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检索条件"任意字段=Knowledge-Based Software Engineering Conference"

共 17482 条记录，以下是931-940 订阅

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Research on Ornamental Fish Recognition Base on Improved Shufflenet V2 Network 4

Research on Ornamental Fish Recognition Base on Improved Shu...

引用

4th International conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2023

作者： Zhang, Xingyun Chen, Hongming Zhang, Longqing Chen, Qian Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China Guangdong University of Science and Technology School of Computer Science Dongguan523083 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

ISBN: (纸本)9798350329490

With the advancement of computer vision technology, fish recognition plays a crucial role in various fields such as aquaculture processing and disseminating fish knowledge. In this study, we propose an enhanced and lightweight fish recognition algorithm to accomplish fish classification recognition with limited data samples. based on the ShuffleNet V2 network, we incorporate Transfer Learning and the SE attention mechanism. Additionally, we replace the ReLu activation function with the AM-Softmax activation function. Comparative evaluations are conducted against Resnet50, MobileNet V2, MobileNet V3, and the original ShuffleNet V2 network model. Our experimental findings demonstrate that the proposed algorithm outperforms the aforementioned models, resulting in recognition accuracy improvements of 16.01%, 19.05%, 15.04%, and 14.44% respectively. The final recognition accuracy achieved is 97.20%, successfully attaining the desired outcome. © 2023 IEEE.

关键词： Computer vision

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Control Strategy based on Vision for Security in Communication Systems 5

Control Strategy Based on Vision for Security in Communicati...

引用

5th IEEE International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Bhati, Sunita Bhati, Anita Shah, Bharti Pachauri, Divya Alleema, N.Noor Patel, Mayank Jecrc University Jaipur India Jaipur National University Jaipur India Gls University Faculty of Computer Applications & It Ahmedabad India Nitra Technical Campus Ghaziabad India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Geetanjali Institute of Technical Studies Udaipur India

ISBN: (纸本)9798350383522

Forests are crucially important to maintaining ecological balance and providing all essential life services. Still, the constant protection against illegal, destructive exploitation is a difficult challenge that many people do not think about. - To address this issue, a new camera-based access control system for entry and exit to the protected forest areas that minimizes the number of people with insider knowledge is used for. Unauthorized access to sensitive or restricted areas is eliminated. - In an increasingly vulnerable world where greedy exploitation and human invaders abound, it has now become an urgent need to look after forests. To protect protected forest areas against such threats as loss of habitat, illegal logging, and poaching and the damage done to ecosystems (once their borders are crossed), one must rely on stringent security measures. - Traditional forest preservation strategies lack comprehensive monitoring and access control. But that's why security and refurbishment of forests are such pressing issues now-and why this project is so vital. - Our system aspires to provide real-time surveillance, access control, and automated alerts if any unauthorized human activity occurs within protected forest areas;for this we need cutting edge hardware and software technologies. The main goal is to create a secure, reliable, efficient access control system for protected forest areas, which presents special problems. - The System will use a camera and software on the basis of the ESP32 to capture and analyze images for identity verification. Unauthorized individuals trying to gain entry will trigger an alert for subsequent investigation or logging. - The study of the camera-based access control system covers the design, installation and testing as well as its operation. This includes hardware/software components and system architecture, which integrates with the Blynk cloud-based server to provide data storage and analytics. - Emphasis: there should be a conce

关键词： alert and notification cloud flood monitoring iot

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A New Online Continuing Education Course on Control engineering 62

A New Online Continuing Education Course on Control Engineer...

引用

62nd IEEE conference on Decision and Control (CDC)

作者： Maciejowski, Jan Univ Cambridge Dept Engn Cambridge England

ISBN: (纸本)9798350301243

We describe an online 'continuing education' course on Control engineering for graduates of any branch of engineering or other scientific discipline. The course runs mostly asynchronously over 8 weeks, with the expectation that each student will devote 7-9 hours per week to it. Initially, motivational material is presented in the form of examples of control systems and their benefits. The feedback structure is emphasised, but feedforward, cascade and multivariable structures are also discussed. Sensors and actuators are introduced, several examples of each being given. Mathematical analysis is introduced after a qualitative understanding of feedback has been established. Emphasis is given throughout the course to PID controllers, including their implementation and limitations, as well as approaches to tuning them. 'Classical' frequency-domain analysis and design methods for SISO systems are presented and emphasised. Later parts of the course cover more advanced material such as state feedback, observers, and LQG controllers. More advanced material, including MPC, adaptive and robust control, is introduced very briefly. The course is assessed by graded assignments based on a nonlinear model of an industrial process. Students develop a working knowledge of Matlab and Simulink software during the course.

关键词： Continuing education online course mature students control engineering

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Android Threat Detection Using Deep Learning (CNN)

Android Threat Detection Using Deep Learning (CNN)

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2024 International conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Benedict, J.N. Praveen, J. Santhosh Kumar, G. Senthil Pandi, S. Rajalakshmi Engineering College Department of CSE Chennai India

ISBN: (纸本)9798350381887

Man-made consciousness (computer-based intelligence) has become progressively well known. Is quickly progressing inside the field of software engineering. Throughout the long term we've seen an ascent, in the quantity of utilizations using man-made intelligence across spaces. Profound realizing, which falls under the umbrella of AI includes utilizing models to handle existing errands. This approach empowers us to extricate highlights bringing about better precision for acknowledgment purposes. Malware alludes to unsafe programming that can get to client's secret data for purposes. With the flood in cell phone utilization, we've additionally noticed an expansion in malware focusing on these gadgets. While numerous malware discovery arrangements depend on application marks their adequacy is lessening as malware advances. To battle this challenge a few distributions have proposed outfitting knowledge methods in this space. In this study dive into ideas connected with Android stages. Investigate profound learning calculations such, as Convolutional Neural Network (CNN). Furthermore, I examine an answer in light of document portrayal and familial order. © 2024 IEEE.

关键词： Convolutional neural networks

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Latex moisture content measurement and Analysis using RGB-based indices

Latex moisture content measurement and Analysis using RGB-ba...

引用

2023 IEEE International conference on Agrosystem engineering, Technology and Applications, AGRETA 2023

作者： Madzhi, Nina Korlina Ismail, Faridatul Aima Abdullah, Noor Ezan Universiti Teknologi MARA School of Electrical Engineering College of Engineering Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9798350347333

Malaysia Rubber Research Institute of Malaysia (RRIM) produces several rubber clone series at the research institute such as RRIM 2002, RRIM 2007, RRIM 2008, RRIM 2014 and RRIM 3001. We investigated the use of MCS600 Carl Zeiss spectrometer with the wavelength of the visible light spectrum (VIS) ranging from 400 to 900 to identify the rubber clone series based on the various moisture content. The obtained reflectance measurement index was next analyzed to observe the differences for these clones based on RGB color spectrum using SPSS (Statistical Package for the Social Sciences) software tool. From the results obtained, one-way ANOVA analysis were used to observe the relationship between rubber clone identification by using color spectrum and moisture content. From the findings, it can be concluded that green and blue color spectrum is able to differentiate the various clones at 100 % level of moisture content. © 2023 IEEE.

关键词： Color

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A Lithium-Ion Battery Cathode Material Literature Entity Recognition Method based on Deep Learning 4th

A Lithium-Ion Battery Cathode Material Literature Entity Rec...

引用

4th International conference on Data and Information in Online Environments

作者： Yin, Ziyu Lei, Gang Xiao, Jianmao Qiu, Xinji Zhang, Qian Chang, Lei Chen, Haohui Wu, Musheng Zhao, Xushan Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China Jiangxi Normal Univ Dept Phys Nanchang 330022 Jiangxi Peoples R China Contemporary Amperex Technol Co Ltd Innovat Lab Ningde 352100 Fujian Peoples R China Jiangxi Prov Engn Res Ctr Blockchain Data Secur & Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9783031807121;9783031807138

With the continuous deepening of research on lithium-ion battery materials, the number of literature in related fields has shown explosive growth, posing a challenge to researchers in terms of literature reading and information screening. Named entity recognition technology can automatically mark the pre-defined entity content in the literature, helping researchers to quickly locate the key information of the articles, and improve the reading rate of the literature. In this paper, the classification system of lithium-ion battery materials literature is constructed through the combination of automation and manual analysis, and the named entity recognition operation is conducted. We adopt the named entity recognition model based on BiLSTM-CRF, combined with Bert word embedding technology, to automatically identify and extract entities in the literature, and develope an automatic document annotation tool. Our work provides strong support for researchers to quickly understand the literature content and key information, and lays a foundation for subsequent relationship extraction and the construction of a knowledge graph.

关键词： Lithium-ion Battery Materials Information Extraction Classification System Named Entity Identification

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Big Data & Artificial Intelligence & software engineering (ICBASE), International conference on

作者： Ya Zhou Chen Chen Guimin Huang School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

In the research methods of sentence similarity, sentence similarity is often calculated from the semantic aspect, while the influence of syntactic structure is ignored. We propose an enhanced knowledge language representation model (ExtKBRCNN) based on CNN and Bi-GRU, which effectively uses the fine-grained word relations in the knowledge base to evaluate semantic similarity and models the relationship between knowledge structure and text structure. In order to make full use of the syntactic information of the sentence, we also propose a dependency tree kernel-based method (Dep-SIF), which combines syntactic information and semantic features to evaluate syntactic similarity. Finally, we propose a comprehensive model that integrates semantic and syntactic information to comprehensively evaluate sentence similarity. Experimental results show that the accuracy of the model on the MRPC dataset is 77.63% and the F1 value is 83.90%.

关键词： knowledge engineering Accuracy Semantics knowledge based systems Syntactics Natural language processing software engineering

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Employing Live Scripts for Implementing Virtual Laboratories and Activities

Employing Live Scripts for Implementing Virtual Laboratories...

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2023 ASEE Annual conference and Exposition - The Harbor of engineering: Education for 130 Years, ASEE 2023

作者： Hill, Rick Department of Mechanical Engineering University of Detroit Mercy United States

Significant research exists demonstrating the benefits of active and inquiry-based instruction for student engagement, learning, and knowledge retention. The emergence of ever improving software tools provide instructors valuable resources for developing virtual activities to be used within lecture courses, or in place of physical laboratory experiments. This paper describes the use of the MATLAB Live Editor for creating interactive live scripts. These live scripts combine code, formatted text, graphics, and live controls such as numeric sliders, buttons, and drop-down lists, for the creation of lectures and virtual activities that illustrate complex topics through interactive figures and animations. When deployed as in-class activities for students and as interactive homework assignments, students are able to build intuition for course material and construct meaning for themselves. This paper provides techniques and examples for developing and employing live scripts for teaching a range of topics from mathematics, engineering, and science. The author has piloted these techniques in online and in-person courses related to modeling and control over the past two years at University of Detroit Mercy. Student survey data from the winter 2023 semester indicates students found significant value from the live script activities implemented in their course. © American Society for engineering Education, 2023.

关键词： Students

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Data-to-text Generation with Data Control and Multi-loss Fusion 2

Data-to-text Generation with Data Control and Multi-loss Fus...

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2nd International conference on Frontiers of Artificial Intelligence and Machine Learning, FAIML 2023

作者： Luo, Weier Yue, Xi Zhong, Chaotie Chengdu University of Information Technology Chengdu610225 China Sichuan Province Engineering Technology Research Center of Support Software of Informatization Application China

ISBN: (纸本)9798400707544

The goal of data-to-text generation is to generate fluent and fidelity natural language based on input data. At present, most models focus on data planning. Generating text according to the planning can effectively improve the fidelity to the generated text, but they lack attention to the fluency of the generated text, resulting in poor readability of the generated text. In order to address this issue, we propose a data-to-text generation model based on a pre-training language model. A control module is cleverly designed inside the language model to improve the fidelity to generated text. In training stage, multiple loss functions are cleverly designed to optimize the fluency and fidelity of generated text. Finally, the experimental results of RotoWire datasets show that the strong prior knowledge based on the Generative Pre-Training 2.0 (GPT2) can effectively improve the fluency of the generated text, and the proposed model in this paper can generate and control the fluency and fidelity of the generated text simultaneously. © 2023 Copyright held by the owner/author(s).

关键词： knowledge based systems

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Unsupervised Cross-Domain Adaptation through Mutual Mean Learning and GANs for Person Re-identification 3

Unsupervised Cross-Domain Adaptation through Mutual Mean Lea...

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3rd IEEE International conference on Artificial Intelligence, ICAI 2023

作者： Yao, Leethar Lin, Bo-Yu Ul Haq, Qazi Mazhar Ul Islam, Ihtesham National Taipei University of Technology Department of Electrical Engineering Taipei Taiwan Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan National University of Sciences and Technology Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350322125

Unsupervised cross-domain adaptation is a challenging task for person re-identification due to the unavailability of target domain labels. Among existing methods, pseudo-Iabels-based methods have considerable performance but most of them use target domain data without labels which are challenging difficult for the target model to learn enough features. In this paper, we use generative based models that generate more target data. In cooperation with the generative model, a mutual learning model is used to transfer knowledge of one model to another model that ultimately improves overall model performance. Ex-tensive experiments are performed on Duke and Market datasets that significantly achieve improved performance in comparison to state-of-the-art methods. © 2023 IEEE.

关键词： Generative adversarial networks

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