Medical cyber physical systems are information applications of medical industry.A lagrge amount of medical data is stored in MCPS,and there are many challenges in the secure store and data sharing.Using blockchain technology into medical Cyber Physical system has become popular.Blockchain has remarkable features such as tamper proof and privacy protection, and has the function of protecting data in the medical Cyber Physical system.In this paper,we propose a hybrid blockchain,which applied private blockchain and consortium blockchain, After the medical source data is hashed, a hash tree is generated and stored in the private chain of the hospital. The hospital server extracts information to build a new transaction on the consortium chain.the system ensure the secure storage and fast access of data.Still,a threshold signature system is proposed.Aiming at the situation that medical accidents are easy to occur in multidisciplinary joint consultation in the medical process, this paper proposes to use threshold signature for joint consultation.Using the security and tamper-proof of the threshold signature, when the consensus is reached,treatment can be carried out and the medical data is uploaded to the consortium blockchain. The security analysis and performance analysis show that the scheme has advantages in safety and performance and is suitable for the medical environment.
{"title":"Blockchain-Based Medical Cyber Physical Systems With Decentralized Threshold signature Scheme","authors":"Xianfei Zhou, Hongfang Cheng, Min Li, Fulong Chen","doi":"10.46300/9106.2023.17.7","DOIUrl":"https://doi.org/10.46300/9106.2023.17.7","url":null,"abstract":"Medical cyber physical systems are information applications of medical industry.A lagrge amount of medical data is stored in MCPS,and there are many challenges in the secure store and data sharing.Using blockchain technology into medical Cyber Physical system has become popular.Blockchain has remarkable features such as tamper proof and privacy protection, and has the function of protecting data in the medical Cyber Physical system.In this paper,we propose a hybrid blockchain,which applied private blockchain and consortium blockchain, After the medical source data is hashed, a hash tree is generated and stored in the private chain of the hospital. The hospital server extracts information to build a new transaction on the consortium chain.the system ensure the secure storage and fast access of data.Still,a threshold signature system is proposed.Aiming at the situation that medical accidents are easy to occur in multidisciplinary joint consultation in the medical process, this paper proposes to use threshold signature for joint consultation.Using the security and tamper-proof of the threshold signature, when the consensus is reached,treatment can be carried out and the medical data is uploaded to the consortium blockchain. The security analysis and performance analysis show that the scheme has advantages in safety and performance and is suitable for the medical environment.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90164921","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 a new approach with stability analysis, simulation and experimental investigation of a sliding mode based estimator for rotor-position and torque-load calculation in high performance speed-sensor-less AC motor drive. The proposed algorithm is built based on the induction motor (IM) fluxes equations for two rotationg referential frames. The First equation calculates the stator flux vector while the second gives the rotor flux vector. Moreover, the stator flux equation is linked to a stator-flux rotating referential frame and the rotor flux equation is linked to a rotor-flux rotating referential frame. Among merits of the proposed technique is no necessity to rotor-speed measurement and adaptation. Thus, it is well suitable to the fully speed-sensorless scheme. The whole observer stability is verified by using of Lyapunov’s principle. Simulations are done by using Matlab-Simulink and experimental implementation is performed in order to prove the feasibility of proposed algorithm. The illustrated results are shown by using a DS1104 controller board.
{"title":"An Approach of Position and Torque Estimation for Induction Motor based Sensor-less Drive","authors":"A. Ahriche","doi":"10.46300/9106.2023.17.5","DOIUrl":"https://doi.org/10.46300/9106.2023.17.5","url":null,"abstract":"This paper presents a new approach with stability analysis, simulation and experimental investigation of a sliding mode based estimator for rotor-position and torque-load calculation in high performance speed-sensor-less AC motor drive. The proposed algorithm is built based on the induction motor (IM) fluxes equations for two rotationg referential frames. The First equation calculates the stator flux vector while the second gives the rotor flux vector. Moreover, the stator flux equation is linked to a stator-flux rotating referential frame and the rotor flux equation is linked to a rotor-flux rotating referential frame. Among merits of the proposed technique is no necessity to rotor-speed measurement and adaptation. Thus, it is well suitable to the fully speed-sensorless scheme. The whole observer stability is verified by using of Lyapunov’s principle. Simulations are done by using Matlab-Simulink and experimental implementation is performed in order to prove the feasibility of proposed algorithm. The illustrated results are shown by using a DS1104 controller board.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78345184","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}
Y. Ramalakshmanna, Dr. P. Shanmugaraja, D. V. R. Raju, Dr T.V. Hymalakshmi
Infinite Impulse Response (IIR) systems identification is complicated by traditional learning approaches. When reduced-order adaptive models are utilised for such identification, the performance suffers dramatically. The IIR system is identified as an optimization issue in this study. For system identification challenges, a novel population-based technique known as Elitist teacher learner-based optimization (ETLBO) is used to calculate the best coefficients of unknown infinite impulse response (IIR) systems. The MSE function is minimised and the optimal coefficients of an unknown IIR system are found in the system identification problem. The MSE is the difference between an adaptive IIR system's outputs and an unknown IIR system's outputs. For the unknown system coefficients of the same order and decreased order cases, exhaustive simulations have been performed. In terms of mean square error, convergence speed, and coefficient estimation, the results of actual and reduced-order identification for the standard system using the novel method outperform state-of-the-art techniques. For approximating the same-order and reduced-order IIR systems, four benchmark functions are examined utilizing GA, PSO, CSO, and BA. To demonstrate the improvements, the approach is evaluated on three conventional IIR systems of 2nd, 3rd, and 4th order models. On the basis of computing the mean square error (MSE) and fitness function, the suggested ETLBO approach for system identification is proven to be the best among others. Furthermore, it is confirmed that the suggested ETLBO method outperforms some of the other known system identification strategies. Finally, the efficiency of the dynamic nature of the control parameters of DE, TLBO, and BA in finding near parameter values of unknown systems is demonstrated through comparison data. The simulation results show that the suggested system identification approach outperforms the current methods for system identification.
{"title":"Adaptive Infinite Impulse Response System Identification Using Elitist Teaching-Learning- Based Optimization Algorithm","authors":"Y. Ramalakshmanna, Dr. P. Shanmugaraja, D. V. R. Raju, Dr T.V. Hymalakshmi","doi":"10.46300/9106.2023.17.1","DOIUrl":"https://doi.org/10.46300/9106.2023.17.1","url":null,"abstract":"Infinite Impulse Response (IIR) systems identification is complicated by traditional learning approaches. When reduced-order adaptive models are utilised for such identification, the performance suffers dramatically. The IIR system is identified as an optimization issue in this study. For system identification challenges, a novel population-based technique known as Elitist teacher learner-based optimization (ETLBO) is used to calculate the best coefficients of unknown infinite impulse response (IIR) systems. The MSE function is minimised and the optimal coefficients of an unknown IIR system are found in the system identification problem. The MSE is the difference between an adaptive IIR system's outputs and an unknown IIR system's outputs. For the unknown system coefficients of the same order and decreased order cases, exhaustive simulations have been performed. In terms of mean square error, convergence speed, and coefficient estimation, the results of actual and reduced-order identification for the standard system using the novel method outperform state-of-the-art techniques. For approximating the same-order and reduced-order IIR systems, four benchmark functions are examined utilizing GA, PSO, CSO, and BA. To demonstrate the improvements, the approach is evaluated on three conventional IIR systems of 2nd, 3rd, and 4th order models. On the basis of computing the mean square error (MSE) and fitness function, the suggested ETLBO approach for system identification is proven to be the best among others. Furthermore, it is confirmed that the suggested ETLBO method outperforms some of the other known system identification strategies. Finally, the efficiency of the dynamic nature of the control parameters of DE, TLBO, and BA in finding near parameter values of unknown systems is demonstrated through comparison data. The simulation results show that the suggested system identification approach outperforms the current methods for system identification.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76336558","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":"Unsupervised Deep Learning of Sparse Signals against Low-Rank Backgrounds with Application to Online Lung Sound Separation","authors":"","doi":"10.18178/ijsps.11.1.1-6","DOIUrl":"https://doi.org/10.18178/ijsps.11.1.1-6","url":null,"abstract":"","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80156350","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 : 2022-12-01DOI: 10.18178/ijsps.10.4.18-24
R. Sabre
Abstract— Consider a symmetric continuous time α stable process observed with an additive constant error. The objective of this paper is to give a non-parametric estimator of this error by using discrete observations. As the time of process is continuous and the observations are discrete, we encountered the aliasing phenomenon. Our process sample is taken in a way to circumvent the difficulty related to aliasing and we smoothed the periodogram by using Jackson Kernel. The rate of convergence of this estimator is studied when the spectral density is zero at origin. Few long memory processes are taken here as examples. We have applied our estimator to the concrete case of modeling noise of a bird captured under stress.
{"title":"Aliasing-Free and Additive Error in Mixed Spectra for Stable Processes. Application: Sound of a Bird just Captivated in Stress","authors":"R. Sabre","doi":"10.18178/ijsps.10.4.18-24","DOIUrl":"https://doi.org/10.18178/ijsps.10.4.18-24","url":null,"abstract":" Abstract— Consider a symmetric continuous time α stable process observed with an additive constant error. The objective of this paper is to give a non-parametric estimator of this error by using discrete observations. As the time of process is continuous and the observations are discrete, we encountered the aliasing phenomenon. Our process sample is taken in a way to circumvent the difficulty related to aliasing and we smoothed the periodogram by using Jackson Kernel. The rate of convergence of this estimator is studied when the spectral density is zero at origin. Few long memory processes are taken here as examples. We have applied our estimator to the concrete case of modeling noise of a bird captured under stress.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73650717","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 : 2022-10-07DOI: 10.46300/9106.2022.16.137
H. Fouad, Hesham Kamel, Adel Youssef
Telemedicine applications run at very low input voltages, necessitating the use of Great Precision Rectifier with high sensitivity to function at low input voltages. In this study, we used a 65 nm CMOS rectifier to achieve a 0.2V input voltage for Energy Harvesting Telemedicine application. The suggested rectifier, which has two-stage structure and operates at frequency of 2.4GHz, has been found to perform better in cases where the minimum operating voltage is lower than previously published papers, and the rectifier can operate over a wide range of low input voltage amplitudes. Full-Wave Fully gate cross-coupled Rectifiers (FWFR) CMOS Rectifier Efficiency at Freq of 2.4 GHz: With an input voltage amplitude of 2V, the minimum and maximum output voltages are 0.49V and 1.997V, respectively, with a peak VCE of 99.85 percent and a peak PCE of 46.86 percent. This enables the suggested rectifier to be used in a variety of vibration energy collecting systems, including electrostatic, electromagnetic, and piezoelectric energy harvesters. The proposed rectifier, which is built at 2.4GHz and has a two-stage structure, performs better in the event of low input voltage amplitude and has lower minimum operation voltage than previously published papers. Full-wave fully gate cross-coupled rectifiers (FWFR) CMOS Rectifier Performance Summary at Freq of 2.4 GHz: With a 2V input voltage amplitude, the minimum and maximum output voltages are 0.49V and 1.997V, respectively, with a maximum VCE of 99.85% and a maximum PCE of 46.86%.
{"title":"High Precision Low Input Voltage of 65nm CMOS Rectifier for Energy Harvesting using Threshold Voltage Minimization in Telemedicine Embedded System","authors":"H. Fouad, Hesham Kamel, Adel Youssef","doi":"10.46300/9106.2022.16.137","DOIUrl":"https://doi.org/10.46300/9106.2022.16.137","url":null,"abstract":"Telemedicine applications run at very low input voltages, necessitating the use of Great Precision Rectifier with high sensitivity to function at low input voltages. In this study, we used a 65 nm CMOS rectifier to achieve a 0.2V input voltage for Energy Harvesting Telemedicine application. The suggested rectifier, which has two-stage structure and operates at frequency of 2.4GHz, has been found to perform better in cases where the minimum operating voltage is lower than previously published papers, and the rectifier can operate over a wide range of low input voltage amplitudes. Full-Wave Fully gate cross-coupled Rectifiers (FWFR) CMOS Rectifier Efficiency at Freq of 2.4 GHz: With an input voltage amplitude of 2V, the minimum and maximum output voltages are 0.49V and 1.997V, respectively, with a peak VCE of 99.85 percent and a peak PCE of 46.86 percent. This enables the suggested rectifier to be used in a variety of vibration energy collecting systems, including electrostatic, electromagnetic, and piezoelectric energy harvesters. The proposed rectifier, which is built at 2.4GHz and has a two-stage structure, performs better in the event of low input voltage amplitude and has lower minimum operation voltage than previously published papers. Full-wave fully gate cross-coupled rectifiers (FWFR) CMOS Rectifier Performance Summary at Freq of 2.4 GHz: With a 2V input voltage amplitude, the minimum and maximum output voltages are 0.49V and 1.997V, respectively, with a maximum VCE of 99.85% and a maximum PCE of 46.86%.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88390399","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 : 2022-10-07DOI: 10.46300/9106.2022.16.138
B. Sucharitha, D. Sheela
Tensor decomposition methods have beenrecently identified as an effective approach for compressing high-dimensional data. Tensors have a wide range of applications in numerical linear algebra, chemo metrics, data mining, signal processing, statics, and data mining and machine learning. Due to the huge amount of information that the hyper spectral images carry, they require more memory to store, process and send. We need to compress the hyper spectral images in order to reduce storage and processing costs. Tensor decomposition techniques can be used to compress the hyper spectral data. The primary objective of this work is to utilize tensor decomposition methods to compress the hyper spectral images. This paper explores three types of tensor decompositions: Tucker Decomposition (TD_ALS), CANDECOMP/PARAFAC (CP) and Tucker_HOSVD (Higher order singular value Decomposition) and comparison of these methods experimented on two real hyper spectral images: the Salinas image (512 x 217 x 224) and Indian Pines corrected (145 x 145 x 200). The PSNR and SSIM are used to evaluate how well these techniques work. When compared to the iterative approximation methods employed in the CP and Tucker_ALS methods, the Tucker_HOSVD method decomposes the hyper spectral image into core and component matrices more quickly. According to experimental analysis, Tucker HOSVD's reconstruction of the image preserves image quality while having a higher compression ratio than the other two techniques.
张量分解方法最近被认为是一种有效的高维数据压缩方法。张量在数值线性代数、化学度量、数据挖掘、信号处理、静力学、数据挖掘和机器学习中有着广泛的应用。由于高光谱图像所携带的信息量巨大，需要更多的内存来存储、处理和发送。为了降低存储和处理成本，需要对高光谱图像进行压缩。张量分解技术可用于压缩高光谱数据。本文的主要目的是利用张量分解方法对高光谱图像进行压缩。本文探讨了三种张量分解:Tucker分解(TD_ALS)、CANDECOMP/PARAFAC (CP)和Tucker_HOSVD(高阶奇异值分解)，并在Salinas图像(512 x 217 x 224)和Indian Pines校正图像(145 x 145 x 200)两幅真实高光谱图像上进行了实验比较。PSNR和SSIM用于评估这些技术的工作效果。与CP和Tucker_ALS方法的迭代逼近方法相比，Tucker_HOSVD方法能够更快地将高光谱图像分解为核心矩阵和分量矩阵。实验分析表明，与其他两种技术相比，Tucker HOSVD重建的图像在保持图像质量的同时具有更高的压缩比。
{"title":"Compression of Hyper Spectral Images using Tensor Decomposition Methods","authors":"B. Sucharitha, D. Sheela","doi":"10.46300/9106.2022.16.138","DOIUrl":"https://doi.org/10.46300/9106.2022.16.138","url":null,"abstract":"Tensor decomposition methods have beenrecently identified as an effective approach for compressing high-dimensional data. Tensors have a wide range of applications in numerical linear algebra, chemo metrics, data mining, signal processing, statics, and data mining and machine learning. Due to the huge amount of information that the hyper spectral images carry, they require more memory to store, process and send. We need to compress the hyper spectral images in order to reduce storage and processing costs. Tensor decomposition techniques can be used to compress the hyper spectral data. The primary objective of this work is to utilize tensor decomposition methods to compress the hyper spectral images. This paper explores three types of tensor decompositions: Tucker Decomposition (TD_ALS), CANDECOMP/PARAFAC (CP) and Tucker_HOSVD (Higher order singular value Decomposition) and comparison of these methods experimented on two real hyper spectral images: the Salinas image (512 x 217 x 224) and Indian Pines corrected (145 x 145 x 200). The PSNR and SSIM are used to evaluate how well these techniques work. When compared to the iterative approximation methods employed in the CP and Tucker_ALS methods, the Tucker_HOSVD method decomposes the hyper spectral image into core and component matrices more quickly. According to experimental analysis, Tucker HOSVD's reconstruction of the image preserves image quality while having a higher compression ratio than the other two techniques.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79351017","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 : 2022-09-19DOI: 10.46300/9106.2022.16.136
C. Laxmi, Narendra Kumar, Rajendra Kumar Khad
This paper introduces to control a standalone hybrid renewable system, involving PV cell and wind turbines as the primary energy sources along with fuel cell and battery energy source as an emotionally supportive system. While trying to work on the solidness and security of the hybrid renewable system sustainable framework, a battery bank, is incorporated as supporting units, due to the discontinuous and fluctuation in primary energy sources commitment. In this paper we model an independent sustainable source micro grid with various sources, which are wind energy with PMSG, PV panel, Fuel cell and battery storage system. Analysis of each source is done under variation conditions and variation of source parameters, such as wind speed of wind turbine, illumination, temperature of PV cell and state of charge of the battery. If main power sources of PV panel and wind turbines is not available, the battery storage device act as backup supply for load. This storage source (battery) can be charged when abundance power is produced from the PV panel and wind generation system. Investigation on each sources with dynamic changes of boundaries are studied with siumulation results analysis is studied by utilizing MATLAB/ SIMULINK software.
{"title":"An Effective Load Management for Grid Connected Hybrid Energy Sources","authors":"C. Laxmi, Narendra Kumar, Rajendra Kumar Khad","doi":"10.46300/9106.2022.16.136","DOIUrl":"https://doi.org/10.46300/9106.2022.16.136","url":null,"abstract":"This paper introduces to control a standalone hybrid renewable system, involving PV cell and wind turbines as the primary energy sources along with fuel cell and battery energy source as an emotionally supportive system. While trying to work on the solidness and security of the hybrid renewable system sustainable framework, a battery bank, is incorporated as supporting units, due to the discontinuous and fluctuation in primary energy sources commitment. In this paper we model an independent sustainable source micro grid with various sources, which are wind energy with PMSG, PV panel, Fuel cell and battery storage system. Analysis of each source is done under variation conditions and variation of source parameters, such as wind speed of wind turbine, illumination, temperature of PV cell and state of charge of the battery. If main power sources of PV panel and wind turbines is not available, the battery storage device act as backup supply for load. This storage source (battery) can be charged when abundance power is produced from the PV panel and wind generation system. Investigation on each sources with dynamic changes of boundaries are studied with siumulation results analysis is studied by utilizing MATLAB/ SIMULINK software.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84307165","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 : 2022-09-16DOI: 10.46300/9106.2022.16.135
Yang Liu, Ze Chen, Zhongyan Liu, Xin Liu, Guochen Yu, Shun Na
The leakage of water in pipelines severely affects the environment and economy. However, there are limitations in the effectiveness of existing leak detection and localization techniques and methodologies. In this paper, we propose a novel leakage detection and localization method based on the multiple time-frequency features, a neural network, and an adaptive time delay estimation algorithm. First, we use spectral subtraction and wavelet denoising to reduce the effects of noise. In addition, to ensure and improve the accuracy of leakage detection in complex realistic environments, we propose the use of multi time-frequency features that can comprehensively represent the leak signal and make the neural network more robust to train a radial basis function (RBF)neural network to detect the leak signal. Further, we extract multiple features of the leakage signal and input into the RBF neural network to train. Moreover, to prevent the impulsive components of environmental noise and improve localization accuracy, we further propose the use of a fractional lower-order statistics (FLOS) based adaptive time delay estimation algorithm to estimate the time delay and locate the leakage. The simulation results show that the detection and localization performance of the proposed method is superior to those of existing schemes.
{"title":"Leakage Detection and Localization of Water Pipeline Using Multi-features and Adaptive Time Delay Estimation","authors":"Yang Liu, Ze Chen, Zhongyan Liu, Xin Liu, Guochen Yu, Shun Na","doi":"10.46300/9106.2022.16.135","DOIUrl":"https://doi.org/10.46300/9106.2022.16.135","url":null,"abstract":"The leakage of water in pipelines severely affects the environment and economy. However, there are limitations in the effectiveness of existing leak detection and localization techniques and methodologies. In this paper, we propose a novel leakage detection and localization method based on the multiple time-frequency features, a neural network, and an adaptive time delay estimation algorithm. First, we use spectral subtraction and wavelet denoising to reduce the effects of noise. In addition, to ensure and improve the accuracy of leakage detection in complex realistic environments, we propose the use of multi time-frequency features that can comprehensively represent the leak signal and make the neural network more robust to train a radial basis function (RBF)neural network to detect the leak signal. Further, we extract multiple features of the leakage signal and input into the RBF neural network to train. Moreover, to prevent the impulsive components of environmental noise and improve localization accuracy, we further propose the use of a fractional lower-order statistics (FLOS) based adaptive time delay estimation algorithm to estimate the time delay and locate the leakage. The simulation results show that the detection and localization performance of the proposed method is superior to those of existing schemes.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74349364","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 : 2022-09-01DOI: 10.18178/ijsps.10.3.14-17
{"title":"Recapitulation of Synthetic ECG Signal Generation methods and Analysis","authors":"","doi":"10.18178/ijsps.10.3.14-17","DOIUrl":"https://doi.org/10.18178/ijsps.10.3.14-17","url":null,"abstract":"","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78847995","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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