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Integration of Slip Detection and Grip Force Control in an Autonomous Robot Assembly Task for Space applications 20

Integration of Slip Detection and Grip Force Control in an A...

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ieee 20th international conference on Automation Science and Engineering (CASE)

作者： Ishrat, I. Barat, S. Diaz, Gustavo H. Santra, Shreya Yoshida, Kazuya Palanthandalam-Madapusil, Harish J. Indian Inst Technol Dept Mech Engn IITGN Robot Lab Gandhinagar 382424 India Tohoku Univ Grad Sch Engn Dept Aerosp Engn Space Robot Lab SRL Sendai Miyagi 9808579 Japan

ISBN: (纸本)9798350358513;9798350358520

Establishing human habitat using in-situ materials on the Moon or Mars autonomously by robotic systems is of great interest to humans. In such autonomous assembly tasks, it is crucial to integrate a slip detection capability that can detect any slipping in the grip and take corrective actions autonomously. This will enable safe handling of insitu materials during assembly by not unnecessarily applying excessive gripping force. We construct and evaluate sliding slip detection methods (based on both heuristic and learning methods), using only the normal force recorded by a uni-axial force senor in a robotic gripper particularly from the point of view of autonomous assembly tasks for space applications. We were able to achieve over 75% sensitivity and specificity in slip detection using LSTM (Long Short-Term Memory) based model. We also observe that training the model on simulation data alone yields very robust performance in experiments and real-time prediction, and contrary to our expectation, labeling slip in the incipient phase did not yield better performance. Further, we integrate slip detection and corrective actions with an autonomous assembly task through Robot Operating System. In an experimental demonstration of autonomous assembly task using magnetic blocks, YOLO v8 for object detection and LSTM model for slip detection, our results indicate that we are able to successfully complete with correct detection of slip and corrective actions in 9 out of the 10 demonstrations.

关键词： Robot Operating System

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Scenario-Based Thermal Management Parametrization Through Deep Reinforcement Learning 27

Scenario-Based Thermal Management Parametrization Through De...

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27th ieee international conference on Intelligent Transportation systems, ITSC 2024

作者： Rudolf, Thomas Muhl, Philip Hohmann, Soren Eckstein, Lutz Porsche Engineering Group GmbH Department for Software Calibration and Diagnosis Weissach71287 Germany Dr. Ing. H.c. F. Porsche Ag Department for Thermal Management Systems Weissach71287 Germany Karlsruhe76131 Germany Rwth Aachen Aachen52056 Germany

ISBN: (纸本)9798331505929

The thermal system of battery electric vehicles demands advanced control. Its thermal management needs to effectively control active components across varying operating conditions. While robust control function parametrization is required, current methodologies show significant drawbacks. They consume considerable time, human effort, and extensive real-world testing. Consequently, there is a need for innovative and intelligent solutions that are capable of autonomously parametrizing embedded controllers. Addressing this issue, our paper introduces a learning-based tuning approach. We propose a methodology that benefits from automated scenario generation for increased robustness across vehicle usage scenarios. Our deep reinforcement learning agent processes the tuning task context and incorporates an image-based interpretation of embedded parameter sets. We demonstrate its applicability to a valve controller parametrization task and verify it in real-world vehicle testing. The results highlight the competitive performance to baseline methods. This novel approach contributes to the shift towards virtual development of thermal management functions, with promising potential of large-scale parameter tuning in the automotive industry. © 2024 ieee.

关键词： Automotive industry

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Dynamic Emotion Regulation through Reinforcement Learning: A Neurofeedback System with EEG Data and Custom Wearable Interventions

Dynamic Emotion Regulation through Reinforcement Learning: A...

引用

2024 international conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Joseph, Jeethu Judy, M.V. Department Of Computer Applications Kochi India

ISBN: (纸本)9798331540067

Neurofeedback, a form of biofeedback that uses brainwave activity to provide real-time feedback, is a powerful approach for emotion regulation. Recent advancements in affective computing and machine learning offer new possibilities for improving the efficacy of neurofeedback systems, particularly through adaptive algorithms for personalizing the intervention process. This study presents an innovative neurofeedback mecha-nism that uses reinforcement learning (RL) to regulate emotions. The system uses Q-learning algorithm to dynamically adjusts its intervention strategies based on sampled EEG data from the DEAP dataset that monitors brain activity associated with emotional states. The therapeutic interventions consist of a mix of music therapy sessions, mindfulness exercises, and tactile feedback provided by a custom-built wearable device. The device utilizes tactile vibration as a neurofeedback signal to help users become more conscious of and effectively regulate their emotional well-being. The system's performance was evaluated through detailed analysis of the Q-learning process and observing how various states, actions and rewards influenced effective emotional regulation. Using sampled EEG data from the DEAP dataset enabled comprehensive testing and refinement of the system's algorithms in a controlled environment, thereby setting the stage for potential future use with real time EEG devices. The results of this study highlight the power of RL-based neurofeedback to help transform emotional wellbeing, while also showcasing the potential to expand the spectrum of neurofeedback tools to be more responsive and adaptive to individual users, ultimately promoting improved mental health. © 2024 ieee.

关键词： Reinforcement learning

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A Novel Path Planning-Aware Vehicular Task Offloading Strategy 23

A Novel Path Planning-Aware Vehicular Task Offloading Strate...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Yufei Zhao, Liang Yang, Kaiqi Li, Yunpeng School of Computing Shenyang Aerospace University Shenyang China Ciac China P&t Appliances Beijing China

ISBN: (纸本)9781665494571

In intelligent transportation system (ITS), the combination of vehicle network (VN) and mobile edge computing (MEC) can provide low delay networking services to vehicles. Since the quality of vehicle access network is mainly determined by location, the service quality of vehicle computation task unloading (CTO) should be considered in the process of vehicle path planning, so as to further improve the performance of a single vehicle in unloading throughput, delay, and cost. In this paper, with employing the SDN-based system architecture, we propose a novel path planning-aware vehicular task offloading strategy for connected vehicles in real-time by considering CTO to provide drivers a good driving experience. To achieve the efficient path planning, an effective road performance evaluation method is proposed, which combines the road capacity and the throughput of computation tasks into the driving index to evaluate the priority selected by vehicle drivers. The simulation results show that the proposed method can guarantee the driving time of the vehicle, while it can also give the driver a better quality of experience. © 2021 ieee.

关键词： Performance evaluation Roads Simulation systems architecture Throughput real-time systems Path planning

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Surveying Keyword Extractors: Classification, applications, and Empirical Analysis 6

Surveying Keyword Extractors: Classification, Applications, ...

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6th Parul international conference on Engineering and Technology, PICET 2024

作者： Musunuru, Varun Nagam, Sai Pavan Krishna Thushara, M.G. Villuri, Praneeth Vasantha, Gopikrishna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350369748

Natural Language Processing (NLP) has seen some rapid developments. One of the areas is keyword Extraction which has seen rapid growth. Many varied and complex extractors have been brought to light recently, and several extractors have been upgraded. These can be used in several NLP applications. The analysis and working of some of the newest and benchmark extractors that shed new light in this field have been discussed. The goodness of any extractor can only be measured by how it works on real-life data. Instead of going down the set route, a detour was taken, and the working of keyword extractors on movie plots was observed and discussed. Thus for the analysis part, a new dataset has been created from several movie plots taken from various credible sources like IMDB, Rotten Tomatoes, etc. © 2024 ieee.

关键词： Natural language processing systems

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Deep Learning based Efficient Parking Management System Framework

Deep Learning based Efficient Parking Management System Fram...

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2024 international conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Sathishkumar, P. Boopalan, R. Shree, S. Kiruthiga Dhanish, R. K. S. Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode637 215 India

ISBN: (纸本)9798350359688

In urban areas, efficient parking management is crucial for optimizing traffic flow and enhancing overall transportation systems. This paper introduces a novel approach to parking management utilizing the YOLO v5 (You Only Look Once) deep learning architecture. YOLO v5 is renowned for its real-time object detection capabilities, making it an ideal candidate for applications in dynamic environments such as parking lots. By integrating YOLO v5 into our framework, we achieve accurate and fast detection of vacant parking spaces in real-time. This enables proactive decision-making for drivers seeking parking spots, thereby reducing congestion and improving overall traffic efficiency. We present experimental results demonstrating the effectiveness and efficiency of our proposed system compared to traditional methods. Our approach not only enhances parking management efficiency but also lays the foundation for intelligent transportation systems in smart cities. © 2024 ieee.

关键词： Intelligent systems

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Innovative Wireless Solutions for real-time ECG Monitoring: Leveraging BLE, Wi-Fi, and LoRa Technologies

Innovative Wireless Solutions for Real-Time ECG Monitoring: ...

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2024 international conference on Telecommunications and Intelligent systems, ICTIS 2024

作者： Meftah, El-Hadi Benmahmoud, Slimane Rabehi, Abdelhalim USTHB LISIC Laboratory Department of Electronics Algiers16000 Algeria M'sila University Department of Electronics Engineering M'sila28000 Algeria Djelfa University Lab of Telecom and Smart Systems Djelfa17000 Algeria

ISBN: (纸本)9798331527396

The rapid advancement of wireless technologies and the Internet of Things (IoT) has transformed healthcare, enabling real-time monitoring and data transmission for improved patient care. This paper presents an in-depth study of the wireless transmission of electrocardiogram (ECG) signals using Bluetooth Low Energy (BLE), Wi-Fi, and Long Range (LoRa) technologies. We explore the architecture, security, energy efficiency, and reliability of each protocol, determining their suitability for healthcare applications. Leveraging IoT integration, we utilize ESP32 and ESP8266 microcontrollers for real-time data transmission to cloud-based platforms, facilitating continuous health monitoring through web and mobile application interfaces. The study also delves into the design and testing of the electrical circuits, incorporating ECG sensors and wireless communication units to establish a robust monitoring system. By automating the collection and analysis of critical health data, the proposed solution enhances diagnostic capabilities, offering flexible and scalable approaches for modern healthcare needs. © 2024 ieee.

关键词： Wi-Fi

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AI-Driven Fault Detection in Smart Grids 3

AI-Driven Fault Detection in Smart Grids

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3rd Odisha international conference on Electrical Power Engineering, Communication and computing Technology, ODICON 2024

作者： Pradeep, S. Rex, C. R. Edwin Selva Kalaiyarasi, D. Dhandapani, Ramya Sakthivel, M. Vijaipriya, K. S. A. Engineering College Department of Electronics and Communication Engineering Tamil Nadu 600077 India Vignana Bharathi Institute of Technology Department of Electrical and Electronic Engineering Telangana Hyderabad501301 India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600123 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai India Sri Sai Ranganathan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350354379

The use of artificial intelligence (AI) for fault finding in smart grids, with the end goal of improving the reliability and efficiency of ultramodern power systems. Smart grids, which are distinguished by their intricate architectural design and massive data collection, are equipped with cutting-edge approaches for the quick and precise identification of breakdowns. Through the utilization of artificial intelligence techniques, such as Deep Reinforcement Learning (DRL) method can analyze big datasets, identify anomalies, and accurately predict implicit errors with a high degree of precision. In this research, artificial intelligence algorithms that are colorful are investigated, along with their effectiveness in real-time monitoring and defect finding within smart grids. The advantages of AI-driven defect discovery over traditional methods are demonstrated through case studies and simulations. These advantages include important breakthroughs in response time, delicacy, and functional adaptability. The incorporation of artificial intelligence into the management of smart grids holds the potential to bring about substantial breakthroughs in the areas of grid stability maintenance, time-out reduction, and energy distribution optimization. The purpose of this research is to provide a complete analysis of the role that artificial intelligence plays in defect finding. It also offers insight into future advances and practical applications in smart grid systems. © 2024 ieee.

关键词： Deep reinforcement learning

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IoT-Enhanced Innovations in Drug Delivery systems for Targeted Therapy in Autoimmune Disorders 10

IoT-Enhanced Innovations in Drug Delivery Systems for Target...

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10th international conference on Advanced computing and Communication systems, ICACCS 2024

作者： Bhuvaneshwari, V. Sadaieswaran, R. Malathi, P. Legapriyadharshini, N. Thirumalaikumari, T. Raja, S.R. Acharya College of Engineering Department of Computer Science and Engineering Achariyapuram Puducherry India Tagore College of arts and science Department of Computer Applications Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Data Analytics Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350384369

The growing number of autoimmune diseases necessitates the needs novel drug delivery system alternatives to improve targeted therapy. The study deals with the immediate requirement for improvements by investigating the limitations of existing systems, highlighting their limitations in precision, patient adherence, and real-time monitoring. The proposed system combines Internet of Things (IoT) technology into medicine distribution, providing an extensive structure that makes use of smart devices, sensors, and networking solutions. The integration attempts to improve patient participation and treatment efficacy by ensuring tailored regimens. The study demonstrates promising discoveries in optimizing therapeutic interventions, cost-effectiveness, and data-driven insights through an in-depth evaluation of case studies and successful implementations. The proposed IoT-enhanced drug delivery system not only addresses the limitations of existing systems but also represents an evolution for healthcare professionals and pharmaceutical businesses. Patient privacy and data security are essential for ethical and regulatory reasons. The study's results and analysis highlight the proposed system's potential for overcoming limitations and enhancing targeted therapy for autoimmune disorders, setting the groundwork for a future in which IoT seamlessly interacts with drug delivery systems to transform patient care. © 2024 ieee.

关键词： Targeted drug delivery

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AI Innovations in Photovoltaic Power Prediction

AI Innovations in Photovoltaic Power Prediction

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2024 ieee international conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Mansouri, Nawres Zitouni, Naoufel Mouelhi, Aymen Faculty of Sciences of Tunis Tunis Tunisia University of Tunis Ensit Laboratory Sime Tunis1008 Tunisia

ISBN: (纸本)9798350389838

Accurate prediction of solar energy production is essential for integrating photovoltaic (PV) systems into the global energy grid, ensuring grid stability, and optimizing energy management. Traditional PV forecasting methods often face challenges in accounting for the inherent variability in solar radiation and atmospheric conditions. This paper proposes an innovative approach using machine learning (ML) and deep learning (DL) techniques, particularly Long Short-Term Memory (LSTM) networks, to improve short-term PV energy forecasts. Unlike previous models, our work introduces a new combination of input variables, including real-time meteorological data and optimized preprocessing techniques, leading to improved prediction accuracy under varying environmental conditions. The study also demonstrates the importance of high-quality data acquisition, advanced feature selection, and rigorous model training processes for achieving reliable forecasting results. By evaluating multiple models across different scenarios, this research contributes a novel methodology for enhancing the precision of PV power prediction, making it highly suitable for real-world applications in renewable energy integration. © 2024 ieee.

关键词： AI DL forecasting LSTM ML PV systems

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