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检索条件"任意字段=7th International Conference on the Art, Science, and Engineering of Programming, Programming 2023"

共 1136 条记录，以下是451-460 订阅

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Advancing Residential Energy Management: Integrating Renewable Resources and Vehicle-to-Grid Systems for Optimized Electrical Grid Performance

Advancing Residential Energy Management: Integrating Renewab...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Rakesh Chandrashekar D Shobha Rani Ginni Nijhawan A Balaram Prateek Chaturvedi B Sankara Babu Department of Mechanical Engineering New Horizon College of Engineering Bangalore Karnataka India Department of Electrical and Electronics Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Lovely Professional University Phagwara Punjab India Department of Computer Science & Engineering MLR Institute of Technology Hyderabad Telangana India Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering GRIET Bachupally Hyderabad India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

the term “smart grid” describes the future generation of electrical power networks, which are very complicated systems. It must take into account all aspects of the electrical system, increasing its intelligence and adaptability. In response to shifts in the electrical market, this idea is put out with the intention of controlling the rising demand while guaranteeing higher service quality and enhanced safety. this research presents an urban scenario that incorporates dispersed energy sources into a smart grid. the research incorporates renewable energy sources like solar panels and tiny windmills into the system, as well as battery storage and grid-connected vehicles that can power the electrical grid by drawing electricity from the storage device. So, to maximize the energy production and absorption systems as well as the recharging and discharging time for a home user’s electric car, a mixed integer linear programming is proposed. Additionally, a number of case studies are provided by changing important variables using the Taguchi technique of design of experiments in order to identify the best solution and demonstrate how each influences the system’s complexity.

关键词： Vehicle-to-grid Renewable energy sources Electricity Production Safety Mixed integer linear programming Solar panels Informatics Energy management Optimization

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7th international Working conference on Human-Centered Software engineering, HCSE 2018

7th International Working Conference on Human-Centered Softw...

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7th international Working conference on Human-Centered Software engineering, HCSE 2018

ISBN: (纸本)9783030059088

the proceedings contain 23 papers. the special focus in this conference is on Human-Centered Software engineering. the topics include: A visual tool for analysing IoT trigger/action programming;software support for coherent prototyping of 3D gesture interactions;towards tool-support for robot-assisted product creation in fab labs;usability evaluation of model-driven cross-device web user interfaces;absolute indirect touch interaction: Impact of haptic marks and animated visual feedback on usability and user experience;factors affecting the choice of usability evaluation methods for interactive adaptive systems;towards a model to address the interplay between IoT applications and users in complex heterogeneous contexts;user evaluations of virtually experiencing mount everest;early incremental user testing design approach validation for satellite command center’s application;Get realistic! - UCD course design and evaluation;graphical user interface redefinition addressing users’ diversity;Integrating HCD into BizDevOps by using the subject-oriented approach;Intuitive user-centered interaction design for ATV;trade-Off between system effectiveness and context constraints in the design of an iot system giving access to health care in African rural villages;helping teams to help themselves: An industrial case study on interdependencies during sprints;participatory ideation for gamification: Bringing the user at the heart of the gamification design process;a method for optimizing complex graphical interfaces for fast and correct perception of system states;data-driven usability test scenario creation;MIODMIT: A generic architecture for dynamic multimodal interactive systems;adding measures to task models for usability inspection of the cloud access control services.

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Adaptive Dynamic Programing for Optimal Consensus of Multi-agent Systems Using ANFIS 7

Adaptive Dynamic Programing for Optimal Consensus of Multi-a...

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7th IEEE international conference on Advanced Robotics and Mechatronics

作者： Yu, Yinsheng Xie, Songbo Zhang, Xianxia Shanghai Univ Sch Mechatron & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200444 Peoples R China Shandong Laigang Yongfeng Steel Corp Dezhou Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

A new adaptive dynamic programming approach is presented in this paper. It proposes ANFIS-based distributed consensus control of linear multi-agent systems. Firstly, the optimal tracking control problem is formed for a multi-agent system. Secondly, a policy iteration-based ADHDP approach is used to compute the performance index and control policy. Moreover, the convergence analysis of the optimal solutions is given. thirdly, the control policy is approximated using an ANFIS based actor network, while the iterative performance index function is approximated using an NN based critic network, and this algorithm implements the policy iteration approach online without using system dynamic knowledge. Finally, the suggested optimal tracking control approach is demonstrated accroding to simulation results.

关键词： Knowledge engineering Mechatronics System dynamics Heuristic algorithms Simulation Approximation algorithms Iterative algorithms

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Development of intelligent integrated design system for hydraulic plane steel gate 7

Development of intelligent integrated design system for hydr...

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7th international conference on Hydraulic and Civil engineering and Smart Water Conservancy and Intelligent Disaster Reduction Forum, ICHCE and SWIDR 2021

作者： Zhang, Liaojun Yang, Wenyun Hohai University College of Water Conservancy and Hydropower Engineering Nanjing China

ISBN: (纸本)9781665408653

In this paper, the parametric programming language APDL of structural analysis software ANSYS and python object-oriented intelligent interface development tool pyqt5 are used to develop the intelligent integrated design system of plane steel gate. the system is composed of three parts. In the first part, the preliminary design module of plane gate based on design specification is developed by python. In the second part, APDL parametric language is used to build the database of gate components, loads, constraints and other parameters, and the three-dimensional finite element model of the overall gate is established. In the third part, the finite element structural calculation is carried out by using ANSYS software. According to the finite element calculation results, further optimization analysis is carried out to optimize the component parameters and optimize the stress state of the gate. In these parts, pyqt5 interface development tool, APDL parametric programming language and the powerful structural analysis function of ANSYS are integrated. the visual interface is used for integrated design. the data transmission of the three parts is seamlessly connected through the developed interface module, forming an intelligent and integrated plane steel gate design system, which greatly improves the design quality and design efficiency. © 2021 IEEE.

关键词： Python

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Advanced Automotive Quality Assurance: A Comprehensive Approach to Power Window Functionality Testing through Software Test Automation

Advanced Automotive Quality Assurance: A Comprehensive Appro...

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international conference on Computer Applications In Electrical engineering-Recent Advances (CERA)

作者： Neha Rathod Rohan Kulkarni S. M. Kakade Asesh Ray Dept. of Electrical Engineering COEP Technological University Pune Dept. of Electrical Engineering KJ College of Engineering and Management Research Pune Electronics Department Brose India Automotive Systems Private Limited Pune

Continuous progress in control methodologies within the automotive industry harnesses electronic hardware and software to achieve heightened functionality. Ensuring the top-notch the effectiveness and endurance of power window operations in automobile systems requires the implementation of automated software testing. this study is dedicated to the automation of power window testing, with a focus on key capabilities, including obstacle detection, heat and voltage protection, and accurate position tracking. As a mechatronic application, the power window relies on software algorithms. the power window Electronic Control Unit (ECU), an external DC power supply, a PCB layout design, and software tools such as the vector CANoe tool and the Communication Access programming Language (CAPL) browser comprise the experimental setting for this investigation. the proposed automated testing method significantly enhances efficiency and mitigates the risks associated with ECU malfunctions and potential human accidents. the manual completion of 400 scenario checks, taking six weeks, can be streamlined to just two weeks through automation. the automated process generates a detailed report encompassing specifics like start times, end times, and logged values.

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Hybird Clustering Algorithm Based on BP Neural Network and K-means

Hybird Clustering Algorithm Based on BP Neural Network and K...

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Software engineering and Computer science (CSECS), international conference on

作者： Yongzhi Zhu Qian Zhang College of Information Engineering Shandong Vocational University of Foreign Affair Weihai China Department of Electronic Information Engineering Jining Vocational and Technical College Jining China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

In the era of big data, efficient and accurate clustering algorithms are indispensable for organizing and mining information. this paper focuses on the optimization of clustering algorithms, particularly by integrating the advantages of the Backpropagation (BP) neural network and the K-means algorithm to enhance clustering accuracy and stability. this paper proposed improvement strategy introduces a novel fusion method that combines the self-learning characteristic of the BP neural network with the crowd wisdom of the K-means algorithm, resulting in a hybrid model that dynamically adjusts cluster centroids. In this model, the BP network is responsible for generating and updating initial cluster centroids, while the K-means algorithm allocates samples based on these centroids. this complementary approach forms an iterative optimization process. the aim is to address the limitations of traditional K-means when dealing with complex data structures and the initialization challenge faced by the BP network in clustering tasks. the implementation section involves programming the improved algorithm using the sklearn library and testing it on the classic iris dataset. through comparisons with the original K-means algorithm and clustering solely with the BP neural network,this demonstrates the superiority of the improved algorithm in clustering performance.

关键词： Iris Accuracy Heuristic algorithms Neural networks Software algorithms Clustering algorithms Big Data Stability analysis Classification algorithms Optimization

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A Progressive Single-Modality to Multi-modality Classification Framework for Alzheimer’s Disease Sub-type Diagnosis 7th

A Progressive Single-Modality to Multi-modality Classificat...

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7th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Liu, Yuxiao Liu, Mianxin Zhang, Yuanwang Sun, Kaicong Shen, Dinggang School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai201210 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

ISBN: (纸本)9783031787607

the current clinical diagnosis framework of Alzheimer’s disease (AD) involves multiple modalities acquired from multiple diagnosis stages, each with distinct usage and cost. Previous AD diagnosis research has predominantly focused on how to directly fuse multiple modalities for an end-to-end one-stage diagnosis, which practically requires a high cost in data acquisition. Moreover, a significant part of these methods diagnose AD without considering clinical guideline and cannot offer accurate sub-type diagnosis. In this paper, by exploring inter-correlation among multiple modalities, we propose a novel progressive AD sub-type diagnosis framework, aiming to give diagnosis results based on easier-to-access modalities in earlier low-cost stages, instead of all modalities from all stages. Specifically, first, we design 1) a text disentanglement network for better processing tabular data collected in the initial stage, and 2) a modality fusion module for fusing multi-modality features separately. Second, we align features from modalities acquired in earlier low-cost stage(s) with later high-cost stage(s) to give accurate diagnosis without actual modality acquisition in later-stage(s) for saving cost. Furthermore, we follow the clinical guideline to align features at each stage for achieving sub-type diagnosis. third, we leverage a progressive classifier that can progressively include additional acquired modalities (if needed) for diagnosis, to achieve the balance between diagnosis cost and diagnosis performance. We evaluate our proposed framework on large diverse public and in-home datasets (8280 subjects in total) and achieve superior performance over state-of-the-art methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Design and Implementation of Low-Voltage Distribution Line Load Pre-Balanced System Based on Commutation Method

Design and Implementation of Low-Voltage Distribution Line L...

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2023 7th international conference on Electrical, Mechanical and Computer engineering (ICEMCE)

作者： Dilixiati Hayireding Yiteng Lei Yang Li School of Electronic and Engineering Yili Normal University Yining Xinjiang China

Extensive research and development efforts have been dedicated to the investigation and advancement of the load commutation method-based automatic commutation device (CD) as a means to mitigate three-phase imbalance in low-voltage distribution networks (LVDNs). However, the present automatic commutation approach employs real-time balancing techniques. this entails that when the device detects an imbalance surpassing the threshold, it promptly executes commutation operations on certain power consumers where the user experiences a significant level of current, which poses challenges in the process of commutation. In this paper, a commutation-based load-pre-balanced system for low-voltage distribution line is created and put into practice. the system has the capability to perform the commutation operation prior to the user's utilization of electricity, hence eliminating the necessity for conducting high current commutation, which can mitigate the potential risks associated with automatic CD commutation. Firstly, a novel pre-balanced phase change strategy is introduced, accompanied by the construction of a pre-balanced system. Two commutation strategies, namely current-based and voltage-based, are designed for the pre-balanced system. Additionally, the effectiveness of the pre-balanced system in controlling the three-phase line load imbalance is verified by simulation on the MATLAB/Simulink platform. Finally, the hardware component of the system is realized through the use of the PIC18F27QI0 micro controller as the central controller. On the other hand, the system software component is developed by employing the programming languages C and Python, also a prototype is made for real environment testing. the experimental findings indicate that the designed pre-balanced system has the capability to efficiently regulate the imbalance in the three-phase line load.

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Segmentation of Smoke Plumes Using Fast Local Laplacian Filtering 1

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7th international conference on Computer Vision and Image Processing, CVIP 2022

作者： Koranne, Vedant Anand Ientilucci, Emmett J. Dey, Abhishek Datta, Aloke Ghosh, Susmita Rochester Institute of Technology Electrical Engineering RochesterNY United States Rochester Institute of Technology Center for Imaging Science RochesterNY United States Bethune College University of Calcutta Kolkata India The LNM Institute of Information Technology Jaipur India Jadavpur University Kolkata India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

In this paper, we address the problem of smoke plume segmentation from background clutter. Smoke plumes can be generated from fires, explosions, etc. In the mining industry, plumes from blasts need to be characterized in terms of their volume and concentration, for example. Plume segmentation is required in order to start such an analysis. We present a new image processing approach based on a fast local Laplacian filtering (FLLF) technique. In addition, we discuss how we designed and executed our own field experiments to acquire actual test data of smoke plumes from RGB video cameras. Lastly, we show how the FLLF technique can be used to generate thousands of training samples with applications in machine learning. Results show that the FLLF technique outperforms state-of-the-art approaches (i.e., SFFCM and an approach by Wang et al.) when tested using metrics such as Accuracy, the Jaccard Index, F1-score, False Alarms and Misses. We also show that the FLLF technique is more computationally efficient. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image segmentation

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CT Image Synthesis from MR Image Using Edge-Aware Generative Adversarial Network 1

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7th international conference on Computer Vision and Image Processing, CVIP 2022

作者： Joseph, Jiffy Prasanth, Rita Maret, Sebin Abraham Pournami, P.N. Jayaraj, P.B. Puzhakkal, Niyas Department of Computer Science and Engineering National Institute of Technology Calicut Kerala Calicut India Department of Medical Physics MVR Cancer Centre and Research Institute Kerala Calicut India

ISBN: (数字)9783031314070

ISBN: (纸本)9783031314063

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are two widely used medical imaging techniques in radiology to form pictures of the anatomy and the human body’s physiological processes. Radiotherapy planningrequires the use of CT as well as MRimages. the high radiation exposure of CT and the cost of acquiring multiple modalities moti-vateareliableMRI-to-CT ***-to-CTsynthesiserintroduced in this paper implements a deep learning model called Edge-aware Generative Adversarial Network (EaGAN). this model includes edge information into the traditional Generative Adversarial Network (GAN) using the Sobel operator to compute edge maps along two directions. Doing this allows us to focus on the structure of the image and the boundary lines present in the image. three variants of the EaGAN model are trained and tested. A model called discriminator-induced EaGAN (dEaGAN) that adversarially learns the edge information in the images is proven to generate the best results. It possesses a Mean Absolute Error (MAE) of 67.13HU, a Peak Signal to Noise Ratio (PSNR) of 30.340 dB, and a Structural Similarity Index (SSIM) of 0.969. the proposed model outperforms the state-of-the-art models and generates CT images closerto the ground truth CTs. the synthesised CTs are beneficial in medical diagnostic and treatment purposes to a greater extent. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Generative adversarial networks

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