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Integrated control of traffic signal and automated vehicles for mixed traffic: Platoon-based bi-level optimization approach 25

Integrated control of traffic signal and automated vehicles ...

引用

ieee 25th international conference on intelligent Transportation systems (ITSC)

作者： Zou, Yangang Zheng, Fangfang Fan, Zhichen Tang, Youhua Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Peoples R China Southwest Jiaotong Univ Sch Transportat & Logist Natl Engn Lab Integrated Transportat Big Data App Natl United Engn Lab Integrated & Intelligent Tra Chengdu 611756 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Connected and autonomous vehicle (CAV) technologies are widely recognized to help improve the operational efficiency of urban road traffic systems and thus mitigate traffic congestion. In this study, we propose a bi-level integrated control method to optimize traffic signals and automated vehicle trajectories simultaneously for mixed traffic with human-driven vehicles (HDVs) and CAVs. Instead of considering individual vehicles in the optimization process, the vehicles are divided into platoons, which are used as the basic units for optimal control, such that the computational cost can be reduced. A mixed integer linear programming (MILP) model is developed to optimize the signal timing scheme and arrival time of the platoon at the stop line with the objective of minimizing the total delay. Based on the optimized arrival time, the trajectory of the first CAV in the moving platoon is optimized to reduce the start-stop phenomenon at the intersection. Numerical experiments are performed and the results demonstrate that the proposed method outperforms the vehicle-actuated control method significantly in terms of the delay and fuel consumption reduction under different traffic conditions. Furthermore, the performance improvement of the proposed model compared to that of the vehicle-actuated control method increases substantially with the increase of the CAV penetration rate.

关键词： Roads Optimal control Process control Optimization methods Trajectory Delays Numerical models

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Design of Integrated Robotic System for Object Recognition and intelligent Grasping based on STM32 4

Design of Integrated Robotic System for Object Recognition a...

引用

4th ieee international conference on Power, intelligent Computing and systems, ICPICS 2022

作者： Gu, Zhihao Wang, Meng Peng, Huaibo School of Electrical Engineering and Automation Harbin Institute of Technology Institute of Technology Harbin China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

With the development of intelligent robots and the proposal of 'Industry 5.0', the intelligent robotic systems have been increasingly used in industrial scenarios. However, the current robotic grasping system is facing a series of problems, including low integration, low movability and limited application scenarios. To counter these problems, this paper designs an integrated robotic system for object recognition and intelligent grasping, consisting of an STM32F405RGT6 microcontroller board, path planning module, object recognition and intelligent grasping module, and environmental monitoring and communication module. This paper details the framework, the hardware and software design of the robotic system respectively. The results show that the robotic system achieves the design goal and realizes robust intelligent recognition and automatic grasping in a constantly moving process with high accuracy, stability and information-based degree. © 2022 ieee.

关键词： Internet of things

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FNOC: A Superficial Learning Methodology to Predict Digital Image Forgeries Using Fuzzy Enabled Neural Optimization Classifier

FNOC: A Superficial Learning Methodology to Predict Digital ...

引用

2024 international conference on Innovative Computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Franklin Thamil Selvi, M.S. Malar, R. Jeya Vasuki, A. Vinotha Pavithra, N. Adkane, R.V. Odeh, M. Sathyabama Institute of Science and Technology Department of Mathematics Chennai600119 India Kings Engineering College Department of Cse Chennai India J.P. College of Engineering Department of Cse Tenkasi India Jeppiaar Institute of Technology Department of Information Technology Sunguvarchatram Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

There has been an explosion in the production and dissemination of altered and false images in recent years, thanks to the widespread availability and user-friendliness of image editing programs. Many methods have been suggested for determining whether a image is genuine and, in certain instances, for identifying where the image has been changed or falsified. In order to assess the efficacy of the suggested system, this study introduces a novel deep learning model named Fuzzy enabled Neural Optimization Classifier (FNOC) and uses it in conjunction with the more traditional learning technique known as Support Vector Machine (SVM). In order to identify fakes, the suggested method employs a FNOC to glean characteristics based on residual noise from images. In order to differentiate between genuine and altered images, this method entails discovering the pattern of noise that the counterfeit process leaves behind. The capacity to handle undetected forgeries is a major advantage of employing a FNOC for image forgery detection. Traditional methods of detecting image forgeries can be challenged by more advanced technology. Yet, FNOCs can pick up on patterns that aren't established explicitly, so they can spot forgeries that weren't there before. The use of FNOCs to the identification of image forgeries has enormous promise in the fight against the issue of image manipulation. Digital images used in contexts ranging from medical reports to crime scene investigations might benefit from this technology's increased dependability and trustworthiness with more development. © 2024 ieee.

关键词： FNOC Forgeries Detection Fuzzy Logic Image Forgery Neural Optimization Classifier Superficial Learning Support Vector Machine SVM

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A New Method for Forming Rideshare Groups 15

A New Method for Forming Rideshare Groups

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15th international conference on Knowledge and systems Engineering, KSE 2023

作者： Tran, Van Manh Nhan Vu, Thi Hong University of Engineering and Technology Faculty of Information Technology Vnu Hanoi Viet Nam

ISBN: (纸本)9798350329742

Nowadays, ridesharing has gained popularity as an alternative mode of transportation. It offers cost savings, convenience, environmental benefits, and social opportunities. This research aims at providing riders with the reliability of the route and the flexibility of the service. For this purpose, we mine the longest frequent movement from historical data. A novel algorithm for discovering rides hare groups based on the similarity of the users' longest frequent movements is proposed. In this way, the user does not have to search for a ride many times and does not have to wait too long as well because the group with a specific schedule and driver information can be excited and sent to the users in advance. The algorithm is evaluated based on synthetic datasets under different settings and compared with the existing ones. The experimental results of the proposed method is superior to the results of the previous algorithms in terms of execution time and the total travel distance. © 2023 ieee.

关键词： Clustering Frequent Movement Ridesharing

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intelligent Route Planning for Post-Earthquake Rescues Using IoT 6

Intelligent Route Planning for Post-Earthquake Rescues Using...

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6th international conference on Electrical Engineering and Information and Communication technology, ICEEICT 2024

作者： Tamzid, Rahat Mahee, Ehfaz Faisal Rahman, Ashikur Tamanna, Tahira Akhtar Rahman, Muntasir Hossain, Md. Mamun Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350385779

Starting rescue operations quickly after an earth-quake is the best way to save lives in such disasters. In the case of large-scale earthquakes, current post-earthquake response systems cannot determine which building or rescue route would save the most lives due to the lack of real-time data availability. Research gaps include a lack of automation in earthquake damage assessment of buildings, location identification, and estimation of the number of trapped people. To fill these gaps, this article proposed an innovative solution using advanced sensors and cloud services for post-earthquake rescue route planning systems. This system aims to improve rescue operations to save the maximum number of lives in the shortest time and minimize impacts by optimizing resource allocation and overall response efficiency. By filling the existing research gap, our system seeks to provide more responsive and effective solutions to post-earthquake emergencies. Tested in a simulated environment with 16 samples, our system successfully collects and processes data from IoT devices deployed in buildings, classifies structural damage, and estimates the number of people affected. Our approach significantly reduces the time required to initiate rescue operations by providing rescuers with optimized routes to disaster sites, which has potential implications for disaster management strategies. © 2024 ieee.

关键词： Earthquakes

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Obstacle Detection of Unmanned Surface Vessel based on Faster RCNN

Obstacle Detection of Unmanned Surface Vessel based on Faste...

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Industrial cyber-Physical systems (ICPS)

作者： Jiahe Cai Sheng Du Chengda Lu Bo Xiao Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Guangzhou Marine Geological Survey Guangzhou China

With the development of unmanned surface vessel in the world today, obstacle avoidance using environmental information is the basis to ensure its high maneuvering performance and safety. However, directly using standard algorithms will lead to missing and wrong identification severely for characteristics of marine obstacles. This paper adds a multi-scale feature extraction layer of dilation convolution and group convolution to Faster Region based Convolutional Neural Network (Faster-RCNN), the baseline model, and changes the classification algorithm to improve its robustness and accuracy. Soft-Non Maximum Suppression (Soft-NMS) is used to enhance the prediction effects further. After improvements, the mean average precision value increases by 3.35%, and the final loss value decreases by 0.20. Given the phenomenon of missing and misidentification in the prediction by the baseline model, the results of our new model show outstanding performance.

关键词： Convolution Predictive models Feature extraction Prediction algorithms Robustness Classification algorithms Safety

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Crane Hook Motion Monitoring Based on Trajectory Filtering and Tracking Video Monitoring 2

Crane Hook Motion Monitoring Based on Trajectory Filtering a...

引用

2nd international conference on Innovations and Development of Information Technologies and Robotics, IDITR 2023

作者： Li, Jin Lv, Zhiyong You, Jinhui Guo, Wei Wuhan University of Technology Automation College Wuhan China Guangdong Inland Port and Shipping Industry Research Co. Ltd Intelligent Transportation Systems Research Center Wuhan China

ISBN: (纸本)9798350345575

Crane hoisting is an important part of smelting operations. The traditional operation method is that the ground dispatcher and the driver work together, which is not only inefficient but also has great potential safety hazards. To solve the above problems, this paper designs a video monitoring system. The PTZ camera is used for video monitoring of the crane hook operation, the laser sensor and the rotary encoder collect data, and the Kalman-based detection adaptive filtering algorithm is used for filtering processing to obtain the precise motion trajectory of the hook. On the basis of the traditional PID controller, the DMC controller based on predictive control is designed to solve the control lag problem in the control process of PTZ cameras. The experimental results show that the monitoring system can clearly display the hook in the center of the screen, help the crane operator to grasp the status of the hook, improve the production efficiency of the workshop, and ensure the safety of hoisting. © 2023 ieee.

关键词： Adaptive filters

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Design of tidal level measuring system based on STM32 5

Design of tidal level measuring system based on STM32

引用

5th ieee international conference on Power, intelligent Computing and systems, ICPICS 2023

作者： Huang, Hongzhi He, Haijing Du, Libin Cui, Tong Liu, Zezheng Liu, Jie Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350333442

Tidal variation is one of the fundamental elements of the ocean. To address the problem of low accuracy in ocean exploration, this article introduces a tide gauge measurement system design based on STM32. The system uses the STM32 microcontroller as the control core, collects pressure and temperature data through high-precision ADC chips, and employs the Kalman filter algorithm to store the data in Flash memory. The results of the water experiment demonstrate that the system exhibits high accuracy and minimal error when compared to actual tidal data. At the same time, the phenomenon of 'jumping point' in actual measurement is solved by fitting the mathematical model of tidal level data, which provides a more scientific theoretical basis for the field of tidal level observation. © 2023 ieee.

关键词： Tide gages

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An Energy-Efficient Metaheuristic for Offloading Security-Critical Tasks in Mobile Edge Computing with Integrated DPUs 3

An Energy-Efficient Metaheuristic for Offloading Security-Cr...

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3rd ieee international conference on intelligent technology and Embedded systems, ICITES 2023

作者： Hong, Shaoying Zhu, Jiayin Yuan, Yiqi Sun, Jin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China International Joint Audit Institute Nanjing Audit Technology Nanjing211815 China

ISBN: (纸本)9798350343755

In mobile edge computing (MEC) paradigm, for the security-critical computation tasks offloaded from the mobile device, the MEC server needs to decrypt the encrypted task data before task execution, leading to heavy computation load on the CPUs of the MEC server. The integration of data processing unit (DPU) into the MEC server can release CPUs from data decrpytion for the purpose of reducing computation and energy overheads. This paper studies a task offloading problem in a MEC system with integrated DPUs, which aims at reducing the total energy consumption under the deadline constraint on task completion time. We formulate the studied problem as an combinatorial optimization model and propose a group mapping-based cuckoo search (GMCS) metaheuristic algorithm to explore high-quality energy-efficient solutions to the formulated model. The proposed GMCS introduces a group mapping operator and a greedy-based task offloading strategy for converting cuckoo individuals into offloading solutions and evaluating the objective values of the converted solutions, respectively. We provide a theoretical analysis to justify that the mapping operator can improve the diversity of the converted solutions and in turn the metaheuristic’s searching capability. We create various testing instances for a MEC system equipped with DPUs to demonstrate the proposed GMCS produces high-quality solutions to the studied offloading problem, with reduced energy consumption compared with baseline algorithms. © 2023 ieee.

关键词： Mobile edge computing

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AI-Driven Solutions for Cloud Security Implementing intelligent Threat Detection and Mitigation Strategies

AI-Driven Solutions for Cloud Security Implementing Intellig...

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2024 ieee international conference on intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Yasani, Rajashekar Reddy Prasad, Putalpattu Muni Srinivas, Pattlola Reddy, N V Raja Sekhar Jawarkar, Parag Raghunath, Vedaprada Cloud Security Cloud Computing Cyber Security India University of Rome Tor Vergata Department of Industrial Engineering Italy Department of Computer Science and Engineering Telangana India Mlr Institute of Technology Department of It India Shri Ramdeobaba College of Engineering and Management Electronics Engineering Nagpur India Visveswaraya Technological University India

ISBN: (纸本)9798331508432

Adapting threat detection and response procedures is essential for businesses to stay ahead of the constantly developing cyberattack landscape. Researching cutting-edge tools like XDR, SIEM, SOAR, and NDR - which shed light on the dynamic realm of detection and response systems - is crucial. Other areas of investigation include real-time monitoring and network forensics. Security and threat detection have both been greatly enhanced since businesses began storing their apps and data in the cloud. In order to safeguard vulnerable network infrastructures in cloud settings against advanced attacks, traditional security procedures must be revised. Artificial intelligence (AI) is used to learn more about this problem so that threat reaction and identification can be done more quickly and accurately. Artificial intelligence has changed cloud security and threat identification, as this study shows. Because cyberattacks on cloud infrastructures and service providers are becoming more common, strong security steps that are also easy to use are still needed. This piece will talk about how cloud security and AI operations work together, with a focus on how this collaboration speeds up the time it takes to respond to incidents. The paper will also show how this relationship helps a company improve its defences and lessen the impact of security events. Organisations that want to keep up with the constantly changing danger landscape need to use cloud security and AI and understand how the two work together. This is because it is necessary to keep a security stance that is both flexible and strong. © 2024 ieee.

关键词： Cloud security

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