Pub Date : 2023-10-23DOI: 10.1080/23080477.2023.2270820
Athira Jayavarma, None Preetha, Manjula G Nair
ABSTRACTIn today’s smart communities, small-scale energy systems are essential for sustainable development and efficient resource management. However, ensuring the confidentiality, safety, and accurate prediction of energy consumption patterns in energy trading is a major challenge. To address these issues, an innovative solution that synergistically combines two cutting-edge technologies: blockchain and machine learning is proposed. This paper unveils a novel approach that harmoniously merges blockchain with the Recalling-Enhanced Recurrent Neural Network (RERNN) to revolutionize energy trading systems called ‘Blockchain-Enhanced Energy Trading with Recalling-Enhanced Recurrent Neural Network (BET-RERNN).’ Data from IoT-enabled smart devices is securely stored in blockchain blocks, ensuring data integrity and immutability. Blockchain’s decentralized nature creates a trust-less environment for energy trading, protecting the privacy and anonymity of participants while maintaining transparency. At the heart of our system lies the advanced machine-learning capabilities of the RERNN model. By processing the data stored on the blockchain, RERNN accurately predicts optimal power generation for small-scale energy systems, enabling smart communities to make informed decisions and optimize their energy consumption. The BET-RERNN scheme provides a plethora of strengths. First, participants can securely engage in energy trading without compromising sensitive information, fostering a more resilient and efficient market. Second, blockchain technology ensures that all energy-related data is protected from tampering and unauthorized access, ensuring system reliability and trust. An in-depth comparison of RERNN’s performance to traditional General Regression Neural Network (GRNN) and Gradient Boost Decision Tree (GBDT) methods is conducted. To verify the strategy’s effectiveness, MATLAB simulations are employed, demonstrating its real-world applicability and scalability. By combining blockchain and machine learning, a secure and privacy-preserving smart community is established, promoting sustainable energy practices for a greener future.KEYWORDS: Machine learningblockchainRecalling-Enhanced Recurrent Neural Networkpeer-to-peer energy tradingsmart communityinternet of Things Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"A secure energy trading in a smart community by integrating Blockchain and machine learning approach","authors":"Athira Jayavarma, None Preetha, Manjula G Nair","doi":"10.1080/23080477.2023.2270820","DOIUrl":"https://doi.org/10.1080/23080477.2023.2270820","url":null,"abstract":"ABSTRACTIn today’s smart communities, small-scale energy systems are essential for sustainable development and efficient resource management. However, ensuring the confidentiality, safety, and accurate prediction of energy consumption patterns in energy trading is a major challenge. To address these issues, an innovative solution that synergistically combines two cutting-edge technologies: blockchain and machine learning is proposed. This paper unveils a novel approach that harmoniously merges blockchain with the Recalling-Enhanced Recurrent Neural Network (RERNN) to revolutionize energy trading systems called ‘Blockchain-Enhanced Energy Trading with Recalling-Enhanced Recurrent Neural Network (BET-RERNN).’ Data from IoT-enabled smart devices is securely stored in blockchain blocks, ensuring data integrity and immutability. Blockchain’s decentralized nature creates a trust-less environment for energy trading, protecting the privacy and anonymity of participants while maintaining transparency. At the heart of our system lies the advanced machine-learning capabilities of the RERNN model. By processing the data stored on the blockchain, RERNN accurately predicts optimal power generation for small-scale energy systems, enabling smart communities to make informed decisions and optimize their energy consumption. The BET-RERNN scheme provides a plethora of strengths. First, participants can securely engage in energy trading without compromising sensitive information, fostering a more resilient and efficient market. Second, blockchain technology ensures that all energy-related data is protected from tampering and unauthorized access, ensuring system reliability and trust. An in-depth comparison of RERNN’s performance to traditional General Regression Neural Network (GRNN) and Gradient Boost Decision Tree (GBDT) methods is conducted. To verify the strategy’s effectiveness, MATLAB simulations are employed, demonstrating its real-world applicability and scalability. By combining blockchain and machine learning, a secure and privacy-preserving smart community is established, promoting sustainable energy practices for a greener future.KEYWORDS: Machine learningblockchainRecalling-Enhanced Recurrent Neural Networkpeer-to-peer energy tradingsmart communityinternet of Things Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"24 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135413040","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}
Pub Date : 2023-10-17DOI: 10.1080/23080477.2023.2270819
Ayushi Thakur, Umesh Kumar Vates, Sanjay Mishra
ABSTRACTThe present study aimed to optimize the composition of 3D printing critical process parameters (nozzle temperature, layer thickness, and printing speed) to maximize the tensile strength and flexural strength of the biodegradable 3D printed PLA specimen using response surface methodology. For this purpose, after using the CCD of experiments with three independent parameters with two levels, 20 flat PLA parts were produced with an FDM-based 3D printer. The mechanical behavior of the 3D-printed PLA part was investigated, and a model was developed from the three parameters to get the scientific information to optimize the responses. As a result, it was noticed that the layer thickness and nozzle temperature greatly influenced mechanical response. One of the major aspects of the coronary stent is the mechanical behavior should be in accordance with the medical requirements such as flexibility, which is very necessary to facilitate the placement of the vessel in the artery, and sufficient radial rigidity is also required to support the vessel. Based on this aspect the identified responses are tensile and flexural strength.KEYWORDS: FDMPLAtensile & flexural strengthresponse surface methodologycentral composite designoptimization AcknowledgmentsThe support from Amity University CAM LAB is gratefully acknowledged.Disclosure statementNo potential conflict of interest was reported by the author(s).Author(s) contributionAuthor(s) contribution in the manuscript entitled ‘Prediction of Mechanical Properties of FDM printed PLA parts using response surface methodology’ is as follows: Ayushi Thakur is a Research Scholar at Amity University Uttar Pradesh, Noida, India. She is pursuing Ph.D. in Mechanical Engineering. She has done the experimental investigation of optimization parameters for 3D printed parts using Minitab software. Dr. Umesh Kumar Vates is an Associate professor at the Mechanical Engineering Department of Amity University, Uttar Pradesh, India. He has completed his Ph.D. in Mechanical Engineering from IIT Dhanbad (An Institute of National Importance). His role is as an expert in this work while monitoring and motivating the above PhD scholar. He has suggested the optimization technique in this research work. Dr. Sanjay Mishra is an Associate Professor at Madan Mohan Malviya University of Technology, Gorakhpur, India. He has motivated the above PhD scholar and interpreted the optimized results.Future Scope of the workIn the future, further efforts will be dedicated to Design optimizations of PLA stent structure by FEM and investigating its function in a simulated plaque artery.
{"title":"Parametric optimization of 3D-printed PLA part using response surface methodology for mechanical properties","authors":"Ayushi Thakur, Umesh Kumar Vates, Sanjay Mishra","doi":"10.1080/23080477.2023.2270819","DOIUrl":"https://doi.org/10.1080/23080477.2023.2270819","url":null,"abstract":"ABSTRACTThe present study aimed to optimize the composition of 3D printing critical process parameters (nozzle temperature, layer thickness, and printing speed) to maximize the tensile strength and flexural strength of the biodegradable 3D printed PLA specimen using response surface methodology. For this purpose, after using the CCD of experiments with three independent parameters with two levels, 20 flat PLA parts were produced with an FDM-based 3D printer. The mechanical behavior of the 3D-printed PLA part was investigated, and a model was developed from the three parameters to get the scientific information to optimize the responses. As a result, it was noticed that the layer thickness and nozzle temperature greatly influenced mechanical response. One of the major aspects of the coronary stent is the mechanical behavior should be in accordance with the medical requirements such as flexibility, which is very necessary to facilitate the placement of the vessel in the artery, and sufficient radial rigidity is also required to support the vessel. Based on this aspect the identified responses are tensile and flexural strength.KEYWORDS: FDMPLAtensile & flexural strengthresponse surface methodologycentral composite designoptimization AcknowledgmentsThe support from Amity University CAM LAB is gratefully acknowledged.Disclosure statementNo potential conflict of interest was reported by the author(s).Author(s) contributionAuthor(s) contribution in the manuscript entitled ‘Prediction of Mechanical Properties of FDM printed PLA parts using response surface methodology’ is as follows: Ayushi Thakur is a Research Scholar at Amity University Uttar Pradesh, Noida, India. She is pursuing Ph.D. in Mechanical Engineering. She has done the experimental investigation of optimization parameters for 3D printed parts using Minitab software. Dr. Umesh Kumar Vates is an Associate professor at the Mechanical Engineering Department of Amity University, Uttar Pradesh, India. He has completed his Ph.D. in Mechanical Engineering from IIT Dhanbad (An Institute of National Importance). His role is as an expert in this work while monitoring and motivating the above PhD scholar. He has suggested the optimization technique in this research work. Dr. Sanjay Mishra is an Associate Professor at Madan Mohan Malviya University of Technology, Gorakhpur, India. He has motivated the above PhD scholar and interpreted the optimized results.Future Scope of the workIn the future, further efforts will be dedicated to Design optimizations of PLA stent structure by FEM and investigating its function in a simulated plaque artery.","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136034632","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}
ABSTRACTHome area network (HAN) devices send the electricity consumption and other important data to the smart meter that must remain confidential from other devices. This paper proposes a novel one-dimensional hybrid chaotic map. The proposed map shows excellent chaotic properties when analyzed by bifurcation diagram, Lyapunov exponent & Shannon entropy. We further design an encryption strategy for data transfers between the smart meter and HAN devices. The proposed encryption scheme uses the existing lightweight key management in advanced metering infrastructure (LKM-AMI) architecture for data transfers, in which the encrypted data is transferred through an insecure channel and private keys are provided by trusted third party (TTP) through secure channels. The 2-way communication between HAN devices and the smart meter sends messages that are encrypted by using the proposed novel hybrid one-dimensional chaotic map. The encryption strategy mainly consists of three steps. In the first step, the seed and the control parameters are initialized. The second phase generates two intermediate keys using the proposed hybrid chaotic map. In the last phase, we encrypt the message by applying permutation followed by diffusion using intermediate keys. The proposed encryption strategy is resistant to various attacks.KEYWORDS: Smart gridsmart meterhome area networkchaos theoryAMIadvanced metering infrastructure Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementOn behalf of all authors, I, Lokesh Goel, declare that this article does not involve any generated or analyzed datasets. Data sharing does not apply to this study.Compliance with ethical standardsOn behalf of all authors, I, Lokesh Goel, hereby declare that: No external funding was obtained for this study.The authors and the submitted manuscript have no conflicts of interest to declare.This article does not include any studies involving human participants or animals that were performed by any of the authors.
摘要:家庭局域网(HAN)设备将用电量等重要数据发送到智能电表，这些数据必须对其他设备保密。提出了一种新的一维混合混沌映射。通过分岔图、李雅普诺夫指数和香农熵分析，表明该映射具有良好的混沌特性。我们进一步设计了智能电表和HAN设备之间数据传输的加密策略。该加密方案采用LKM-AMI (advanced metering infrastructure)架构中现有的轻量级密钥管理进行数据传输，加密后的数据通过不安全通道传输，私钥通过安全通道由可信第三方(trusted third party, TTP)提供。HAN设备和智能电表之间的双向通信发送的消息使用所提出的新型混合一维混沌映射进行加密。加密策略主要包括三个步骤。在第一步，初始化种子和控制参数。第二阶段使用所提出的混合混沌映射生成两个中间密钥。在最后一个阶段，我们通过使用中间密钥应用排列和扩散来加密消息。提出的加密策略能够抵抗各种攻击。关键词:智能电网智能电表家庭区域网络混沌理论先进计量基础设施披露声明作者未报告潜在的利益冲突。我，Lokesh Goel，代表所有作者声明，本文不涉及任何生成或分析的数据集。数据共享不适用于本研究。我，Lokesh Goel，谨代表所有作者声明:本研究未获得外部资助。作者与投稿文章无利益冲突需要申报。本文不包括任何作者进行的涉及人类参与者或动物的研究。
{"title":"Novel hybrid chaotic map-based secure data transmission between smart meter and HAN devices","authors":"Lokesh Goel, Hardik Chawla, Mohit Dua, Shelza Dua, Deepti Dhingra","doi":"10.1080/23080477.2023.2264564","DOIUrl":"https://doi.org/10.1080/23080477.2023.2264564","url":null,"abstract":"ABSTRACTHome area network (HAN) devices send the electricity consumption and other important data to the smart meter that must remain confidential from other devices. This paper proposes a novel one-dimensional hybrid chaotic map. The proposed map shows excellent chaotic properties when analyzed by bifurcation diagram, Lyapunov exponent & Shannon entropy. We further design an encryption strategy for data transfers between the smart meter and HAN devices. The proposed encryption scheme uses the existing lightweight key management in advanced metering infrastructure (LKM-AMI) architecture for data transfers, in which the encrypted data is transferred through an insecure channel and private keys are provided by trusted third party (TTP) through secure channels. The 2-way communication between HAN devices and the smart meter sends messages that are encrypted by using the proposed novel hybrid one-dimensional chaotic map. The encryption strategy mainly consists of three steps. In the first step, the seed and the control parameters are initialized. The second phase generates two intermediate keys using the proposed hybrid chaotic map. In the last phase, we encrypt the message by applying permutation followed by diffusion using intermediate keys. The proposed encryption strategy is resistant to various attacks.KEYWORDS: Smart gridsmart meterhome area networkchaos theoryAMIadvanced metering infrastructure Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementOn behalf of all authors, I, Lokesh Goel, declare that this article does not involve any generated or analyzed datasets. Data sharing does not apply to this study.Compliance with ethical standardsOn behalf of all authors, I, Lokesh Goel, hereby declare that: No external funding was obtained for this study.The authors and the submitted manuscript have no conflicts of interest to declare.This article does not include any studies involving human participants or animals that were performed by any of the authors.","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135899446","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}
Pub Date : 2023-09-23DOI: 10.1080/23080477.2023.2263239
Göknur Arzu Akyüz, Dursun Balkan
ABSTRACTSmartness journey involves increasing knowledge availability and maturity in technological, organizational, managerial, and human dimensions for the transformation of an enterprise. This article implements a systematic methodology and multi-dimensional maturity assessment tool to measure the maturity in the mentioned dimensions by offering a Turkish case study. Contrary to frequent mention of sectors such as automotive in relation to smartness concept, this study offers a real-life application in an adverse sector: glass balcony manufacturing. The methodology calculates weighted maturity index, determines the maturity levels for each dimension, and aggregates them into an overall enterprise maturity assessment. While applying the methodology, ratings are obtained via case company interview, and weight set utilized are determined via academic experts having practical sectoral expertise. Based on the findings, specific suggestions are provided to the case company for smart manufacturing implementation in multiple dimensions for their smartness journey. The study is original in comprehensively handling all maturity dimensions; demonstrating how smartness maturity can be practically measured in a case company by a flexible and weighted approach; obtaining simple, easy-to-interpret measurements; and authenticity of the sector.KEYWORDS: Smart manufacturingmaturity assessmenttechnological maturityTurkey Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Smart manufacturing maturity assessment: a Turkish case study in glass balcony manufacturing enterprise","authors":"Göknur Arzu Akyüz, Dursun Balkan","doi":"10.1080/23080477.2023.2263239","DOIUrl":"https://doi.org/10.1080/23080477.2023.2263239","url":null,"abstract":"ABSTRACTSmartness journey involves increasing knowledge availability and maturity in technological, organizational, managerial, and human dimensions for the transformation of an enterprise. This article implements a systematic methodology and multi-dimensional maturity assessment tool to measure the maturity in the mentioned dimensions by offering a Turkish case study. Contrary to frequent mention of sectors such as automotive in relation to smartness concept, this study offers a real-life application in an adverse sector: glass balcony manufacturing. The methodology calculates weighted maturity index, determines the maturity levels for each dimension, and aggregates them into an overall enterprise maturity assessment. While applying the methodology, ratings are obtained via case company interview, and weight set utilized are determined via academic experts having practical sectoral expertise. Based on the findings, specific suggestions are provided to the case company for smart manufacturing implementation in multiple dimensions for their smartness journey. The study is original in comprehensively handling all maturity dimensions; demonstrating how smartness maturity can be practically measured in a case company by a flexible and weighted approach; obtaining simple, easy-to-interpret measurements; and authenticity of the sector.KEYWORDS: Smart manufacturingmaturity assessmenttechnological maturityTurkey Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135966735","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}
Pub Date : 2023-09-23DOI: 10.1080/23080477.2023.2260023
B. S. Sharmila, Rohini Nagapadma
ABSTRACTOver the past few years, many intellectuals have focused on unsupervised learning for anomaly detection in IoT networks. Deploying an unsupervised Autoencoder algorithm for Intrusion Detection System (IDS) is computationally intensive for IoT devices with limited resources. In this work, we propose two distinct AI models using Post-Training Quantization; Quantized Autoencoder float16 (QAE-float16) and Quantized Autoencoder uint8 (QAE-uint8). QAE models are derived using Autoencoder models, which work on the assumption of generating high Reconstruction Error (RE) for anomaly data. Post Training Quantization includes pruning, clustering, and Quantization techniques. The proposed models were tested against the RT-IoT23 dataset, which includes normal and attack traces. This study is focused on the three major types of attacks, namely SSH brute-force, UFONet and DDoS (Distributed Denial of Service) exploitation. Since these attacks are the gateway for future exploitation. The model performance evaluated on IoT devices reveals that QAE-uint8 is the most efficient model by a wide margin, with average memory utilization decreased by 70.01%, memory size compressed by 92.23%, and peak CPU utilization decreased by 27.94%. Therefore, the proposed QAE-uint8 model has the potential to be used in low-power IoT Edge devices to detect anomalies.KEYWORDS: Anomaly detectionartificial intelligenceautoencodersIoTIDSpost-quantization training Disclosure statementNo potential conflit of interest was reported by the authors.
{"title":"QAE-IDS: DDoS anomaly detection in IoT devices using Post-Quantization Training","authors":"B. S. Sharmila, Rohini Nagapadma","doi":"10.1080/23080477.2023.2260023","DOIUrl":"https://doi.org/10.1080/23080477.2023.2260023","url":null,"abstract":"ABSTRACTOver the past few years, many intellectuals have focused on unsupervised learning for anomaly detection in IoT networks. Deploying an unsupervised Autoencoder algorithm for Intrusion Detection System (IDS) is computationally intensive for IoT devices with limited resources. In this work, we propose two distinct AI models using Post-Training Quantization; Quantized Autoencoder float16 (QAE-float16) and Quantized Autoencoder uint8 (QAE-uint8). QAE models are derived using Autoencoder models, which work on the assumption of generating high Reconstruction Error (RE) for anomaly data. Post Training Quantization includes pruning, clustering, and Quantization techniques. The proposed models were tested against the RT-IoT23 dataset, which includes normal and attack traces. This study is focused on the three major types of attacks, namely SSH brute-force, UFONet and DDoS (Distributed Denial of Service) exploitation. Since these attacks are the gateway for future exploitation. The model performance evaluated on IoT devices reveals that QAE-uint8 is the most efficient model by a wide margin, with average memory utilization decreased by 70.01%, memory size compressed by 92.23%, and peak CPU utilization decreased by 27.94%. Therefore, the proposed QAE-uint8 model has the potential to be used in low-power IoT Edge devices to detect anomalies.KEYWORDS: Anomaly detectionartificial intelligenceautoencodersIoTIDSpost-quantization training Disclosure statementNo potential conflit of interest was reported by the authors.","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135966219","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}
Pub Date : 2023-09-16DOI: 10.1080/23080477.2023.2258643
Yuvraj V. Parkale, Sanjay L. Nalbalwar
ABSTRACTIn this article, we have investigated the 1-D discrete wavelet transform (DWT)-based measurement matrices for electrocardiogram (ECG) compression. Moreover, the current work examines the suitability of the diverse DWT matrices, namely Symlets, Battle, Coiflets, Vaidyanathan, and Beylkin wavelet families, for ECG compression. Furthermore, this article shows the comparative performance study of the proposed DWT matrices with conventional deterministic and random measurement matrices. Overall, the Battle1 wavelet-based measurement matrices demonstrate the enhanced performance against the db3, coif5, and sym6 based measurement matrices in terms of Percentage Root-Mean Squared Difference (PRD), Root Mean Square Error (RMSE), and Signal-to-Noise Ratio (SNR). Finally, it was seen that the proposed Battle1 matrix demonstrates the improved performance against the conventional measurement matrices such as the Karhunen–Loeve transform (KLT), Discrete Cosine Transform (DCT) matrix, and random Hadamard measurement matrix. Thus, the result shows the adequacy of DWT measurement matrices for the compression of ECG.KEYWORDS: ECG compressionCompressed sensing (CS)Wavelet transform Disclosure statementNo potential conflict of interest was reported by the author(s).Ethics Approval and Consent to ParticipateThe authors declare that they have no human participants, their data or biological material used in this work.Consent for PublicationInformed consent was obtained from all authors included in the study.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/23080477.2023.2258643Additional informationFundingThe author(s) reported that there is no funding associated with the work featured in this article.
{"title":"Compressed sensing for ECG signal compression using DWT based sensing matrices","authors":"Yuvraj V. Parkale, Sanjay L. Nalbalwar","doi":"10.1080/23080477.2023.2258643","DOIUrl":"https://doi.org/10.1080/23080477.2023.2258643","url":null,"abstract":"ABSTRACTIn this article, we have investigated the 1-D discrete wavelet transform (DWT)-based measurement matrices for electrocardiogram (ECG) compression. Moreover, the current work examines the suitability of the diverse DWT matrices, namely Symlets, Battle, Coiflets, Vaidyanathan, and Beylkin wavelet families, for ECG compression. Furthermore, this article shows the comparative performance study of the proposed DWT matrices with conventional deterministic and random measurement matrices. Overall, the Battle1 wavelet-based measurement matrices demonstrate the enhanced performance against the db3, coif5, and sym6 based measurement matrices in terms of Percentage Root-Mean Squared Difference (PRD), Root Mean Square Error (RMSE), and Signal-to-Noise Ratio (SNR). Finally, it was seen that the proposed Battle1 matrix demonstrates the improved performance against the conventional measurement matrices such as the Karhunen–Loeve transform (KLT), Discrete Cosine Transform (DCT) matrix, and random Hadamard measurement matrix. Thus, the result shows the adequacy of DWT measurement matrices for the compression of ECG.KEYWORDS: ECG compressionCompressed sensing (CS)Wavelet transform Disclosure statementNo potential conflict of interest was reported by the author(s).Ethics Approval and Consent to ParticipateThe authors declare that they have no human participants, their data or biological material used in this work.Consent for PublicationInformed consent was obtained from all authors included in the study.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/23080477.2023.2258643Additional informationFundingThe author(s) reported that there is no funding associated with the work featured in this article.","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135305602","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}
Pub Date : 2023-09-10DOI: 10.1080/23080477.2023.2256531
Sannistha Banerjee, Partha Sarathee Bhowmik
A new machine learning-based method to classify the different transient events in distributed generation (DG) system has been proposed in this article. An existing hybrid DG-based network which consists of three microgrids (MGs), i.e. thermal, wind, and solar power, is used as test network to create transient conditions for both the islanding and grid-connected circumstances. The transient case studies include the symmetrical and unsymmetrical fault at distribution line, intentional islanding, variation of power demand, switching of capacitor bank, addition of nonlinear load, motor starting condition, etc. This recommended methodology starts with generating the sampled voltage signals of three different phases of different locations, and each signal has been decomposed using discrete wavelet transform. The significant features are extracted from the computed energy values of detailed wavelet coefficient for co-training of fine K-nearest neighbor (KNN) and ensemble KNN classification in the following stage. The results and the performance indices of the trained classifiers prove that the proposed method has been detected and classified all the transient events with 98% accuracy. Such type of multiple transient event classification in MG by a single algorithm is truly beneficial with respect to the power quality issues of modern power system.
{"title":"Multiclass transient event classification in hybrid distribution network based on co-training of fine KNN and ensemble KNN classifier","authors":"Sannistha Banerjee, Partha Sarathee Bhowmik","doi":"10.1080/23080477.2023.2256531","DOIUrl":"https://doi.org/10.1080/23080477.2023.2256531","url":null,"abstract":"A new machine learning-based method to classify the different transient events in distributed generation (DG) system has been proposed in this article. An existing hybrid DG-based network which consists of three microgrids (MGs), i.e. thermal, wind, and solar power, is used as test network to create transient conditions for both the islanding and grid-connected circumstances. The transient case studies include the symmetrical and unsymmetrical fault at distribution line, intentional islanding, variation of power demand, switching of capacitor bank, addition of nonlinear load, motor starting condition, etc. This recommended methodology starts with generating the sampled voltage signals of three different phases of different locations, and each signal has been decomposed using discrete wavelet transform. The significant features are extracted from the computed energy values of detailed wavelet coefficient for co-training of fine K-nearest neighbor (KNN) and ensemble KNN classification in the following stage. The results and the performance indices of the trained classifiers prove that the proposed method has been detected and classified all the transient events with 98% accuracy. Such type of multiple transient event classification in MG by a single algorithm is truly beneficial with respect to the power quality issues of modern power system.","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136071713","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}
Pub Date : 2023-08-24DOI: 10.1080/23080477.2023.2244190
Abdullah, A. Irwan, Nor Ain, Nafia Fitrawati, Sarmila
ABSTRACT The conversion of glycerol into high-value-added chemicals has begun to take the attention of researchers in recent years. Approximately 10% (w/w) of glycerol is produced as a by-product in the manufacture of biodiesel. Through a catalytic oxidation reaction, glycerol can be converted to formic acid. In this study, glycerol was converted to formic acid as the main product by involving a heterogeneous catalyst of Cu-formate which was embedded in the ash of gelam wood (Melaleuca leucadendron). The catalyst was made with variations in the ratio of Cu-formate to gelam wood ash (GWA) = 0.125; 0.25; 0.50; and 0.75 (w/w). The catalysts were characterized by FTIR, XRD, SEM-EDX, and SAA. The Cu-formate/GWA catalyst was then tested for its activity on glycerol oxidation with various catalyst ratios, amount of catalyst (0.5–3% (w/w)), reaction temperature (50°−90°C), and reaction time (1–11 hours). The results showed that the yield of the products increased with increase in catalyst ratio. The optimal amount of catalyst was used at a concentration of 2% (w/w), a reaction temperature of 70°C, and a reaction time of 3 hours. GRAPHICAL ABSTRACT
{"title":"Preparation and characterization of Cu-formate heterogeneous catalysts from ash of gelam wood (Melaleuca leucadendron) for glycerol oxidation","authors":"Abdullah, A. Irwan, Nor Ain, Nafia Fitrawati, Sarmila","doi":"10.1080/23080477.2023.2244190","DOIUrl":"https://doi.org/10.1080/23080477.2023.2244190","url":null,"abstract":"ABSTRACT The conversion of glycerol into high-value-added chemicals has begun to take the attention of researchers in recent years. Approximately 10% (w/w) of glycerol is produced as a by-product in the manufacture of biodiesel. Through a catalytic oxidation reaction, glycerol can be converted to formic acid. In this study, glycerol was converted to formic acid as the main product by involving a heterogeneous catalyst of Cu-formate which was embedded in the ash of gelam wood (Melaleuca leucadendron). The catalyst was made with variations in the ratio of Cu-formate to gelam wood ash (GWA) = 0.125; 0.25; 0.50; and 0.75 (w/w). The catalysts were characterized by FTIR, XRD, SEM-EDX, and SAA. The Cu-formate/GWA catalyst was then tested for its activity on glycerol oxidation with various catalyst ratios, amount of catalyst (0.5–3% (w/w)), reaction temperature (50°−90°C), and reaction time (1–11 hours). The results showed that the yield of the products increased with increase in catalyst ratio. The optimal amount of catalyst was used at a concentration of 2% (w/w), a reaction temperature of 70°C, and a reaction time of 3 hours. GRAPHICAL ABSTRACT","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"11 1","pages":"695 - 701"},"PeriodicalIF":2.3,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48859732","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}
Pub Date : 2023-08-11DOI: 10.1080/23080477.2023.2246285
Kavita Singh, U. Chauhan, L. Varshney
ABSTRACT Due to their impaired immune systems, lung cancer (LC) patients are especially sensitive to COVID-19 and are more susceptible to it as well as its related effects. The diagnosis, treatment and aftercare of LC patients are exceedingly difficult and time-consuming throughout an epidemic due to a multitude of factors. In these situations, the care of LC patients using cutting-edge technologies offers the potential to enhance the diagnosis, treatment, and advancements using machine learning (ML) algorithms and artificial intelligence (AI). The researchers might be able to differentiate between lung problems brought on by the corona virus and those brought on by, for example, chemotherapy and radiation, using therapeutic and imaging data as well as ML techniques. AI ensures that the appropriate individuals are enrolled in LC clinical research more effectively and rapidly than in the past, when it was done in a conventional and time-consuming manner. To effectively treat cancer patients and find new, more potent treatments, it is critical to move past traditional research approaches and make use of artificial intelligence and machine learning (AIML). When applied to various patient populations, AI based algorithms can swiftly identify lung cancer CT scans with COVID-19-linked pneumonia and accurately distinguish non-COVID connected pneumonia, which is significant for thoughtful mechanisms of an outbreak that is significant to AI. It is possible to use the present challenges and projected futures in this study to direct the best application of AI and ML in an epidemic. GRAPHICAL ABSTRACT
{"title":"Impact of covid-19 in lung cancer detection using image processing techniques, artificial intelligence and machine learning approaches","authors":"Kavita Singh, U. Chauhan, L. Varshney","doi":"10.1080/23080477.2023.2246285","DOIUrl":"https://doi.org/10.1080/23080477.2023.2246285","url":null,"abstract":"ABSTRACT Due to their impaired immune systems, lung cancer (LC) patients are especially sensitive to COVID-19 and are more susceptible to it as well as its related effects. The diagnosis, treatment and aftercare of LC patients are exceedingly difficult and time-consuming throughout an epidemic due to a multitude of factors. In these situations, the care of LC patients using cutting-edge technologies offers the potential to enhance the diagnosis, treatment, and advancements using machine learning (ML) algorithms and artificial intelligence (AI). The researchers might be able to differentiate between lung problems brought on by the corona virus and those brought on by, for example, chemotherapy and radiation, using therapeutic and imaging data as well as ML techniques. AI ensures that the appropriate individuals are enrolled in LC clinical research more effectively and rapidly than in the past, when it was done in a conventional and time-consuming manner. To effectively treat cancer patients and find new, more potent treatments, it is critical to move past traditional research approaches and make use of artificial intelligence and machine learning (AIML). When applied to various patient populations, AI based algorithms can swiftly identify lung cancer CT scans with COVID-19-linked pneumonia and accurately distinguish non-COVID connected pneumonia, which is significant for thoughtful mechanisms of an outbreak that is significant to AI. It is possible to use the present challenges and projected futures in this study to direct the best application of AI and ML in an epidemic. GRAPHICAL ABSTRACT","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"11 1","pages":"728 - 743"},"PeriodicalIF":2.3,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49395592","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}
Pub Date : 2023-08-07DOI: 10.1080/23080477.2023.2244262
C. Roy, D. Das
ABSTRACT In a day ahead electricity market, all candidates of the electricity market, i.e. electricity users, aggregator, and grid operator urge to grow individual profit, but it is quite challenging to assure profit for all the candidates at a time. In this work, a multi-objective problem is formulated by combining the concept of demand side management (DSM) and Dynamic Economic Emission Dispatch (DEED). The multi-objective DSM – DEED problem is optimized by class topper optimization algorithm. In addition, an energy management algorithm (EMA) is proposed for optimal power utilization from various energy sources and to match the load demand with generated energy in presence of uncertainties of RESs. To get an accurate model, a random forest regression-based machine learning approach is considered in this paper to predict load demand, wind, and solar power on an hourly basis for a span of 24 hours. The objective here is to optimally schedule load consumption and power generation patterns simultaneously for a day to improve load factor, minimize operational cost, and maximize the profit of all the candidates of the electricity market simultaneously. The simulation findings highlight the effects of the proposed EMA and modified DSM program on the smart grid’s economy and performance. GRAPHICAL ABSTRACT
{"title":"Performance enhancement of smart grid with demand side management program contemplating the effect of uncertainty of renewable energy sources","authors":"C. Roy, D. Das","doi":"10.1080/23080477.2023.2244262","DOIUrl":"https://doi.org/10.1080/23080477.2023.2244262","url":null,"abstract":"ABSTRACT In a day ahead electricity market, all candidates of the electricity market, i.e. electricity users, aggregator, and grid operator urge to grow individual profit, but it is quite challenging to assure profit for all the candidates at a time. In this work, a multi-objective problem is formulated by combining the concept of demand side management (DSM) and Dynamic Economic Emission Dispatch (DEED). The multi-objective DSM – DEED problem is optimized by class topper optimization algorithm. In addition, an energy management algorithm (EMA) is proposed for optimal power utilization from various energy sources and to match the load demand with generated energy in presence of uncertainties of RESs. To get an accurate model, a random forest regression-based machine learning approach is considered in this paper to predict load demand, wind, and solar power on an hourly basis for a span of 24 hours. The objective here is to optimally schedule load consumption and power generation patterns simultaneously for a day to improve load factor, minimize operational cost, and maximize the profit of all the candidates of the electricity market simultaneously. The simulation findings highlight the effects of the proposed EMA and modified DSM program on the smart grid’s economy and performance. GRAPHICAL ABSTRACT","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":"11 1","pages":"702 - 727"},"PeriodicalIF":2.3,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46815786","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}
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