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Train Safety Control Based on Deep Deterministic Policy Gradient with Control Barrier Function 21

Train Safety Control Based on Deep Deterministic Policy Grad...

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21st international conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Guoteng Lv, Jidong Liu, Hongjie Chai, Ming Tang, Tao Zhang, Wentao School of Automation and Intelligence Beijing Jiaotong University Beijing China Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing China Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing China Beijing Zhengshi Environmental Engneering Company Beijing China

ISBN: (纸本)9798350365221

Train Relative Distance Braking Mode (RDBM) has great potential to increase line capacity by enabling shorter following distances based on train-to-train communication in-formation. However, it is difficult for traditional train control methods to ensure avoidance of potential collision risks in RDBM while dealing with the nonlinearity and uncertainty of train dynamics. In this paper, a novel train safety control method combining Deep Deterministic Policy Gradient (DDPG) and Control Barrier Function(Cbf)is proposed. Firstly, the safety constraint set for adjacent trains, considering their braking capabilities, is analyzed, and the uncertainty of train dynamics is modeled using a Gaussian process. Secondly, DDPG is used to explore the optimal control strategy under the action of uncertain train dynamics, and CBF provides compensation control input to ensure safety during the learning process. Then, a DDPG-CBF algorithm is presented to achieve efficient safety controller design and implementation. Finally, sufficient numerical simulations demonstrate that, compared with DDPG algorithm alone, the DDPG-CBF algorithm can not only ensure the safe and stable operation of trains, but also learn train control strategies expe-ditiously and accurately under different operating environments, with an average efficiency increase of 38.94 %. © 2024 IEEE.

关键词： Braking

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Multiple Objects Identification for Autonomous Car using YOLO and CNN 7

Multiple Objects Identification for Autonomous Car using YOL...

引用

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

作者： Prakash, M. Janarthanan, M. Devi, D. Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

One of the most anticipated 21st-century technologies and one of the subjects of the current study that is gaining significant research attention is autonomous driving. By detecting and responding to the vehicle's immediate environment, autonomous driving tries to navigate roads without human assistance. It presents significant difficulties for computer vision and machine learning. Processing several candidate object locations, which are frequently referred to as 'proposals,' is one difficulty. These choices only offer basic localization, which needs to be refined in order to obtain precise localization. However, solutions to these issues frequently come at the expense of efficiency, precision, or simplicity. R-CNN first introduced in 2013, and their upgraded versions Fast R-CNN and Faster R-CNN are recent examples of cutting-edge deep learning models that tackle the issue of object detection. YOLO, a paradigm for object detection created for speed and real-time use, was released in 2015. © 2023 IEEE.

关键词： Autonomous vehicles

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A Lightweight and Responsive On-Line IDS Towards intelligent Connected Vehicles System 43rd

A Lightweight and Responsive On-Line IDS Towards Intelligent...

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43rd international conference on Computer Safety, Reliability and Security (SAFECOMP)

作者： Liu, Jia Fan, Wenjun Dai, Yifan Lim, Eng Gee Lisitsa, Alexei Univ Liverpool Dept Comp Sci Liverpool L69 7ZF England Xian Jiaotong Liverpool Univ Sch Adv Technol Suzhou 215123 Jiangsu Peoples R China Tsinghua Univ Suzhou Automot Res Inst Suzhou 215200 Jiangsu Peoples R China

ISBN: (纸本)9783031686054;9783031686061

The current intelligent connected vehicles (ICV) system often shares the detected intrusion event to the cloud for further collaborative investigation. The upstream channel leading to the Internet of Vehicles (IoV) cloud is typically vendor-proprietary and costly, and the congestion caused by false alarms even exacerbates the situation. Machine learning (ML) can improve intrusion detection performance by reducing the false alarm rate. However, as a computation-intensive approach, traditional ML is not appropriate for real-time detection. Therefore, this paper proposes a lightweight and responsive on-line intrusion detection approach aiming for the ICV system requiring real-time detection. More specifically, we design a model termed Machine learning integrated with Blacklist Filter (ML-BF), which leverages the feature engineering and the Bloom filter techniques built on ML to enhance both detection and real-time performances. To evaluate the proposed solution, several experiments are conducted by using the Car-Hacking and CIC-IDS-2017 datasets. The experimental results show that our approach can detect intrusion at a microsecond level with a lower computational cost as well as a lower false positive rate than that in the state-of-the-art.

关键词： intelligent Connected Vehicles Intrusion Detection Responsive Detection Bloom Filter Machine learning

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Cultivating Knowledge: Exploring the Impact of Virtual Reality and Augmented Reality on Education 23th

Cultivating Knowledge: Exploring the Impact of Virtual Reali...

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23rd international conference on intelligent Systems Design and Applications, ISDA 2023

作者： Elamrani, Lobna Moughit, Mohamed Research Team LASTI: Laboratory Science and Technology for Engineering Transmission and Communication of Information Department Sultan Moulay Slimane University Beni Mellal Khouribga Morocco

ISBN: (纸本)9783031647789

Virtual Reality and Augmented Reality offering new avenues for enhancing learning experiences. This research investigates the potential of VR and AR to enrich education, catering to a wide spectrum of learners from elementary to university levels. By immersing students in simulated environments or overlaying digital elements onto the real world, these technologies enable innovative ways of engaging students and enhancing comprehension while addressing the limitations of traditional teaching methods. The paper highlights the VR and AR in education, they empower students to interact with complex concepts, connect with experts, and experience subjects firsthand. Furthermore, the paper underscores the importance of VR and AR in cultivating 21st-century skills. These technologies have the potential to create immersive, interactive learning environments that transcend traditional pedagogy. It emphasizes that as technology evolves, the potential for educational transformation continues to expand. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Augmented reality

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Deep learning-based Detection of Deep-sea Hydrothermal Polymetallic Sulfides, Plume, and Their Biological Communities 21

Deep Learning-based Detection of Deep-sea Hydrothermal Polym...

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21st IEEE international conference on Mechatronics and Automation (IEEE ICMA)

作者： Zhao, Shengya Ye, Xiufen Guo, Shuxiang Li, Zhengguang Mei, Xinkui Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China Natl Deep Sea Ctr Deep Sea Technol Dept Qingdao Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen Peoples R China

ISBN: (纸本)9798350388084;9798350388077

Because of their rich mineral resources and extreme ecological environments, deep-sea hydrothermal vent areas are significantly valuable both scientifically and economically. Optical imaging is the most direct method for verifying deep-sea hydrothermal activity. Autonomous detection and recognition of hydrothermal activity using optical imaging can significantly improve underwater robot detection efficiency. However, because of the difficulty in obtaining optical images of deep-sea hydrothermal activity and the limited accumulation of data, to date, there have been few studies on the autonomous detection of various characteristic targets of hydrothermal activity. To achieve autonomous detection and recognition of deep-sea hydrothermal polymetallic sulfides, plumes, and their biological communities, this study developed an optical image dataset of deep-sea hydrothermal activity, proposed an improved YOLOv8-based multi-target real-time detection algorithm, introduced the CBAM attention mechanism, and modified the network structure. Experimental results show that the improved algorithm achieves better target detection performance, and the response time reaches 6.8ms, which satisfies the real-time performance for underwater robots to autonomously identify hydrothermal vents.

关键词： deep learning deep-sea hydrothermal object detection YOLOv8

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A Comparative Analysis of Selected Machine learning Classifiers for Early Detection of Breast Cancer

A Comparative Analysis of Selected Machine Learning Classifi...

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2024 international conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Toyin, Okebule Adeyemo, Oluwaseyi A. Oguntimilehin, Abiodun Obamiyi, stephen E. Badeji-Ajisafe, Bukola Abiola, O.B. Aweh, Opani M. Sanya, O.A. Akinduyite, Christianah O. Afe Babalola University Department of Computer Science Ado-Ekiti Nigeria

ISBN: (纸本)9798350358155

Breast cancer is the principal causes of death among women in Nigeria and the world at large. The high prevalence of breast cancer in women has rapidly increased in recent decades. X-ray mammography and magnetic resonance imaging (MRI) are currently the major methods used to diagnose cancer in Nigeria. Machine learning techniques are the trendiest tool of the 21st century for discovery where intelligent approaches are utilized to detect breast cancer classification patterns. A technique for excellently recognizing and forecasting early cancer of the breast is needed by specialists for an unequivocal detection. This work provides a comparative analysis of selected Machine learning classifiers for early detection of breast cancer. Locally dataset was obtained from Ekiti state University Teaching Hospital. statistical approaches were employed to clean the data by assigning the missing value based on the neighboring feature values, in contrast to other methods that exclude all cases. This study examined three of Machine learning techniques - Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB) models on the basis of which we idetified the best predictive model. Accuracy, Precision, Recall, and F1 Score metrics were implemented for evaluating each model's performance using python programing language. The findings of this study demonstrate that the suggested SVM-based model has greater promise for breast cancer classification than the LR and NB classifiers yielding a 96% accuracy rate and suggested that the mechanism is effective than traditional techniques when assessed using several indicators of effectiveness to detect breast cancer. © 2024 IEEE.

关键词： Contrastive learning

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data-Driven intelligent Feeding System for Pet Care 22

Data-Driven Intelligent Feeding System for Pet Care

引用

22nd international conference on Control, Automation and Systems, ICCAS 2022

作者： Ravi, Ghimire Choi, Jae Weon Pusan National University Department of Mechanical Engineering Busan46241 Korea Republic of

ISBN: (纸本)9788993215243

The rapid development of artificial intelligence, the internet of things, and digital information processing technology has a huge impact on our daily lives with smart devices and wearables. The well-being of companion animals such as dogs and cats has become a large challenge. An increasing number of pet owners, their emotional attachment with their pets, and the 21st-century's lifestyle importantly need the safety and welfare of pets by harnessing a smart technological approach. This paper analyzes and compares different machine learning algorithms for data-driven intelligent feeding system for pet care application. Different parameters such as body weight growth, body temperature, heart rate, eating habits, activity, sleep, and urine pH have been considered with other correlated sub-variables in creating virtual datasets. The supervised machine learning models: linear regression, gaussian process regression, narrow neural network, linear support vector machine, and fine tree are evaluated and discussed for estimating feed quantity. The machine learning model was verified by training, validation, and testing datasets. The developed model will be an innovative breakthrough for pet care applications. Feed estimation can be automated using the pet's health parameters, this will help the pet to prevent obesity and related disorders. © 2022 ICROS.

关键词： Feeding

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Parameter Selection of Generalized Morse Wavelets for Water Leakage Classification 21

Parameter Selection of Generalized Morse Wavelets for Water ...

引用

21st international conference on Electrical engineering, Computing Science and Automatic Control, CCE 2024

作者： Martinez-Rios, Erick Axel Bustamante-Bello, Rogelio School of Engineering and Sciences Tecnologico de Monterrey Mexico City Mexico

ISBN: (纸本)9798350377545

Water leakage detection in water distribution networks is crucial to mitigating water scarcity. Machine learning algorithms have been utilized to generate models that detect water leakages by employing vibration, acoustic, flow, and pressure signals. In this sense, time-frequency methods have been widely employed to extract features or represent the signals for water leakage detection. Nevertheless, the challenge of using time-frequency analysis, such as the wavelet transform, is to select an adequate mother wavelet and its parametrization to represent the time-frequency information of the signal correctly. The selection of a mother wavelet is frequently performed arbitrarily or through trial and error, or the parameterization of the wavelet transform is not reported, hindering the studies' reproducibility. Using acceleration data, this paper proposed using the Heisen-berg area and the Average Reconstruction Mean Squared Error (ARMSE) to select the Generalized Morse Wavelets (GMWs) parameters to perform water leakage classification in looped and branched water networks. The generated scalogram was used to fine-Tune the GoogLeNet to classify the water leakages in the two water networks. The results of this study showed that the use of the Heisenberg area and the ARMSE to set the parameters of the GMWs led to select a β = 1.6667 and one voice per octave for the looped water network and β =2 and two voices per octave for the branched water network. Furthermore, fine-Tuning the GoogLeNet with the generated scalogram shows a testing accuracy of 78.33% for the looped water network and 76.25% for the branched water network. © 2024 IEEE.

关键词： Mean square error

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Prediction of High Heating Value Using ANN Technique Aftermath Natural Disasters 21

Prediction of High Heating Value Using ANN Technique Afterma...

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2024 IEEE/ACS 21st international conference on Computer Systems and Applications, AICCSA 2024

作者： Thakur, Disha Kumar, Sanjay Sharma, Bhisham Dhaou, Imed Ben UIT Himachal Pradesh University Department of Civil Engineering Shimla India UIT Himachal Pradesh University Department of Electrical Engineering Shimla India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Centre for Research Impact & Outcome Punjab 140401 India Hekma School of Engineering Computing and Design Dar Al-Hekma University Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798331518240

Natural disasters, particularly earthquakes and floods, can generate substantial waste, presenting challenges for waste management and energy recovery. This study explores the application of machine learning, specifically Artificial Neural Networks (ANN), to optimize Waste-to-Energy (WTE) conversion processes for disaster-generated Municipal Solid Waste (MSW). Using data from Sundernagar City, India, we constructed and evaluated multiple ANN models (collectively named ANNSNAGAR) to estimate High Heating Value, HHV, based on ultimate/elemental analysis of MSW. The models were rigorously assessed using statistical error metrics. Our best-performing network, with a 9-50-1 architecture (ANNMS), achieved a mean absolute percentage error (MAPE) of 1.47%, demonstrating high predictive accuracy. © 2024 IEEE.

关键词： Artificial Neural Network Energy Energy Content Machine learning Municipal Solid Waste

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An intelligent Digital System For Foot-Weight Distribution Analysis 21

An Intelligent Digital System For Foot-Weight Distribution A...

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21st international Bhurban conference on Applied Sciences and Technology, IBCAst 2024

作者： Hussain, Tassadaq Haider, Amna Ahmad, Uman Haris, Muhammad Awan, Muhammad Zeerak Ayguade, Eduard Centre for AI and Big Data Department of Electrical Engineering Mianwali Pakistan UCERD Pakistan Supercomputing Foot Analytic Islamabad Pakistan Centre for AI and Big Data Namal University Mianwali Pakistan Barcelona Supercomputing Centre Computer Sciences Department Spain

ISBN: (纸本)9798331516680

Maintaining proper weight distribution on the feet during daily activities can reduce postural and ambulatory issues. Therefore, accurate foot weight distribution analysis is crucial for medical science, occupational rehabilitation, physical therapy, sports, military training, and daily life postural training, influencing diagnostic and preventive healthcare. Despite the growing significance of body weight analysis, existing research in this domain is limited. The complexity of this issue arises from inherent variations in factors such as weight, height, Body Mass Index (BMI), physiological parameters, and accidentally conceived abnormalities. This paper introduces a novel approach to address this challenge by developing a smart digital system designed to analyze human body weight distribution across different parts of the foot. The system uses a data acquisition module connected with Force Sensing Resistor (FSR)-based matrix sensors placed under the plantar surface of the foot. The collected data is processed by an embedded system that uses analytic techniques to identify foot patterns and diagnose defects in the body. The proposed foot weight distribution system can be a helpful diagnostic tool for analyzing foot pressure distribution to identify and suggest potential medical problems (such as osteoporosis and prostheses). To validate the effectiveness of the system, a study involving 200 participants (comprising 100 males and 100 females) was conducted. The results show a significant level of accuracy in identifying abnormalities in osteoporosis and detecting prosthetic misalignment. © 2024 IEEE.

关键词： Medical problems

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