Energy routers are recent topics of interest for scientific community working on alternative energy. Enabling technologies supporting installation and monitoring energy efficiency in building are discussed in this paper, by focusing the attention on innovative aspects and on approaches to predict risks and failures conditions of energy router devices. Infrared (IR) Thermography and Augmented Reality (AR) are indicated in this work as potential technologies for the installation testing and tools for predictive maintenance of energy networks, while thermal simulation, image post-processing and data mining improve the analysis of the prediction process. Image postprocessing has been applied on thermal images and for WiFi AR. Concerning data mining we applied k-Means and Artificial Neural Network –ANNobtaining outputs based on measured data. The paper proposes some tools procedure and methods supporting the Building Information ModelingBIMin smart grid applications. Finally we provide some ISO standards matching with the enabling technologies by completing the overview of scenario .
{"title":"Overview and Application of Enabling Technologies Oriented on Energy Routing Monitoring, on Network Installation and on Predictive Maintenance","authors":"A. Massaro, A. Galiano, Giacomo Meuli, S. Massari","doi":"10.5121/IJAIA.2018.9201","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9201","url":null,"abstract":"Energy routers are recent topics of interest for scientific community working on alternative energy. Enabling technologies supporting installation and monitoring energy efficiency in building are discussed in this paper, by focusing the attention on innovative aspects and on approaches to predict risks and failures conditions of energy router devices. Infrared (IR) Thermography and Augmented Reality (AR) are indicated in this work as potential technologies for the installation testing and tools for predictive maintenance of energy networks, while thermal simulation, image post-processing and data mining improve the analysis of the prediction process. Image postprocessing has been applied on thermal images and for WiFi AR. Concerning data mining we applied k-Means and Artificial Neural Network –ANNobtaining outputs based on measured data. The paper proposes some tools procedure and methods supporting the Building Information ModelingBIMin smart grid applications. Finally we provide some ISO standards matching with the enabling technologies by completing the overview of scenario .","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"9 1","pages":"01-20"},"PeriodicalIF":0.0,"publicationDate":"2018-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2018.9201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43570576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the proposal for the identification of residential equipment in non-intrusive load monitoring systems. The system is based on a Convolutional Neural Network to classify residential equipment. As inputs to the system, transient power signal data obtained at the time an equipment is connected in a residence is used. The methodology was developed using data from a public database (REED) that presents data collected at a low frequency (1 Hz). The results obtained in the test database indicate that the proposed system is able to carry out the identification task, and presented satisfactory results when compared with the results already presented in the literature for the problem in question.
{"title":"Home Appliance Identification for Nilm Systems Based on Deep Neural Networks","authors":"D. Penha, A. Castro","doi":"10.5121/IJAIA.2018.9206","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9206","url":null,"abstract":"This paper presents the proposal for the identification of residential equipment in non-intrusive load monitoring systems. The system is based on a Convolutional Neural Network to classify residential equipment. As inputs to the system, transient power signal data obtained at the time an equipment is connected in a residence is used. The methodology was developed using data from a public database (REED) that presents data collected at a low frequency (1 Hz). The results obtained in the test database indicate that the proposed system is able to carry out the identification task, and presented satisfactory results when compared with the results already presented in the literature for the problem in question.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"9 1","pages":"69-80"},"PeriodicalIF":0.0,"publicationDate":"2018-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2018.9206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47357517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Prevention of Heart Problem Using Artificial Intelligence","authors":"Nimai Chand Das Adhikari","doi":"10.5121/IJAIA.2018.9202","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9202","url":null,"abstract":"","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"9 1","pages":"21-35"},"PeriodicalIF":0.0,"publicationDate":"2018-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44012477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Things like growing volumes and varieties of available data, cheaper and more powerful computational processing, data storage and large-value predictions that can guide better decisions and smart actions in real time without human intervention are playing critical role in this age. All of these require models that can automatically analyse large complex data and deliver quick accurate results – even on a very large scale. Machine learning plays a significant role in developing these models. The applications of machine learning range from speech and object recognition to analysis and prediction of finance markets. Artificial Neural Network is one of the important algorithms of machine learning that is inspired by the structure and functional aspects of the biological neural networks. In this paper, we discuss the purpose, representation and classification methods for developing hardware for machine learning with the main focus on neural networks. This paper also presents the requirements, design issues and optimization techniques for building hardware architecture of neural networks.
{"title":"Hardware Design for Machine Learning","authors":"P. Jawandhiya","doi":"10.5121/IJAIA.2018.9105","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9105","url":null,"abstract":"Things like growing volumes and varieties of available data, cheaper and more powerful computational processing, data storage and large-value predictions that can guide better decisions and smart actions in real time without human intervention are playing critical role in this age. All of these require models that can automatically analyse large complex data and deliver quick accurate results – even on a very large scale. Machine learning plays a significant role in developing these models. The applications of machine learning range from speech and object recognition to analysis and prediction of finance markets. Artificial Neural Network is one of the important algorithms of machine learning that is inspired by the structure and functional aspects of the biological neural networks. In this paper, we discuss the purpose, representation and classification methods for developing hardware for machine learning with the main focus on neural networks. This paper also presents the requirements, design issues and optimization techniques for building hardware architecture of neural networks.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"9 1","pages":"63-84"},"PeriodicalIF":0.0,"publicationDate":"2018-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2018.9105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42222600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes two algorithms of crack detection one using fuzzy logic (FL) and the other artificial neural networks (ANN). Since modal parameters are very sensitive to damages, the first three relative natural frequencies are used as three inputs and the corresponding relative crack location, relative crack depth are used as the two outputs in the algorithms. The three natural frequencies for an undamaged beam and different cases of damaged beam (Single crack at various locations with varying depths) were obtained by modelling and simulating the beams using a finite element based (FEM) software. Results concluded that both the approaches can be successfully employed in crack detection in a beam like structure but FL approach performed better in determining relative crack depth whereas ANN approach performed better in determining relative crack location. All the comparisons made in the study are based on the R 2 values.
{"title":"Comparison of Artificial Neural Networks and Fuzzy Logic Approaches for Crack Detection in a Beam Like Structure","authors":"B. PrakruthiGowd, K. Jayasree, M. N. Hegde","doi":"10.5121/IJAIA.2018.9103","DOIUrl":"https://doi.org/10.5121/IJAIA.2018.9103","url":null,"abstract":"This paper proposes two algorithms of crack detection one using fuzzy logic (FL) and the other artificial neural networks (ANN). Since modal parameters are very sensitive to damages, the first three relative natural frequencies are used as three inputs and the corresponding relative crack location, relative crack depth are used as the two outputs in the algorithms. The three natural frequencies for an undamaged beam and different cases of damaged beam (Single crack at various locations with varying depths) were obtained by modelling and simulating the beams using a finite element based (FEM) software. Results concluded that both the approaches can be successfully employed in crack detection in a beam like structure but FL approach performed better in determining relative crack depth whereas ANN approach performed better in determining relative crack location. All the comparisons made in the study are based on the R 2 values.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"9 1","pages":"35-51"},"PeriodicalIF":0.0,"publicationDate":"2018-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2018.9103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45462506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amir E. Sarabadani Tafreshi, Amir E. Sarabadani Tafreshi, A. Ralescu
{"title":"Ranking Based on Collaborative Feature Weighting Applied to the Recommendation of Research Papers","authors":"Amir E. Sarabadani Tafreshi, Amir E. Sarabadani Tafreshi, A. Ralescu","doi":"10.5121/ijaia.2018.9204","DOIUrl":"https://doi.org/10.5121/ijaia.2018.9204","url":null,"abstract":"","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"9 1","pages":"47-53"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/ijaia.2018.9204","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70613215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wdnei R. da Paixao, T. M. Paixão, Mateus Barcellos Costa, J. O. Andrade, F. G. Pereira, K. S. Komati
A collaborative system for cataloging sea turtles activity that supports picture/video content demands automated solutions for data classification and analysis. This work assumes that the color characteristics of the carapace are sufficient to classify each species of sea turtles, unlikely to the traditional method that classifies sea turtles manually based on the counting of their shell scales, and the shape of their head. Particularly, the aim of this study is to compare two features extraction techniques based on color, Color Histograms and Chromaticity Moments, combined with two classification methods, K-nearest neighbors (KNN) and Support Vector Machine (SVM), identifying which combination of techniques has a higher effectiveness rate for classifying the five species of sea turtles found along the Brazilian coast. The results showed that the combination using Chromaticity Moments with the KNN classifier presented quantitatively better results for most species of turtles with global accuracy value of 0.74 and accuracy of 100% for the Leatherback sea turtle, while the descriptor of Color Histograms proved to be less precise, independent of the classifier. This work demonstrate that is possible to use a statistical approach to assist the job of a specialist when identifying species of sea turtle.
{"title":"Texture Classification of Sea Turtle Shell Based on Color Features: Color Histograms and Chromaticity Moments","authors":"Wdnei R. da Paixao, T. M. Paixão, Mateus Barcellos Costa, J. O. Andrade, F. G. Pereira, K. S. Komati","doi":"10.5121/ijaia.2018.9205","DOIUrl":"https://doi.org/10.5121/ijaia.2018.9205","url":null,"abstract":"A collaborative system for cataloging sea turtles activity that supports picture/video content demands automated solutions for data classification and analysis. This work assumes that the color characteristics of the carapace are sufficient to classify each species of sea turtles, unlikely to the traditional method that classifies sea turtles manually based on the counting of their shell scales, and the shape of their head. Particularly, the aim of this study is to compare two features extraction techniques based on color, Color Histograms and Chromaticity Moments, combined with two classification methods, K-nearest neighbors (KNN) and Support Vector Machine (SVM), identifying which combination of techniques has a higher effectiveness rate for classifying the five species of sea turtles found along the Brazilian coast. The results showed that the combination using Chromaticity Moments with the KNN classifier presented quantitatively better results for most species of turtles with global accuracy value of 0.74 and accuracy of 100% for the Leatherback sea turtle, while the descriptor of Color Histograms proved to be less precise, independent of the classifier. This work demonstrate that is possible to use a statistical approach to assist the job of a specialist when identifying species of sea turtle.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"9 1","pages":"55-67"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/ijaia.2018.9205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70613251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The work in this paper shows intensive empirical experiments using 13 datasets to understand the regularization effectiveness of ridge regression, the lasso estimate, and elastic net regularization methods. The study offers a deep understanding of how the datasets affect the goodness of the prediction accuracy of each regularization method for a given problem given the diversity in the datasets used. The results have shown that datasets play crucial rules on the performance of the regularization method and that the predication accuracy depends heavily on the nature of the sampled datasets.
{"title":"On the Prediction Accuracies of Three Most Known Regularizers : Ridge Regression, The Lasso Estimate and Elastic Net Regularization Methods","authors":"Adel Aloraini","doi":"10.5121/IJAIA.2017.8603","DOIUrl":"https://doi.org/10.5121/IJAIA.2017.8603","url":null,"abstract":"The work in this paper shows intensive empirical experiments using 13 datasets to understand the regularization effectiveness of ridge regression, the lasso estimate, and elastic net regularization methods. The study offers a deep understanding of how the datasets affect the goodness of the prediction accuracy of each regularization method for a given problem given the diversity in the datasets used. The results have shown that datasets play crucial rules on the performance of the regularization method and that the predication accuracy depends heavily on the nature of the sampled datasets.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"8 1","pages":"29-36"},"PeriodicalIF":0.0,"publicationDate":"2017-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2017.8603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44128218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Algosaibi, Saleh Albahli, Samer F. Khasawneh, Austin Melton
{"title":"Web Evolution - The Shift from Information Publishing to Reasoning","authors":"A. Algosaibi, Saleh Albahli, Samer F. Khasawneh, Austin Melton","doi":"10.5121/IJAIA.2017.8602","DOIUrl":"https://doi.org/10.5121/IJAIA.2017.8602","url":null,"abstract":"","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"8 1","pages":"11-28"},"PeriodicalIF":0.0,"publicationDate":"2017-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2017.8602","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49193734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Misguided information in health care has caused much havoc that have led to the death of millions of people as a result of misclassification, and inconsistent health care records; hence the objective of this paper is to develop an improved clinical decision support system. This system incorporated hybrid system of non-knowledge based and knowledge based decision support system for the diagnosis of diseases and proper health care delivery records using prostate cancer and diabetes datasets to train and validate the model. The min-max method was adopted in normalizing the datasets, while genetic algorithm was deployed in initiating the training weights of the MLP. The result obtained in this paper yielded a classification accuracy of 98%, sensitivity of 0.98 and specificity of 100 for prostate cancer and accuracy of 94%, sensitivity of 0.94 and specificity of 0.67 for diabetes.
{"title":"An Improved Model for Clinical Decision Support System","authors":"O. Henry, U. Chidiebere, Inyiama Hycinth","doi":"10.5121/IJAIA.2017.8604","DOIUrl":"https://doi.org/10.5121/IJAIA.2017.8604","url":null,"abstract":"Misguided information in health care has caused much havoc that have led to the death of millions of people as a result of misclassification, and inconsistent health care records; hence the objective of this paper is to develop an improved clinical decision support system. This system incorporated hybrid system of non-knowledge based and knowledge based decision support system for the diagnosis of diseases and proper health care delivery records using prostate cancer and diabetes datasets to train and validate the model. The min-max method was adopted in normalizing the datasets, while genetic algorithm was deployed in initiating the training weights of the MLP. The result obtained in this paper yielded a classification accuracy of 98%, sensitivity of 0.98 and specificity of 100 for prostate cancer and accuracy of 94%, sensitivity of 0.94 and specificity of 0.67 for diabetes.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"8 1","pages":"37-55"},"PeriodicalIF":0.0,"publicationDate":"2017-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2017.8604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42589924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：