Pub Date : 2022-08-24DOI: 10.46300/9106.2022.16.134
Dhanalaxmi H R, A. G S, Sunil Kumar A V
The fundamental inputs used as a renewable energy source are wind speed and solar radiation. Both parameters are very nonlinear and depending on their surroundings. As a result, reliable prediction of these characteristics is required for usage in a variety of agricultural, industrial, transportation, and environmental applications since they reduce greenhouse gas emissions and are environmentally benign. In this study, we used a Feed Forward Back Propagation Neural Network (FFBPN) technique to predict proper data such as temperature, relative moisture, sun radiations, rain, and wind speed. The FFBPN will be trained in such a way that it can conduct hybrid forecasting with little changes to the programming codes, ranging from hourly (short term forecasting) to daily forecasting (medium term forecasting). This feature is one of the significant improvements, showing the suggested hybrid renewable energy forecasting system's high robustness. Because the hybrid forecasting system is a unique approach, the system's accuracy will be determined by comparing the findings to the corresponding values of the persistent model, a stand-alone forecasting model. Finally, the completely created system package could be sold and/or used in future research initiatives to help researcher’s analyses, validate, and illustrate their models across a variety of areas.
{"title":"Short Term Renewable Energy Forecasting Based on Feed Forward Back Propagation Neural Network Strategy","authors":"Dhanalaxmi H R, A. G S, Sunil Kumar A V","doi":"10.46300/9106.2022.16.134","DOIUrl":"https://doi.org/10.46300/9106.2022.16.134","url":null,"abstract":"The fundamental inputs used as a renewable energy source are wind speed and solar radiation. Both parameters are very nonlinear and depending on their surroundings. As a result, reliable prediction of these characteristics is required for usage in a variety of agricultural, industrial, transportation, and environmental applications since they reduce greenhouse gas emissions and are environmentally benign. In this study, we used a Feed Forward Back Propagation Neural Network (FFBPN) technique to predict proper data such as temperature, relative moisture, sun radiations, rain, and wind speed. The FFBPN will be trained in such a way that it can conduct hybrid forecasting with little changes to the programming codes, ranging from hourly (short term forecasting) to daily forecasting (medium term forecasting). This feature is one of the significant improvements, showing the suggested hybrid renewable energy forecasting system's high robustness. Because the hybrid forecasting system is a unique approach, the system's accuracy will be determined by comparing the findings to the corresponding values of the persistent model, a stand-alone forecasting model. Finally, the completely created system package could be sold and/or used in future research initiatives to help researcher’s analyses, validate, and illustrate their models across a variety of areas.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87518001","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 : 2022-07-27DOI: 10.46300/9106.2022.16.133
K. Mahafzah, M. Obeidat
Induction motors are commonly used in many different applications. The importance of these motors comes from their ruggedness, reliability, and low maintenance cost. Generally, the driving system of these motors can be vulnerable to injury of different types of faults during the operation, which leads to failure in continuous optimal operation of the system. This paper proposes a new simple algorithm to detect and classify the fault may occur in the driving system (in the Voltage Source Inverter VSI) of induction motor. The proposed method uses three parameters: First, the per phase average value of the stator current. Second, the Pulse Width Modulation (PWM) signal. Third, the switch voltage (drain to source voltage). The method is designed based on using the logic system. It is designed to decide whether the driving system is healthy or faulty. Moreover, the logic system can specify the fault location over the driving system. MATLAB 2020a is used to validate the results.
{"title":"Logic System Design for Fault Detection and Classification of Voltage Source Inverter Driving Induction Motor","authors":"K. Mahafzah, M. Obeidat","doi":"10.46300/9106.2022.16.133","DOIUrl":"https://doi.org/10.46300/9106.2022.16.133","url":null,"abstract":"Induction motors are commonly used in many different applications. The importance of these motors comes from their ruggedness, reliability, and low maintenance cost. Generally, the driving system of these motors can be vulnerable to injury of different types of faults during the operation, which leads to failure in continuous optimal operation of the system. This paper proposes a new simple algorithm to detect and classify the fault may occur in the driving system (in the Voltage Source Inverter VSI) of induction motor. The proposed method uses three parameters: First, the per phase average value of the stator current. Second, the Pulse Width Modulation (PWM) signal. Third, the switch voltage (drain to source voltage). The method is designed based on using the logic system. It is designed to decide whether the driving system is healthy or faulty. Moreover, the logic system can specify the fault location over the driving system. MATLAB 2020a is used to validate the results.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"16 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83067773","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 : 2022-07-26DOI: 10.46300/9106.2022.16.132
Shengbo Liu, Yi Guo, Yandong Zhao
With the development of sensor and image processing technology, computer vision plays an increasingly significant role in the chemical engineering because of its characteristics such as low cost, high resolution, and non-contact measurement. In this paper, the motion probability map can be obtained by sparse optical flow based on Harris corner point. Then the coarse contour of silicon dioxide particles which is the input of kernelized correlation filtering (KCF) algorithm can be generalized. KCF algorithm can easily complete tracking task under the influence of disturbance including light change, video shaking and so forth. A contour refining and tracking method are proposed. The geometric active contour (GAC) algorithm can use function as implicit expression of contour and can design the different energy functional to control contour evolution. By minimizing of energy functional, the refining contour is evolved. Then the target tracking is realized according to the refined contour.
{"title":"Research on Target Tracking Algorithm Based on Kernel Correlation","authors":"Shengbo Liu, Yi Guo, Yandong Zhao","doi":"10.46300/9106.2022.16.132","DOIUrl":"https://doi.org/10.46300/9106.2022.16.132","url":null,"abstract":"With the development of sensor and image processing technology, computer vision plays an increasingly significant role in the chemical engineering because of its characteristics such as low cost, high resolution, and non-contact measurement. In this paper, the motion probability map can be obtained by sparse optical flow based on Harris corner point. Then the coarse contour of silicon dioxide particles which is the input of kernelized correlation filtering (KCF) algorithm can be generalized. KCF algorithm can easily complete tracking task under the influence of disturbance including light change, video shaking and so forth. A contour refining and tracking method are proposed. The geometric active contour (GAC) algorithm can use function as implicit expression of contour and can design the different energy functional to control contour evolution. By minimizing of energy functional, the refining contour is evolved. Then the target tracking is realized according to the refined contour.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75521854","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 : 2022-07-26DOI: 10.46300/9106.2022.16.130
Q. Hamarsheh
This article proposes an ECG (electrocardiography) database system based on linear filtering, wavelet transform, PSD analysis, and adaptive AR modeling technologies to distinguish 19 ECG beat types for classification. This paper uses the Savitzky-Golay filter and wavelet transform for noise reduction, and wavelet analysis and AR modeling techniques for feature extraction to design a database system of AR coefficients describing the ECG signals with different arrhythmia types. In the experimental part of this work, the proposed algorithm performance is evaluated using an ECG dataset containing 19 different types including normal sinus rhythm, atrial premature contraction, ventricular premature contraction, ventricular tachycardia, ventricular fibrillation, supraventricular tachycardia, and other types from the MIT-BIH Arrhythmia Database. The simulation is performed in a MATLAB environment.
{"title":"Autoregressive Modeling based ECG Cardiac Arrhythmias’ Database System","authors":"Q. Hamarsheh","doi":"10.46300/9106.2022.16.130","DOIUrl":"https://doi.org/10.46300/9106.2022.16.130","url":null,"abstract":"This article proposes an ECG (electrocardiography) database system based on linear filtering, wavelet transform, PSD analysis, and adaptive AR modeling technologies to distinguish 19 ECG beat types for classification. This paper uses the Savitzky-Golay filter and wavelet transform for noise reduction, and wavelet analysis and AR modeling techniques for feature extraction to design a database system of AR coefficients describing the ECG signals with different arrhythmia types. In the experimental part of this work, the proposed algorithm performance is evaluated using an ECG dataset containing 19 different types including normal sinus rhythm, atrial premature contraction, ventricular premature contraction, ventricular tachycardia, ventricular fibrillation, supraventricular tachycardia, and other types from the MIT-BIH Arrhythmia Database. The simulation is performed in a MATLAB environment.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80953617","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 : 2022-07-26DOI: 10.46300/9106.2022.16.129
A. A. C., R. Dhanesha, Shrinivasa Naika C. L., K. A. N., Parinith S. Kumar, Parikshith P. Sharma
Agriculture and farming as a backbone of many developing countries provides food safety and security. Arecanut being a major plantation in India, take part an important role in the life of the farmers. Arecanut growth monitoring and harvesting needs skilled labors and it is very risky since the arecanut trees are very thin and tall. A vision-based system for agriculture and farming gains popularity in the recent years. Segmentation is a fundamental task in any vision-based system. A very few attempts been made for the segmentation of arecanut bunch and are based on hand-crafted features with limited performance. The aim of our research is to propose and develop an efficient and accurate technique for the segmentation of arecanut bunches by eliminating unwanted background information. This paper presents two deep-learning approaches: Mask Region-Based Convolutional Neural Network (Mask R-CNN) and U-Net for the segmentation of arecanut bunches from the tree images without any pre-processing. Experiments were done to estimate and evaluate the performances of both the methods and shows that Mask R-CNN performs better compared to U-Net and methods that apply segmentation on other commodities as there were no bench marks for the arecanut.
{"title":"Arecanut Bunch Segmentation Using Deep Learning Techniques","authors":"A. A. C., R. Dhanesha, Shrinivasa Naika C. L., K. A. N., Parinith S. Kumar, Parikshith P. Sharma","doi":"10.46300/9106.2022.16.129","DOIUrl":"https://doi.org/10.46300/9106.2022.16.129","url":null,"abstract":"Agriculture and farming as a backbone of many developing countries provides food safety and security. Arecanut being a major plantation in India, take part an important role in the life of the farmers. Arecanut growth monitoring and harvesting needs skilled labors and it is very risky since the arecanut trees are very thin and tall. A vision-based system for agriculture and farming gains popularity in the recent years. Segmentation is a fundamental task in any vision-based system. A very few attempts been made for the segmentation of arecanut bunch and are based on hand-crafted features with limited performance. The aim of our research is to propose and develop an efficient and accurate technique for the segmentation of arecanut bunches by eliminating unwanted background information. This paper presents two deep-learning approaches: Mask Region-Based Convolutional Neural Network (Mask R-CNN) and U-Net for the segmentation of arecanut bunches from the tree images without any pre-processing. Experiments were done to estimate and evaluate the performances of both the methods and shows that Mask R-CNN performs better compared to U-Net and methods that apply segmentation on other commodities as there were no bench marks for the arecanut.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73357142","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 : 2022-07-26DOI: 10.46300/9106.2022.16.127
Roselind Tei, Abdulrazak Yahya Saleh
The aim of this article is to analyse the Deep Spiking Neural Network (DSNN) performance in flood prediction. The DSNN model has been trained and evaluated with 30 years of data obtained from the Drainage and Irrigation (DID) department of Sarawak from 1989 to 2019. The model's effectiveness is measured and examined based on accuracy (ACC), RMSE, Sensitivity (SEN), specificity (SPE), Positive Predictive Value (PPV), NPV and the Average Site Performance (ASP). Furthermore, the proposed model's performance was compared with other classifiers that are commonly used in flood prediction to evaluate the viability and capability of the proposed flood prediction method. The results indicate that a DSNN model of greater ACC (98.10%), RMSE (0.065%), SEN (93.50%), SPE (79.0%), PPV (88.10%), and ASP (89.60 %) is predictable. The findings were fair and efficient and outperformed the other BP, MLP, SARIMA, and SVM classification models.
{"title":"Flood Prediction using Deep Spiking Neural Network","authors":"Roselind Tei, Abdulrazak Yahya Saleh","doi":"10.46300/9106.2022.16.127","DOIUrl":"https://doi.org/10.46300/9106.2022.16.127","url":null,"abstract":"The aim of this article is to analyse the Deep Spiking Neural Network (DSNN) performance in flood prediction. The DSNN model has been trained and evaluated with 30 years of data obtained from the Drainage and Irrigation (DID) department of Sarawak from 1989 to 2019. The model's effectiveness is measured and examined based on accuracy (ACC), RMSE, Sensitivity (SEN), specificity (SPE), Positive Predictive Value (PPV), NPV and the Average Site Performance (ASP). Furthermore, the proposed model's performance was compared with other classifiers that are commonly used in flood prediction to evaluate the viability and capability of the proposed flood prediction method. The results indicate that a DSNN model of greater ACC (98.10%), RMSE (0.065%), SEN (93.50%), SPE (79.0%), PPV (88.10%), and ASP (89.60 %) is predictable. The findings were fair and efficient and outperformed the other BP, MLP, SARIMA, and SVM classification models.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87591028","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 : 2022-07-26DOI: 10.46300/9106.2022.16.128
Haifeng Luo, Yue Han, J. Kan
This paper presents an improved SURF (Speeded Up Robust Features) for image matching which considers color information. Firstly, a new color difference scale space is constructed based on color information to detect feature point. Then we extracted a 192-dimensional vector to describe feature point, which includes a 64-dimensional vector representing the brightness information and a 128-dimensional vector representing the color information in a color image. Finally, in the process images matching, a new weighted Murkovski distance is used to measure the distance between two descriptors. From the experiment results, we can know that, compared the other methods, the feature points detection method proposed is more robust. The matching scores and precision of our method are dominant among different methods of color image matching. Compared with SURF, the number of feature points detected by the proposed method increases by 163%, the average matching scores and matching precision increase by 16% and 15.81% respectively.
{"title":"Improved SURF in Color Difference Scale Space for Color Image Matching","authors":"Haifeng Luo, Yue Han, J. Kan","doi":"10.46300/9106.2022.16.128","DOIUrl":"https://doi.org/10.46300/9106.2022.16.128","url":null,"abstract":"This paper presents an improved SURF (Speeded Up Robust Features) for image matching which considers color information. Firstly, a new color difference scale space is constructed based on color information to detect feature point. Then we extracted a 192-dimensional vector to describe feature point, which includes a 64-dimensional vector representing the brightness information and a 128-dimensional vector representing the color information in a color image. Finally, in the process images matching, a new weighted Murkovski distance is used to measure the distance between two descriptors. From the experiment results, we can know that, compared the other methods, the feature points detection method proposed is more robust. The matching scores and precision of our method are dominant among different methods of color image matching. Compared with SURF, the number of feature points detected by the proposed method increases by 163%, the average matching scores and matching precision increase by 16% and 15.81% respectively.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85544953","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 : 2022-07-26DOI: 10.46300/9106.2022.16.131
Kok Luong Goh, G. Ng, Muzaffar Hamzah, S. Chai
Semi-automated segmentation or more commonly known as interactive image segmentation is an algorithm that extracts a region of interest (ROI) from an image based on the input information from the user. The said algorithm will be repetitively fed with such input information until required region of interest is successfully segmented. To accelerate this segmentation procedure as well as enhancing the result, pre-processing steps can be applied. The application of superpixel is an example of such pre-processing step. Superpixel can be defined as a collection of pixels that share common features such as texture and colours. Though employed as pre-processing step in many interactive segmentation algorithms, to date, no study has been conducted to assess the effects of such incorporations on the segmentation algorithms. Thus, this study aims to address this issue. In this study, five different types of superpixels ranging from watershed, density, graph, clustering and energy optimization categories are evaluated. The superpixels generated by these five algorithms will be used on two interactive image segmentation algorithms: i) Maximal Similarity based Region Merging (MSRM) and ii) Graph-Based Manifold Ranking (GBMR) with single and multiple strokes on various images from the Berkeley image dataset. The result of testing had shown that MSRM achieved better result compared to GBMR in both single and multiple input strokes using SEEDS superpixel algorithm. This study summary concluded that at different superpixel algorithms produced different results and that it is not possible to single out one particular superpixel algorithm that can work well for all the interactive segmentation algorithms. As such, the key to achieving a decent segmentation result lies in choosing the right superpixel algorithms for a given interactive segmentation algorithm.
{"title":"Effects of Different Superpixel Algorithms on Interactive Segmentations","authors":"Kok Luong Goh, G. Ng, Muzaffar Hamzah, S. Chai","doi":"10.46300/9106.2022.16.131","DOIUrl":"https://doi.org/10.46300/9106.2022.16.131","url":null,"abstract":"Semi-automated segmentation or more commonly known as interactive image segmentation is an algorithm that extracts a region of interest (ROI) from an image based on the input information from the user. The said algorithm will be repetitively fed with such input information until required region of interest is successfully segmented. To accelerate this segmentation procedure as well as enhancing the result, pre-processing steps can be applied. The application of superpixel is an example of such pre-processing step. Superpixel can be defined as a collection of pixels that share common features such as texture and colours. Though employed as pre-processing step in many interactive segmentation algorithms, to date, no study has been conducted to assess the effects of such incorporations on the segmentation algorithms. Thus, this study aims to address this issue. In this study, five different types of superpixels ranging from watershed, density, graph, clustering and energy optimization categories are evaluated. The superpixels generated by these five algorithms will be used on two interactive image segmentation algorithms: i) Maximal Similarity based Region Merging (MSRM) and ii) Graph-Based Manifold Ranking (GBMR) with single and multiple strokes on various images from the Berkeley image dataset. The result of testing had shown that MSRM achieved better result compared to GBMR in both single and multiple input strokes using SEEDS superpixel algorithm. This study summary concluded that at different superpixel algorithms produced different results and that it is not possible to single out one particular superpixel algorithm that can work well for all the interactive segmentation algorithms. As such, the key to achieving a decent segmentation result lies in choosing the right superpixel algorithms for a given interactive segmentation algorithm.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90914867","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 : 2022-06-23DOI: 10.46300/9106.2022.16.126
B. Gilev, Gergana Vacheva, Plamen Stanchev, N. Hinov
In current research a hybrid autonomous supplying system for electric vehicles applications is presented. The hybrid system is consisted of fuel cell, micro gas turbine and supercapacitor. There are realized with averaged models in MATLAB/Simulink environment. The supplying elements are connected to a DC bus for charging a different type of EVs. In this case as a load is use two EVs: BMW-i3 and Nissan Leaf. This system can operate autonomously in hard-to-reach places where there is no supplying from the distributed grid and other sources. These places could be remote holiday villages, research centers positioned at hard-to-reach places and also for production of agricultural crops with the aids of electric vehicles. This requires the necessity for searching of different structural and conceptual solutions for production and storage of electric energy. An optimization problem is resolved in order to reduce the value of the capacitance of the supercapacitor with which it will decrease his price. Thus, it also decreases the price for construction of the entire charging station. Recently, the usage of natural gas and his transportation is well organized which can contribute for assuring of the reserved energy for the autonomous charging station.
{"title":"Design Consideration of Charging Station with Hybrid Energy Sources","authors":"B. Gilev, Gergana Vacheva, Plamen Stanchev, N. Hinov","doi":"10.46300/9106.2022.16.126","DOIUrl":"https://doi.org/10.46300/9106.2022.16.126","url":null,"abstract":"In current research a hybrid autonomous supplying system for electric vehicles applications is presented. The hybrid system is consisted of fuel cell, micro gas turbine and supercapacitor. There are realized with averaged models in MATLAB/Simulink environment. The supplying elements are connected to a DC bus for charging a different type of EVs. In this case as a load is use two EVs: BMW-i3 and Nissan Leaf. This system can operate autonomously in hard-to-reach places where there is no supplying from the distributed grid and other sources. These places could be remote holiday villages, research centers positioned at hard-to-reach places and also for production of agricultural crops with the aids of electric vehicles. This requires the necessity for searching of different structural and conceptual solutions for production and storage of electric energy. An optimization problem is resolved in order to reduce the value of the capacitance of the supercapacitor with which it will decrease his price. Thus, it also decreases the price for construction of the entire charging station. Recently, the usage of natural gas and his transportation is well organized which can contribute for assuring of the reserved energy for the autonomous charging station.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82613251","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 : 2022-06-01DOI: 10.46300/9106.2022.16.123
Taiguo Li, Tiance Zhang, Quanqin Li
Fatigue driving is one of the main causes of traffic accidents. The eye features are the important cues of fatigue detection. In order to improve the accuracy and robustness of detection based on a single eye feature, we propose a fatigue detection algorithm based on the eye feature (EFV) vector. Firstly, the coordinates of the eye region were localized with facial landmarks detector and the landmarks geometric relation (LGR) was calculated as a feature value. Secondly, a deep transfer learning network was designed to classify the driver eye state on a small dataset. The probability value of the eyes being open state was calculated. Then an eye feature vector was constructed to overcome the limitations of a single fixed threshold and a support vector machine (SVM) model was trained for eye state classification recognition. Finally, the performance of the proposed detection model was evaluated by the percentage of eyelid closure over time (PERCLOS) criterion. The results show that the accuracy of this model can reach 91.67% on the test database, which is higher than the single-feature-based method. This work lays a foundation for the online fatigue detection of train drivers and the deployment of the train driving monitoring system.
{"title":"Train Driver Fatigue Detection Using Eye Feature Vector and Support Vector Machine","authors":"Taiguo Li, Tiance Zhang, Quanqin Li","doi":"10.46300/9106.2022.16.123","DOIUrl":"https://doi.org/10.46300/9106.2022.16.123","url":null,"abstract":"Fatigue driving is one of the main causes of traffic accidents. The eye features are the important cues of fatigue detection. In order to improve the accuracy and robustness of detection based on a single eye feature, we propose a fatigue detection algorithm based on the eye feature (EFV) vector. Firstly, the coordinates of the eye region were localized with facial landmarks detector and the landmarks geometric relation (LGR) was calculated as a feature value. Secondly, a deep transfer learning network was designed to classify the driver eye state on a small dataset. The probability value of the eyes being open state was calculated. Then an eye feature vector was constructed to overcome the limitations of a single fixed threshold and a support vector machine (SVM) model was trained for eye state classification recognition. Finally, the performance of the proposed detection model was evaluated by the percentage of eyelid closure over time (PERCLOS) criterion. The results show that the accuracy of this model can reach 91.67% on the test database, which is higher than the single-feature-based method. This work lays a foundation for the online fatigue detection of train drivers and the deployment of the train driving monitoring system.","PeriodicalId":13929,"journal":{"name":"International Journal of Circuits, Systems and Signal Processing","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73158804","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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