In India a large part of goods transportation is carried out by sea, leading to an emerging requirement for remote maritime patrolling system, which also serves as an asset during wartime and peacetime for defence. In this research paper, we propose an automated maritime patrolling solution by making a Deep Learning Model Pipeline for Ship Detection from satellite images using existing State of the art Object Detection Algorithms like Faster-RCNN, SSD, YOLOv3, and YOLOv4. We compare results based on various Evaluation Metrics. Further we also release our own dataset which consists of around 300 satellite images of the top 13 busiest Sea-ports of India. After performing the validations, we found that the YOLO v4 displayed the best re-sults with a balanced mAP and FPS score to detect the ships in the satellite images.
{"title":"Monitoring Maritime Traffic with Ship Detection via YOLOv4","authors":"Gaurav Verma, Aditya Gupta, Shobhit Bansal, Himanshu Dhiman","doi":"10.1109/AISP53593.2022.9760632","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760632","url":null,"abstract":"In India a large part of goods transportation is carried out by sea, leading to an emerging requirement for remote maritime patrolling system, which also serves as an asset during wartime and peacetime for defence. In this research paper, we propose an automated maritime patrolling solution by making a Deep Learning Model Pipeline for Ship Detection from satellite images using existing State of the art Object Detection Algorithms like Faster-RCNN, SSD, YOLOv3, and YOLOv4. We compare results based on various Evaluation Metrics. Further we also release our own dataset which consists of around 300 satellite images of the top 13 busiest Sea-ports of India. After performing the validations, we found that the YOLO v4 displayed the best re-sults with a balanced mAP and FPS score to detect the ships in the satellite images.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"46 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87050822","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760530
Ramesh Babu Pedditi, Kumar Debasis
In a Wireless Sensor Network (WSN), sensor nodes are small autonomous devices that run on batteries. When the battery of a sensor node dies, it stops functioning. Generally, a sensor node loses more energy in communication than in sensing or processing activities. The proposed model, Low Energy Consuming Medium Access Control (LECMAC), minimizes energy consumption by reducing the number of communications in the network. Hence, the WSN lifetime increases. Two existing models, ES-MAC and LEACH, are compared to LECMAC in four various layouts. The experimental results show that LECMAC outperforms ES-MAC and LEACH in extending network lifetime.
{"title":"A Low Energy Consuming MAC Protocol for Wireless Sensor Networks","authors":"Ramesh Babu Pedditi, Kumar Debasis","doi":"10.1109/AISP53593.2022.9760530","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760530","url":null,"abstract":"In a Wireless Sensor Network (WSN), sensor nodes are small autonomous devices that run on batteries. When the battery of a sensor node dies, it stops functioning. Generally, a sensor node loses more energy in communication than in sensing or processing activities. The proposed model, Low Energy Consuming Medium Access Control (LECMAC), minimizes energy consumption by reducing the number of communications in the network. Hence, the WSN lifetime increases. Two existing models, ES-MAC and LEACH, are compared to LECMAC in four various layouts. The experimental results show that LECMAC outperforms ES-MAC and LEACH in extending network lifetime.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"73 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75294203","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760684
Faisal Farhan, Thahmidul Islam Nafi
Crime trend analysis has become a mandatory task as the scale of crime is increasing rapidly all over the globe. In recent years, Bangladesh has encountered various types of crimes and the rate is increasing with its increased population. Both physical and digital based crimes have become very common and their fatality can disrupt the advancement of any country. To tackle the situation, it is important to investigate and forecast the crime patterns that can assist the law enforcement agencies for easier investigation. Machine Learning and Deep Learning based crime analysis has become very popular as they can accurately and efficiently analyze large criminal dataset. In this study, the authors implemented machine learning techniques as well as ANN architecture to assess and forecast crime trends in Bangladesh. The dataset in this experiment was collected from the Bangladesh Police website that consists of criminal records of various crimes during the years 2010 to 2019. Authors evaluated the performance of ANN with other regression models named linear Regression and Support Vector Regression for crime prediction. The proposed model(ANN) outperforms other two models in this study compared to all the performance evaluation metrics. Hence ANN model is suggested by the authors to forecast and analyze future crime trends.
{"title":"ANN based approach to predict criminal trends in Bangladesh","authors":"Faisal Farhan, Thahmidul Islam Nafi","doi":"10.1109/AISP53593.2022.9760684","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760684","url":null,"abstract":"Crime trend analysis has become a mandatory task as the scale of crime is increasing rapidly all over the globe. In recent years, Bangladesh has encountered various types of crimes and the rate is increasing with its increased population. Both physical and digital based crimes have become very common and their fatality can disrupt the advancement of any country. To tackle the situation, it is important to investigate and forecast the crime patterns that can assist the law enforcement agencies for easier investigation. Machine Learning and Deep Learning based crime analysis has become very popular as they can accurately and efficiently analyze large criminal dataset. In this study, the authors implemented machine learning techniques as well as ANN architecture to assess and forecast crime trends in Bangladesh. The dataset in this experiment was collected from the Bangladesh Police website that consists of criminal records of various crimes during the years 2010 to 2019. Authors evaluated the performance of ANN with other regression models named linear Regression and Support Vector Regression for crime prediction. The proposed model(ANN) outperforms other two models in this study compared to all the performance evaluation metrics. Hence ANN model is suggested by the authors to forecast and analyze future crime trends.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"8 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79831263","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}
Diabetes is infamous for clutching individuals into the disarray of health degradation. The count of affected patients is rising with each passing day and an increasing number of them are afflicted with that variety of this disease which is of the incurable kind. The high cost of treatment is another major shortcoming associated with this despicable matter. These cases require immediate attention and sitting on the fence cannot be an option with respect to treatment procedures as wrong treatments can lead to early readmission. This can be very expensive for the patients and it begs the need to look for solutions that can help avoid such situations. Thus, predicting the readmission of patients is a leading matter of concern with respect to both treatment and cost effectiveness. To this end, we review the literature and develop a novel data-driven approach that draws from the previous works to make better predictions. It helps to find hidden dependencies in the data to outperform basic methods.
{"title":"A data-driven approach to predict the risk of readmission among patients with Diabetes Mellitus","authors":"Sachin Parajuli, Sanjaya Parajuli, Manoj Kumar Guragai","doi":"10.1109/AISP53593.2022.9760601","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760601","url":null,"abstract":"Diabetes is infamous for clutching individuals into the disarray of health degradation. The count of affected patients is rising with each passing day and an increasing number of them are afflicted with that variety of this disease which is of the incurable kind. The high cost of treatment is another major shortcoming associated with this despicable matter. These cases require immediate attention and sitting on the fence cannot be an option with respect to treatment procedures as wrong treatments can lead to early readmission. This can be very expensive for the patients and it begs the need to look for solutions that can help avoid such situations. Thus, predicting the readmission of patients is a leading matter of concern with respect to both treatment and cost effectiveness. To this end, we review the literature and develop a novel data-driven approach that draws from the previous works to make better predictions. It helps to find hidden dependencies in the data to outperform basic methods.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"66 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79988449","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760615
Ankam Anirudh, U. Nanda, Milan Biswal
The binary tree adder is a critical component of most digital circuit designs and is often used in digital signal processors and other data processing units. The key component of DSP is the binary adder, which is the basic unit that contains all the inputs and outputs of a binary adder structure. Many researches continue to analyze and improve the performance of the binary adders. In VLSI, Binary adders are one of the most essential logic elements within a digital system. A binary adder is a logic element that is commonly used in digital systems. They can also be utilized in non - ALU units such as memory addressing, dividers and multipliers. This paper compares the proposed Ripple Carry Adder with the existing RCA models and shows that it has better efficiency. In this project Xilinx-Vivado, Xilinx-ISE are the tools used for simulation, logical verification, and further synthesis purpose. This Design is implemented in Xilinx-Vivado 19.1 and Xilinx-ISE 14.7 version.
{"title":"Power and Area Efficient Multi-operand Binary Tree Adder","authors":"Ankam Anirudh, U. Nanda, Milan Biswal","doi":"10.1109/AISP53593.2022.9760615","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760615","url":null,"abstract":"The binary tree adder is a critical component of most digital circuit designs and is often used in digital signal processors and other data processing units. The key component of DSP is the binary adder, which is the basic unit that contains all the inputs and outputs of a binary adder structure. Many researches continue to analyze and improve the performance of the binary adders. In VLSI, Binary adders are one of the most essential logic elements within a digital system. A binary adder is a logic element that is commonly used in digital systems. They can also be utilized in non - ALU units such as memory addressing, dividers and multipliers. This paper compares the proposed Ripple Carry Adder with the existing RCA models and shows that it has better efficiency. In this project Xilinx-Vivado, Xilinx-ISE are the tools used for simulation, logical verification, and further synthesis purpose. This Design is implemented in Xilinx-Vivado 19.1 and Xilinx-ISE 14.7 version.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"2 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86731340","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}
Mobile robots is one of the most alluring fields and has constantly sparked the attention of industry, academia and research agencies to carry out advanced research. Microcomputers, single board computers, and embedded systems have all aided in the development of low-cost robots. In this study, several systems and methodologies are examined that are implemented on Raspberry Pi. Some of the state-of-the-art applications are discussed in this review such as Surveillance Robots, MultiUtility/Multi-functionality Robots, Line Following Robots, Obstacle Avoidance and Object Detection Robots, Crop Disease Detection, Super Market Service, Tunnel Mapping, Sprayer Robot, Wall Painting Robot, Rescue Robot, Fire Exterminating Robot. This work will help many open-source communities, where the raspberry pi is mostly used in Multi-Utility/Multifunctionality Robotic applications.
{"title":"A Review on Raspberry Pi and its Robotic Applications","authors":"Sudha Ellison Mathe, Ashok Chakravarthy Pamarthy, Hari Kishan Kondaveeti, Suseela Vappangi","doi":"10.1109/AISP53593.2022.9760590","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760590","url":null,"abstract":"Mobile robots is one of the most alluring fields and has constantly sparked the attention of industry, academia and research agencies to carry out advanced research. Microcomputers, single board computers, and embedded systems have all aided in the development of low-cost robots. In this study, several systems and methodologies are examined that are implemented on Raspberry Pi. Some of the state-of-the-art applications are discussed in this review such as Surveillance Robots, MultiUtility/Multi-functionality Robots, Line Following Robots, Obstacle Avoidance and Object Detection Robots, Crop Disease Detection, Super Market Service, Tunnel Mapping, Sprayer Robot, Wall Painting Robot, Rescue Robot, Fire Exterminating Robot. This work will help many open-source communities, where the raspberry pi is mostly used in Multi-Utility/Multifunctionality Robotic applications.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"35 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89343063","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760588
Kodali Radha, Mohan Bansal, Shaik Mulla Shabber
To combat the Covid-19 outbreak, the education system shifted away from the classroom to distinct e-learning on digital platforms, which made effective use of voice-based recognition systems, especially for preliterate children. Children’s speech recognition systems face multiple challenges owing to their immature vocal tracts, and they demand more intelligence due to the fact that children with diverse accents utter words differently. Accent refers to a unique style of pronouncing a language, particularly one associated with a specific nation, place, or socio-economic background. This paper aims to extract reliable acoustic and prosodic speech cues of accent for classification of native and non-native preschool children using harmonic pitch estimation along with Mel Frequency Cepstral Coefficients (MFCCs) to train the k-Nearest Neighbour (k-NN) classifier. The experimental results reveal that the proposed robust model outperforms various feature extractors in accent classification of native and non-native children in terms of accuracy & F-Measure and more discriminate against noisy environments.
{"title":"Accent Classification of Native and Non-Native Children using Harmonic Pitch","authors":"Kodali Radha, Mohan Bansal, Shaik Mulla Shabber","doi":"10.1109/AISP53593.2022.9760588","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760588","url":null,"abstract":"To combat the Covid-19 outbreak, the education system shifted away from the classroom to distinct e-learning on digital platforms, which made effective use of voice-based recognition systems, especially for preliterate children. Children’s speech recognition systems face multiple challenges owing to their immature vocal tracts, and they demand more intelligence due to the fact that children with diverse accents utter words differently. Accent refers to a unique style of pronouncing a language, particularly one associated with a specific nation, place, or socio-economic background. This paper aims to extract reliable acoustic and prosodic speech cues of accent for classification of native and non-native preschool children using harmonic pitch estimation along with Mel Frequency Cepstral Coefficients (MFCCs) to train the k-Nearest Neighbour (k-NN) classifier. The experimental results reveal that the proposed robust model outperforms various feature extractors in accent classification of native and non-native children in terms of accuracy & F-Measure and more discriminate against noisy environments.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"23 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82833919","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760668
Naveen Cheggoju, K. Madhavi, Mallela Jayadeep
In the recent years Artificial Intelligence (AI) has become a go to approach to solve any kind of real-world problems. In these pandemic times, there are many issues arising around the world. One of such issues is the identification of a masked person. It has become difficult to even recognize the very well-known persons due to masks, which has made the field of surveillance suffer a lot. The Object of this research is to solve this issue by reconstructing the face behind the mask using deep learning models. We are proposing a network called as Intelligent Design Painter (InDiP) with capabilities of reconstructing the face behind the mask at low resolutions. This would make the recognition of the masked persons easier. To achieve this target initially work has been done on reconstructing the general human images for fine tuning. After fine tuning the algorithm it has applied on masked faces to obtain the desired results. When checked the accuracy with the original image, the results seem satisfactory. In this approach a sequence Transformer is trained to forecast pixels based on data from a succession of 2D inputs without taking into consideration any of the 2D input structure. Even though the GPT-2 scale model has been trained on low-resolution ImageNet datasets without labels, the network is able to perform satisfactorily. So by using this model ONE can deduce the facial images of the masked people.
{"title":"InDiP: Intelligent Digital Painter for Low Resolution Face Inpainting","authors":"Naveen Cheggoju, K. Madhavi, Mallela Jayadeep","doi":"10.1109/AISP53593.2022.9760668","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760668","url":null,"abstract":"In the recent years Artificial Intelligence (AI) has become a go to approach to solve any kind of real-world problems. In these pandemic times, there are many issues arising around the world. One of such issues is the identification of a masked person. It has become difficult to even recognize the very well-known persons due to masks, which has made the field of surveillance suffer a lot. The Object of this research is to solve this issue by reconstructing the face behind the mask using deep learning models. We are proposing a network called as Intelligent Design Painter (InDiP) with capabilities of reconstructing the face behind the mask at low resolutions. This would make the recognition of the masked persons easier. To achieve this target initially work has been done on reconstructing the general human images for fine tuning. After fine tuning the algorithm it has applied on masked faces to obtain the desired results. When checked the accuracy with the original image, the results seem satisfactory. In this approach a sequence Transformer is trained to forecast pixels based on data from a succession of 2D inputs without taking into consideration any of the 2D input structure. Even though the GPT-2 scale model has been trained on low-resolution ImageNet datasets without labels, the network is able to perform satisfactorily. So by using this model ONE can deduce the facial images of the masked people.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"2014 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87934793","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760583
G. Ganesh, Annaadi Nithish Reddy, Annadi Sai Siddu Vardhan Reddy, N.V.Punith Chowdary
Smart home security plays a a significant job which makes a difference to give better security in home application. The proposed work is to convey a message to the door from a tablet or mobile device by using a Bluetooth system. This permits the individual to lock and open an entryway from inside or outside a house with a Bluetooth gadget accessible. The ideal motivation behind the work is in case the entryway isn’t locked on the primary floor or some other floor, the client from the beginning can open the entryway or open the entryway from a cell phone or PC, which causes an individual to lessen its energy or save time. The latest Arduino board, a Solenoid lock or Servo motor, and a Bluetooth module standard protocol for wireless communication are the main components of the system.
{"title":"Wireless Biometric Lock Using Arduino with the IoT","authors":"G. Ganesh, Annaadi Nithish Reddy, Annadi Sai Siddu Vardhan Reddy, N.V.Punith Chowdary","doi":"10.1109/AISP53593.2022.9760583","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760583","url":null,"abstract":"Smart home security plays a a significant job which makes a difference to give better security in home application. The proposed work is to convey a message to the door from a tablet or mobile device by using a Bluetooth system. This permits the individual to lock and open an entryway from inside or outside a house with a Bluetooth gadget accessible. The ideal motivation behind the work is in case the entryway isn’t locked on the primary floor or some other floor, the client from the beginning can open the entryway or open the entryway from a cell phone or PC, which causes an individual to lessen its energy or save time. The latest Arduino board, a Solenoid lock or Servo motor, and a Bluetooth module standard protocol for wireless communication are the main components of the system.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"3 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89063972","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760545
Muniprakash T, V. K, P. Naik, R. Varma, R. Supraja, Rahul Vijay Lingadhal
Electric vehicles are one of the highly on-going innovations that are getting the most attention. This paper provides an overview of EV technologies, charging systems, and optimization methodologies. First, discussed the main features of HEVs and EVs. Next, the present results of current research on charging methods for electric vehicles, such as BSS, WPT, and CC. This paper shows the review of optimization techniques of battery charging in electric vehicles.
{"title":"A review on optimization techniques of battery charging in electric vehicles","authors":"Muniprakash T, V. K, P. Naik, R. Varma, R. Supraja, Rahul Vijay Lingadhal","doi":"10.1109/AISP53593.2022.9760545","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760545","url":null,"abstract":"Electric vehicles are one of the highly on-going innovations that are getting the most attention. This paper provides an overview of EV technologies, charging systems, and optimization methodologies. First, discussed the main features of HEVs and EVs. Next, the present results of current research on charging methods for electric vehicles, such as BSS, WPT, and CC. This paper shows the review of optimization techniques of battery charging in electric vehicles.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"181 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76940941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：