Implementation and performance results of neural network for power quality event detection

2008 ◽  
Author(s):  
Weijian Huang ◽  
Wenzhi Tian
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Event Detection ◽  
And Performance ◽  
Performance Results

scholarly journals Cascaded Neural Network Based Data Mining Strategy for Cloud Intrusion Detection

2018 ◽  
Vol 11 (3) ◽  
pp. 1094
Author(s):  
P Purniemaa ◽  
R Jagadeesh Kannan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Knowledge Discovery ◽  
Detection System ◽  
Data Sets ◽  
And Performance ◽  
Performance Results ◽  
Data Mining Strategy

In recent years data mining has acquired huge popularity in the field of knowledge discovery. Thus, this approach has inspired several researches for anomaly detection, fraud detection and intrusion detection with higher accuracy, all round generalization of the problem and its sub cases; all giving higher performance in conditions subjected to continuous alteration. Though there remain quite a few challenging problems in design and implementation of a data mining based cloud intrusion detection system, as deception tactics and modeling of behavior remains a daunting problem to compute for anomaly owing to massive size of data to process in reasonable time. In this study we present a cascaded neural network based data mining strategy for cloud intrusion detection systems (IDSs) and presents the comparison and performance results tested on DARPA Intrusion Detection (ID) Data Sets, Knowledge Discovery and Data Mining Cup, NSL-KDD dataset. The study exhibits numerous advantages offered by the presented method and give reliable results of anomaly detection in real time scenario.

Expectation Propagation with Factorizing Distributions: A Gaussian Approximation and Performance Results for Simple Models

2011 ◽  
Vol 23 (4) ◽  
pp. 1047-1069 ◽  
Author(s):  
Fabiano Ribeiro ◽  
Manfred Opper
Keyword(s):  
Neural Network ◽  
Limit Theorem ◽  
Gaussian Approximation ◽  
Network Models ◽  
Expectation Propagation ◽  
Neural Network Models ◽  
And Performance ◽  
Approximate Bayesian ◽  
Performance Results ◽  
Approximate Bayesian Inference

We discuss the expectation propagation (EP) algorithm for approximate Bayesian inference using a factorizing posterior approximation. For neural network models, we use a central limit theorem argument to make EP tractable when the number of parameters is large. For two types of models, we show that EP can achieve optimal generalization performance when data are drawn from a simple distribution.

The Relation Between Financial Incentives, Motivation, and Performance

2017 ◽  
Vol 16 (2) ◽  
pp. 61-76 ◽  
Author(s):  
Anaïs Thibault Landry ◽  
Marylène Gagné ◽  
Jacques Forest ◽  
Sylvie Guerrero ◽  
Michel Séguin ◽  
...  
Keyword(s):  
Financial Incentives ◽  
Work Performance ◽  
Autonomous Motivation ◽  
Compensation Plans ◽  
Work Settings ◽  
Path Analyses ◽  
Three Samples ◽  
And Performance ◽  
Performance Results

Abstract. To this day, researchers are debating the adequacy of using financial incentives to bolster performance in work settings. Our goal was to contribute to current understanding by considering the moderating role of distributive justice in the relation between financial incentives, motivation, and performance. Based on self-determination theory, we hypothesized that when bonuses are fairly distributed, using financial incentives makes employees feel more competent and autonomous, which in turn fosters greater autonomous motivation and lower controlled motivation, and better work performance. Results from path analyses in three samples supported our hypotheses, suggesting that the effect of financial incentives is contextual, and that compensation plans using financial incentives and bonuses can be effective when properly managed.

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

Neural Network Distillation on IoT Platforms for Sound Event Detection

2019 ◽  
Author(s):  
Gianmarco Cerutti ◽  
Rahul Prasad ◽  
Alessio Brutti ◽  
Elisabetta Farella
Keyword(s):  
Neural Network ◽  
Event Detection ◽  
Sound Event ◽  
Iot Platforms ◽  
Sound Event Detection
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