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检索条件"任意字段=International Conference on Advances in Intelligent Control and Computer Engineering"

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Reconsidering Stochastic Policy Gradient Methods for Traffic Signal control 37th

Reconsidering Stochastic Policy Gradient Methods for Traffic...

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37th international conference on Industrial, engineering and Other Applications of Applied intelligent Systems (IEA-AIE)

作者： Kato, Masahiro Kojima, Ryosuke Kyoto Univ Undergrad Sch Engn Sci Kyoto Japan Kyoto Univ Grad Sch Med 54 Shogoin Kawahara ChoSakyo Ku Kyoto Kyoto Japan

ISBN: (纸本)9789819746767;9789819746774

Traffic management through traffic light control is essential for reducing congestion and improving traffic flow in cities. Recent progress in deep reinforcement learning (DRL) has shown potential for enhancing traffic light efficiency. Intuitively, probabilistic policies can promote exploration in reinforcement learning and prevent it from becoming stuck in local solutions. However, the conditions under which probabilistic policies work well in actual traffic light control are not fully understood. In this study, we focused on the use of probabilistic policies within DRL and investigated the effectiveness of stochastic policies for signal control under various conditions using a traffic simulator. We scrutinized four main stochastic approaches: using a softmax function with a temperature parameter, adding noise to the intermediate layer, employing NoisyNet, and implementing Bayes by Backprop. These methods were evaluated against the reinforcement learning benchmarks for traffic signal control (RESCO) and, a set of standards for assessing traffic control systems. The results revealed that the simplest approach, that is the softmax function with appropriate temperature parameters, performed the best in tuning the temperature parameter. The findings of this study provide empirical insights into how the temperature parameter in this function correlates with different traffic conditions.

关键词： Reinforcement learning Stochastic Policy Multi-Agent Systems

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Static Modeling of A Soft Robotic Manipulator Using Neural Networks and computer Vision Techniques 12

Static Modeling of A Soft Robotic Manipulator Using Neural N...

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12th RSI international conference on Robotics and Mechatronics, ICRoM 2024

作者： Shojaeezadeh, Ali Rad, Pouya Firuzy Ghafarirad, Hamed Amirkabir University of Technology Tehran Polytechnic Mechanical Engineering Department Tehran Iran

ISBN: (纸本)9798331529734

This research explores the static modeling of a planar soft robotic arm composed of two parallel silicon-based actuators using neural networks. Soft robots, characterized by their pliable structure and pneumatic actuation, present challenges in traditional control and modeling due to their nonlinear behavior and material flexibility. Conventional methods often fail to accurately predict their motion, especially under varying internal pressures and structural imperfections. In contrast, neural network-based approaches offer robust solutions by effectively capturing these complexities through data-driven techniques. Utilizing a Levenberg-Marquardt algorithm for training, this study develops a neural network model that predicts the 2D spatial position of the robotic arm's end-effector based on input pressure variables. Experimental data, obtained via image processing techniques for precise position tracking, informs the model's training and validation phases. Results demonstrate high accuracy in predicting end-effector positions, validated by low mean squared errors and high coefficients of determination. This approach not only advances static modeling capabilities for soft robotic systems but also lays groundwork for future dynamic modeling and real-time control applications. © 2024 IEEE.

关键词： End effectors

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Research on intelligent control Technology of Maglev Robot Based on Neural Network 2

Research on Intelligent Control Technology of Maglev Robot B...

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2nd international conference on computer, control and Robotics, ICCCR 2022

作者： He, Qin Kong, Min Wenhua College Mechanical and Electrical Engineering Department Wuhan China

ISBN: (纸本)9781665466745

The fusion of magnetic levitation technology and robot is an important direction in the research of precision robot. Aiming at the motion characteristics of magnetic levitation robot realized by elastic deformation, this paper proposes that in the aspect of intelligent target positioning of magnetic levitation robot, multi-target recognition and detection calculation method of convolution neural network is used to accurately locate the target object;in the model-based torque control structure, the magnetic levitation robot is established. The parallel processing RBF neural network model of the inverse motion problem of the floating robot could improve the solution speed while maintaining a certain accuracy. In order to improve the trajectory tracking accuracy of the maglev robot, BP neural network has been used based on the traditional sliding mode variable structure control. Simultaneous interpreting the mapping relationship between the master control module and the sensor, the neural network algorithm reduces the network weight by means of error back propagation. Then, when the heading angle and longitudinal velocity of the maglev robot are time-varying, the path following has been controlled. The application of these strategies would have a certain reference value for the effective intelligent control of Maglev robot. © 2022 IEEE.

关键词： Neural network models

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AMRM: Machine Learning Based intelligent Recommendation of Preference of Music

AMRM: Machine Learning Based Intelligent Recommendation of P...

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2022 IEEE international conference on advances in Electrical engineering and computer Applications, AEECA 2022

作者： Zhou, Weixi Fu, Min Chengdu Shishi A-Level Center Chengdu China Macquarie University School of Computing Sydney Australia

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

With an increasing quantity of music applications in recent years, recommendation of music became the key determination for these apps to attract users. However, for sun-rise music app firms, they are not able to obtain enormous data of users to create their AI recommendation mechanism to catch the music preference of users accurately. In this paper, we introduce an artificial intelligent recommendation model called AMRM, which can analyse a small amount of dataset related to end users to recommend the songs which match the music tastes of the users to the best extent, for small companies to create their recommendation mechanism. Our method is proposed based on the real world questionnaires about the features in user dimension and the information learning models. The traditional machine learning model of GBDT and the deep-learning model CNN are applied to compare one other to find out which are the users' favorite songs on the Netease Cloud. Based on the evaluation experiments, the experimental results show that the traditional machine learning model GBDT is the more feasible and more appropriate to the analysis on our small-sized dataset. © 2022 IEEE.

关键词： Surveys

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Novel Approach to Monitor Location of the Student in the Institution 15

Novel Approach to Monitor Location of the Student in the Ins...

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15th international conference on advances in Computing, control, and Telecommunication Technologies, ACT 2024

作者： Kulkarni, R.N. Suma, Rochana Y. Sravanthi, B. Rakshitha, G. Krathika, T. Ballari Institute of Technology and Management Dept. of Computer Science & Engineering Ballari India

ISBN: (纸本)9798331300579

During the last few decades there is a tremendous growth in the information and communication technology wherein many organizations are making use of the hardware and software for various applications. Marking the attendance of a student in the school or college as well as the presence of the employee in the workplace is to be either captured or manually marked in the registers. Performing various analyses by taking the manual input is cumbersome because the number of students in the class or number of employees in the organizations are increasing. To overcome the difficulties of managing the things manually in this paper we are proposing an automated tool to capture the presence of the student in the specific location. This paper proposes a novel approach to mark the presence of a student in the college premises. The tool developed here is an Android Application (App) wherein the students can send the details to the App. Two modules are proposed here, one for the student and another for the Administrator. Through the student module, each student can login to the App and capture the self-image and send it to the Administrator Module. The captured image comprises the GPS details like Longitude, Latitude, Time, and date information. The information sent through the App determines the location of the student whether the student is in the college premise such as Class room, Laboratory and Library. The application which is managed by the teacher or the administrator or proctor will get the details about the student's image, GPS locations such as latitude, longitude, date and time. The mobile app verifies the GPS location and image of the student and compares it with the database. If the captured information matches with the appropriate GPS location then the students attendance is marked as present in that particular duration and appropriate files are updated. © Grenze Scientific Society, 2024.

关键词： Face recognition

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Adaptive Traffic Management for Emergency Vehicles in Work Zones 11

Adaptive Traffic Management for Emergency Vehicles in Work Z...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Bandarian, Fatemeh Ghanadbashi, Saeedeh Malekjafarian, Abdollah Golpayegani, Fatemeh Structural Dynamics and Assessment Laboratory School of Civil Engineering University College Dublin Dublin Ireland School of Computer Science University College Dublin Dublin Ireland

ISBN: (纸本)9789897587450

A work zone is a section of road with closed lanes for maintenance, forcing vehicles to merge and creating congestion bottlenecks on the highway. Emergency Vehicles (EVs) are vital for incident response, with response times closely tied to fatality rates. EVs often face challenges when navigating work zones, and despite their importance, little attention has been given to improving their movement through these areas, highlighting the need for a system that enables quicker EV passage in work zones. While traffic management strategies are implemented in work zones, their effectiveness for EVs remains unexplored. This leaves a gap in understanding work zone management for EVs, ensuring their fast and safe passage. This paper proposes the ADAPtive Emergency MERGing (ADAPT-EMERG) algorithm to address this gap. This algorithm controls vehicles longitudinally for smooth merging into the open lane. It integrates merging approaches, headway adjustment between vehicles, and Variable Speed Limit (VSL) rules to set speed limits. Simulation results show that the ADAPT-EMERG algorithm reduces average travel times by 40%, minimises time loss for EVs by an average of 5%, and achieves a throughput increase across various traffic scenarios compared to the state-of-the-art strategies. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Travel time

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Prediction of Insulin-Dependent T2DM in Patients Using Machine Learning Model 2

Prediction of Insulin-Dependent T2DM in Patients Using Machi...

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2nd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gowthami, S. Venkata Siva Reddy, R. Ahmed, Mohammed Riyaz School of Electronics and Communication Engineering REVA University Bangalore560064 India HKBK College of Engineering Department of Computer Science and Engineering Bangalore560045 India

ISBN: (纸本)9798350327533

Diabetes, a widespread and enduring chronic ailment, exerts its impact on millions of people across the globe. Individuals with diabetes need regular insulin injections to sustain optimal blood sugar levels. Insulin is indispensable for persons with type 1 diabetes, although persons with type 2 diabetes may require insulin at a later stage. However, the current approaches for suggesting insulin dosage frequently prove inadequate, exhibiting either imprecision or complex procedures. Certain approaches require individuals to bear the responsibility, necessitating the manual calculation of insulin dosage using blood sugar levels and other factors - a complex and prone-to-error undertaking. Some individuals require closed-loop insulin administration devices, which are expensive and include wearing a continuous glucose monitoring (CGM) system. Recent estimates indicate that over 40% of patients who are suffering from (T2DM) in India and Gulf countries are utilizing insulin either on its own or in conjunction with Oral Antidiabetic Drugs (OADs) at any one moment. Lack of sufficient understanding regarding the use of insulin is expected to impact the acceptance, adherence, and effectiveness of therapy. This highlights the urgent requirement to examine the knowledge, and attitude, and suggest the usage of insulin to patients with both juvenile and type 2 diabetes. Insulin therapy is a crucial component of diabetes treatment;individuals with juvenile diabetes and the majority of those with type 2 diabetes will need insulin at some point. Proper injection technique is crucial for achieving glycemic control when using injectable medications such as human insulin, insulin analog, and glucagon-like peptide-l receptor agonists to manage diabetes. The proposed study focuses on determining the subset of individuals with diabetes who are taking both insulin and Oral Antidiabetic Drugs (OAD), and to assess the influence of insulin on glycemic control, ultimately resulting in improved

关键词： Insulin pens Machine learning algorithms Oral Antidiabetic drugs Precision medicine Type 2 Diabetes Mellitus

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Performance Analysis of Network Coverage Area in WSN Using Optimization Techniques 2nd

Performance Analysis of Network Coverage Area in WSN Using O...

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2nd international conference on advances in Data-driven Computing and intelligent Systems, ADCIS 2023

作者： Prabhu, R. Nagarajan, R. Kalis, K.R.N. Kannadhasan, S. Department of Electronics and Communication Engineering Gnanamani College of Technology Tamil Nadu Namakkal India Department of Electrical and Electronics Engineering Gnanamani College of Technology Tamil Nadu Namakkal India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamil Nadu Trichy India Department of Electronics and Communication Engineering Study World College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9789819995233

Wireless sensor networks (WSNs) are created by deploying various sensor units in harsh environments for target monitoring, as well as by deploying various tiny sensors collectively. The sensor units monitor the data in their vicinity and send it to the sink for processing. However, due to various parameters such as coverage area, in-built sensing and processing units, and so on, the sensor nodes may consume more power when forwarding information to the sink. We concentrate on the existing issues in the coverage control of various sensor models for quantitatively and qualitatively solving the sensor network coverage area. Various optimization techniques such as spotted hyena optimization (SHO), particle swarm optimization (PSO), and moth flame optimization (MFO) are studied and discussed. The trade-off between coverage rate, total number of sensor nodes, and energy consumption was observed, and the techniques’ significance was compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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A Study of Deep Learning based Techniques for the Detection of Maize Leaf Disease: A Short Review 7

A Study of Deep Learning based Techniques for the Detection ...

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

作者： Nourish, Ayisha Batra, Salil Sharoon, Kunhimma Sharma, Ritika Sharma, Mohit Lovely Professional University Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350397253

The process of detecting and recognizing disease in plants are crucial for the early control of pests and diseases that significantly affect plant growth. Although maize is the most productive food crop worldwide, it can suffer from physiological lesions caused by viral and fungal infections primarily visible in the leaves. The automatic identification and classification of plant diseases are essential for sustainable agriculture, but it remains a significant challenge. Existing techniques proposed for this purpose are limited in scope and rely solely on models for deep learning. Using cropped photos, convolutional neural networks have demonstrated to be the most adequate method for identifying and forecasting illnesses. An overview of deep learning methods for identifying plant diseases is given in this article, which also includes data collection sources, deep learning architectures, and image processing methods. The study evaluates existing deep learning models and highlights their results, with a focus on future research to improve system performance and accuracy for detecting crop diseases using better deep learning capabilities. The main objective of this review is to strengthen the quality of maize plant leaf disease detection and inspire further research in this field. © 2023 IEEE.

关键词： Feature extraction

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LNKDSEA: Machine Learning Based IoT/IIoT Attack Detection Method 1

LNKDSEA: Machine Learning Based IoT/IIoT Attack Detection Me...

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1st IEEE international conference on advances in Electronics, Communication, Computing and intelligent Information Systems, ICAECIS 2023

作者： Koppula, Manasa Leo Joseph, L.M.I. Sr University Dept. Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798350348057

The Internet of Things (IoT) brings together more devices that can communicate with one another while requiring little user input. IoT is one of the computer disciplines that is expanding rapidly, but the fact is that with the increasingly intimidating Internet world, IoT is susceptible to different kinds of cyberattacks. Practical defenses against this, including network anomaly detection, must be built to secure IoT networks. Attacks cannot be completely prevented forever, but practical defense depends on the ability to identify an attack as soon as possible. IoT systems cannot be protected by conventional high-end security solutions because IoT devices have a limited amount of storage and processing capability. This suggests the need for the creation of smart network-based solutions for cyberattacks, such as Machine Learning (ML). Although the application of ML methods in detecting attacks has numerous studies in recent years, attack detection in IoT networks has received less attention. The major goal of this study is to create and evaluate a hybrid ensemble algorithm called LNKDSEA (Logistic regression, Naïve Bayes, K-nearest neighbor, Decision tree, and Support vector machine-based Ensemble Algorithm). The proposed approach can efficiently identify IoT network attacks including DDoS, information gathering, Malware, Injection attacks, and Man-in-The-Middle- Attack. The edge-IIoTset dataset is used to evaluate the proposed model. During the implementation stage, the proposed technique is evaluated by employing binary and multi-class (6 and 15 Class) classifications of cyberattacks, and high performance is accomplished. © 2023 IEEE.

关键词： Internet of things

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