPP
Fig. 9(b)-(d) show the distribution of the low-dimensional feature set after the dimension reduction of KLPP, KPCA, LPP. In Fig. 9(b)-(d), it can be noticed that both the three methods cannot effectively separate the wear fault and broken tooth fault. In Fig. 9(b) and (c), when using KLPP and KPCA, there are some overlaps between the wear fault and broken fault, and the clustering of the samples with same fault is not well. In Fig. 9(d), the three type samples of gear are mixed together. The result verifies that the LPGE can achieve higher classification accuracy than other methods.
The above results demonstrate the superior performance of the proposed method. Dimensionality reduction with LPGE is better than other classical dimension reduction algorithms. For an online vibration monitoring system, it is necessary to install an automated technique for fault diagnosis. The proposed fault diagnosis model can be used as the core diagnosis strategy of online vibration monitoring systems. Such the system enables an objective, reliable detection and diagnosis of mechanical faults. It also saves time of maintenance technicians and improves the economic performance of the equipment.
Bearings and gears are extensively used in wind turbine transmission systems. Defective bearings and gears cause high amplitude of vibration, which can increase power consumption and reduce the economic benefits of rotating machinery. Therefore, a reliable and faster fault diagnosis technique for bearings and gears is important for wind turbine maintenance decisions and reduce operating costs. In this paper, we proposed such a technique to be used for the key components of wind turbine transmission systems. The new technique based on the mixed-domain feature fusion (EEMD-ME) and dimensionality reduction (LPGE) demonstrates a high classification accuracy. The accuracy of the technique was tested on bearing and gear vibration signals. The four classes of the recorded bearing vibration signals, i.e. normal, outer race fault, inner race fault and ball fault operation, and three classes of the recorded gear vibration signals, i.e. normal, wear fault and broken tooth fault operation. The result demonstrates the technology achieves 100 % accuracy.
As part of further work, we plan to introduced the technique into the fault diagnosis of a wind turbine transmission system under variable speeds and alternating loads for wind turbine maintenance decision-making.
This work is supported by the National Science Foundation of China (No. 51767022 and No. 51575469), the Outstanding Doctor Graduate Student Innovation Project (No. XJUBSCX-2016017) and the Graduate Student Innovation Project of Xinjiang Uygur Autonomous Region (No. XJGRI2017006).
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Ricci R., Pennacchi P. Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions. Mechanical System and Signal Processing, Vol. 25, Issue 3, 2011, p. 821-838. [Publisher]
Wu Z., Huang N. E. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, Vol. 1, Issue 1, 2009, p. 1-41. [Publisher]
Lei Y. G., Li Lin et al. N. P. J. Fault diagnosis of rotating machinery based on an adaptive ensemble empirical mode decomposition. Sensor, Vol. 13, 2013, p. 16950-16964. [Publisher]
Zhao H., Sun M., Deng W., et al. A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy, Vol. 19, Issue 1, 2016, p. 14. [Publisher]
Wang J., Gao R. X., Yan R. Integration of EEMD and ICA for wind turbine gearbox diagnosis. Wind Energy, Vol. 17, Issue 5, 2014, p. 757-773. [Publisher]
Imaouchen Y., Kedadouche M., Alkama R., et al. A frequency-weighted energy operator and complementary ensemble empirical mode decomposition for bearing fault detection. Mechanical System and Signal Processing, Vol. 82, 2017, p. 103-116. [Publisher]
Chen J. L., Pan J., Li Z. P., Zi Y. Y., Chen C. F. Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals. Renewable Energy, Vol. 89, 2016, p. 80-92. [Publisher]
Tang B. P., Song T., Li F., Deng L. Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine. Renewable Energy, Vol. 62, 2014, p. 1-9. [Publisher]
Tenenbaum J. B., De Silva V., Langford J. C. A global geometric framework for nonlinear dimensionality reduction. Science, Vol. 290, Issue 5500, 2000, p. 2319-2323. [Publisher]
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He X., Niyogi P. Locality preserving projections. Advances in Neural Information Processing Systems, 2004, p. 153-160. [Search CrossRef]
Bhatia K. K., Rao A., Price A. N. Hierarchical manifold learning for regional image analysis. IEEE Transaction on Medical Imaging, Vol. 33, Issue 2, 2014, p. 444-461. [Publisher]
Liu W., Sawant A., Ruan D. Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach. Physica Medica-European Journal of Medical Physics, Vol. 61, Issue 13, 2016, p. 2016-4989. [Search CrossRef]
Wang X., Zheng Y., Zhao Z., et al. Bearing fault diagnosis based on statistical locally linear embedding. Sensors, Vol. 15, Issue 7, 2015, p. 16225-16247. [Publisher]
Chen R., Chen S., Yang L., et al. Looseness diagnosis method for connecting bolt of fan foundation based on sensitive mixed-domain features of excitation-response and manifold learning. Neurocomputing, Vol. 219, 2017, p. 376-388. [Publisher]
Su Z. Q., Tang B. P., Ma J. H., Deng L. Fault diagnosis method based on incremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier. Measurement, Vol. 48, 2014, p. 136-148. [Publisher]
Ding X., He Q., Luo N. A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification. Journal of Sound and Vibration, Vol. 335, 2015, p. 367-383. [Publisher]
Huang Y., Zha X. F., Lee J., et al. Discriminant diffusion maps analysis: A robust manifold learner for dimensionality reduction and its applications in machine condition monitoring and fault diagnosis. Mechanical System and Signal Processing, Vol. 34, Issue 1, 2013, p. 277-297. [Publisher]
Sun J. D., Xiao Q. Y., Wen J. T., Wang F. Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM. Measurement, Vol. 55, 2014, p. 434-443. [Publisher]
Zhang X., Zhou J. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mechanical System and Signal Processing, Vol. 41, Issue 1, 2013, p. 127-140. [Publisher]
Tiwari R., Gupta V. K., Kankar P. K. Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier. Journal of Vibration and Control, Vol. 21, Issue 3, 2015, p. 461-467. [Publisher]
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Unbalance Vibration Characteristics and Sensitivity Analysis of the Dual-Rotor System in Aeroengines
Journal of Aerospace Engineering
Pingping Ma, Jingyu Zhai, Zihuimin Wang, Hao Zhang, Qingkai Han
Fault diagnosis of rolling bearing using marine predators algorithm-based support vector machine and topology learning and out-of-sample embedding
Xu Chen, Xiaoli Qi, Zhenya Wang, Chuangchuang Cui, Baolin Wu, Yan Yang
A Review on Self-Recovery Regulation (SR) Technique for Unbalance Vibration of High-End Equipment
Chinese Journal of Mechanical Engineering
Xin Pan, Jiaqiao Lu, Jiaji Huo, Jinji Gao, Haiqi Wu


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A small bridge crosses over creek in the proposed Middle May timber-sale tract on July 29 near Gold Bar. (Olivia Vanni / Herald file)
'There's going to be some tears' over 160-acre timber harvest
Despite local resistance, the Middle May tract next to Wallace Falls State Park was sold Monday.
Wednesday, December 2, 2020 8:36pm
Local NewsEnvironment
GOLD BAR — A 160-acre swath of forest next to Wallace Falls State Park will soon be cut for timber, in spite of outcry against felling the trees.
The state Department of Natural Resources on Monday sold the so-called Middle May harvest to Sierra Pacific Industries for nearly $3.15 million.
DNR delayed the sale after a group of Snohomish County residents, called Sky Valley Future, rallied to oppose the harvest last summer.
"I haven't started crying yet, but I promise there's going to be some tears when they start to cut those trees," said Chuck Lie, a resident of nearby Gold Bar for 20 years. "That's a real special forest."
The group argued that a clear-cut so close to the heavily trafficked Wallace Falls State Park would ruin the Reiter Foothills' potential as an outdoor recreation destination. Instead, they proposed a 5,300-acre county park.
On Nov. 20, the Sauk-Suiattle Tribe sent a letter to state Commissioner of Public Lands Hilary Franz requesting a three-month delay of the sale — enough time to discuss the possibility of the county conveying the land from DNR. The county technically owns the land, but DNR manages it.
"I think there should have been a better effort by the county to discuss the options for that property," Lie said.
In October, the tribe found a large number of culturally significant trees, waterfalls and plants in the potential park area.
The Tulalip Tribes told The Daily Herald in August that they oppose a potential county park.
"We've been working with the Tulalip Tribes, they're very active in that landscape," County Councilmember Sam Low said.
On July 29, Rachel Chesley (left) and Sam Chesley point out some of the forested area that was proposed to be cut for timber near Gold Bar. (Olivia Vanni / Herald file)
Over 1,180 acres of timber sales are planned in the Reiter Foothills over the next five years. Most of the harvests have been years in the making and have the potential to generate millions of dollars for junior taxing districts like schools, fire departments, libraries, hospitals and other community services. The Sultan School District is one of the largest beneficiaries.
"I think the (Middle May) sale is great for all the junior taxing districts," Low said.
The Middle May harvest will employ a technique called "variable retention harvest," which can leave as few as eight trees per acre. Work can begin as soon as the contract with Sierra Pacific Industries is complete.
The concept of a park is still a possibility, said Irene Nash, one of the Sky Valley Future organizers.
"The Middle May clear-cut takes some of the heart out of the park, but there's a lot to be gained by preserving that area overall," she said.
But County Councilmember Megan Dunnsaid a park is unlikely.
"It is evident from my conversations that there is not a solution that all can agree on to reconvey over 5,000 acres, nor was there interest from county council members to pursue a reconveyance at this time," she said via email. "This conflict is evidence of the larger issue that our forests are managed to prioritize maximizing profit for our schools, roads and other secondary taxing beneficiaries, which is at odds with sustainable ecosystem management."
Controlled explosion rattles Cathcart and much of the county
16,000 fentanyl pills, pounds of meth, heroin seized in bust


Check the Alumni, News and Contact Us section as well for more information!
Welcome to the Georgia Metros Website. In 2019, we are entering our 36th year of providing mentoring and coaching to young female athletes, as we use basketball to teach them important Life Lessons and to help them earn College Basketball Scholarships. In the Georgia Metros Family, our Coaches are dedicated not only to the development of our young women as talented basketball players, but, more importantly, their growth into responsible, caring adults, who will earn their college degrees and seek to make the world a better place by giving back to their communities, the same way the Metros Family has supported and mentored them. We emphasize values such as teamwork, hard work, empathy, leadership, responsibility, humility, being coachable, and respect for all races, ethnicities, religions and backgrounds.
We currently have OVER 80 METROS PLAYERS who, as young women student/athletes, are now earning a free college education (mainly at NCAA Division I colleges). This amounts to over a million dollars in educational benefits for our Metros Players in College this year alone (including their tuition, books, room and board)! We have helped over 400 young women earn college scholarships in our 35 year history. In 2008 we had TWO FIRST ROUND DRAFT PICKS in the WNBA Draft. In 2011 we had the NUMBER ONE OVERALL PICK in the WNBA Draft. In 2017 the number four overall draft pick and WNBA Rookie Of The Year, Allisha Gray. We curently have over 40 Metros Players playing professionally overseas.
Auburn - UConn - Florida State - Mississippi State - Elon - Maryland - Winthrop - Shorter - Anderson - Kent State - West Virginia - Central Florida - UGA - Samford - Hofstra - Michigan State - Texas A&M - UNC-G - Hofstra - UNC-A - College of Charleston - and Lee University, among others.


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Governor Brown Moves to Align Medical and Adult Use Marijuana Regulation in California
by Drug Policy Alliance April 6, 2017
April 5, 2017 – Late Tuesday afternoon, Governor Jerry Brown released his proposal to align medical marijuana regulations passed by the Legislature in 2015 and the 2016 Adult Use of Marijuana Act (Prop 64), both of which call for commercial licensing and regulation to begin in January 2018. The Drug Policy Alliance (DPA) applauds the Administration's framework, which adheres closely to the language of Prop 64, which was passed by California voters in 2016 with more than 57 percent of the vote. DPA's partner organization, Drug Policy Action, was a co-drafter and funder of Proposition 64, which legalized marijuana for adults 21 and over in California.
Critical to protecting against potential federal intervention, the Governor proposes a unified regulatory system while maintaining distinct licenses for medical and recreational. Based on remarks from U.S. Attorney General Jeff Sessions some fear that a federal crackdown on states with legal marijuana sales will target recreational markets first.
"As social justice advocates, we are excited that the Governor committed to implementing Prop 64 on-time and intact," said Lynne Lyman, California state director for the Drug Policy Alliance. "Brown's administration has designed a tight comprehensive regulatory framework that protects consumers, workers, public health, the environment, and small business stakeholders, while ensuring an inclusionary framework that opens up access for low income people and communities of color."
Any efforts to roll back the sentencing reforms, to create new forms of discrimination or additional penalties for activities that are now legal, to limit community reinvestment, or to create barriers to economic participation by persons with limited capital or prior criminal justice records would undermine the basic fairness of Prop. 64, and reinforce the historical and institutional racism of the war on drugs.
The Governor's proposed legislation was published on the Department of Finance website and addresses a range of regulatory issues, including licensing types, distribution models, cultivation limits, vertical integration, licensing equity, and the appeals process.
"The fight for a fair, just and safe system continues," said Lyman. "Opponents of Prop 64, including narcotics officers and prohibitionists may try to twist arms in the legislature to undermine the voter's intent."
The Governor's proposal will now be debated, first in the Senate and Assembly budget committees, then on the floor, and hopefully pass with the budget in June.
www.drugpolicy.org
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Is Cognex Corporation (CGNX) A Good Stock To Buy ?
A whopping number of 13F filings filed with U.S. Securities and Exchange Commission has been processed by Insider Monkey so that individual investors can look at the overall hedge fund sentiment towards the stocks included in their watchlists. These freshly-submitted public filings disclose money managers' equity positions as of the end of the three-month period that ended March 31, so let's proceed with the discussion of the hedge fund sentiment on Cognex Corporation (NASDAQ:CGNX).
Hedge fund interest in Cognex Corporation (NASDAQ:CGNX) shares was flat at the end of last quarter. This is usually a negative indicator. At the end of this article we will also compare CGNX to other stocks including Snap-on Incorporated (NYSE:SNA), Albemarle Corporation (NYSE:ALB), and Grupo Aval Acciones y Valores S.A. (NYSE:AVAL) to get a better sense of its popularity.
Let's go over the key hedge fund action surrounding Cognex Corporation (NASDAQ:CGNX).
What have hedge funds been doing with Cognex Corporation (NASDAQ:CGNX)?
At the end of the first quarter, a total of 13 of the hedge funds tracked by Insider Monkey were long this stock, a change of 0% from the fourth quarter of 2018. The graph below displays the number of hedge funds with bullish position in CGNX over the last 15 quarters. With hedgies' positions undergoing their usual ebb and flow, there exists a few notable hedge fund managers who were adding to their holdings substantially (or already accumulated large positions).
According to Insider Monkey's hedge fund database, Joho Capital, managed by Robert Karr, holds the biggest position in Cognex Corporation (NASDAQ:CGNX). Joho Capital has a $97.2 million position in the stock, comprising 16.4% of its 13F portfolio. The second most bullish fund manager is Royce & Associates, led by Chuck Royce, holding a $57.3 million position; 0.5% of its 13F portfolio is allocated to the stock. Other hedge funds and institutional investors that are bullish contain Brian Ashford-Russell and Tim Woolley's Polar Capital, Cliff Asness's AQR Capital Management and Peter Rathjens, Bruce Clarke and John Campbell's Arrowstreet Capital.
Because Cognex Corporation (NASDAQ:CGNX) has faced a decline in interest from the smart money, it's safe to say that there exists a select few funds that slashed their full holdings last quarter. Intriguingly, Andreas Halvorsen's Viking Global cut the biggest investment of all the hedgies watched by Insider Monkey, worth about $34.2 million in stock. Peter Muller's fund, PDT Partners, also dumped its stock, about $14.2 million worth. These moves are intriguing to say the least, as aggregate hedge fund interest stayed the same (this is a bearish signal in our experience).
Let's go over hedge fund activity in other stocks – not necessarily in the same industry as Cognex Corporation (NASDAQ:CGNX) but similarly valued. We will take a look at Snap-on Incorporated (NYSE:SNA), Albemarle Corporation (NYSE:ALB), Grupo Aval Acciones y Valores S.A. (NYSE:AVAL), and Bausch Health Companies Inc. (NYSE:BHC). This group of stocks' market values resemble CGNX's market value.
SNA 25 498590 0
ALB 25 186420 -3
AVAL 8 15040 -1
BHC 31 1801427 -1
As you can see these stocks had an average of 22.25 hedge funds with bullish positions and the average amount invested in these stocks was $625 million. That figure was $231 million in CGNX's case. Bausch Health Companies Inc. (NYSE:BHC) is the most popular stock in this table. On the other hand Grupo Aval Acciones y Valores S.A. (NYSE:AVAL) is the least popular one with only 8 bullish hedge fund positions. Cognex Corporation (NASDAQ:CGNX) is not the least popular stock in this group but hedge fund interest is still below average. This is a slightly negative signal and we'd rather spend our time researching stocks that hedge funds are piling on. Our calculations showed that top 20 most popular stocks among hedge funds returned 6.2% in Q2 through June 19th and outperformed the S&P 500 ETF (SPY) by nearly 3 percentage points. Unfortunately CGNX wasn't nearly as popular as these 20 stocks (hedge fund sentiment was quite bearish); CGNX investors were disappointed as the stock returned -9.9% during the same time period and underperformed the market. If you are interested in investing in large cap stocks with huge upside potential, you should check out the top 20 most popular stocks among hedge funds as 13 of these stocks already outperformed the market so far in Q2.
How Microsoft Corporation and Other Hedge Fund Favorites Performed in Q2 How Amazon.com Inc and Other Hedge Fund Favorites Performed in Q2 How Alphabet Inc and Other Hedge Fund Favorites Performed in Q2 Here's What Hedge Funds Think About Halcon Resources Corp (HK) Hedge Funds Have Never Been This Bullish On Ring Energy Inc (REI) Here's What Hedge Funds Think About Del Frisco's Restaurant Group...
Is CGNX A Good Investment Right Now? Is CGNX A Good Stock To Buy? NASDAQ:CGNX Yahoo Finance
Here's Why These Stocks Are Moving in Extended Market Trading Here is What Hedge Funds Think About Cognex Corporation (CGNX) Should You Avoid Cognex Corporation (CGNX)? Rise of the Machines: Why It's Time to Invest in Robots Chuck Royce Is Overweight Industrials; Here's Why Should You Buy Cognex Corporation (CGNX)? Tiger Cub Robert Karr's Top Tech Bets Paid Off Big in Q3 25 Biggest Activist Short Sellers in the Hedge Fund World 21 Best Tinder Bio Examples for Newly Single Moms 17 Best Excuses to Get Out of Work


1.	We are going to create an abstract Employee class and an abstract calculatePay method. The abstract Employee class will prevent a programmer from creating an object based on Employee. Only objects based on Salaried and Hourly will be allowed. The abstract calculatePay method in Employee will force the child classes to implement calculatePay.
2.	We are going to implement Polymorphism and dynamic binding by creating generalized methods that accept generalized Employee objects to collect input and display information. However, in the main method we will pass derived objects of the Employee class into the methods.
•	Zip the project files.
•	Put the zip file and screenshots (Word document) in the Dropbox.
Notice in the updated UML diagram that the Employee class is designated as abstract by having the class name Employee italicized. Also, the calculatePay method is italicized, which means that it is an abstract method and needs to be implemented in the derived classes.
Create a new project and name it CIS247B_WK6_Lab_LASTNAME. Copy all the source files from the Week 5 project into the Week 6 project.
Before you move on to the next step, build and execute the Week 6 project.
1.	Make the Employee class abstract.
2.	Define calculatePay as an abstract method.
Implement the calculatePay method. Since calculatePay is not declared as an abstract method in Employee, all subclasses of Employee must provide an implementation for calculatePay. Unlike the Salaried class, the Hourly class does not currently have an implementation for calculatePay and this will cause an error!
We are also going to implement error handing this week using a try/catch block. Specifically, you should wrap the code used to acquire and set the life insurance amount and vacation days in try-catch blocks as shown below. The "try" portion of the code looks for an error. If an error occurs parsing the integer, it jumps down to the "catch" portion of the code and displays the error message. Because badInput is still true, it loops back to the "do" and starts over. Once the integer is parsed correctly, badInput is set to false so it exits the loop and correctly sets the value for vacation.
Create a method called displayEmployeeInformation that accepts an Employee object, provides an output header string, and then displays Employee information.
Create a method called displayNumberOfEmployees to display the number of employee objects.
2.	For the first employeeList index, create a Salaried employee.
3.	Call the collectEmployeeInformation method, passing in the Salaried object.
4.	Use the displayEmployeeInformation method to display the object's state.
5.	Use the displayNumberEmployees method to display the total number of Employees created. Remember, this includes both Salaried and Hourly employees.
6.	For the second employeeList index, create an Hourly employee.
7.	Repeat Steps 3-5 for the Hourly object.
8.	As an educational exercise, you can try to create an Employee object to verify that an error will occur.
When done, compile and execute your code, and debug any errors until your code is error-free.
All of the output values should match last week's output.


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Will Microsoft Edge Browser Replace Google Chrome, Firefox?
IT Solutions OS & Apps Tech & Trends
by Anuj Sharma	September 17, 2015 0 comments
Developed under the codename Project Spartan, Microsoft's new web browser is called Edge and will replace Internet Explorer in Windows 10. Microsoft's new browser is a Windows App, rather than a traditional desktop application. It has morphed into more simplified version of IE, sports the borderless frames and minimalist aesthetic design.
Microsoft Edge is light on resources and is designed using EdgeHTML layout engine that removes support for legacy technologies such as ActiveX in favor of extensions and integration with other Microsoft services and is interoperable with other modern browsers. There's also integration with Cortana to provide additional information – for example, when you're on a web page for a restaurant, Cortana will make a booking and display information such as opening time. The new browser will be the default browser for PCs, laptops, tablets and smartphones running Windows 10.
Windows 10's voice assistant seems to be present everywhere, and Edge is no exception in this regard. When you are on a webpage for which directions are required—say you're on a hotel or a restaurant's webpage—Cortana surfaces in her blue circle in the browser toolbar giving relevant information.You can also right-click on selected text to have Cortana find info about the selection.
New-Tab Page
The new-tab page still shows top sites like IE, but now also pops app suggestions, weather, sports scores, and video suggestions. The page doesn't show an address bar, but you can type a URL into its search box that will take you to the desired web page.
This feature has been available in other browsers (particularly in Apple's Safari) but is now landing to Microsoft's new browser. This re-structures the web page eliminating advertisements, menus and other distractions aside from the main text and images, sidebars into an easy to read layout.
It is a way of temporarily bookmarking a page without adding it to your favorites. The Reading List links are shown with large pictures taken from the content along with large clear headlines, making it really easy to quickly scan through and find the link you want.
Page Annotations
Not present in rivals like Mozilla Firefox, Google Chrome, and Apple Safari, this feature lets you mark up web pages with a highlighter or drawing tool and then share them as an image file in email or social apps.
Edge vs Chrome vs Internet Explorer
We ran benchmarks on the latest versions of Edge, Internet Explorer and Google Chrome, in order to see which was quickest. Test bed comprised of Intel Pentium Processor clocked at 2.41Ghz, 8GB RAM, SSD storage.
All the benchmarks ran a predefined list of tasks to measure the browser's efficiency in completing a predefined list of tasks. Typical test tasks include rendering and animation, DOM transformations, string operations, mathematical calculations, sorting algorithms, graphic performance tests and memory instructions.
Browsermark indicated that Edge was a little quicker than IE, but was still well behind Chrome and Firefox. In Octane also Edge was level up with Chrome and Firefox, and easily outrunning IE (scoring lowest). SunSpider measures core JavaScript performance on tasks relevant to the current and near future use of JavaScript in the real world, such as encryption and text manipulation. Here also Edge topped the list completing the whole test in just 325.8 miliseconds. However, Edge scored last in Peacekeeper. Thus in general, Edge seems to be a bit quicker than Internet Explorer.
We also did testing on system real estate that a browser uses. To perform this, we fire up five tabs in each browser with the same sites in each. The homepage includes Youtube.com, mashable.com, facebook.com, ibnlive.com and cnet.com. We then used the Task Manager to total up the memory usage of all the processes used and CPU usage. In this test the edge browser seemed to be memory hungry.
It's early days obviously and we think it's not going to be a issue on laptops or desktops but while running on tablets and phones as well, the browser needs to maintain a much smaller presence than this as they are limited to resources.
Google Chrome microsoft microsoft edge Mozilla Firefox
Unlockar transforms your phone lock screen into an information stream
Google 'Fast Share' to replace Android Beam in Android Q
Is Connected System Hype or Reality?
three × nine =


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Stuck in Jail
Home > Barbour County Jail, AL > Attorneys > Robinson J David Attorney At Law
Robinson J David Attorney At Law
(334) 503-1005 - Main Phone
http://robinsonattorneyatlaw.com/
214 S Daleville Ave
With more than 30 years of experience and a reputation for getting results, residents throughout Daleville, AL, have turned to J. David Robinson in their time of need. Serving all of Dale County and surrounding areas, this one-of-a-kind attorney works tirelessly to ensure that every client he represents gets treated fairly and receives the best possible outcome in their case. Specializing in representing clients in DUI, criminal defense, family law, and personal injury cases, attorney J. David Robinson has the experience and the knowledge to fight for you in multiple areas of the law. Whether you are looking for counsel on what your next steps should be or seeking full representation in a court of law, his office can help you navigate the legal system and will walk you through every step of the case to help you reach your desired goal. From traffic tickets and violations to more serious criminal charges and accusations, you need an attorney who understands the legal system and can formulate a strategy perfectly tailored to the unique circumstances of your situation. Whether you need a divorce attorney or you're facing a child custody dispute, attorney J. David Robinson understands how vital the outcome of your case is to you and your family. With his help, you can expect results that will help you move on with your life. When you need legal representation, don't settle for just any attorney. Turn to J. David Robinson and get the results you deserve. To schedule an appointment with his office, call (334) 503-1005 today.
What is a bail hearing?
After a defendant is arrested, a bail hearing is scheduled to determine if the defendant is a flight risk and to set the amount of his or her bail or deny the bail and bonds altogether if the defendant is deemed a flight risk.
How long after an arrest does it take to set bail?
States have laws that specify when a bail hearing must take place after an arrest. For most states, the hearing must be held 48 to 72 hours after the arrest – but weekends and holidays will not count towards this time.
What is a bail bond?
A bail bond is a contractual guarantee, issued by a licensed bail bond agent, between the agent the defendant and the court, that promises the court the full bail payment if the defendant does not show up for his or her scheduled court hearing.
How quickly can a bail bond be issued?
Once defendants have received a bail amount at their bail hearing, and contacted a bail bond service, the bail and bonds are usually accomplished within 24 hours of the bail hearing.
What is the cost of a bail bond?
States set the fee for a bail bond, and in most states, the fee is 10 percent of the total bail. This fee is paid to the bond agent for the bond service, and it is a non-refundable fee.
Are there any alternatives to a bail bond?
Yes. Defendants may pay the full price of the bail, opt for a property bond or try to gain a release through their own recognizance.
What if a defendant cannot afford a bail bond?
Defendants who cannot afford a bail bond can try for a release on their own recognizance, search for a bail bond agent who offers a payment plan, or remain in jail until their court hearing.
What happens if a defendant flees?
If a defendant flees the full amount of the bail is due, the defendant will be charged with costs associated with their recovery and the amount of the bail is forever forfeit even after the defendant is recovered.
What type of collateral can be used for a bail bond?
Almost anything of value can be used as collateral for a bail bond, including retirement or private savings, property, or personal items of value such as jewelry or antique collections that belong to the defendant or to family members or friends who accept responsibility for the collateral.
When does the bail bond end?
The process for bail and bonds ends when defendants appear for their court hearing. Regardless of the outcome of their hearing, as long as they show up, the bonds are terminated.
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UN Secretary General Ban Ki-moon wasn't so thrilled with the outcome of the recent G8 climate change talks, rebuking member nations for not doing more. Now IPCC chairman Dr Rajendra Pachauri has some harsh words for the G8, the AP reports. Pachauri started by saying that they "clearly ignored" the IPCC's recommendations on how to prevent climate change, and then laid into the G8:Though it was a good thing that the G8 agreed to the aspirational goal of limiting global average temperature rise to 2°C by 2050, Pachauri said he found it "interesting" that the G8 then proceeded to pay no heed to when the IPCC says carbon emissions should peak.
Now if the G8 leaders agreed on this 2 degree increase as being the limit that could be accepted, then I think they should have also accepted the attendant requirement of global emissions peaking by 2015. And if that were to be the case, then they should most categorically have said that ... by 2020 there would have to be deep cuts in emissions.