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An Energy Management Strategy in Hybrid Electric Vehicles: The Present and The Future Scenario 2

An Energy Management Strategy in Hybrid Electric Vehicles: T...

2nd international conference on Advancements in Electrical, Electronics, Communication, Computing and automation, ICAECA 2023

作者： Sabarish, G. Maria, Nivetha A Shanthini, M.K. Mayurappriyan, P.S. Ganesh, C. Shanmugasundaram, R. Dept of Electronics and Instrumentation Engineering Kumaraguru College of Technology Coimbatore India Dept of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India Dept of Electrical and Electronics Engineering Sri Ramakrishna College of Engineering Coimbatore India

ISBN: (纸本)9798350306811

There has been a significant increase in interest towards "The electrification of transportation"in the past decade. The environmental crisis caused due to global warming and the depletion of oil supplies are factors that have made the automobile sector adopt strategies to produce eco-friendly, sustainable vehicles instead of their commercial fuel propelled vehicles. Most governments have expressed their interest in alleviating the environmental impact caused due to vehicle pollution by introducing various policies that eventually help people switch to Electric Vehicles (EV). Though there has been a surge of interest among people to adopt environmentally friendly, economically affordable electric vehicles, the major concern is the state of charge and range offered by these EVs. The global automobile sector has started to invest in research dedicated to the energy management system in EVs to overcome these issues. The emergence of artificial intelligence (AI) and the advancement of control systems are clear signs for developing smart-energy management systems in EV. This paper reviews the methods of energy storage and usage in EV following the various Energy Management Systems (EMS) present in EV such as Rule-based EMS and Optimization-based EMS and its types. Also, it gives an insight into the emergence of futuristic AI-based chips that would revolutionize the energy-management system in E-vehicles. © 2023 IEEE.

关键词： Neural networks

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A multi-view comparative learning method for spatial transcriptomics data clustering

A multi-view comparative learning method for spatial transcr...

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2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Peng, Wei Zhang, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350337488

Clustering individual cells or spots based on their gene expression profiles in a spatial context is a powerful approach to uncovering the underlying biological diversity and relationships among cells. The intricate information within spatial transcriptomics data demands sophisticated algorithms that effectively integrate gene expression, cell position, and tissue image data for accurate cell or spot clustering. This work proposes a Multi-View Comparative Learning method for clustering Spatial Transcriptomics data (MVCLST). MVCLST first builds on two data views using gene expression profiles, cell space coordinates, and image features. Then it employs four different encoders to capture the common and private features of the two views. The model employs a contrastive learning loss to encourage effective interaction between the two views and ensure feature consistency. The shared and private features from both views are fused using corresponding decoders. Finally, the model employs the Leiden algorithm for downstream clustering of the learned features. We test the MVCLST method on a human dorsolateral prefrontal cortex dataset. The results show that MVCLST outperforms other state-of-the-art methods in most cases. Additionally, the clusters identified by MVCLST align closely with manual annotations and established neuroscience definitions. © 2023 IEEE.

关键词： contrast learning multiview learning spatial transcriptome data clustering

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Hybrid Algorithm of Gradient Boosted Decision Tree and Multiple Linear Regression and Its Application on Decesion Prediction 3

Hybrid Algorithm of Gradient Boosted Decision Tree and Multi...

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3rd IEEE international conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Wen, Yingzi He, Xin Lv, Dawei Li, Fei College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China College of Management Science Chengdu University of Technology Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9798350333862

Gradient boosted decision tree is a supervised learning algorithm model constructed by improving tree structure, replacing residual with gradient and using the concept of entropy in informatics. GBDT produces classification bias due to the imbalance of the sample. Therefore, we propose a way to accelerate the output quality of a decision tree by combining linear regression and GBDT and preprocessing sample data through linear regression before training. In the experiment, the new algorithm is applied to the nonlinear classification decision. It is found that the regression coefficient of the apportion of independent directors is 0.034, the regression coefficient is 0.712, the regression coefficient for table size is 0.197, the regression coefficient for the size of the board of directors is -0.197, and the regression coefficient for the share of institutional investors is -0.018. The regression coefficient for the institutional investor share is -0.018. It is found that this algorithm effectively improves classification accuracy and has good practical value. © 2023 IEEE.

关键词： Multiple linear regression

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Detection of a Novel Object-Detection-Based Cheat Tool for First-Person Shooter Games Using Machine Learning 21

Detection of a Novel Object-Detection-Based Cheat Tool for F...

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21st IEEE/ACIS international conference on Software Engineering Research, Management and Applications, SERA 2023

作者： Xiao, Zhang Goto, Takaaki Ghosh, Partha Kirishima, Tadaaki Tsuchida, Kensei Toyo University Graduate School of Information Sciences and Arts Saitama Japan Toyo University Faculty of Information Sciences and Arts Saitama Japan Academy of Technology Department of Computer Science and Engineering West Bengal India

ISBN: (纸本)9798350345889

Detection of novel game cheating tools is critical for ensuring fair online play. Such cheating tools are visual-based and effectively avoid detection because they do not change the data of game software. With the development and popularity of artificial intelligence technology, it has become easier for individuals to develop cheating tools, such as a new cheating tool for first-person shooter games that searches for characters on the game screen and automatically targets them. Therefore, in this study, a new cheat detection method is proposed using machine learning. The proposed method can be used to detect new cheating tools based on object detection. © 2023 IEEE.

关键词： Machine learning

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Underwater image recovery method considering target polarization characteristics 4

Underwater image recovery method considering target polariza...

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4th international conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Wang, Zhe Yu, Haiyang Peng, Jin School of Computer Science and Information Engineering Hefei University of Technology AnHui Hefei China

ISBN: (纸本)9798350326444

In underwater polarization imaging, the estimation of the polarization characteristics of the target is also the key to recover the original image. Most current recovery methods ignore the polarization characteristics of the target and require manual selection of the region for parameter estimation. This not only causes errors in the recovery results, but also has the limitation that a background region must exist. In this paper, we derive the reconstruction model of the original underwater image from the underwater imaging model and the polarization imaging model, considering the polarization characteristics of the target. In this paper, the background light polarization degree is obtained using the filtered median algorithm, and the bright channel principle is used to obtain the background light at infinity. Compared with the traditional method, the method in this paper can reduce the error. Finally, this paper makes improvements to the perfect reflection algorithm and enhances the recovered image. © 2023 IEEE.

关键词： Image enhancement

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Data-Driven Learning of Strong Conjunctive Invariants 23

Data-Driven Learning of Strong Conjunctive Invariants

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23rd international conference on Formal Methods in computer-Aided Design, FMCAD 2023

作者： Thakkar, Arkesh D'Souza, Deepak Indian Institute of Science Department of Computer Science and Automation Bengaluru India

ISBN: (纸本)9783854480600

Coming up with adequate inductive invariants is the key step in the deductive verification of programs. Moreover, coming up with strong inductive invariants is important in several application scenarios like compositional verification. Houdini-style approaches are popular techniques that learn the strongest conjunctive subset of a given set of constraints that is adequate to prove a given pre-post specification for a program. However, both the adequacy and strength of the learned invariants depend on the quality of the initial set of constraints. In this work, we propose a data-driven way of learning an initial set of constraints from sample runs of the program. We use a novel combination of linear regression for equality constraints and linear programming for inequality constraints. The evaluation of our technique shows that it is more efficient and leads to stronger adequate invariants than some of the state-of-the-art data-driven techniques. © 2023 FMCAD Association and individual authors.

关键词： Linear programming

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A Survey Paper on Weed Identification Using Deep Learning Techniques

A Survey Paper on Weed Identification Using Deep Learning Te...

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2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Gayathri, U. Praveena, V. Dr. N. G. P Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350348217

Agriculture is the fundamental art every country will embrace for its economy, which is necessary for all living things to live. The Farmers can reduce their total workload by applying technology to improve grain quality. The genuine steps that need to be taken by the farmers' weed control and eradication practices result in increased plant growth and improved water-use efficiency, thereby allowing them to maximize crop yields with less effort and a higher percentage of growth. An Automatic detection and classification of weeds can play an important role in weed management to maximize the use of resources and minimize the cost of weed control. It is critical to use deep learning technology to identify and estimate weed. This survey paper describes the progress in weed detection and classification using the most recent technologies in the field of artificial intelligence, and image processing techniques. It includes detailed literature on the major strategies. © 2023 IEEE.

关键词： Deep learning

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Visual Design Of Artificial Pancreas Glucose Control Method

Visual Design Of Artificial Pancreas Glucose Control Method

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2023 international Annual conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Zhou, Ying Song, Rui Zhuo Xia, Li Na School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education Beijing100083 China

ISBN: (纸本)9798350309003

The number of diabetic patients is predicted to increase and the age of diabetic patients is slowly becoming younger. Diabetes is generally categorized into the following types: type I diabetes, type II diabetes, gestational diabetes, and other types of diabetes caused by pancreatic disease, endocrine disease, medications, and other factors. The traditional method of treating diabetes involves injecting insulin using a device outside the body. This method is physically and psychologically harmful to the diabetic. Therefore, this paper focuses on the glycemic control of patients with type I diabetes who have completely lost their insulin secretion, and whose pancreas is unable to secrete insulin. Therefore, it is necessary to use a blood glucose sensor to measure the blood glucose concentration in the patient's body. Then the measured blood glucose concentration in the diabetic patient's body is fed back to the computer terminal. Finally the controller is utilized for regulation. This paper focuses on the regulation of blood glucose in the body of type I diabetic patients and combines a fuzzy controller and a PID controller to regulate the blood glucose concentration in type I diabetic patients. In this paper, the blood glucose-insulin metabolism models of healthy people and diabetic patients are firstly established. Then the parameter values of the PID controller are adjusted through several trials to build a fuzzy rule base. Then the fuzzy controller is designed so that the diabetic's blood glucose can be successfully tracked to the healthy person's blood glucose changes. In this way, the purpose of regulating the blood glucose concentration of diabetic patients is realized. In addition, on the basis of realizing the control of blood glucose concentration. In this paper, the GUI toolbox of MATLAB is also used to design the visualization interface for the simulation process and the results. © 2023 IEEE.

关键词： Insulin

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Intelligent Home IoT Intrusion Detection System Based on RISC-V 3

Intelligent Home IoT Intrusion Detection System Based on RIS...

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3rd IEEE international conference on Power, Electronics and computer Applications, ICPECA 2023

作者： Liang, Quanyi Xie, Shaobin Cai, Boyang School of Computer Science and Technology South China Business College Guangdong University of Foreign Studies Guangzhou510545 China

ISBN: (纸本)9781665472784

This paper analyzes the security requirements of smart home IoT system, and constructs its intrusion detection system environment on RISC-V. By simulating the attack behavior, the key nodes of the Internet of Things are tested for intrusion detection and warning. The test results show that the deployment of intrusion detection system in smart home IoT can effectively improve its security. © 2023 IEEE.

关键词： Internet of things

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