(tie) Westside 5-4 6.22 t17
16. Sissonville 5-4 5.78 20
17. Philip Barbour 5-5 5.60 14
18. River View 5-4 5.44 —
19. Liberty Raleigh 5-4 5.33 t15
20. Mingo Central 4-5 5.11 t17
(tie) Tolsia 4-5 5.11 —
CLASS A
1. Tucker County 9-0 11.22 1
2. Magnolia 8-1 10.00 4
3. Wahama 9-0 9.78 2
4. Greenbrier West 9-1 9.40 3
5. Madonna 8-1 9.22 5
6. St. Marys 8-1 8.89 8
7. Clay-Battelle 9-0 8.78 6
8. Valley Fayette 8-1 8.33 9
9. East Hardy 8-1 8.11 11
10. Buffalo 7-2 7.67 7
11. Wheeling Central 7-3 7.40 13
12. Meadow Bridge 8-2 7.30 12
13. Wirt County 6-3 7.11 10
14. Tug Valley 7-2 6.56 15
15. Bishop Donahue 6-3 6.00 14
(tie) Moorefield 5-4 6.00 16
17. Doddridge County 6-3 5.89 17
(tie) Williamstown 5-4 5.89 19
19. Parkersburg Cath. 5-5 5.00 20
20. Pocahontas County 5-5 4.50 —&gt;you did not like this tweet or my decision to repost it in this subreddit and for that, I apologize.

You're apologizing for the way that I feel? That doesn't make any sense.

If you're sorry for posting it, take the post down. If you're not sorry, keep it up. It doesn't really matter to me, my goal isn't to get your post removed. But making an apology for how I *feel* about something is meaningless.

&gt;Never would I do anything to offend any person, especially those in my community

Just saying something doesn't make it so. It seems that /u/FieryPhoenix56 was at least somewhat offended, and saying "I would never do that thing I just did" is just a blatant lie.

Edit: this sounds mean or angry, that's on me. I don't think you did anything wrong simply by posting a tweet, but this response really rubbed me the wrong way.Brazil’s civil aviation authority ANAC has granted a type certificate to the Embraer Praetor 600 super-midsize business jet, a milestone coming roughly six months after the airframer publicly launched the Praetor programme.
Embraer calls the Praetor 600 “the most disruptive and technologically advanced super-midsize business jet”, noting the aircraft has 4,020nm (7,445km) range with four passengers.
That is range enough to fly routes such as London to New York, Miami to Sao Paulo and Dubai to London.
The Praetor 600 can carry up to 12 passengers.
Embraer has previously said it expects the US Federal Aviation Administration to certify the Praetor 600 early in the second half of 2019.
The company unveiled the Praetor 600 and its smaller sibling, the Praetor 500, at the NBAA trade show in October 2018.
Both aircraft are derivatives of the Legacy 450 and Legacy 500 jets, sharing the same fuselage and wings.
Honeywell HTF7500E turbofans power the Praetor, and the aircraft has Collins Aerospace Pro Line Fusion flight deck electronics with 15.1in displays. Cockpit systems include an enhanced vision system, synthetic vision guidance and predictive wind shear capability, Embraer says.
The Praetor 500 and 600 also have fly-by-wire technology.
An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.


Introduction
As one of the core components of rotating machinery, rolling element bearings are used to constrain relative motions to only the desired motion and reduce friction between moving parts. Bearings are always expected to work 24 h per day in actual production. Any failure with the bearings may lead to unexpected consequence of the whole machine. As bearing failures always bring downtime, expensive repair and hidden cost to enterprises, real time monitoring and precise fault diagnosis are critical to avoid catastrophic damages.
In the past decades, a variety of methods have been developed in bearing fault diagnosis, such as vibration analysis [1], acoustic analysis [2], noise analysis [3], thermal imaging analysis [4] and so on, among which the vibration analysis has proven to be the most efficient [5]. Many vibration signal processing tools have been used in signal preprocessing such as Fourier spectral analysis [6], wavelet analysis [7], empirical mode decomposition [8], and multi-wavelet transformation [9]. These vibration analysis methods have achieved good performance from non-adaptive analysis to adaptive analysis and from qualitative analysis to quantitative analysis [10]. However, good performance with these methods highly depends on the expert experience and knowledge.
In addition to the application of diverse signal preprocessing approaches, many artificial intelligence methods have been applied to features extraction and classification, two main steps in bearing fault diagnosis. Time domain statistical analysis, multi-wavelet transformation and fast Fourier transformation are always used for features extraction. Feature selection methods such as principal component analysis (PCA) [11] and independent component analysis (ICA) [12] are employed to select most useful features. Classifiers such as Bayes Classifier [13], k-nearest neighbor (KNN) [14], Multi-layer Perceptron neural networks (MLP) [15], support vector machines (SVM) [16], Decision Trees (DT) [17], and Random Forests (RF) [18] have all been applied to bearing fault diagnosis. Among them, MLP is known for its capability to learn features with complex and nonlinear patterns. It has also been reported that SVM is superior to the other AI algorithms in fault diagnosis due its memory efficiency and good performance when the number of features is greater than that of samples [19][20][21].
Recently, deep learning methods have received amazing success in pattern recognition and machine learning application domains due to their outstanding capability to learn complex and robust representations. Many classification problems have been successfully solved by various deep learning models such as deep belief network [22], deep Boltzmann machine [23], deep auto encoder [24], convolutional neural network (CNN) [25] and so on. Deep learning has also been applied to fault diagnosis recently. Guo [26] proposed a hierarchical adaptive deep convolution neural network (ADCNN) on bearing fault analysis, which first output the fault type and then analyze the fault size. Chen et al. [27] proposed a 2D-CNN model for gearbox fault diagnosis through combining time domain statistical features with 256 statistical frequency domain root mean squared (RMS) values. An 1D CNN model based on the raw time series data was proposed for motor fault detection by Ince et al. [28]. Tran et al. [29] developed a deep belief model for reciprocating compressor fault analysis.
Although the deep learning methods have achieved great success in fault diagnosis, there still exist two potential issues. First, selecting appropriate network structures, parameters, and algorithms is critical to the success of deep learning approaches. Second, the signal data in all current deep learning methods for fault diagnosis are from a single sensor, which may not provide reliable information in terrible working environments. The adaptability and the anti-interference ability of different sensors varies widely so prediction models based on single source of information could lead to misdiagnosis. It is desirable to exploit multi-sensor information source for more reliable fault diagnosis by effective information fusion approach, which is complicated by the fact that different sensor locations and sensor qualities.
In this paper, we propose a novel algorithm that integrates deep neural networks with multiple sensor information with an improved Dempster-Shafer (D-S) evidence theory [30] based data fusion model for bearing fault diagnosis. Our fusion model can combine multiple uncertain evidences and give the fusion result through merging consensus information and excluding conflicting information. In recent years, D-S evidence theory and its variants have been widely used in multi-sensors data fusion, decision analysis, fault detection and other industrial fields. However, two defects in traditional D-S evidence theory still exist, which we addressed in our proposed fusion model: one is evaluating the basic function of the evidence body objectively; and the other is solving the conflict evidences coming from different sources.
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