A Non - Intrusive Load Identification Algorithm Combined with Event Detection

Background: The traditional state-based non-intrusive load monitoring method mainly deploys the aggregate load as the characteristic to identify the states of every electrical appliance. Each identification is relatively independent, and there is no correlation between the identification results. Objective: This paper combines the event detection results with the state-based non-intrusive load identification algorithm to improve accuracy. Methods: Firstly, the load recognition model based on an artificial neural network is constructed, and the state-based recognition results are obtained. An event recognition and detection model is then built to identify electrical state transitions, that is, the current moment based on the event recognition results obtained from the previous moment. Finally, a reasonable decision method is constructed to determine the identification result of the electrical states. Result: Experimental results on the public data set REDD show that in the Long Short-Term Memory (LSTM) fusion model, the macro-F1 is increased by an average of 6%, and the macro-F1 of the Artificial Neural Network (ANN) fusion model is increased by an average of 5.3% compared with LSTM and ANN. Conclusion: The proposed model can effectively improve the accuracy of identification compared with the state-based load identification method.

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189164 ◽

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.

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Walking gait event detection based on electromyography signals using artificial neural network

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2018.08.030 ◽

2019 ◽

Vol 47 ◽

pp. 334-343 ◽

Cited By ~ 24

Author(s):

Nurhazimah Nazmi ◽

Mohd Azizi Abdul Rahman ◽

Shin-Ichiroh Yamamoto ◽

Siti Anom Ahmad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Event Detection ◽

Walking Gait ◽

Artificial Neural ◽

Electromyography Signals

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Multi-radioisotope identification algorithm using an artificial neural network for plastic gamma spectra

Applied Radiation and Isotopes ◽

10.1016/j.apradiso.2019.01.005 ◽

2019 ◽

Vol 147 ◽

pp. 83-90 ◽

Cited By ~ 5

Author(s):

Jinhwan Kim ◽

Kyeongjin Park ◽

Gyuseong Cho

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Identification Algorithm ◽

Artificial Neural ◽

Gamma Spectra

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Estimation of monthly global solar irradiation using the Hargreaves–Samani model and an artificial neural network for the state of Alagoas in northeastern Brazil

Theoretical and Applied Climatology ◽

10.1007/s00704-015-1541-8 ◽

2015 ◽

Vol 125 (3-4) ◽

pp. 743-756 ◽

Cited By ~ 5

Author(s):

Gustavo Bastos Lyra ◽

Sidney Sára Zanetti ◽

Anderson Amorim Rocha Santos ◽

José Leonaldo de Souza ◽

Guilherme Bastos Lyra ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

The State ◽

Northeastern Brazil ◽

Solar Irradiation ◽

Global Solar Irradiation ◽

Artificial Neural

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An artificial neural network based b jet identification algorithm at the CDF experiment

Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment ◽

10.1016/j.nima.2011.10.024 ◽

2012 ◽

Vol 663 (1) ◽

pp. 37-47 ◽

Cited By ~ 8

Author(s):

J. Freeman ◽

W. Ketchum ◽

J.D. Lewis ◽

S. Poprocki ◽

A. Pronko ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Identification Algorithm ◽

Artificial Neural

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Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

10.20944/preprints201711.0190.v1 ◽

2017 ◽

Author(s):

Atul Anand ◽

L Suganthi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Tamil Nadu ◽

Consumer Price Index ◽

Optimization Methods ◽

Electricity Demand ◽

The State ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Optimizing Algorithm

In  the present study, a hybrid  optimizing algorithm has been proposed using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of  electricity demand of  the state of Tamil Nadu in India. The GA-PSO model optimizes  the coefficients of factors of  gross state domestic product (GSDP), per capita demand, income and  consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models  are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as  ANN-BP, ANN-GA, ANN-PSO models. Further  the paper also forecasts the electricity demand of Tamil Nadu  based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario  is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that the direct causality exists between  GSDP and the electricity demand of the state.

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Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

10.20944/preprints201801.0216.v1 ◽

2018 ◽

Author(s):

Atul Anand ◽

L Suganthi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Tamil Nadu ◽

Consumer Price Index ◽

Electricity Consumption ◽

Optimization Methods ◽

Electricity Demand ◽

The State ◽

Artificial Neural ◽

Artificial Neural Network Ann

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

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Improvement of the method of estimation and forecasting of the state of the monitoring object in intelligent decision support systems

Eastern-European Journal of Enterprise Technologies ◽

10.15587/1729-4061.2021.237996 ◽

2021 ◽

Vol 4 (3(112)) ◽

pp. 43-55

Author(s):

Areej Adnan Abed ◽

Iurii Repilo ◽

Ruslan Zhyvotovskyi ◽

Andrii Shyshatskyi ◽

Spartak Hohoniants ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Decision Support ◽

Decision Support Systems ◽

Support Systems ◽

The State ◽

Intelligent Decision ◽

Intelligent Decision Support Systems ◽

Artificial Neural ◽

Intelligent Decision Support

In order to objectively and completely analyze the state of the monitored object with the required level of efficiency, the method for estimating and forecasting the state of the monitored object in intelligent decision support systems was improved. The essence of the method is to provide an analysis of the current state of the monitored object and short-term forecasting of the state of the monitored object. Objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The novelty of the method is the use of an improved procedure for processing initial data in conditions of uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The procedure of forecasting the state of the monitored object allows for multidimensional analysis, accounting and indirect influence of all components of the multidimensional time series with their different time shifts relative to each other in conditions of uncertainty. The method allows increasing the efficiency of data processing at the level of 12–18 % using additional advanced procedures. The proposed method can be used in decision support systems of automated control systems (ACS DSS) for artillery units, special-purpose geographic information systems. It can also be used in ACS DSS for aviation and air defense and ACS DSS for logistics of the Armed Forces of Ukraine

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Remaining useful life prediction for lithium-ion batteries using particle filter and artificial neural network

Industrial Management & Data Systems ◽

10.1108/imds-03-2019-0195 ◽

2019 ◽

Vol 120 (2) ◽

pp. 312-328 ◽

Cited By ~ 1

Author(s):

Wei Qin ◽

Huichun Lv ◽

Chengliang Liu ◽

Datta Nirmalya ◽

Peyman Jahanshahi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Particle Filter ◽

Prediction Accuracy ◽

Lithium Ion ◽

Remaining Useful Life ◽

Content Type ◽

Public Data ◽

Useful Life ◽

Artificial Neural

Purpose With the promotion of lithium-ion battery, it is more and more important to ensure the safety usage of the battery. The purpose of this paper is to analyze the battery operation data and estimate the remaining life of the battery, and provide effective information to the user to avoid the risk of battery accidents. Design/methodology/approach The particle filter (PF) algorithm is taken as the core, and the double-exponential model is used as the state equation and the artificial neural network is used as the observation equation. After the importance resampling process, the battery degradation curve is obtained after getting the posterior parameter, and then the system could estimate remaining useful life (RUL). Findings Experiments were carried out by using the public data set. The results show that the Bayesian-based posterior estimation model has a good predictive effect and fits the degradation curve of the battery well, and the prediction accuracy will increase gradually as the cycle increases. Originality/value This paper combines the advantages of the data-driven method and PF algorithm. The proposed method has good prediction accuracy and has an uncertain expression on the RUL of the battery. Besides, the method proposed is relatively easy to implement in the battery management system, which has high practical value and can effectively avoid battery using risk for driver safety.

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