scholarly journals Steady State Modification Method Based On Backpropagation Neural Network For Non-Intrusive Load Monitoring (NILM)

2018 ◽  
Vol 218 ◽  
pp. 02013
Author(s):  
Sigit Tri Atmaja ◽  
Abdul Halim
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Electric Power ◽  
Recognition Rate ◽  
Electric Energy ◽  
Backpropagation Neural Network ◽  
Local Data ◽  
Load Power ◽  
Modification Method ◽  
Load Monitoring

Household electric power sector is highlighted as one of significant contributors to national energy consumption. To reduce electric energy usage in this sector, a technique called Non-Intrusive Load Monitoring (NILM) has been developed recently. NILM is a load disaggregating and monitoring tool that can be used to identify the daily usage behavior of individual electric appliance. Different to conventional method, NILM promises the reduction of sensor deployment significantly. NILM commonly uses either transient or steady state signal. Based on load/appliance signal condition, many NILM’s research results have been published. In this paper, steady state modification method of backpropagation neural network (NN) is applied for developing NILM. We use steady state signal to disaggregate the sum of load power signal. In the proposed method, NN is explored for feature extraction of electric power consumption of individual appliance. The presented method is powerful for load power signal which has almost same value. To verify the effectiveness of proposed method, data provided by tracebase.org has been used. The presented method can be applied for local data. It is obvious from simulation results that the proposed method could improve the recognition rate of appliances until 100 %.

scholarly journals Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA

2013 ◽  
Vol 7 (2) ◽  
pp. 189
Author(s):  
Silviani E Rumagit ◽  
Azhari SN
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Statistical Method ◽  
Moving Average ◽  
Electric Energy ◽  
Backpropagation Neural Network ◽  
The Difference ◽  
The Government ◽  
Increasing Demand

AbstrakLatar Belakang penelitian ini dibuat dimana semakin meningkatnya kebutuhan listrik di setiap kelompok tarif. Yang dimaksud dengan kelompok tarif dalam penelitian ini adalah kelompok tarif sosial, kelompok tarif rumah tangga, kelompok tarif bisnis, kelompok tarif industri dan kelompok tarif pemerintah. Prediksi merupakan kebutuhan penting bagi penyedia tenaga listrik dalam mengambil keputusan berkaitan dengan ketersediaan energi listik. Dalam melakukan prediksi dapat dilakukan dengan metode statistik maupun kecerdasan buatan.            ARIMA merupakan salah satu metode statistik yang banyak digunakan untuk prediksi dimana ARIMA mengikuti model autoregressive (AR) moving average (MA). Syarat dari ARIMA adalah data harus stasioner, data yang tidak stasioner harus distasionerkan dengan differencing. Selain metode statistik, prediksi juga dapat dilakukan dengan teknik kecerdasan buatan, dimana dalam penelitian ini jaringan syaraf tiruan backpropagation dipilih untuk melakukan prediksi. Dari hasil pengujian yang dilakukan selisih MSE ARIMA, JST dan penggabungan ARIMA, jaringan syaraf tiruan tidak berbeda secara signifikan. Kata Kunci— ARIMA, jaringan syaraf tiruan, kelompok tarif.  AbstractBackground this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.            ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary  use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly different. Keyword—ARIMA, neural network, tarif groups

Analysis of the Energy Metering Problem in Dynamic Environment

2014 ◽  
Vol 668-669 ◽  
pp. 673-676
Author(s):  
Zhuo Wang ◽  
Hai Bao
Keyword(s):  
Steady State ◽  
Electric Power ◽  
Electric Energy ◽  
Dynamic Environment ◽  
First Order ◽  
Energy Metering ◽  
Analytic Expressions ◽  
Engineering Environment ◽  
Linear Elements ◽  
Power Metering

The traditional electric power metering theory is a kind of no-error metering theory in sinusoidal steady-state circuit. However, the applying condition is too rigor, and the engineering environment is hard to fulfil, the application premise should be extended to dynamic. Ideal linear elements are adopted to build a first-order dynamic circuit. And the analytic expressions of the capacitor cumulative electric energy in the charging process are derived theoretically. It points out that the cumulative electric energy of capacitor in dynamic circuit is a nonzero value. This fully demonstrates that the energy metering principle brings error when it is used in dynamic environment.

EXPLORING FEATURES AND CLASSIFIERS TO CLASSIFY GENE EXPRESSION PROFILES OF ACUTE LEUKEMIA

2002 ◽  
Vol 16 (07) ◽  
pp. 831-844 ◽  
Author(s):  
SUNG-BAE CHO
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Recognition Rate ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Self Organizing Map ◽  
Backpropagation Neural Network ◽  
Self Organizing

Bioinformatics has recently drawn a lot of attention to efficiently analyze biological genomic information with information technology, especially pattern recognition. In this paper, we attempt to explore extensive features and classifiers through a comparative study of the most promising feature selection methods and machine learning classifiers. The gene information from a patient's marrow expressed by DNA microarray, which is either the acute myeloid leukemia or acute lymphoblastic leukemia, is used to predict the cancer class. Pearson's and Spearman's correlation coefficients, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Backpropagation neural network, self-organizing map, structure adaptive self-organizing map, support vector machine, inductive decision tree and k-nearest neighbor have been used for classification. Experimental results indicate that backpropagation neural network with Pearson's correlation coefficients produces the best result, 97.1% of recognition rate on the test data.

scholarly journals Sliding Window Based Machine Learning System for the Left Ventricle Localization in MR Cardiac Images

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Abdulkader Helwan ◽  
Dilber Uzun Ozsahin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Left Ventricle ◽  
Recognition Rate ◽  
Detection Task ◽  
Learning System ◽  
Backpropagation Neural Network ◽  
Vision Systems ◽  
Test Set ◽  
Cardiac Images

The most commonly encountered problem in vision systems includes its capability to suffice for different scenes containing the object of interest to be detected. Generally, the different backgrounds in which the objects of interest are contained significantly dwindle the performance of vision systems. In this work, we design a sliding windows machine learning system for the recognition and detection of left ventricles in MR cardiac images. We leverage on the capability of artificial neural networks to cope with some of the inevitable scene constraints encountered in medical objects detection tasks. We train a backpropagation neural network on samples of left and nonleft ventricles. We reformulate the left ventricles detection task as a machine learning problem and employ an intelligent system (backpropagation neural network) to achieve the detection task. We treat the left ventricle detection problem as binary classification tasks by assigning collected left ventricle samples as one class, and random (nonleft ventricles) objects are the other class. The trained backpropagation neural network is validated to possess a good generalization power by simulating it with a test set. A recognition rate of 100% and 88% is achieved on the training and test set, respectively. The trained backpropagation neural network is used to determine if the sampled region in a target image contains a left ventricle or not. Lastly, we show the effectiveness of the proposed system by comparing the manual detection of left ventricles drawn by medical experts and the automatic detection by the trained network.

IDENTIFIKASI POLA IRIS MENGGUNAKAN METODE BACKPROPAGATION

2019 ◽  
Vol 8 (02) ◽  
pp. 43-48
Author(s):  
Yoan Elviralita ◽  
Asrul Hidayat
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Unknown Parameter ◽  
Recognition Rate ◽  
Backpropagation Neural Network ◽  
Excellent Performance ◽  
Controlling System

In recent years, there has been a lot of research related to pattern recognition is conducted to identify various forms of patterns and controlling system. Utilizing backpropagation neural network in pattern identifying is very useful to solve problems with unknown parameter and difficult to determined. And then the data of the pattern are trained and tested. The results obtained from the recognition rate indicates a backpropagation neural network, provide excellent performance, which is an average of 98%. This neural network is expected to be developed by other researchers for the advancement of knowledge in all fields.

Application of Ultrasonic Test System for Test Performance Improvement of Welding Flaw

2006 ◽  
Vol 321-323 ◽  
pp. 1517-1521 ◽  
Author(s):  
Chang Hyun Kim ◽  
Jae Yeol Kim ◽  
Kyung Seok Song ◽  
Yong Hoon Cha
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Recognition Rate ◽  
Test System ◽  
Ultrasonic Test ◽  
Inspection System ◽  
Backpropagation Neural Network ◽  
Ultrasonic Probe ◽  
Inspection Method ◽  
Probability Neural Network

In this research, we used nondestructive test based on ultrasonic test as inspection method, and made up inspection robot in order to control of ultrasonic probe on the SWP surface, and programmed to signal processing code and pattern classifying code by user made programming code. For evaluation of flaw signal is reflected on welding flaw, user-made program codes are composed of signal processing and probability neural network (PNN) and backpropagation neural network (BPNN). And then, we actually confirmed to the theoretical advantage of each neural network method compared probability neural network with backpropagation neural network for classification and recognition rate. For the application of classifier to SWP inspection system, BPNN classifier is adequate in the first stage. And then, the application of PNN classifier is adequate as the next stage. Because of PNN application need enough sample data that is due to probabilistic density function.

scholarly journals Acoustic-Based Fault Diagnosis of Commutator Motor

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 299 ◽  
Author(s):  
Adam Glowacz
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Acoustic Analysis ◽  
Recognition Rate ◽  
Gear Train ◽  
Self Organizing Map ◽  
Nearest Neighbour ◽  
Classification Methods ◽  
Backpropagation Neural Network ◽  
Selection Of

In the paper, the author presents acoustic-based fault diagnosis of a commutator motor (CM). Five states of the commutator motor were considered: healthy commutator motor, commutator motor with broken rotor coil, commutator motor with shorted stator coils, commutator motor with broken tooth on sprocket, commutator motor with damaged gear train. A method of feature extraction MSAF-15-MULTIEXPANDED-8-GROUPS (Method of Selection of Amplitudes of Frequency Multiexpanded 8 Groups) was described and implemented. Classification methods, such as nearest neighbour (NN), nearest mean (NM), self-organizing map (SOM), backpropagation neural network (BNN) were used for acoustic analysis of the commutator motor. The paper provides results of acoustic analysis of the commutator motor. The results had a good recognition rate. The results of acoustic analysis were in the range of 88.4–94.6%. The NM classifier and the MSAF-15-MULTIEXPANDED-8-GROUPS provided TERCM = 94.6%.

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