scholarly journals Energy Theft Detection in an Edge Data Center Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Guixue Cheng ◽  
Zhemin Zhang ◽  
Qilin Li ◽  
Yun Li ◽  
Wenxing Jin
Keyword(s):  
Neural Network ◽  
Data Center ◽  
Information Acquisition ◽  
Distributed Processing ◽  
Small Data ◽  
Good Convergence ◽  
Operation Speed ◽  
Convergence Performance ◽  
Energy Theft ◽  
Edge Layer

With the development of smart grid information physical systems, some of the data processing functions gradually approach the edge layer of end-users. To better realize the energy theft detection function at the edge, we proposed an energy theft detection method based on the power consumption information acquisition system of power enterprises. The method involves the following steps. In the centralized data center, K-means is used to decompose a large amount of data into small data and then input and train neural network parameters to realize feature extraction. We design a neural network named DWMCNN, which can extract features from the day, week, and month and can extract more accurate features. In the edge data center, the random forest (RF) algorithm is used to classify the extracted features. The experimental results show that the clustering method accords with the idea of edge computing-distributed processing and improves the operation speed and that the feature extractor has good convergence performance. In addition, compared with the methods based on various classifiers, this method has higher accuracy and lower computational complexity, which is suitable for the deployment of edge data centers.

A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

2005 ◽  
Author(s):  
Jungeui Hong ◽  
Elizabeth A. Cudney ◽  
Genichi Taguchi ◽  
Rajesh Jugulum ◽  
Kioumars Paryani ◽  
...  
Keyword(s):  
Neural Network ◽  
Small Data ◽  
Data Sets ◽  
Comparison Study ◽  
Data Set ◽  
Set Size ◽  
Breast Cancer Study ◽  
Discriminant Ability ◽  
Small Data Sets ◽  
Multivariate Pattern

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

scholarly journals Abnormal Detection to Big Data Using Deep Neural Networks

2021 ◽  
Vol 2068 (1) ◽  
pp. 012025
Author(s):  
Jian Zheng ◽  
Zhaoni Li ◽  
Jiang Li ◽  
Hongling Liu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Risk Model ◽  
Small Sample ◽  
Small Samples ◽  
Small Data ◽  
Data Segmentation ◽  
Sample Data ◽  
Segmentation Algorithms ◽  
The Common

Abstract It is difficult to detect the anomalies in big data using traditional methods due to big data has the characteristics of mass and disorder. For the common methods, they divide big data into several small samples, then analyze these divided small samples. However, this manner increases the complexity of segmentation algorithms, moreover, it is difficult to control the risk of data segmentation. To address this, here proposes a neural network approch based on Vapnik risk model. Firstly, the sample data is randomly divided into small data blocks. Then, a neural network learns these divided small sample data blocks. To reduce the risks in the process of data segmentation, the Vapnik risk model is used to supervise data segmentation. Finally, the proposed method is verify on the historical electricity price data of Mountain View, California. The results show that our method is effectiveness.

Signal processing algorithm for neural networks with integrodifferential splines as an activation function and its particular case of image classification

2021 ◽  
Author(s):  
T.K. Biryukova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Classification ◽  
Activation Function ◽  
Experimental Comparison ◽  
Training Time ◽  
Operation Speed ◽  
The Neural Network ◽  
Linear Algebraic Equations ◽  
Network Operation

Classic neural networks suppose trainable parameters to include just weights of neurons. This paper proposes parabolic integrodifferential splines (ID-splines), developed by author, as a new kind of activation function (AF) for neural networks, where ID-splines coefficients are also trainable parameters. Parameters of ID-spline AF together with weights of neurons are vary during the training in order to minimize the loss function thus reducing the training time and increasing the operation speed of the neural network. The newly developed algorithm enables software implementation of the ID-spline AF as a tool for neural networks construction, training and operation. It is proposed to use the same ID-spline AF for neurons in the same layer, but different for different layers. In this case, the parameters of the ID-spline AF for a particular layer change during the training process independently of the activation functions (AFs) of other network layers. In order to comply with the continuity condition for the derivative of the parabolic ID-spline on the interval (x x0, n) , its parameters fi (i= 0,...,n) should be calculated using the tridiagonal system of linear algebraic equations: To solve the system it is necessary to use two more equations arising from the boundary conditions for specific problems. For exam- ple the values of the grid function (if they are known) in the points (x x0, n) may be used for solving the system above: f f x0 = ( 0) , f f xn = ( n) . The parameters Iii+1 (i= 0,...,n−1 ) are used as trainable parameters of neural networks. The grid boundaries and spacing of the nodes of ID-spline AF are best chosen experimentally. The optimal selection of grid nodes allows improving the quality of results produced by the neural network. The formula for a parabolic ID-spline is such that the complexity of the calculations does not depend on whether the grid of nodes is uniform or non-uniform. An experimental comparison of the results of image classification from the popular FashionMNIST dataset by convolutional neural 0, x< 0 networks with the ID-spline AFs and the well-known ReLUx( ) =AF was carried out. The results reveal that the usage x x, ≥ 0 of the ID-spline AFs provides better accuracy of neural network operation than the ReLU AF. The training time for two convolutional layers network with two ID-spline AFs is just about 2 times longer than with two instances of ReLU AF. Doubling of the training time due to complexity of the ID-spline formula is the acceptable price for significantly better accuracy of the network. Wherein the difference of an operation speed of the networks with ID-spline and ReLU AFs will be negligible. The use of trainable ID-spline AFs makes it possible to simplify the architecture of neural networks without losing their efficiency. The modification of the well-known neural networks (ResNet etc.) by replacing traditional AFs with ID-spline AFs is a promising approach to increase the neural network operation accuracy. In a majority of cases, such a substitution does not require to train the network from scratch because it allows to use pre-trained on large datasets neuron weights supplied by standard software libraries for neural network construction thus substantially shortening training time.

Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques

2016 ◽  
Vol 7 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Ahmad Al-Khasawneh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Diagnosis ◽  
Multilayer Perceptron ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Algorithms ◽  
Small Data ◽  
Self Organizing Map ◽  
Term Survival ◽  
Self Organizing

Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilayer perceptron has been implemented as a supervised neural network and a self-organizing map as an unsupervised one. Both models were simulated using a variety of parameters and tested using several combinations of those parameters in independent experiments. It was concluded that the multilayer perceptron neural network outperforms Kohonen's self-organizing maps in diagnosing breast cancer even with small data sets.

Steady State and Transient Comparison of Perimeter and Row-Based Cooling Employing Controlled Cooling Curves

2015 ◽  
Author(s):  
Husam A. Alissa ◽  
Kourosh Nemati ◽  
Bahgat Sammakia ◽  
Alfonso Ortega ◽  
David King ◽  
...  
Keyword(s):  
Steady State ◽  
Heat Exchanger ◽  
Data Center ◽  
Cooling System ◽  
Air Flow ◽  
Thermal Inertia ◽  
Small Data ◽  
Cfd Model ◽  
Controlled Cooling ◽  
Failure Scenario

The perpetual increase of data processing has led to an ever increasing need for power and in turn to greater cooling challenges. High density (HD) IT loads have necessitated more aggressive and direct approaches of cooling as opposed to the legacy approach by the utilization of row-based cooling. In-row cooler systems are placed between the racks aligned with row orientation; they offer cool air to the IT equipment more directly and effectively. Following a horizontal airflow pattern and typically occupying 50% of a rack’s width; in-row cooling can be the main source of cooling in the data center or can work jointly with perimeter cooling. Another important development is the use of containment systems since they reduce mixing of hot and cold air in the facility. Both in-row technology and containment can be combined to form a very effective cooling solution for HD data centers. This current study numerically investigates the behavior of in-row coolers in cold aisle containment (CAC) vs. perimeter cooling scheme. Also, we address the steady state performance for both systems, this includes manufacturer’s specifications such as heat exchanger performance and cooling coil capacity. A brief failure scenario is then run, and duration of ride through time in the case of row-based cooling system failure is compared to raised floor perimeter cooling with containment. Non-raised floor cooling schemes will reduce the air volumetric storage of the whole facility (in this small data center cell it is about a 20% reduction). Also, the varying thermal inertia between the typical in-row and perimeter cooling units is of decisive importance. The CFD model is validated using a new data center laboratory at Binghamton University with perimeter cooling. This data center consists of one main Liebert cooling unit, 46 perforated tiles with 22% open area, 40 racks distributed on three main cold aisles C and D. A computational slice is taken of the data center to generalize results. Cold aisle C consists of 16 rack and 18 perforated tiles with containment installed. In-row coolers are then added to the CFD model. Fixed IT load is maintained throughout the simulation and steady state comparisons are built between the legacy and row-based cooling schemes. An empirically obtained flow curve method is used to capture the flow-pressure correlation for flow devices. Performance scenarios were parametrically analyzed for the following cases: (a) Perimeter cooling in CAC, (b) In-row cooling in CAC. Results showed that in-row coolers increased the efficiency of supply air flow utilization since the floor leakage was eliminated, and higher pressure build up in CAC were observed. This reduced the rack recirculation when compared to the perimeter cooled case. However, the heat exchanger size demonstrated the limitation of the in-row to maintain controlled set point at increased air flow conditions. For the pump failure scenario, experimental data provided by Emerson labs were used to capture the thermal inertia effect of the cooling coils for in-row and perimeter unit, perimeter cooled system proved to have longer ride through time.

scholarly journals Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4408 ◽  
Author(s):  
Hyun-Myung Cho ◽  
Heesu Park ◽  
Suh-Yeon Dong ◽  
Inchan Youn
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Mental Arithmetic ◽  
Small Data ◽  
Data Sets ◽  
Learning Method ◽  
Data Set ◽  
Electrocardiogram Signals ◽  
Proposed Model ◽  
Small Data Set

The goals of this study are the suggestion of a better classification method for detecting stressed states based on raw electrocardiogram (ECG) data and a method for training a deep neural network (DNN) with a smaller data set. We suggest an end-to-end architecture to detect stress using raw ECGs. The architecture consists of successive stages that contain convolutional layers. In this study, two kinds of data sets are used to train and validate the model: A driving data set and a mental arithmetic data set, which smaller than the driving data set. We apply a transfer learning method to train a model with a small data set. The proposed model shows better performance, based on receiver operating curves, than conventional methods. Compared with other DNN methods using raw ECGs, the proposed model improves the accuracy from 87.39% to 90.19%. The transfer learning method improves accuracy by 12.01% and 10.06% when 10 s and 60 s of ECG signals, respectively, are used in the model. In conclusion, our model outperforms previous models using raw ECGs from a small data set and, so, we believe that our model can significantly contribute to mobile healthcare for stress management in daily life.

scholarly journals An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 113 ◽  
Author(s):  
Joao Ferreira ◽  
Gustavo Callou ◽  
Albert Josua ◽  
Dietmar Tutsch ◽  
Paulo Maciel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Data Center ◽  
Data Centers ◽  
New Technologies ◽  
Operational Costs ◽  
The Us ◽  
Artificial Neural

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option.

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