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Design and implementation of personal online disk system based on DBN image recognition algorithm 3

Design and implementation of personal online disk system bas...

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3rd International conference on Signal Image Processing and Communication, ICSIPC 2023

作者： Wen, Mingxing Chen, Tongfei Tang, Wenyang Chen, Elaine Pan, Zhihong School of Information and Intelligent Engineering Guangzhou Xinhua University Guangzhou China School of Art Media Guangzhou Xinhua University Guangzhou China

ISBN: (纸本)9781510670945

With the growth of the Internet and the age of information explosion, more and more people need network drives to store and share the resources they find online. While downloading files to a local hard drive can ensure data security, this also brings with it many inconveniences, such as the need to share with others using third-party websites or communication software, or the need to transfer files using a removable hard drive. As a result, personal network drives have emerged on the market to make data sharing easier by transferring files to a server. This project will retain the basic functions of a traditional network drive system, such as file uploading, downloading, and deletion, while taking into account the security of the data and using deep learning-based DBN algorithms for image recognition, using artificial intelligence and image recognition techniques to detect pirated and illegal image material. Through the combined application of the above technologies, an efficient personal web-disk system is designed and implemented. © 2023 SPIE.

关键词： Image recognition

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Street cleanliness monitoring system using deep learning 3

Street cleanliness monitoring system using deep learning

引用

3rd International conference on intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Ghildiyal, Anirudh Sharma, Sachin Kumar, Ayush Apoorv Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9780738111834

Street cleanliness is a major concern for any country. It gives an idea of the country's well-being to its tourists and individuals in general. One of the major causes of illnesses in a city is typically the lack of cleanliness in the streets. In many ways, artificial intelligence today shapes society. The current artificial intelligence technologies investigation can regulate this street cleanliness problem. The street-clean dataset developed containing photos from the web. The images in the dataset are divided into three classes that can classify a model (good, average, and poor). The model produced more than 90% accuracy. Everyone in our society is concerned about the cleanliness of their buildings, whether it is their home or their workplace, but no one cares about working outside of the clean environment. The dirty surroundings are home may cause many virus diseases. © 2021 IEEE.

关键词： Deep learning

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PI parameter optimization of Asynchronous motor vector control based on MIGA 3

PI parameter optimization of Asynchronous motor vector contr...

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2022 3rd International conference on Mechatronics Technology and intelligent Manufacturing, ICMTIM 2022

作者： Jing, Bowen Guo, Zhijun Wu, Jingbo School of Vehicle and Traffic Engineering Henan University of Science and Technology Henan Luoyang471003 China

In motor control, the stable control of motor speed and the stability in the face of sudden torque are two important indicators to evaluate the quality of the control system. This paper proposes a multi-island genetic algorithm (MIGA) and fuzzy PI vector control algorithm. Combined with the asynchronous motor control method, the simulation test and bench test are carried out. Taking the Integral Time Multiplied by the Absolute Value of Error (ITAE) minimum as the optimization goal, the MIGA optimization algorithm is used to obtain the optimal solution for the quantization factors ke0, kec0, proportional factors kup, kui of the fuzzy PI controller. The optimal parameters are imported into the asynchronous motor control model for simulation verification and bench test verification. The simulation test shows that the fuzzy PI control method optimized by MIGA can effectively reduce the speed rise time, overshoot and steady-state error. When the load is suddenly applied, the speed drop is 5.16 r/min, and the adjustment time is 0.015s. The bench test shows that: under the changing load, the change trend of the speed curve of the simulation and the test is basically the same, which proves the correctness and effectiveness of the algorithm proposed in this paper. © Published under licence by IOP Publishing Ltd.

关键词： Induction motors

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A Mobile Based Garbage Collection System 3

A Mobile Based Garbage Collection System

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3rd International Informatics and Software Engineering conference, IISEC 2022

作者： Wijendra, D. De Silva, D.I. Gunawardhena, N.M. Wijayarathna, S.M. Aluthwaththage, J.H. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

Garbage disposal and collection is an ongoing global crisis amplified by the increasing world population, lack of funds and public awareness, and recently because of the Covid-19 pandemic. Information Technology can be utilized as a solution for the existing garbage collection methods that are old-fashioned, time-consuming, and energy-consuming due to the lack of a unified and consistent system that incorporates all the parties involved in garbage production and collection. A mobile-based garbage collection system is proposed to overcome the issues aforementioned through route and schedule optimization, AI chatbot, and optimized GPS tracking. The route and schedule optimization is achieved through vehicle routing problem with time windows(VRPTW) with synchronization and precedence that was optimized using LNS;the total travel cost went from 172 minutes to 144 minutes. The AI chatbot feature facilitates reporting garbage collection issues and complaints and enquiring about waste management tips (reduce, recycle, and reuse tips) to be used at home. The most prominent role of developing this AI chatbot is replacing the manual process of reporting garbage collection issues in Sri Lanka with an efficient and interactive way. The chatbot has waste management tips Q and A. In Optimized GPS Tracking, the user can use the map to find the nearest garbage disposal place based on the type of rubbish they generate. The truck driver can find the optimal path to the closest current garbage disposal centres and public trash bins and view the location of Homeowners on the map. The optimized path between two points is displayed based on distance, time, and fuel consumption. The main goal of the component is to show the location of garbage disposal bins and the optimal paths for truck drivers using Linear regression and the Node2vec algorithm. © 2022 IEEE.

关键词： Machine learning

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Stress analysis of key components of finger rehabilitation exoskeleton robot based on finite element method 3

Stress analysis of key components of finger rehabilitation e...

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2021 3rd International conference on Robotics, intelligent Control and Artificial Intelligence, ICRICA 2021

作者： Zhang, Qiuyue Lu, Guangda Chen, Ying Guo, Tinghang Han, Yu Qie, Tongtong Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China

This paper analyzes the flexion and extension of three joints of a single exoskeleton mechanical finger. According to the motion characteristics of the finger, a finger rehabilitation exoskeleton robot is designed to assist patients with hand function for rehabilitation training. The three-dimensional model was established by SolidWorks, and the initial state of the robot was set under the action of gravity. The finite element analysis method was used to analyze the stress of the key components, including calculate and analyze its stress and strain. The simulation showed that the maximum stress of key components in rehabilitation training was 2140 (Pa), which is far less than the yield strength of the material 60 (MPa), and the maximum deformation was 0.092 (mm). The simulation result revealed that our design did not lead to failure of mechanical structure, which would provide a certain reference basis for its structural optimization design. © Published under licence by IOP Publishing Ltd.

关键词： Stress analysis

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NRKM: News Recommendation Based on Knowledge Graph with Multi-View learning 22

NRKM: News Recommendation Based on Knowledge Graph with Mult...

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3rd International conference on Control, Robotics and intelligent System, CCRIS 2022

作者： Xu, Yong Chen, Bao Zhen, Jingru Ma, Guoqing Chen, Gongbin Liu, Yan Fang, Qun Anhui Normal University Wuhu Anhui China

ISBN: (纸本)9781450396851

News recommendation is necessary to help users find interesting news, improve their experience, and alleviate information overload. Accurately learning news and user representations is a key task in news recommendation systems. News texts usually contain rich entities, however existing recommender systems ignore the importance of news entities. In order to effectively alleviate the above problems, we design a multi-view news recommendation system based on knowledge graph. First, with news headlines, summaries, categories, and knowledge graph features, we learn news representations using a graph interactive attention network and a multi-head attention mechanism. Second, we combine a recurrent neural network and an interactive attention network to learn user representations from user historical click news records. Finally, predict the probability that the user will click on the candidate news. This method effectively alleviates the problem that the current news recommendation model has a weak ability to capture news representations and user interest representations. Experiments on real datasets show that this method can effectively improve the performance of news recommendation. © 2022 ACM.

关键词： Knowledge graph

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A Supervised Method Base on Multi Labels for Vehicle Re-identification 3

A Supervised Method Base on Multi Labels for Vehicle Re-iden...

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3rd International conference on Machine learning and Computer Application, ICMLCA 2022

作者： Zhu, Jingmiao Deng, Shizhuo Chen, Dongyue College of Information Science and Engineering Northeastern University Liaoning Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Liaoning Shenyang110819 China Ministry of Education Liaoning Shenyang110819 China

ISBN: (纸本)9781510664814

With the rise of smart city construction, the importance of vehicle re-identification based on video surveillance has become increasingly prominent. The task of vehicle re-identification mainly focuses on recognizing the same vehicle image under different cameras. In this paper, we propose a multi-label fusion framework for vehicle re-identification based on VIT network. We design a method of anti-angle distortion data augmentation to solve the problem that the performance of VIT is limited by the relative position relationship inside the vehicle structure and the angle deviation caused by the different relative positions of camera and vehicle. At the same time, the key position weight information module is inserted into the coding layer to improve the network's attention to key information. Finally, in view of the insufficient use of the location information between patches in the VIT network, we design a location code based on the Minkowski distance metric to add the relative location relationships within the vehicle individuals to the network. The experiment shows that under the same conditions, our vehicle weight recognition system is superior to most of the more advanced work in vehicle-ID and VERI776 datasets compared with the most advanced supervised vehicle weight recognition work. © 2023 SPIE.

关键词： Cameras

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Design and optimization of intelligent Control Unit for Enhanced Electric Motorcycle Performance

Design and Optimization of Intelligent Control Unit for Enha...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Edward Boris Paraduan Manurung Jason Atmadja Dept. Mechatronics Department Swiss German University Serpong Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

This research paper addresses the challenge of enhancing driving performance in electric motorcycles, particularly during slow-speed driving and slope starts. The current system utilizes a BLDC motor control alongside standard switches for lights, brakes, and a display gauge cluster. The objective is to introduce an Electronic Control Unit (ECU) to augment the system’s capabilities. The proposed ECU will be integrated with the existing setup to enable easy programmability and precise control over motor acceleration, especially at low speeds. The system will feature modes for slow acceleration, fast acceleration, and hill-start assist, all governed by a microcontroller. The ECU will also record critical data such as angle, RPM, and torque applied to the motorcycle’s wheels, which can be accessed via an SD card for future analysis or optimization. The results demonstrate that the new ECU significantly improves driving performance and safety. This study concludes that the implementation of an intelligent ECU can effectively enhance electric motorcycle performance, providing a basis for further advancements in this field.

关键词： Motor drives Torque Digital systems Microcontrollers Motorcycles Wheels Motors Safety optimization Standards

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Deep Neural Network Based intelligent Multi-Microgrid Energy Management

Deep Neural Network Based Intelligent Multi-Microgrid Energy...

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Sustainable Energy and Future Electric Transportation (SEFET), International conference on

作者： Sri Suresh Mavuri Jayaram Nakka Aswini Kotla Engineering Dept. National Institute of Technology Andhra Pradesh India

In this paper, a Deep Neural Network (DNN)-based efficient interconnected energy management system and Reinforcement learning (RL) technique are proposed. In this paper being investigated, interconnected M Gs are linked to a main grid, and to maintain local usage of power, they share power from the main grid. The objective from the viewpoint of the distribution system operator (DSO) is to reduce the demand-side peak-to-average ratio (PAR) but also to increase the profit from selling energy. DSO uses a DNN to train the Multi-MG response without having actual access to customer data in order to protect customer privacy. In addition, the DSO optimizes the prediction- based choice by applying the Monte Carlo method of reinforcement learning to determine its selling price strategy. Simulation findings from proposed computation deep learning method and comparison with conventional model-based techniques provide indication for the utility of the proposed method for resolving power system challenges with incomplete or ambiguous information.

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Efficient Chromosome Abnormal Detection using Yolo-V8 model

Efficient Chromosome Abnormal Detection using Yolo-V8 model

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International Symposium on Advanced Computing and Communication (ISACC)

作者： V. UmaRani Augustian Isaac K. Premnath Kannan G Sanjay A Hari Haran.L. K Dept of Computer Science and Engineering Saveetha Engineering College Chennai TamilNadu India Dept of Artificial Intelligence & Machine Learning Saveetha Engineering College Chennai TamilNadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Human chromosomes exhibit complex and intricate patterns, making their analysis and data interpretation challenging. Studying chromosomes is crucial for identifying genetic disorders, such as Down syndrome, and various types of cancer. Traditional karyotyping methods depend heavily on manual method, which causes human error and high cost. To address these challenges, a deep learning-based approach is proposed to automate the karyotyping process, improving both accuracy and efficiency while minimizing human involvement and errors. This research leverages advanced deep learning techniques, specifically the YOLOv8 object detection framework, to enhance chromosomal analysis. From experiments, the proposed YOLO V8 model achieves a 95.2% accuracy, surpassing other models such as DCNN, CNN, and VGG-19. This work represents a significant step toward developing a more reliable and efficient diagnostic tool for detecting genetic disorders.

关键词： Deep learning YOLO Training Analytical models Accuracy Manuals Genetics Reliability intelligent systems Biological cells

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