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Design and Implementation of a Web GIS-based Quality Management Information System for Reservoir Projects

Design and Implementation of a Web GIS-based Quality Managem...

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international conference on AI and Metaverse in Supply Chain Management (AIMSCM)

作者： Cai, Hualong Zhuang, Yiyan Liu, Shijie Xu, Zhenwei Jiaying Univ Sch Civil Engn Meizhou Peoples R China

ISBN: (纸本)9798400708251

With the rapid development of society, the project construction scale is expanding, the technology is more complex, the project quality management puts forward higher requirements, the traditional information processing and management methods are difficult to meet the requirements of modern project quality management, project quality management information has become an inevitable trend. This paper takes Baitouwang Reservoir in Guizhou Province as an example, in order to better motivate Baitouwang Reservoir to achieve the functions of flood control, water supply and irrigation, and to realise the informatisation, networking and intelligent management of Baitouwang Reservoir, and at the same time, to solve the problems such as the low level of quality management of traditional reservoir projects. The system is centred on a water resources database and uses Web GIS technology, Internet of Things (IoT) technology and computer applications to design a quality management information system for reservoir projects. The use of the system can achieve the reservoir information query, gate control, video surveillance and monitoring data real-time monitoring and analysis functions, at the same time, can also timely and accurately grasp the real-time dynamic situation of the reservoir, effectively improve the quality of the reservoir project management and decision-making level to provide a scientific basis, to achieve the efficient operation of the reservoir information technology.

关键词： Web GIS Quality Management Reservoir Projects Information engineering

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On controlling Drones for Disaster Relief 26

On Controlling Drones for Disaster Relief

引用

26th international conference on Knowledge-Based and intelligent Information and engineering Systems, KES 2022

作者： Hewett, Rattikorn Puangpontip, Supadchaya Texas TechUniversity Lubbock79409 United States

Drones or unmanned aerial vehicles (UAVs) are increasingly deployed for disaster relief, from aiding in drug or supply delivery to search and rescue victims. For a large-scale mission, the more drones are used, the more difficult they are to control and manage in a timely manner. Automated control is one of the central techniques used in knowledge-based drone management systems to enhance the quality and efficiency of the disaster relief mission. Although many drone management systems have been developed, automatic control of these systems remains a difficult problem. Many approaches have "human in the loop" that trades the degree of autonomy with the efficiency and perhaps accuracy. Our goal is not to fully automate the system but to reduce user interactions where intelligible (e.g., selecting appropriate drones/actions based on prioritized needs) by means of significant advances in control mechanisms. This paper presents a knowledge-based framework for intelligent drone management by explicitly representing control knowledge for use in selecting appropriate task-specific actions. The paper describes how control decisions are derived using domain knowledge relevant to drones and disaster relief. By employing a blackboard control architecture, our framework provides adaptability for dynamic control behaviors and flexibility to handle unanticipated situations during the drone mission activities. The paper illustrates the use of the framework on specific scenarios. © 2022 The Authors. Published by Elsevier B.V.

关键词： intelligent systems

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A neural network model for adversarial defense based on deep learning 3

A neural network model for adversarial defense based on deep...

引用

3rd international conference on Image Processing and intelligent control, IPIC 2023

作者： Wang, Zhiying Wang, Yong College of Computer Science and Technology Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781510668140

Deep learning has achieved great success in many fields, such as image classification and target detection. Adding small disturbance which is hard to be detected by the human eyes to original images can make the neural network output error results with high confidence. An image after adding small disturbance is an adversarial example. The existence of adversarial examples brings a huge security problem to deep learning. In order to effectively defend against adversarial examples attacks, an adversarial example defense method based on image reconstruction is proposed by analyzing the existing adversarial examples attack methods and defense methods. Our data set is based on ImageNet 1k data set, and some filtering and expansion are carried out. Four attack modes, FGSM, BIM, DeepFool and C&W are selected to test the defense method. Based on the EDSR network, multi-scale feature fusion module and subspace attention module are added. By capturing the global correlation information of the image, the disturbance can be removed, while the image texture details can be better preserved, and the defense performance can be improved. The experimental results show that the proposed method has good defense effect. © 2023 SPIE.

关键词： Image classification

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Research on automatic switching of hydraulic feed dual process in honing processing 4

Research on automatic switching of hydraulic feed dual proce...

引用

2024 4th international conference on Mechatronics, Automation and intelligent control, MAIC 2024

作者： Lu, Yong Pei, Hailong Li, Guang Deng, Haijun Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems School of Automation Science and Engineering South China University of Technology Guangzhou510640 China School of Information Guangdong Polytechnic of Science and Trade Guangzhou510440 China

Honing is a finishing process used for the inner cylindrical surfaces of engine blocks or liners. The process involves abrasive particles bonded to the surface of a honing head, which come into contact with the inner bore surface, performing an oscillating and rotational motion. These motions follow a specific pattern, allowing for the creation of crosshatch patterns on the inner bore surface, thereby improving dimensional accuracy, form accuracy and reducing surface roughness. Common honing processes are divided into four distinct phases: material removal, crosshatching, finish honing, and plateau honing. Each phase corresponds to specific tasks such as material removal from the bore, creating crosshatch patterns, honing the peaks in the crosshatch, and final surface finishing. In computer numerical control (CNC)-based honing machine, individual control commands are typically used to manage each of these phases separately. The commands are combined according to process requirements to achieve the desired honing results. To enhance the efficiency of the honing process, this study focuses on the hydraulic feed of the abrasive strips on the honing head, specifically examining the automatic continuous switching between finish honing and plateau honing. The research analyzes the honing principles, develops a control algorithm for dual-process automatic switching, and validates the process through machining tests. Compared to single-process control, the proposed algorithm significantly improves processing efficiency. Additionally, this algorithm can be applied to other dual-process automatic switching controls, providing valuable insights for the design of CNC honing systems. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Honing machines

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The Real-Time Monitoring and control of intelligent Sensor Networks in Electrical Automation

The Real-Time Monitoring and Control of Intelligent Sensor N...

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computer engineering and intelligent control (ICCEIC) international conference on

作者： Xiuzhen Zhou Wuhan Polytechnic Hubei Wuhan China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In order to understand the real-time monitoring and control of intelligent sensor networks in electrical automation, a research based on intelligent sensor networks for real-time monitoring and control in electrical automation has been proposed. This paper first utilizes the intelligent sensor nodes of the intelligent sensor network in the system data layer to collect signal data of building electrical equipment, enabling the central node to gather and integrate the collected data. Secondly, the Zigbee intelligent network at the communication layer is used to aggregate the data integrated by the central node into an internal gateway, which uploads the data to the application foundation platform at the application layer. Finally, the experimental results indicate that setting the number of cluster heads in the intelligent sensing network to 25 can achieve an ideal network node lifespan; This system can accurately diagnose the type of fault, identify the location of the fault, and effectively save energy consumption of building electrical equipment.

关键词： Energy consumption Automation Scalability Buildings Zigbee Logic gates Real-time systems Safety intelligent sensors Monitoring

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Employee Behaviour and Attention of Career Building 7

Employee Behaviour and Attention of Career Building

引用

7th international conference on intelligent Computing and control Systems, ICICCS 2023

作者： Suneetha, T.V. Shivani, Bhanda Shreshta, Chidrapu Begum, Mehrajunnisa Nargees Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Department Hyderabad India

ISBN: (纸本)9798350397253

More study into machine learning's potential uses in corporate settings is warranted in light of rising interest among business executives and stakeholders. The loss of skilled workers would result in less productivity of the organization. In this study, ML approaches is applied to investigate the root reasons for employee dissatisfaction. There were three major studies that employed the IBM Watson-generated dataset to foretell employee turnover. Many different machine-learning models, mostly built from scratch but some using the original lesson dataset, were trained in our initial experiment. Two such models were a small-sample size support vector machine (SVM) and a stochastic K-nearest neighbour (KNN) approach. The dataset is updated using aforementioned machine learning methods and employed the ADASYN synthetic methodology to reduce class disparity in our experiment. In the third trial, data types were manually standardised through an iterative and gradual process. Regarding this, it is found that KNN training on an ADASYN-balanced dataset with a K-size of 3 yielded the best results (0.93 F1-score). Using feature selection and random forest, F1-score up to 0.909 is achieved while using just 12 of the possible 29 characteristics. The accuracy achieved is 85% at value K= *** is concluded that this model would help a lot of organizations to Figure out the reasons behind the low performance of the employees. Also, it tries to improve whatever is causing the employees to leave the company. © 2023 IEEE.

关键词： Stochastic systems

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Self-Organized Multi-Hop Bridge Reservation Medium Access control for Cyber Physical Autonomous Vehicles 6

Self-Organized Multi-Hop Bridge Reservation Medium Access Co...

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6th international conference on Trends in Electronics and Informatics, ICOEI 2022

作者： Srithar, S. Sivaraju, S.S. Vetrimani, E. Kanaga Priya, P. Athinarayanan, S. Kalyan, G. Ramesh Koneru Lakshmaiah Education Foundation Department of Computer science and Engineering Andhra Pradesh Vaddeswaram India R V S College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641 402 India Presidency University Department of Computer science and Engineering Bangalore India KPR Institute of Engineering and Technology Department of Computer science and Engineering Coimbatore India Annamacharya Institute of Technology and Sciences Department of Computer science and Engineering Tirupati517520 India SNS College of Technology Department of Computer science and Engineering Coimbatore India

ISBN: (纸本)9781665483285

The cyber-physical intelligent systems (CPIS) are integrated with internet-connected network devices for communication and content sharing. The CPIS is widely used in intelligent transportation and unmanned ariel vehicle. In an autonomous cyber-physical vehicle system to coordinate and connect with the other vehicle to enhance the perception is a challenging task. The ad-hoc network of nodes should be well connected with the control unit and other authorization entities for dropping less transmission. Mostly the ad-hoc vehicle suffers from connectivity issues whenever the handover from one network into another network. The current self-organized vehicle suffers in terms of hard and soft handover because of its dynamic nature. The proposed approach is named Multi-hop Bridge Reservation Medium Access control (MBR-MAC) for optimal handover between the roadside unit. The approach MBR can handle hidden and exposed terminal problems. Mostly the failure handover happens when there is no channel to carry over the current connectivity. The proposed scheme utilizes the time and code division multiplexing for optimum bandwidth utilization. The experimental result shows the better results in terms of byte transmission rate and handover failure rate. © 2022 IEEE.

关键词： Embedded systems

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Roadside IRS Assisted Task Offloading in Vehicular Edge Computing Network 19th

Roadside IRS Assisted Task Offloading in Vehicular Edge Com...

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19th EAI international conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Xie, Yibin Shi, Lei Li, Zhehao Ding, Xu Liu, Feng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

ISBN: (纸本)9783031545207

Vehicular edge computing (VEC) has been recognized as a promising technique to process delay-sensitive vehicular applications. Nevertheless, in order to accommodate the rapid growth in the number of connected vehicles, it’s inevitable that there will be an increasing deployment of conventional infrastructure with limited communication ranges. This could potentially lead to escalating costs and impede the full realization of the VEC system. In this paper, a roadside intelligent reflecting surface (IRS) assisted VEC network is introduced, where the IRS is deployed outside the coverage of roadside units (RSUs) to extend the service range. Furthermore, the maximum total number of successful offloading tasks problem within the scheduling time problem is formulated, encompassing the optimization of offloading decisions, computation resource allocation and phase shift of IRS. To tackle the formulated challenging problem, we first decouple the original problem into two subproblems. Then, a heuristic algorithm is proposed, where a many-to-one matching algorithm is proposed to joint optimize offloading decision and the computation resource, and an iterative algorithm is utilized to optimize the phase shift coefficients of IRS. The simulation results validate the effectiveness of the proposed algorithm in comparison to other schemes, and the IRS can effectively maintain network performance even when there are intervals in RSU coverage areas. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Resource allocation

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Amalgamation of AI and Blockchain in Moving Towards Smart Cities

Amalgamation of AI and Blockchain in Moving Towards Smart Ci...

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2023 international conference on Power Energy, Environment and intelligent control, PEEIC 2023

作者： Suneetha, K. Sharma, Bhuvnesh Shahid, Mohammad Department of Computer Science Bangalore India Vivekananda Global University Department of Mechanical Engineering Jaipur India Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350357769

The Internet of Things, otherwise called the IoT, with 6G capacities has collected more consideration as of late as it endeavors to resolve issues connected with ongoing applications. With its capacity to give precise ongoing information investigation, computerized reasoning (man-made intelligence) is a key empowering influence of huge information examination. Nonetheless, issues with security, protection, preparing data, and unified engineering emerge when enormous information examination utilizing artificial intelligence is created. To defeat the previously mentioned issues with IoT applications, this paper proposes a man-made consciousness incorporated blockchain-based IoT stage. This proposed design's exhibition is assessed utilizing subjective as well as quantitative measurements. Subjectively, the paper portrays exhaustively how consolidating blockchain innovation with man-made brainpower resolves various issues, offering decisions about Agi-oriented Blockchain (BC) and Bp-situated artificial intelligence. The exhibition evaluation of simulated intelligence BC design is done and contrasted and current strategies in subjective measures. Concurring on trial study, the proposed system performs better compared to the cutting edge strategies at present being used. This clever procedure consolidates the upsides of blockchain innovation with man-made brainpower to work on the viability and proficiency of 6G-empowered Internet of Things applications. © 2023 IEEE.

关键词： AI-BC Framework Blockchain Internet of Things (IoT) Smart Cities

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A Query-Based Approach to Mitigate the Shortcomings of Widely Used Learning Methods Through E-Learning 1

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3rd international conference on Advanced Computing and intelligent Technologies, ICACIT 2023

作者： Zaman, Khandoker Ashik Uz Islam, Yusuf Mahbubul Sayed, Md Abu Computer Science & Engineering Independent University Bangladesh Plot 16 Aftab Uddin Ahmed Rd Dhaka1229 Bangladesh Department of Computer Science & Engineering South-East University Dhaka Bangladesh

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

This paper begins with a review of existing learning methods, highlighting the limitations that hinder motivation, development of confidence, and memory retention. To address these gaps, we propose an e-learning strategy that uses technology to create a peer collaborative question development marketplace. The mobile application developed for this purpose is part of a progressive web application that functions as the peer collaborative questioning questions platform. The students’ exchanges of questions are ranked using Bloom’s Taxonomy, encouraging anonymous student questioning that should gradually lead to a deeper topic exploration. Our analysis, comparing an experimental group with a control, reveals that the proposed strategy creates a positive impact on students, enhancing their confidence, motivation, and querying skills. Furthermore, we found that enhanced questioning correlates with improved academic performance. Finally, we also discuss the potential challenges and how the dataset obtained from this study can be utilized for machine learning and artificial intelligence applications in e-learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： E-learning

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