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Deep Reinforcement Learning for IoT-Based Smart Traffic Management Systems 7

Deep Reinforcement Learning for IoT-Based Smart Traffic Mana...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Kansal, Vipashi Shnain, Ammar Hameed Deepak, A. Rana, Aditya Manjunatha Dixit, Krishna Kant Rajkumar, K. Varada Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamil Nadu Chennai India Uttar Pradesh India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Electrical Engineering Mathura India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

The increasing complexity of urban traffic networks demands more intelligent and adaptive solutions for traffic management. This paper presents a novel approach utilizing Deep Reinforcement Learning (DRL) in conjunction with Internet of Things (IoT) technology to develop a smart traffic management system aimed at optimizing traffic flow in real-time. IoT devices, including sensors, cameras, and connected vehicles, provide comprehensive, high-resolution traffic data, which is processed by a DRL-based algorithm to dynamically control traffic signals and manage congestion. The proposed system continuously learns from evolving traffic patterns, adapting its decision-making to optimize key metrics such as vehicle throughput, travel time, and energy consumption. Compared to traditional traffic management systems, the DRL-based approach exhibits superior performance in handling dynamic traffic environments, reducing bottlenecks, and minimizing delays. Extensive simulations demonstrate that the integration of DRL with IoT enhances the system's ability to manage complex, real-world traffic scenarios more efficiently. This research contributes to the growing body of work in smart city development, offering an innovative framework for the deployment of intelligent traffic management systems. The study highlights the potential of DRL and IoT technologies to transform traffic management, promoting sustainable urban mobility and improving overall traffic efficiency.. © 2024 IEEE.

关键词： Travel time

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Design and Implementation of Running Motion Generator for Humanoid Robots using SLIPM and CPG

Design and Implementation of Running Motion Generator for Hu...

引用

2023 international automatic control conference, CACS 2023

作者： Lee, Hui-Hsuan Cho, Kuan-Ting Li, Tzuu-Hseng S. National Cheng Kung University AiRobots Laboratory Department of Electrical Engineering Tainan701 Taiwan

ISBN: (纸本)9798350306354

The purpose of this research is devoted to establishing a running generation technique for bipedal robots. The motion generator is based on the Bipedal Spring-Loaded Inverted Pendulum Model (Bi-SLIPM) and is designed to stabilize running motions. This work incorporates human motion features during the generator process. First, this paper introduces the bipedal robot used for the tasks. The 2D Bi-SLIPM is employed to generate the running pattern. To ensure the applicability of the running motions generated by the 2D Bi-SLIPM in real-life scenarios, this paper introduces the concept of Discrepancy of Leg Compression (DLC) based on the 2D Bi-SLIPM. Additionally, a Central Pattern Generator (CPG) is integrated as an auxiliary synthesizer to generate DLC-CPG motions. These motions serve as the motion generators for the abduction direction of the hip and the ankle. Moreover, to address the challenges posed by tremors and inherent instability in running movements, this paper proposes the integration of feedback data from an inertial measurement unit. The insights gathered from the inertial measurement unit are incorporated into a heuristic algorithm, which systematically explores and refines parameters for the gait model. Through this iterative training process, the resilience and stability of the running gait are significantly improved. Ultimately, the training results demonstrate the successful realization of the proposed motion generation and control techniques on the physical robot. © 2023 IEEE.

关键词： Anthropomorphic robots

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Application of Computer Artificial Intelligence Technology in Image Processing Botany control System of Mikania Micrantha

Application of Computer Artificial Intelligence Technology i...

引用

2023 IEEE international conference on electrical, Automation and Computer engineering, ICEACE 2023

作者： Shuqiang, Li Wei, Chen University of Electronic Science and Technology of China Department of Mechanical and Electrical Engineering Zhongshan Institute Zhongshan Zhongshan China Industry Research Center Wonders Information Co. Ltd. Beijing China

ISBN: (纸本)9798350309614

The Nano-Hyperspec spectrometer was used to preprocess Mikania micrantha images, such as geometric correction, image denoising, radiation correction and bad band elimination. The optimal exponential coefficient method (OIF), adaptive band method (ABS), automatic subspace segmentation (ASP) combined with adaptive band selection method (ASP+ ABS) were used to screen the bands. In this way, the feature bands with relatively large information and low correlation between bands are obtained, and then three remote sensing images are formed. SVM algorithm was used to classify three different types of Mikania micrantha images, evaluate the recognition ability of three kinds of hyperspectral characteristics on Mikania Micrantha images, and screen out the hyperspectral characteristics that can better reflect Mikania Micrantha images. Micrantha Hyperspace was mapped in the study area, and the mapping accuracy was 79.62%. The user accuracy is 67.85%. The ABS spectral selection method was used to locate Micrantha Hyperspace, and the accuracy of mapping was 75.63%, and the accuracy of user was 67.96%. (ASP+ ABS) algorithm was 86.95%. The user accuracy is 92.65%. © 2023 IEEE.

关键词： artificial intelligence image processing mikania micrantha prevention and cure UAV

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Joint Task Offloading and Computation in a Multi-Carrier Multi-Relay MEC System 4th

Joint Task Offloading and Computation in a Multi-Carrier Mul...

引用

4th international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2024

作者： Zhang, Siyu Zhang, Yuexia Li, Junjie Zheng, Hui Zhang, Changyong Zhang, Ruichang Li, Zhili Key Laboratory of Information and Communication Systems Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Beijing Information Science and Technology University Beijing China Datang Microelectronics Technology Company Ltd Beijing China R&D Cente Beijing Smartchip Microelectronics Technology Co. Ltd Beijing China

ISBN: (纸本)9789819653133

As the continuous emergence of new applications, such as face recognition and automatic driving, wireless devices are faced with problems, for instance, limited computing resources and excessive energy consumption. Thus, this study presents a joint task offloading and computation algorithm for multi-carrier and multi-relay mobile edge computing (MEC) systems to resolve aforementioned problems. First, we build a multi-carrier and multi-relay MEC system. Next, we calculate the delay constraints according to the given tasks, optimize resource allocation. To optimize the total energy consumed of relays and users, the optimization of resource allocation is expressed as a mixed integer programming problem. Further, because difficulty in solving the issue, we use continuous relaxation and algebraic transformation to convert the problem to equivalent problem. Finally, we solve the problem utilizing the interior point method, which realizing the highly efficient resource allocation. Simulation results demonstrated that the presented joint collaborative task offloading and computing program decreases energy consumed by 26.67% compared with the relay-only assisted task computing program. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Integer programming

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Exploratory Research of Brain-Tumor Detection and Classification using Deep-Learning-Technique 14

Exploratory Research of Brain-Tumor Detection and Classifica...

引用

14th international conference on Advances in computing, control, and Telecommunication Technologies, ACT 2023

作者： Megha, H.C. Shashikala, M.K. Rajeev Institute of Technology Computer Science & Engineering Hassan India

Good health is important to all of our everyday lives. One of the most important things that we humans have is the brain. Today’s world, damage the lifestyle of people via a hectic schedule. Often, it affects various brain structures and functions. The biggest challenge facing by the medical profession is cancer. A brain tumor is a mass that develops inside the brain, or around the brain. However it is possible to recover from the brain cancer, if it is detected in early stage. Here, we developed Brain tumor detection and classification system by using CNN deep learning technology. The system mainly focused on classification of Normal brain and Spongioblastoma of given input MRI image. The CNN takes less number of features to classify an image. Here, we developed the system in python programming with tensor flow framework. The proposed system gives 99.0% accuracy of detection and classification is represented by confusion matrix. This system is more applicable in medical sector. Within this proposed system we introduced fully automatic segmentation and feature extraction of brain tumor detection and classification using CNN techniques. © Grenze Scientific Society, 2023.

关键词： Brain

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Anycomputing: A Novel Architecture for Orchestrating Various computing Paradigms for Adaptive Workload Management

AnyComputing: A Novel Architecture for Orchestrating Various...

引用

international conference on electrical engineering, computing science and automatic control (CCE)

作者： Seyedreza Taghizadeh Sara Yousefi Department of Computer Science Engineering and Information Technology Shiraz University Shiraz Iran

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

The proliferation of physical objects and intelligent systems, along with their inherent differences, such as levels of device mobility, generated data volume, required storage memory, and complexity in data processing, presents a significant challenge in achieving real-time and optimal responsiveness to the Massive volume of sent requests from these systems. Depending on the nature of each request and its requirements, effective processing must be by available and suitable computational platforms. This situation necessitates the urgent development of novel ideas and frameworks in request orchestration and processing by suitable computational platforms to enhance system performance and efficiently utilize available computational resources. In this article, we propose an integrated computational architecture spanning from edge devices of users and intelligent things to cloud servers. This design encompasses the reception, processing, and response to various computational and storage requests, considering specific requirements, through computational units deployed within this span. Our objective in devising this architecture is to establish mechanisms for categorizing diverse computational requests from users and things, effectively allocating resources to them, thereby reducing response time, optimizing bandwidth utilization and other available resources, managing latency for real-time requests, and minimizing energy consumption in user devices and intelligent objects.

关键词： Performance evaluation System performance Memory management Real-time systems Computational efficiency Resource management Time factors Servers Intelligent systems Optimization

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Efficient Drinking Water Quality Analysis using Machine Learning Model with Hyper-Parameter Tuning 7

Efficient Drinking Water Quality Analysis using Machine Lear...

引用

7th international conference on Intelligent computing and control Systems, ICICCS 2023

作者： Maheswari, M Uma Sudharsanan, R. Arthy, M. Jenefer, Anne Oormila, L. Samuthira Pandi, V. RMD Engineering College Department of Electrical and Electronics Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Information Technology Tamil Nadu Ramapuram India SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Agni College of Technology Department of Computer Science Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Electronics Ad Communication Engineering Tamilnadu Ramapuram India

ISBN: (纸本)9798350397253

The conventional approaches to determining the quality of water include costly and lengthy statistical and laboratory testing;hence, the concept of real-time monitoring is no longer applicable in the modern day. The disastrous implications of poor water quality demand that there be a remedy that is both more expedient and more realistic. This research study examines a variety of supervised machine learning algorithms in order to identify the quality of the water. A variety of variables, such as the pH value, the hardness, the solids, the chloramines, the sulfates, the conductivity, the organic carbon, the trihalomethanes, the turbidity, and the potability of the water, are essential for determining the quality of the water. Random Forest (RF) and Decision Tree (DT) are used in order to ascertain the caliber of the water that is suitable for human consumption. The standard laboratory approach for testing the quality of water is one that takes a lot of time and may be somewhat expensive at times. In a very short length of time, the algorithms that are proposed in this study are able to provide an estimation of the quality of drinking water. DT has a height accuracy F1 score of 99%, whereas RF has a score of 87.86% and a precision of 82.36%. The difference in these two scores is due to the fact that DT's precision is lower. The suggested method demonstrates its potential for usage in real-time water quality monitoring systems by achieving appropriate accuracy with a minimal set of parameters. To prove the usefulness of this program, this is essential. © 2023 IEEE.

关键词： Water quality

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Continuous angle estimation of lower limb multi-joints based on synergy activation model 8

Continuous angle estimation of lower limb multi-joints based...

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8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Pang, Peng Tian, Cong Heilongjiang University of Technology College of Electrical and Information Engineering Jixi China

ISBN: (纸本)9798350302455

The neural information decomposed from EMG signals provides a new way of thinking about human-robot interaction. Distinguished from the traditional EMG time-frequency domain features, muscle synergy analysis can capture synergistic motion information on multiple degrees of freedom, and this information provides more intuitive and natural information for intelligent rehabilitation. In this paper, we propose a neural interface for hip-knee-ankle joint angle prediction of human walking lower limbs based on muscle synergy advanced neural control signals, and compare the prediction results of different walking speeds and different features for the model to find out the best estimation method, which has great application prospects in the scenarios of exoskeleton robot control and rehabilitation training. © 2023 IEEE.

关键词： Degrees of freedom (mechanics)

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Comparative analysis of Workflow Management System using Agents and Multi- agents in Colored Petri Nets

Comparative analysis of Workflow Management System using Age...

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2022 IEEE international conference on Distributed computing and electrical Circuits and Electronics, ICDCECE 2022

作者： Vij, Aanchal Kaswan, Kuldeep Singh Bawa, Chinar Galgotias University School of Computing Science and Engineering Greater Noida India St. Clair College School of Business Information and Technology Windsor Canada

ISBN: (纸本)9781665483162

Agent oriented software engineering has become one of the attractions for the researchers to work on. It includes intelligent element agents which are the combination of computer science and artificial intelligence and thus have common sense ability in them. Multiple agents, which work together to form a single while communicating to each other to achieve a common goal are said to be Multi-agents. There exist various applications of the AOSE and these are required to be implemented in some tool. One of the important applications is Business Area in which agents are used to check and enhance data-usage, control-flow and resource utilization. Resource utilization is the main aspect to be concentrated. In this research paper, multiple techniques are applied on Colored Petri Nets naming, Staff ware and File net, and then there efficiency is being checked to measure the best among them. © 2022 IEEE.

关键词： Workflow management

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A New Quantum CNN Model for Image Classification 8th

A New Quantum CNN Model for Image Classification

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8th international conference on computing, control and Industrial engineering, CCIE 2024

作者： Zhao, Xingqiang Chen, Tianlong School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Department of Science and Technology Service Zhejiang Rural Commercial United Bank Co. Ltd. Hangzhou China

ISBN: (纸本)9789819769360

Quantum density matrix represents all the information of the entire quantum system, and novel models of meaning employing density matrices naturally model linguistic phenomena such as hyponymy and linguistic ambiguity. Earlier work has already demonstrated potential quantum advantage for natural language processing (NLP) in a number of manners. But as far as we know, the idea of the quantum density matrix has had little application in the field of computer vision (CV). Inspired by the quantum density matrix, we argue that the quantum density matrix can enhance the image feature information and the relationship between the features for the classical image classification. Specifically, (i) we combine density matrices and CNN to design a new mechanism;(ii) we apply the new mechanism to some representative classical image classification tasks. A series of experiments show that the application of quantum density matrix in image classification has the generalization and high efficiency on different datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Natural language processing systems

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