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Deep Learning based Advanced Image Recognition in Autonomous Vehicles in Industry 4.0 3

Deep Learning based Advanced Image Recognition in Autonomous...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Aruna, R. Manjula Devi, P. Suresh, D. Arul Pandiyan, M. Sathya, S. Geetha, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vtu Affiliated Amc Engineering College Department of Information Science and Engineering Banglore India Unnamalai College of Arts and Science Department of Computer Science Kovilpatti India Bannari Amman Institute of Technolgy Department of Information Science and Engineering Erode India Bannari Kongu Engineering College Department of Mathematics Erode India Dean of Academics and Research Jct College of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350375190

The automation with integration of numerous devices are important factors in the manufacturing and production sectors in Industry 4.0. This involves the use of autonomous vehicles for diverse applications. They are based on advanced image recognition systems. The efficient functioning of the system is achieved using deep learning techniques. These networks have the ability to automatically learn from complex data. These deep learning techniques are used for the detection of obstacles, lane following and object recognition. The various constraints in obtaining the optimum performance are achieved using ant colony optimization techniques. This helps in obtaining the overall efficiency of autonomous operations. They helps in evaluating optimum solutions through iterative updation of the artificial pheromones. They are used for the optimization of hyperparameters and training parameters. They helps in fine tuning of the model for various requirements of autonomous vehicles in Industry 4.0. They results in reduced computational costs with improved convergence. The integration of deep learning with ACO helps in overcoming the challenges in the deployment of advanced image recognition systems in autonomous vehicles. They helps in improving the perception capabilities of the vehicles. These autonomous vehicles provide adaptive solutions for various processes in the manufacturing and production sectors. © 2024 IEEE.

关键词： Image recognition

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Competitive and Collaborative Learning Accelerates the convergence of Deep Convolutional Neural Networks 7

Competitive and Collaborative Learning Accelerates the Conve...

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7th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2022

作者： Dang, Yanbin Yang, Yuliang Chen, Yueyun Zhu, Mengyu Yin, Dehui School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Shunde China

ISBN: (纸本)9781665487115

In the training of convolutional neural networks (CNNs), the layer-by-layer learning based on the backpropagation (BP) algorithm causes that in each round of weights update, the learning of the latter layer determines the learning of the former layer, while the former layer cannot directly affect the latter layer. This means that the flow of error information is unidirectional, causing non-cooperative learning between layers, thereby reducing the convergence speed of the networks. In this work, we propose a network structure that evaluates the relative contribution of each layer in the CNNs to the final output error. During training, it indirectly realizes the bidirectional flow of information between layers, achieving the purpose of cross-layer collaborative learning. Our algorithm also fuses features at different scales on the detection networks, which we call the flexible feature fusion network(FFN). On public datasets, we have conducted rich experiments. With the help of FFN, the convergence speed of the object detection model is greatly improved. Without pre-training weight initialization, the convergence speed of the model is approximately doubled. © 2022 IEEE.

关键词： Training Power demand Fuses Object detection Detectors Feature extraction Collaborative work

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Simulation Research on Automatic Control System of Urban Light Rail Traffic Signal Based on Ant Colony Algorithm

Simulation Research on Automatic Control System of Urban Lig...

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2023 international conference on Networking, Informatics and computing, ICNETIC 2023

作者： Wang, Yifei Shanghai Institute of Technology Shanghai200235 China

ISBN: (纸本)9798350313314

In the development of urban light rail transit, the signal system of urban light rail transit plays an extremely important role and is the key part of the development of urban light rail transit. However, the huge rail transit system has high complexity, multiple variability, even the accurate mathematical model is still difficult to build, and the limitations of the scheme are difficult to match the variability of the traffic system, so the control effect is not optimistic. In this paper, the simulation research of automatic control system of urban light rail traffic signal based on ACA (Ant colony algorithm) is proposed, and the traffic signal optimization model based on ACA is established. On the basis of the basic ACA, the global update rules are improved, and the local update rules are added. The purpose of global update is to make the search process more instructive. The simulation results show that the convergence speed of the improved ACA algorithm adopted in this paper is obviously better. When the number of iterations of the algorithm is 8, the delay has converged to 97. The feasibility of improve ACA algorithm and model is proved. © 2023 IEEE.

关键词： Light rail transit

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Improved Loop Closing with Panoptic Information and Scan Context++ for Accurate Semantic SLAM 25

Improved Loop Closing with Panoptic Information and Scan Con...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Tan, Louise Lee, Heoncheol Kumoh National Institute of Technology Department of It Convergence Engineering Gumi Korea Republic of

ISBN: (纸本)9798350330014

Loop closing is a key component in SLAM systems to reduce drift accumulations. Most SLAM systems achieve this by using only low-level geometric features of the scene, leaving the high-level features unused. This paper proposes a combinational approach of panoptic information and Scan Context++ to improve the accuracy of loop closure detection. Integrated into the SC-LeGO-LOAM algorithm, the proposed approach is able to exploit LiDAR odometry and panoptic information to perform loop closure detection as well as pose estimation and mapping. Experimental results showed that system was able to leverage panoptic information to detect loops, showing improvement in resulted maps. However, in certain environments, such as environments with reverse loops, the system suffered and resulted in slightly greater drifts. © 2023 IEEE.

关键词： Semantics

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A ML Approach for Predicting The Winner of IPL-24 Using Novel-Hybrid Classifier 9

A ML Approach for Predicting The Winner of IPL-24 Using Nove...

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9th IEEE international conference for convergence in technology, I2CT 2024

作者： Billa, Naga Mallikharjunarao. Akki, Praveena Kaliappan, S. Radha, Marepalli. Priya, G. Gnana Pathuri, Siva Kumar Dept of Cse AP Guntur India Srm Institute of Science And Technology School of Computing Kattankulathur TN Chennai India Lovely Professional University Division of Research And Development Jalandhard Punjab Phagwara144411 India Cvr College of Engineering Dept of Cse Telangana Rangareddy501 510 India Ramco Institute of Technology Department of Electronics And Communication Engineering TN Rajapalayam626117 India Klef Dept of Cse AP Vaddeswaram India

ISBN: (纸本)9798350394474

Cricket is one of those thrilling outdoor activities that has captured everyone's attention. There are a lot of competitions conducted, and one of them, the Indian Premier League, has formed a glorious history in the field of sports (IPL) which is very attractive and attracts everyone's attention. There are several staged series, but the Indian Premier League stands out for having a rich tradition at home (IPL). It has become one of the most popular franchises in the world, with 10 to twelve teams often competing. Few in this study primarily concentrate on the use and measurement of several models for determining which team will be the more appealing in an IPL match. The data set is acquired from the Kaggle repository and used as the model's input during training. Data analytics, on the other hand, is currently required in all businesses to research data sets to extract relevant insights, draw conclusions, and make decisions based on those findings. Algorithms first analyse data to create models to discover various things. The mining model is constructed by choosing and repeating on parameters. The parameters are then imported into a data-set to uncover actionable trends and detailed statistics. The goal of this research was to discover essential information about IPL teams by utilizing Jupyternotebook which facilitates data analysis by providing analysis findings as a visual representation. The data set is collected and pre-processing is performed before feature selection. Compare the results of three algorithms, such as Decision Trees, Naive Bayes, and Hybrid Ensembled Classifier to evaluate the accuracy, recall, and sensitivity. In, which the proposed hybrid classifier may give the best accuracy in predicting the winner of IPL-2024 and displays the results as a graph. © 2024 IEEE.

关键词： Decision trees

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Multi-server Cooperative Offloading Strategy for Dependent Tasks Based on Improved Genetic Algorithm 20th

Multi-server Cooperative Offloading Strategy for Dependent T...

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20th international conference on Intelligent computing (ICIC)

作者： Zheng, Tao Yang, Bin Zaozhuang Univ Sch Informat Sci & Engn Zaozhuang 277160 Peoples R China

ISBN: (纸本)9789819755806;9789819755813

Mobile edge computing (MEC) is an effective means to solve the problem of insufficient processor computation capability and limited battery power, when edge devices are processing computationally intensive and time-sensitive application tasks. Offloading strategy is one of the key technologies of MEC. Current task offloading strategies often lead to problems of low server utilization rate and high delay in the offloading process, because they overlook internal dependency of tasks and time-varying wireless channels. For this problem, a multi-server cooperative offloading strategy based on improved genetic algorithm is designed and implemented for multi-user with multi-server small cell network system scenario. This scenario jointly considers the internal dependency of tasks, time-varying wireless channels and combines with the heterogeneous computation capability of edge devices and servers. The goal of our paper is to find the best task offloading scheme with low latency and high server utilization. The improvements to the genetic algorithm mainly include the crossover and mutation process. Our paper uses the normal distribution crossover operator and the polynomial mutation operator to improve the genetic algorithm, so that it will not fall into the local optimum. The convergence speed is fast and the search space is wider. The simulation results verify that the proposed method can greatly reduce the user's execution delay and increase the utilization rate of the server.

关键词： Mobile edge computing offloading strategy task dependency improved genetic algorithm multi-server cooperation

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An Enhanced YOLOv5s-Based Algorithm for Defect Detection in Steel Box Beam Sections

An Enhanced YOLOv5s-Based Algorithm for Defect Detection in ...

引用

2023 international conference on Cognitive computing and Complex Data, ICCD 2023

作者： Yuhao, Xie Jian, Wu Yisu, Fang Lei, Yin Nanjing University of Science & Technology Jiangsu China Xi'an Jiaotong-Liverpool University Jiangsu China

ISBN: (纸本)9798350306293

Given the limited visibility and obstacles within steel box beams, we propose an improved defect detection algorithm for these sections, using enhanced YOLOV5s architecture. This approach includes a Weighted Bidirectional Feature Pyramid Network (BiFPN), a fusion of Convolutional Block Attention Module (CBAM) and Normalization-based Attention Module (NAM), and replacement of CIoU loss with EIoU. These changes enhance feature extraction and speed up convergence. Thorough experiments are conducted on the NEU-DET dataset, showing that the refined YOLOV5s-based algorithm significantly outperforms the baseline YOLOV5s model. Specifically, the YOLOV5s-NBE algorithm achieves a remarkable 9.9% accuracy improvement and a noticeable 2.3% boost in average precision, making it a promising solution for enhancing defect detection in steel box beams. © 2023 IEEE.

关键词： Defects

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Performance Evaluation of Anti-Predatory Nature-Inspired Algorithm on the CEC'14 Test Suites 6

Performance Evaluation of Anti-Predatory Nature-Inspired Alg...

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6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Sachan, Rohit Kumar Mallick, Shrabani Bennett University SCSET Uttar Pradesh Greater Noida India Dr. B R Ambedkar Institute of Technology Department of CSE Port Blair India

ISBN: (纸本)9798350304480

Anti-Predatory Nature-Inspired Algorithm (APNIA) is a recent proposal in evolutionary computations. It emulates the self-preservation strategies of frogs. It has several advantages, like a reasonable convergence rate, a balance between exploration and exploitation, and preserving diversity in solutions during the evolution process. Apart from different types of evaluations, CEC benchmarking problems are one of the finest test suites that measure the performance of an evolutionary algorithm. With this motive, this work evaluates the performance of the APNIA in solving CEC'14 benchmarking problems. We evaluate the performance of APNIA based on the CEC'14 guidelines. Our experimental evaluations demonstrate the moderated performance of APNIA for solving the CEC'14 test suite problems in different dimensions. © 2023 IEEE.

关键词： Anti-predatory CEC benchmarking Evolution-ary algorithms Nature-inspired algorithms Optimization

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Modification Shuffled Frog Leaping Algorithm (SFLA) Applied with the Single-Objective Function 7th

Modification Shuffled Frog Leaping Algorithm (SFLA) Applied ...

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Daoden, Kanchana Smart Electronics Engineering Faculty of Industrial Technology Uttradit Rajabhat University Uttaradit Thailand

ISBN: (纸本)9783031733239

This proposed algorithm presents the modification of the Shuffled Frog Leaping Algorithm (SFLA) applied with single-objective optimization functions. In the original SFLA, the concept of the moving position of the frogs searching for a better solution is uniformly random. The SFLA constantly updates the positions of frog solutions in memeplexes and shuffles frogs into subgroups to find the optimal solution. In this research, we modify the SFLA by including the angle and sigma in the moving process. The reason is to increase the search space when the frogs move into a straight line. The method is studied in modification SFLA, how the algorithm works when a single-objective function concept. The experiment chooses the two benchmark functions to test the optimization of the proposed method. The Ackley and the De Jong F5 function test the single-objective optimization when the algorithm converts to the optimum. The experimental shown in the graph with the time domain and accuracy domain when the convergence rate of the proposed method is to the optimum solution. The comparison graph is shown in the primary SFLA, the modification SFLA in the convergence rate of the optimum solution in each function. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Optimization algorithms

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Joint Dynamic Resource Allocation and Trajectory Optimization for UAV-Assisted Mobile Edge computing in Internet of Vehicles 29th

Joint Dynamic Resource Allocation and Trajectory Optimizati...

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29th international conference on Cooperative Information Systems, CoopIS 2023

作者： Li, Runji Sun, Haifeng School of Computer Science and Technology Southwest University of Science and Technology Mianyang621010 China

ISBN: (纸本)9783031468452

Computation offloading in Mobile Edge computing (MEC) represents a key technology for the future of the Internet of Vehicles (IoV), reducing the time and energy consumption of vehicles for computation tasks, while Unmanned Aerial Vehicles (UAVs) equipped with computation resources can act as aerial based stations to provide computation offloading services to vehicles moving on the road. In this paper, a joint dynamic resource allocation and UAV trajectory optimization scheme is proposed. In the scheme, an UAV is deployed with an edge server to execute the partially offloaded computation tasks from multiple vehicles. The goal of the problem is to maximize the total computation workload while minimizing the energy consumption of all vehicles by jointly optimizing the computation frequency, the wireless transmission power, the task offloading decisions of vehicles, as well as the flight angle of the UAV in each time slot. Since the problem is non-convex in continuous action space, we consider the Twin Delayed Deep Deterministic (TD3) policy gradient algorithm to solve the problem. Experimental results demonstrate the effectiveness of the TD3 policy gradient algorithm in the proposed optimization scheme in terms of the convergence speed and the system reward. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Antennas

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