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Research on distributed intelligent distribution path architecture based on matrix switch 3

Research on distributed intelligent distribution path archit...

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3rd international Academic Exchange conference on Science and Technology Innovation, IAECST 2021

作者： Shi, Junbiao Ji, Mingming Zheng, Sha He, Xiong Chen, Yoga Huang, Zhiqi Beijing Spacecraft China Technology Corporation and Control Engineering Aerospace Science and Beijing Space Power Conversion Research Center Beijing100094 China

ISBN: (纸本)9781665402675

The existing aerospace power supply and distribution system is difficult to deal with the fault of a single machine in real time, and the timeliness is difficult to ensure. If the fault with potential serious harm cannot be isolated and dealt with in time, the best period of fault treatment will often be missed. In this paper, a satellite intelligent distribution path architecture based on matrix switch is proposed. Through the effective combination of solid-state power switch, interface circuit, current sensor and host computer software, the switch in the matrix is controlled to realize the construction of distribution path and active management of power energy flow under the distributed architecture. Combined with the functional requirements of the distribution channel defined by the traditional hardware and the application characteristics of the new architecture, the design and application experience of the new matrix distribution channel architecture in the aspects of satellite distribution flexibility, intelligence, fault diagnosis and omni-directional operation information monitoring of pre judged power supply and distribution system are summarized, it provides technical reference and theoretical support for the innovation of flexible and intelligent distribution system defined by subsequent software. © 2021 IEEE.

关键词： Interface states

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An Integrated IoT and Wireless System for Energy Optimization and Real-Time monitoring Towards Sustainable and Efficient Building Management in Smart Buildings

An Integrated IoT and Wireless System for Energy Optimizatio...

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2025 international conference on intelligent control, Computing and Communications, IC3 2025

作者： Singh, Someet Malik, Praveen Kumar Chauhan, Ashish Singh Kumar, Manoj Yamsani, Nagendar Singh, Parulpreet School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara India Uttaranchal University Uttarakhand Dehradun India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798331527495

At a time when urbanization is increasing, and the world's energy need is growing, energy efficiency in building elements has become a key factor in the environment impact mitigation and operational cost reduction strategies. Building management is governed by traditional operational features that are often inadequate to contemporary occupation patterns, infrastructural demands, and environmental changes. Consequently, there is a growing tendency towards the implementation of integrated systems such as autonomous management systems which can oversee energy use and other resources without sacrificing comfort and safety. Cloud storage and wireless technologies have now made it possible to improve energy management like never before. By making use of such technologies, smart building systems are able to receive, transmit and monitor data from many networks in real time. This capability further enhances the control of energy-demanding devices and processes such as heating, ventilation, and lighting, which are controlled and manipulated by preset schedules and manual settings. In addition to this, predictive maintenance and detection of inefficiencies is possible due to real time data analysis trends, usage history and anticipation of external consumption forces. © 2025 IEEE.

关键词： Smart homes

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IoT and Wireless Sensor Network Based Autonomous Farming Robot

IoT and Wireless Sensor Network Based Autonomous Farming Rob...

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Knowledge Engineering and Communication Systems (ICKECS), international conference on

作者： K R Swetha D Monisha H B Thejaswini K P Nikhil N U Rahul Department of Computer Science BGS Institute of Technology Adichunchanagiri University BG Nagara India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

This research paper investigates the design, implementation, and performance evaluation of an Autonomous Agriculture Robot for precision farming. Utilizing an Arduino UNO microcontroller, DC motors, and an array of sensors including soil moisture, rain sensor, soil fertility sensor. The robot aims to revolutionize traditional farming practices. The system’s intelligence is harnessed through an Android App, facilitating real-time control and monitoring, enabling tasks such as ploughing,seed sowing, weed cutter, pesticide sprayer, SMS automation, soil moisture sensor, soil fertility sensor and rain sensor. The robot’s adaptability is further enhanced by environmental sensors, ensuring responsive actions based on dynamic field conditions. Wi-Fi connectivity via Node MCU allows for potential remote monitoring, expanding the system’s operational reach. Through comprehensive testing and analysis, this research paper evaluates the robot’s precision, efficiency, and its potential to contribute to sustainable and smart farming. The study develops into the implications of such autonomous systems on resource optimization, productivity and the broader implications for the futureof agriculture in the era of Industry 4.0. Findings highlight the viability and promise of integrating roboticsand automation in addressing contemporary challenges in agriculture, positioning the robot as a noteworthy advancement in the landscape of precision farming technologies.

关键词： Wireless sensor networks Rain Service robots Soil moisture Robot sensing systems Sensor systems intelligent sensors

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Remote Sensing-based Vehicle monitoring System using YOLOv10 and CrossViT

Remote Sensing-based Vehicle Monitoring System using YOLOv10...

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IEEE international conference on Multi Topic

作者： Ghulam Mujtaba Ahmad Jalal Dept. of Computer Sciences Air University Islamabad Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Rapid urbanization and vehicular traffic growth have significantly strained traditional traffic management systems, exacerbating issues such as congestion, safety hazards, and environmental degradation. This paper presents a novel deep learning-based framework designed to address these challenges by enhancing vehicle detection, tracking, and classification in urban traffic settings. The proposed system utilizes Gaussian Blur for effective image preprocessing, followed by DeepLabv3 for accurate vehicle segmentation. Vehicle detection is performed using the YOLOv10 architecture, which excels in detecting vehicles in complex and occluded environments. For vehicle counting, we employ CenterNet, an efficient method that leverages keypoint detection to estimate vehicle counts. Optical flow techniques are applied to track vehicle motion and improve counting accuracy in dynamic traffic conditions. The system’s feature extraction pipeline incorporates deep convolutional networks to capture critical vehicle characteristics, while vehicle classification is enhanced by the CrossViT model, which excels at handling varying vehicle types and scales. Evaluation on the VAID dataset reveals a vehicle detection accuracy of 90%, tracking accuracy of 88%, and classification accuracy of 91%. These results underscore the potential of the proposed framework to offer a scalable, efficient solution for intelligent transportation systems, enabling comprehensive urban traffic monitoring and management.

关键词： Accuracy Vehicle detection Surveillance Urban areas Pipelines Traffic control Trajectory Automobiles Vehicle dynamics Remote sensing

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Implementation of Internet of Things in Wireless Sensor Networks for environmental monitoring

Implementation of Internet of Things in Wireless Sensor Netw...

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Advances in Computing, Communication and Applied Informatics (ACCAI), international conference on

作者： M Vineela V Rama Krishna K. Sandhya Rani Bhaludra R Nadh Singh Kasarla Sri Varsha Kura Venkata Sai Vaishnavi Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Hyderabad Telangana India Department of Computer Science and Engineering Annamacharya Institute of Technology and Sciences Hyderabad Telangana India Department of Computer Science and Engineering MallaReddy College of Engineering & Technology Hyderabad Telangana India Department of Computer Science and Engineering Bhoj Reddy Engineering College For Women Hyderabad-59 India Bhoj Reddy Engineering College for Women Hyderabad-59 Telangana India

Data is collected by the end tags, which in turn send it to the router, which forwards it to the coordinator and provides services to multiple clients, one of which is the display of collected data. Finally, the collected data is sent to the cloud via the base station, which can be accessed remotely via Ethernet. These include sensors for heat, cold, vibration, pressure, humidity, light, and air and water pollution. Recently, attention has been focused on wireless technologies such Wi-Fi [8], [9], Bluetooth [7], [10], and Zig Bee [6], [7]. Ultimate goal in writing this article is to provide a framework that can be utilised on a global scale to control variables in any ecosystem. All environmental data in real time is gathered by a Raspberry Pi and associated sensors, and then retrieved by a web server for display. Since this information is available online, the user is not restricted to their physical location. The Raspberry Pi serves as a hub that establishes a zigzag link between a network of dispersed sensor nodes. In several applications, including habitat monitoring, "Wireless Sensor Networks" (WSN) have been utilized to gather data on physical phenomena. The term "Internet of Things" (IoTs) refers to the interconnection and intelligent linking of items such as smartphones, Internet televisions, sensors, and actuators, which creates new channels of interaction between people and their belongings.

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Applying an Agent-based distributed AI Framework to Forecast Power for the Mini-Grid Stability

Applying an Agent-based Distributed AI Framework to Forecast...

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IEEE international conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Iacovos I. Ioannou Saher Javaid Vasos Vassiliou Andreas Pitsillides Yasuo Tan University of Cyprus and CYENS Centre of Excellence Nicosia Cyprus School of Information Science Japan Advanced Institute of Science and Technology Japan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Renewable energy sources, expected to form about 70% of power systems by 2050, bring challenges like fluctuating outputs and grid instability. Advanced power monitoring systems, crucial in environments like research facilities and hospitals, must navigate these dynamic scenarios. Traditional power management, especially Uninterruptible Power Supply (UPS) systems, often needs to catch up due to high costs and limited response to varied power demands, focusing mainly on constant power supply without differentiating between constant and fluctuating loads. In response, Artificial intelligence (AI) techniques are becoming indispensable for real-time power prediction and control. A distributed AI framework forecasts power needs, considering renewable sources, loads, and storage. This is key to ensuring smooth mini-grid operations, balancing operational demands with environmental considerations, and advancing intelligent energy management. Such systems are essential in optimizing energy usage, aligning it with available power to enhance efficiency and reduce waste. This is particularly important for mini-grids, with or without UPS systems, where predictive monitoring can substantially cut operational costs and extend *** paper focuses on providing consistent, constant, and fluctuating power by predicting mini-grid power needs hourly from the previous day's data. We use a Temporal Convolutional Network (TCN) for time series prediction, integrated within the BDIx agent's belief system through TensorFlow Lite. This approach accurately predicts upcoming power needs, ensures smooth operation, and prevents power outages. The TCN model's predictive capabilities highlight a significant stride in combining AI with energy management to address the complexities of modern power systems.

关键词： Renewable energy sources Costs Time series analysis Power system stability Stability analysis Real-time systems Artificial intelligence

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Temporal Convolution Network and Bi-Directional Long Short-Term Memory -Based Anomaly Detection in Satellite Images for environmental monitoring

Temporal Convolution Network and Bi-Directional Long Short-T...

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distributed Systems, computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： Munimanda Premchander Ammar Hameed Shnain Nagender Aadi Srinivas Aluvala Ramachandra A C Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India School of Computer Science and Engineering SR University Warangal India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In satellite anomaly detection, challenges include unbalanced sample distribution, a limited number of fault samples and subtle anomaly characteristics. Data-driven methods for satellite data anomaly detection often face issues such as high false positive rates and limited interpretability, which negatively impact accuracy. This paper, Multi-Head Attention Mechanism, Temporal Convolution Network (TCN), and Bi-Directional Long Short-Term Memory (Bi-LSTM) technique for anomaly detection in satellite data, achieve better accuracy. The MHA provides flexibility in focusing on different feature and parts of the data for satellite data where anomalies might be influenced by complex, long term patterns. The Bi-LSTM network capture context form both past and future time steps, which is enhance the models ability to understand temporal dependencies and detect anomalies in satellites. The TCS are effective in handling temporal data, especially when dealing with varying sequence length, which is essential for detection anomalies in satellite imagery data. The proposed MHA and TCN-LSTM technique is evaluated for detection anomaly, achieving a high accuracy of 0.92, recall of 0.91, F1-score of 0.92 and precision of 0.92 respectively. This performance is compared to existing techniques such as Random Forest (RF) and Convolutional Neural Network (CNN) and LSTM network.

关键词： Radio frequency Satellites Accuracy Convolution Focusing Bidirectional control Satellite images Convolutional neural networks Anomaly detection Long short term memory

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Towards Accurate And Robust Dynamics and Reward Modeling for Model-Based Offline Inverse Reinforcement Learning

Towards Accurate And Robust Dynamics and Reward Modeling for...

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IEEE/RSJ international conference on intelligent Robots and Systems (IROS)

作者： Gengyu Zhang Yan Yan Department of Computer Science University of Illinois Chicago IL USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper enhances model-based offline inverse reinforcement learning (IRL) by refining conservative Markov decision process (MDP) frameworks, traditionally employing uncertainty penalties to deter exploitation in uncertain areas. Existing methods, dependent on neural network ensembles to model MDP dynamics and quantify uncertainty through ensemble prediction heuristics, face limitations: they presume Gaussian-distributed state transitions, leading to simplified environmental representations. Additionally, ensemble modeling often results in high variance, indicating potential overfitting and a lack of generalizability. Moreover, the heuristic reliance for uncertainty quantification struggles to fully grasp environmental complexities, offering an incomplete foundation for informed decisions. Maintaining multiple models also demands substantial computational resources. Addressing these shortcomings, we propose leveraging score-based diffusion generative models for dynamic modeling. This method significantly broadens the scope of representable target distributions, surpassing Gaussian constraints. It not only improves the accuracy of transition modeling but also roots uncertainty quantification in diffusion models’ theoretical underpinnings, enabling more precise and dependable reward regularization. We further innovate by incorporating a transition stability regularizer (TSR) into the reward estimation. This novel element embeds stability into the reward learning process, diminishing the influence of transition variability and promoting more consistent policy optimization. Our empirical studies on diverse Mujoco robotic control tasks demonstrate that our diffusion-based methodology not only furnishes more accurate transition estimations but also surpasses conventional ensemble approaches in policy effectiveness. The addition of the TSR marks a distinctive advancement in offline IRL by enhancing the reward and policy learning efficacy. Code: https://***/Gabriel

关键词： Uncertainty Accuracy Computational modeling Refining Estimation Stochastic processes Reinforcement learning Predictive models Stability analysis Space exploration

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Wireless Sensor Network based Anomaly Detection using SVM-RFE-MRMR

Wireless Sensor Network based Anomaly Detection using SVM-RF...

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international conference on ent Computing and control Systems (ICICCS)

作者： S. Sakthivel M Agalya R V Sudha V Lathika P Selvi N Suriyapriya Department of Computer Science and Engineering Sona College of Technology Salem Tamilnadu India Department of Computer Science and Engineering Vivekananda College of Technology for women Tiruchengode Namakkal Tamilnadu India Department of Information Technology Vivekananda College of technology for women Tiruchengode Namakkal Tamilnadu India Computer Science and Engineering Jayam College of Engineering and Technology Dharmapuri Tamilnadu India Department of Information Technology Vivekananda College of Technology for women Tiruchengode Namakkal India

There has been a meteoric rise in the use of wireless sensor networks (WSNs) in recent years. WSN’s compact size and low cost are enticing many different industries to adopt them for use in a wide range of applications. There are several applications of IoT technology, including environmental monitoring, building security, and precision agriculture. Given that most WSNs are deployed in an unsupervised and potentially hostile environment, security risks are significant. To ensure the privacy of data during transmission from sensors to the base station, a number of methods have been proposed. The work in question is concerned with a crucial part of data and network security: detecting attacks. With the goal of preventing malicious attacks and maintaining the security of wireless sensor networks, anomaly detection has emerged as a significant obstacle. After the input is given, It consist of three phases such as preprocessing, Feature selection and training the model using SVM-RFE-MRMR. The SVMRFE-MRMR methodology is used for classification in the suggested method. While SVM-RFE takes feature-decision correlation into consideration, it does so by neglecting feature-feature relationships, MRMR is able to provide features with little redundancy and maximum relevance. Because of this SVM-RFE-MRMR is used to generate a set of features that is both minimal in redundancy and maximal in relevance. The proposed model produces an accuracy of about 98.6% when compared other models such as SVM and CNN.

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Robotic Barrier Construction through Weaved, Inflatable Tubes

Robotic Barrier Construction through Weaved, Inflatable Tube...

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IEEE/RSJ international conference on intelligent Robots and Systems (IROS)

作者： Heather Jin Hee Kim Haron Abdel-Raziq Xinyu Liu Alexandra Young Siskovic Shreyas Patil Kirstin H. Petersen Hsin-Liu Kao Department of Human-Centered Design Cornell University Ithaca NY USA Department of Electrical and Computer Engineering Cornell University Ithaca NY USA Sibley School of Mechanical Engineering Cornell University Ithaca NY USA

In this article, we present a mechanism and related path planning algorithm to construct light-duty barriers out of extruded, inflated tubes weaved around existing environmental features. Our extruded tubes are based on everted vine-robots and in this context, we present a new method to steer their growth. We characterize the mechanism in terms of accuracy resilience, and, towards their use as barriers, the ability of the tubes to withstand distributed loads. We further explore an algorithm which, given a feature map and the size and direction of the external load, can determine where and how to extrude the barrier. Finally, we showcase the potential of this method in an autonomously extruded two-layer wall weaved around three pipes. While preliminary, our work indicates that this method has potential for barrier construction in cluttered environments, e.g. shelters against wind or snow. Future work may show how to achieve tighter weaves, how to leverage weave friction for improved strength, how to assess barrier performance for feedback control, and how to operate the extrusion mechanism off of a mobile robot.

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