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Evaluating Optimization Approaches for Deep-Reinforcement-Learning-based Navigation Agents 10

Evaluating Optimization Approaches for Deep-Reinforcement-Le...

引用

10th IEEE international conference on Cybernetics and Intelligent Systems and IEEE conference on Robotics, automation and Mechatronics, CIS-RAM 2023

作者： Kästner, Linh Roberts, Liam Bhuiyan, Teham Lambrecht, Jens Berlin Institute of Technology Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Germany

ISBN: (纸本)9798350303087

In recent years, Deep Reinforcement learning has made remarkable progress in various application areas such as control of robots and vehicles, simulation, and natural language processing. In recent years, various research works applied DRL to conduct different kinds of tasks for autonomous navigation of vehicles and robots such as lane changing, cruise control, or obstacle avoidance. However, DRL training is still a tedious and difficult process due to long training times, catastrophic forgetfulness and the myopic nature of DRL agents. In this paper, we integrate and explore the effect of a variety of state-of-the-art optimization approaches for DRL agents such as imitation learning, behavior cloning, frame stacking, and hindsight replay for autonomous obstacle avoidance. We evaluate the effect of each of these changes to the DRL agent in terms of training- as well as navigational performance. The resulting agents are compared against baseline approaches without those optimizations and we found an increase in navigational performance for some of these methods while other optimization approaches surprisingly resulted in decreased performing. The findings of this paper should aid in development of DRL approaches for autonomous navigation approaches. © 2023 IEEE.

关键词： Natural language processing systems

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AI-Driven Provenance Tracking for Mitigating Fraud in E-Commerce Platforms Using Neural Networks 2

AI-Driven Provenance Tracking for Mitigating Fraud in E-Comm...

引用

2nd IEEE international conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Reddy, Avala Raji Prabhakar, P. B. Edwin Al-Farouni, Mohammed Annapoorna, Errabelli Shukla, Aasheesh Kumar, Neelam Sanjeev Department of Mechanical Engineering CMR Technical Campus Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of CSE Tamil Nadu Chennai600073 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GRIET Bachupally Department of AI&ML Telangana Hyderabad India GLA University Department of Electronics & Communication Engineering Mathura India SRM Institute of Science & Technology Vadapalani Department of Computer Science & Engineering Chennai26 India

ISBN: (纸本)9798350368109

This paper offers an overview of a novel AI-based provenance tracking system that can reduce fraud in e-commerce environments through its neural network. This system has been designed and its effectiveness has been validated on the four datasets - transaction records (500, 000 records), supply chain logs (300, 000 records), user behavioral patterns (1, 000, 000 records), and product images (100, 000 records). Preprocessing steps might include data cleaning, data normalization, and data transformation as we prepared and formatted the data for use in building machine learning models. CNNs were compared along with RNNs and models having a blend of CNN and RNN architectures. In this work, the hybrid model has been proved to give better performance with an accuracy of 96. % 2%, precision of 95. 8%, recall of 94. 88% on a precision level of 92%, a recall of 5%, and an F1 score of 95. This represents a 1% improvement over the previous results, and is indicative of the capabilities of the hybrid model to work with multiple data types for identification of fraudulent patterns. The system was implemented in a real-time e-commerce environment and had an average response time to the user of 150ms, the system was able to handle 200 TPS and the system up-time was 99. 8% It was able to identify 4500 fraudulent transactions out of 300 false and fraudulent positive transactions and 200 false and fraudulent negative transactions to a fraud prevention of 95 %. 5%. The research used 'MATLAB' for data pre-processing and model building, which allows for the development of more optimal and less resource-intensive models. The results prove that the system has a potential of adding a positive value in the ways of e-commerce security promotion and is a holistic approach to combating fraud. © 2024 IEEE.

关键词： Crime

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Indoor Multi-Anchor Collaborative Localization for Unmanned Systems

Indoor Multi-Anchor Collaborative Localization for Unmanned ...

引用

2024 IEEE international conference on Robotics and Biomimetics, ROBIO 2024

作者： Tai, Beijia Wei, Yangjie Zhang, Guangyu He, Yuqing Cui, Siyao Ministry of Education College of Computer Science and Engineering Northeastern University Key Laboratory of Intelligent Computing in Medical Image Wenhua Street 3 Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781665481090

Accurate tracking of unmanned devices is essen-tial for higher-level decision-making in collaborative confrontation scenarios. However, the robustness of current positioning methods based on Ultra-Wideband (UWB) is limit, and the positioning error is unstable. This paper presents a collabo-rative localization algorithm specifically designed for indoor unmanned systems, which leverages UWB technology combined with Least Squares (LS) and Extended Kalman Filter (EKF) models to achieve anchor calibration, as well as precise and stable positioning of unmanned devices. First, the positions of anchors is estimated using fixed-point ranging information;Second, the positions of anchors are simultaneously corrected while locating the tags on unmanned devices. The experimental results show that our algorithm achieves positioning accuracy within 10 cm in the three-dimensional space, even with a ranging error of 5 cm. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Improving Chinese knowledge enhancement models with unified respresentation space

Improving Chinese knowledge enhancement models with unified ...

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2024 international conference on automation and Intelligent Technology, ICAIT 2024

作者： Li, Fei Chen, Zhengyi Wang, Yanyan Xu, Yin Duan, Ticheng School of Computer Science and Technology University of Science and Technology of China Hefei230000 China Innovation + Research Institute GuoChuang Cloud Technology Ltd. Hefei230000 China

ISBN: (纸本)9781510685802

Knowledge Enhanced Pre-trained Language Models have recently improved context-aware representations through external knowledge and linguistic knowledge from grammatical or syntactic analysis. The mismatch between text and knowledge graph embeddings in the feature space cannot be resolved in the fine-tuning phase and input module knowledge augmentation. In this paper, we revisit and advance the development of natural language understanding in Chinese and propose an improving Chinese knowledge Enhancement Models with Unified Respresentation Space. Specifically, knowledge embedded in the knowledge graph triples is effectively injected into it based on a novel pre-training task and knowledge-aware masking strategy. We conducted extensive experiments in seven Chinese nature language process tasks to evaluate the proposed model. The experimental results show that Our Model understands the external knowledge more deeply. We also demonstrate the effectiveness of the proposed method by ablation experiments. © The Authors.

关键词： Knowledge graph

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HEAT SINK DESIGN OPTIMIZATION VIA GAN-CNN COMBINED DEEP-LEARNING

HEAT SINK DESIGN OPTIMIZATION VIA GAN-CNN COMBINED DEEP-LEAR...

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ASME international Design engineering Technical conferences / computers and Information in engineering conference (IDETC-CIE) / 49th Design automation conference (DAC)

作者： Flynn, Nathan Qian, Xiaoping Univ Wisconsin Dept Mech Engn Madison WI 53706 USA

ISBN: (纸本)9780791887301

This work proposes a combined deep learning based approach to improve thermal component heat sinks involving turbulent fluid flow. A Generative Adversarial Network (GAN) is trained to learn and recreate the new ellipse based heat sinks. Simulation data for new designs is efficiently generated using OpenFOAM 7 (Open Source Computational Fluid Dynamics software) along with high throughput computing. To improve the speed of design evaluation, a Convolutional Neural Network (CNN) is trained to predict the entire temperature field for a given design. The trained CNN is able to predict the entire temperature field for the design with a mean average error of 1.140 degrees kelvin in 0.04 seconds (22,500 times faster than the simulation). A combined model is formed using the trained CNN and GAN networks to create and simulate new designs. The combined model optimizes the latent representation of 64 random designs on a Graphical Processing Unit (GPU) in ten minutes. The optimized designs perform fourteen degrees kelvin better on average than the non-optimized designs. The highest preforming design outperforms any design in the training data by 1.83 degrees kelvin.

关键词： Simulation Optimization Heat sinks Errors Design Fluid dynamics Graphics processing units Computational fluid dynamics computer software Temperature Turbulence Artificial neural networks

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Identifying Anomalies in Water Pump Systems Using Machine Learning and an Integrated Ensemble Method 2

Identifying Anomalies in Water Pump Systems Using Machine Le...

引用

2nd international conference on Data Science and Information System, ICDSIS 2024

作者： Sahoo, Sanat Sukumar Singh, Akanksha Kumari, Mupnesh Chandigarh University Department Of Mathematics Mohalli Punjab Gharuan India Chandigarh University Department Of Computer Science Engineering Mohalli Punjab Gharuan India

ISBN: (纸本)9798350381665

Asustainable and continuous water supply requires water pump systems that operate effectively and reliably. Abnormalities in these systems, including equipment malfunctions or fluctuations in water flow, can have detrimental impacts on operations and pose safety risks. To maintain a sustainable water supply, water pump systems must be robust and dependable. This study investigates the use of an ensemble of machine learning techniques, specifically the Random Forest algorithm and K-Nearest Neighbor (KNN) algorithms, for anomaly detection in water pump systems to ensure sustainable and continuous water supply. Real-time sensor data from water pump installations was utilized for analysis. The ensemble model demonstrated over 99% accuracy in detecting abnormalities with low false positive rates across various datasets. The study showcases the reliability and practical applicability of this methodology in enhancing predictive maintenance, reducing downtime, and extending the lifespan of water pump systems. The findings highlight the effectiveness of integrating data-driven methodologies for real-time monitoring and maintenance automation. © 2024 IEEE.

关键词： Water supply

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Dataset constructing method for Foreign Object Debris detection in civil aircraft manufacturing and assembly scenarios 16

Dataset constructing method for Foreign Object Debris detect...

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16th international conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2024

作者： Yu, Minghao Cheng, Sheng Rao, Jun Lu, Jianxia Zhao, Qijie Shanghai University School of Mechatronic Engineering and Automation Shanghai China Commercial Aircraft Corporation of China Ltd Shanghai China

ISBN: (纸本)9798331540241

Foreign Object Debris (FOD) can lead to engine failures and significantly undermine the safety and reliability of civil aircraft. Consequently, FOD detection has garnered increasing attention in computer vision. FOD datasets serve as the foundation for deep learning-based object detection models and are instrumental in evaluating the performance of object detection algorithms, thereby benefiting the aviation industry. This paper presents the construction method of a civil aircraft FOD dataset comprising thirteen representative types of FOD selected based on the characteristics of various manufacturing and assembly workstations. Images and videos of FOD were captured across different workstations using diverse equipment and tools. We examine the impact of varying lighting conditions, quantities, and shooting angles of FOD images, as well as image augmentation techniques, on model testing. This work lays a solid foundation for subsequent safety inspections in civil aircraft workstations and the development of FOD datasets. © 2024 IEEE.

关键词： Civil aircraft Dataset construction method Deep learning Foreign Object Debris

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A Survey of Behavior Tree-Based Task Planning Algorithms for Autonomous Robotic Systems 15

A Survey of Behavior Tree-Based Task Planning Algorithms for...

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15th international conference on Information and Communication Technology Convergence, ICTC 2024

作者： Shin, Mingyu Jung, Soyi Ajou University Dept. Artificial Intelligence Convergence Network Suwon16499 Korea Republic of Ajou University Dept. Electrical and Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350364637

Behavior trees (BTs) have gained recognition for their modularity and scalability, establishing them as a robust framework for task automation and planning in various domains, including robotics, game AI, and autonomous systems. This paper presents a comprehensive review of the integration of BTs with reinforcement learning (RL) and learning-from-demonstration (LfD) to enhance decision making and task planning in robotic systems. The review emphasizes the advantages of BTs, such as increased adaptability and efficiency through RL integration and the simplification of robot programming via LfD. Despite these benefits, challenges persist in the areas of computational complexity, scalability in multi-agent systems, and the automatic generation of BTs. This paper concludes by identifying key areas for future research to address these challenges and further advance the development of autonomous robotic systems. © 2024 IEEE.

关键词： Multi agent systems

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AI based activity tracking and analysis system 10

AI based activity tracking and analysis system

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10th international conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET-SIP-22 2022

作者： Dahiwale, Shaket Pagar, Dhiraj Deval, Revati Chavan, Madhuri R.A.I.T Department of Information Technology Nerul India

ISBN: (纸本)9781665467414

With the increase in the automation in this world, the accuracy of the systems has increase and the cost is decreased. Due to automation the need of activity detection systems has increased which can eliminate the need of human intervention and costly infrastructure. To solve this problem deep learning is used widely. It refers to the use of neural network which classifies the various human activities. In this paper, the model used to classify the human activity is ResNet-34 which is methodology of neural network. The model has an average accuracy of 71%. The error detection and reduction algorithm is used which can increase the accuracy of prediction data considerably. © 2022 IEEE.

关键词： Human computer interaction Deep learning automation Neural networks Decision making Information processing Prediction algorithms

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ROUTESENSE - An AI powered solution to Car pooling systems 10

ROUTESENSE - An AI powered solution to Car pooling systems

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10th international conference on Communication and Signal Processing, ICCSP 2024

作者： Poornimathi, K. Deepak Kumar, A. Srinithi, A. Rajalakshmi Engineering College Department of Computer Science and Business Systems India

ISBN: (纸本)9798350353068

Revolutionizing the urban commuting by introducing a ground-breaking AI-powered carpooling portal. This comprehensive platform provides an ultimate solution combining all the latest technologies for transportation challenges. The system ensures effortless scalability and real-time synchronization of user profiles, tip histories and route information across devices. Using the integration of emerging technologies such as Artificial intelligence and Machine learning algorithms, commuters are given a unique advantage of having a seamless travel and an optimized cost for the travel using AI and ML models and they are matched based on their preferences, schedules and historical data that makes the travel more convenient. The system also provides enhanced route security by analyzing the geospatial information to suggest the safest and most efficient routes for carpooling groups. It also streamlines the billing process by introducing Robotic process automation (RPA), ensuring accurate and tamper-free billing process for the carpooling groups. This combination of different technologies makes carpooling into a sophisticated, seamless and efficient mode of sustainable transportation that also is secure while delivering unparalleled convenience and peace of mind to its users. © 2024 IEEE.

关键词： Robotics

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