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4th International Symposium on Intelligent Robotics and Systems, ISoIRS 2024

作者： Li, Na Ouyang, Yun Deng, Jun Gao, Zhenguo Jiang, Kunliang Huaqiao University School of Mechanical Engineering and Automation Xiamen China Huaqiao University School of Computer Science and Technology Xiamen China

ISBN: (纸本)9798350364798

To enhance the efficiency of motion planning, we introduce a new angle profile searching method known as Progressive Dynamic Local Search (PDLS). Building upon a collision-free multi-segment path established by the RSPM-PS algorithm, PDLS methodically computes angle profiles from the initial to the target pose, focusing searches near each point's predecessor. It refines the path's smoothness by adding intermediate points and sets the local search radius through the Jacobian matrix, optimizing convergence. Our method also innovates in assessing angle profile quality, introducing a combined error function that judiciously weighs the importance of position and attitude accuracy. Furthermore, we mitigate sudden changes in joint acceleration by interpolating angle profiles using a quintic polynomial. Tests in MATLAB and on a JAKA Zu311 manipulator demonstrate PDLS's advantages: reduced run times, precise calculations, and improved convergence rates. © 2024 IEEE.

关键词： Jacobian matrices

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Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing

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Multimedia Tools and Applications 2025年第15期84卷 14359-14386页

作者： Thakur, Rahul Sikka, Geeta Bansal, Urvashi Giri, Jayant Mallik, Saurav Department of Computer Science and Engineering Dr. B R Ambedkar National Institute of Technology Punjab Jalandhar144011 India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi110036 India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Environmental Health Harvard T H Chan School of Public Health BostonMA United States Saveetha University TN Chennai602105 India

Fog computing is an emerging paradigm that extends cloud computing (CC) by providing computation, communication, and storage services at the edge of a network, closer to end devices. It has gained significance due to the rapid development of IoT devices, which generate various types of tasks. Processing these tasks in the cloud can strain its infrastructure and lead to delays in time-sensitive requests. To address this limitation, fog computing (FC) concepts were introduced in 2012 by Cisco. FC is not meant to replace CC but rather to complement and extend its capabilities. One of the challenges in FC is efficiently assigning tasks to appropriate resources to minimize makespan, energy consumption (EC), and increase the number of deadline-satisfied tasks. In this work, the improvement of semi-greedy algorithm has been done by incorporating fuzzy logic (FL). By leveraging FL, the aim is to enhance the algorithm's decision-making process and make it more adaptive to varying conditions and uncertainties in the fog environment. The use of FL allows more nuanced and flexible task scheduling (TS) decisions based on fuzzy sets and fuzzy rules. The simulation experiments demonstrate that the proposed algorithm outperforms PSG (Priority-aware Semi-Greedy) and PSG-M (PSG with multistart), which were identified as the best scheduling algorithms (Algos) in the literature review. The algorithm exhibits better performance in terms of reducing makespan, EC, and increasing the percentage of deadline-satisfied tasks compared to PSG and PSG-M. The inclusion of FL further enhances the algorithm's effectiveness in handling complex scheduling scenarios in a FC environment. To evaluate the performance of the proposed algorithm, different simulation experiments have been conducted using a selected simulator after a systematic review of existing simulators. The experiments involved 300 and 500 random and static tasks, as well as 60 fog nodes in the fog environment. All simulations were impl

关键词： Fog computing

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Energy conservation: Virtual energy audit of an industrial plant

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Materials Today: Proceedings 2023年 72卷 1882-1889页

作者： Joshi, Atharva Khandelwal, Niyati Suryavanshi, Yash Broota, Sumir Kurulekar, Maya Dhaneshwar, Pranav LY B.Tech Computer Engineering Department of Science and Technology Vishwakarma University MS Pune 411048 India TY B.Tech Computer Engineering Department of Science and Technology Vishwakarma University MS Pune 411048 India Department of Engineering Science Vishwakarma University MS Pune 411048 India Department of Mechanical Engineering Vishwakarma University MS Pune 411048 India

Energy consumed by manufacturing industries constitutes a large part of the total energy consumption of the world. In India, almost 50% of the total energy consumption is done by Industries. Energy bills are usually a major contributor to the operating expenses of a company. Industries can profit from the opportunities coming out of energy saving by the implementation of effective energy management using appropriate tools. An industrial plant layout is usually designed within several constraints, mainly operational. The objectives were decided by the company to study and identify energy gaps in the industrial plant and suggest suitable solutions to reduce energy consumption all while reducing the average working temperature inside the plant from 38 °C to an ideal value below 30 °C, preferably without installing an air conditioning system. The study was divided into two parts;Primarily, Data collection and observations of the plant operations and then Building Energy Modelling and simulation. The base model with existing conditions was modelled, simulated and validated with historical energy consumption data and temperature readings taken inside the plant. The proposed solutions include limited modifications in the existing set up considering the operational constraints. The simulations run with proposed modifications indicate a maximum of 11 % savings in energy consumed by lighting and potential to reduce temperature to 33.23 °C from 38 °C. The effect of ambient factors according to the geographical location such as sun exposure, wind directions, humidity were also taken into consideration. © 2023

关键词： Energy utilization

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KB4RL: Towards a Knowledge Base for automatic creation of State and Action Spaces for Reinforcement Learning

KB4RL: Towards a Knowledge Base for automatic creation of St...

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Joint of the ESWC 2024 Workshops and Tutorials, ESWC-JP 2024

作者： Joualy, Lobna Demeester, Eric Tsiogkas, Nikolaos Department of Computer Science KU Leuven Diepenbeek3590 Belgium Department of Mechanical Engineering KU Leuven Diepenbeek3590 Belgium

Reinforcement Learning (RL) is a promising approach for creating adaptive solutions for robotic tasks that are difficult to design directly. Unlike traditional approaches that rely on designing explicit behaviors, RL allows agents to learn skills by interacting with their environment. Inspired by human learning processes, agents acquire knowledge through trial and error guided by the rewards gained from their actions through various experiences. A key step when solving planning problems with RL algorithms is the definition of state and action spaces. Typically, state and action spaces are manually designed in a domain-specific manner to ensure effective problem solving. In this paper, we present an innovative approach to define state and action spaces through a Knowledge Base (KB) encoding information about the environment and physics. We propose a structured and extensible form for knowledge description, and we show how to extend the action space through action learning. This allows the robot to reach its goal in those states where unexpected scenarios are encountered and where, therefore, the action space obtained through the KB is not sufficient. © 2024 Copyright for this paper by its authors.

关键词： Automatic action space Automatic state space Knowledge graphs Reinforcement learning

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IoT-Driven Mushroom Farm Automation Using Neural Turing Machines (NTM) 4

IoT-Driven Mushroom Farm Automation Using Neural Turing Mach...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Manoj Kumar, S. Monica, C.L. Sunith Babu, L. Manikandan, V. Subramani, Dinesh Malik, Subhash Madanapalle Institute of Technology & Science Department of Mechanical Engineering Angallu India V.R.Siddhartha Engineering College Department of Environmental Science Vijayawada India M. S. Ramaiah Institute of Technology Department of Mechanical Engineering Bengaluru India Department of Computer Science and Engineering Bengaluru India Department of Artificial Intelligence and Data Science Erode India Maharishi Markendeshwar Engineering College Department of Mechanical Engineering Mullana India

ISBN: (纸本)9798350352931

Mushroom cultivation leads global agriculture due to its impact on worldwide trade. One of the main challenges in this area is optimal weather, especially temperature and humidity. Unfortunately, traditional farming's inability to achieve these conditions often leads to low yields. The recommended procedure involves collecting sensor data, constructing the model, and training it. Preprocessing includes spectral smoothing, normalization, and reflectance calibration. Sensors measure motion, temperature, light, and soil moisture. Photo feature extraction uses edge detection to identify objects. The model is trained using NTM. The suggested NTM-based model beats top-tier models like Random Forest (RF) and Support Vector Machine (SVM) with 92.35 percent accuracy. This increasing accuracy shows the model's mushroom production optimization. This proposed uses sensor data and current image processing techniques to improve mushroom farming conditions. NTM-based mushroom cultivation is more efficient and yields more than conventional methods. © 2024 IEEE.

关键词： Soil moisture

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Feedback-based safe gradient flow for optimal regulation of virtual power plants 14

Feedback-based safe gradient flow for optimal regulation of ...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Colot, Antonin Cornélusse, Bertrand Cortés, Jorge Dall'Anese, Emiliano Department of Electrical Engineering and Computer Science University of Liège Belgium Department of Mechanical and Aerospace Engineering University of California San Diego United States Department of Electrical and Computer Engineering Boston University United States

ISBN: (纸本)9781837242689

This paper considers the problem of controlling distributed energy resources (DERs) in a distribution network (DN);the paper focuses on the voltage regulation task and on the concept of virtual power plant (VPP). For the latter, we envision an aggregation of DERs as a VPP that tracks power setpoints at the point of common coupling to provide ancillary services to the bulk power system. We propose a feedback-based controller that pursues solutions to a time-varying AC optimal power flow problem. This controller ensures voltage constraints are met in real time, assuming no voltage measurement errors. The feedback from the system mitigates the controller sensitivity to model uncertainties and eliminates the need for measurements of uncontrollable loads at every node. The feedback-based controller is tested on the IEEE37-node system with delta and wye-connected devices in a strongly unbalanced configuration. © The Institution of engineering & Technology 2024.

关键词： Virtual Power Plants

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A Transfer-Learning-based Strategy for Autonomous Driving: Leveraging Driver Experience for Vision-Based Control 20

A Transfer-Learning-based Strategy for Autonomous Driving: L...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Babaahmadi, Amirreza Masoumi, Fatemeh Sadat Ayati, Moosa Amirpour, Mehrimah University of Tehran School of Mechanical Engineering Tehran Iran Allameh Tabataba'i University Faculty of Mathematics Statistics and Computer Science Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

This paper explores the utilization of a novel transformer-based architecture for end-to-end learning in predicting steering angles in self-driving scenarios while leveraging a novel robust image processing pipeline to handle diverse environmental situations. Our approach relies solely on visual perception as the input to generate control commands. We trained and evaluated our methodology using a proprietary dataset from a self-driving car simulator consisting of image frames paired with their corresponding steering angles. The presented methodology is robust against overfitting, and it shows superior performance in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE) compared to previous methods. © 2024 IEEE.

关键词： Autonomous vehicles

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A wake loss model asymmetry induced by the circulation of a vertical axis wind turbine

A wake loss model asymmetry induced by the circulation of a ...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Hassanian, Reza Helgadóttir, Ásdís Riedel, Morris University of Iceland The Faculty of Industrial Engineering Mechanical Engineering and Computer Science Reykjavik Iceland Juelich Supercomputing Centre Germany

ISBN: (纸本)9798350332308

The purpose of this research is to develop a theoretical method for predicting the wake region downstream of the flow from a top view of a vertical axis wind turbine (VAWT) and to examine the wake loss effects of the VAWT. Previous literature has reported that the wake region of the VAWT is not symmetric, and its downstream is similar to a circular cylinder wake street. The airflow of the VAWT is turbulent, and the inviscid incompressible flow can be applied to its range. Therefore, the current study analyzed the flow over a cylinder with circulation to investigate the velocity, pressure coefficient, and tip speed ratio and to observe the similarity between the cylinder and the VAWT. The results of this study demonstrate remarkable similarity, particularly with respect to the asymmetry of the wake region due to the distinct velocity speed in two opposite locations of the VAWT. Moreover, the pressure coefficient functionality and curvature are similar to the available measurements of the VAWT. This model can be further developed and examined under varied conditions to provide a theoretical wake loss model for VAWTs. This will lead to the expansion of VAWTs' applications in green, sustainable, and clean energy production. © 2023 IEEE.

关键词： Vertical axis wind turbine

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Multi-robot Task Allocation in E-Commerce Warehouses: A Comparative Analysis of Distance Minimization and Priority-Based Approaches 21st

Multi-robot Task Allocation in E-Commerce Warehouses: A Comp...

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21st ISME International conference on Advances in mechanical engineering, ISME 2023

作者： Singhal, Rahul Goyal, Pranjay Agarwal, Shreya Makkar, Mohit Kumar, Pushpendra Department of Mechanical-Mechatronics Engineering LNMIIT Rajasthan Jaipur302031 India Department of Computer Science and Engineering LNMIIT Rajasthan Jaipur302031 India Department of Mechanical Engineering Graphic Era University Uttarakhand Dehradun248002 India

ISBN: (纸本)9789819736508

This paper presents a methodology for warehouse management with multiple robots for the efficient picking and dropping of consignments. Task allocation is a critical challenge in this process, as it requires determining a collision-free path for all robots based on warehouse management preferences. Two task allocation preferences are considered: (1) minimizing the total distance traveled by all robots to reduce power consumption and improve overall efficiency and (2) prioritizing task allocation by assigning tasks to the nearest available robot for faster delivery. The proposed approach uses the A* algorithm to determine the shortest path for each robot and evaluates the overall lower cost with the Kuhn-Munkres algorithm. Simulation results show the effectiveness of the methodology in achieving efficient task allocation and reducing the overall distance traveled. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multipurpose robots

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Anomaly detection for sustainable automotive manufacturing

Anomaly detection for sustainable automotive manufacturing

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2024 Low-Cost Digital Solutions for Industrial Automation, LoDiSA 2024

作者： Smallwood, Eleanor Burke, Richard Barnes, Andrew P. Department of Mechanical Engineering University of Bath Bath United Kingdom Department of Computer Science University of Bath Bath United Kingdom

ISBN: (纸本)9781837242672

The automotive sector provides society with the means to move people and goods, however the increasing need for climate change mitigation places responsibility on automotive manufacturers to develop low-carbon technologies to meet these needs. These rapid technological developments are prone to high costs, skill gaps, and sub-optimal energy-intensive practices, particularly within the testing environments for automotive components. Automated safety thresholds are in place to detect large fluctuations in real-time data, whilst the detection of other, more nuanced faults is manual, but these have the risk of undermining vehicle development. As such, this process is resource intensive, such as the personnel time and energy consumed to rerun tests containing faults, and the financial costs associated with these. Here we show the ability of unsupervised, data-driven machine learning based anomaly detection methods to identify anomalous time-periods within an automotive test, including faults in either the test component or in the test facility equipment, without the need for training data. We compare the performance of three clustering algorithms - k-means, agglomerative clustering, and DBSCAN - based on their run time and ability to create defined anomalous clusters of the two anomalies. K-means was able to identify the two anomalies with eight total clusters in half the time of agglomerative clustering. DBSCAN clustered the data in half the time of k-means however was unable to create defined anomalous clusters. These results illustrate the potential for unsupervised data-driven anomaly detection to operate within automotive manufacturer testing environments. These methods provide a low-cost digital solution to the resource demands associated with the traditional processes used by automotive manufacturers when developing sustainable transport options. © This is an open access article published by the IET under the Creative Commons Attribution License (http://creativeco

关键词： Costs

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