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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications III"

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High Frequency Agonist-Antagonist Controller for Shape Memory Alloy Actuators

High Frequency Agonist-Antagonist Controller for Shape Memor...

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IEEE International conference on Soft Robotics (RoboSoft)

作者： Carmen Ballester Víctor Muñoz Dorin Copaci Luis Moreno Dolores Blanco Department of Systems Engineering and Automation University Carlos III of Madrid Leganés Spain

ISBN: (数字)9798331520205

ISBN: (纸本)9798331520212

Soft robotics still requires fundamental advances in sensor and actuator technologies to achieve its full potential. One of the main challenges is the development of effective control strategies for shape memory alloy (SMA) antagonist systems, where coactivation of agonist and antagonist actuators can generate mechanical forces that compromise the integrity of the system. This study presents the Asymmetric Agonist-Antagonist (A3) controller, which is designed to improve safety by preventing the simultaneous heating of both SMAs. The A3 controller employs two activation functions: Pedestrian Safety, that sets an activation offset as a function of position error; and Adaptive Gradient, that attenuates control signals when they are opposite to their primary direction of motion. Comparative tests with passive recovery and traditional PID control show that the A3 controller reduces position error, achieves smoother transitions, and eliminates abrupt control switching between SMA actuators. By mitigating opposing forces, the A3 controller enables safer SMA operation at higher frequencies, allowing these actuators to be integrated into new types of soft robotic applications. This approach represents a critical step toward robust and scalable SMA control, enabling further research into soft actuators.

关键词： Heating systems Actuators Shape memory alloys Soft robotics Stability analysis Safety Complexity theory High frequency Wire Thermal stability

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Simultaneous Fault Diagnosis for sensor and Railway Point Machine for Autonomous Rail System

Simultaneous Fault Diagnosis for Sensor and Railway Point Ma...

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International conference on Intelligent Transportation

作者： Xiaoxi Hu Xiaohan Zhang Fei Chen Zongyang Liu Jin Liu Lei Tan Tao Tang Polytechnic Institute Zhejiang University Hangzhou China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan China Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan China Institute for Transport Studies University of Leeds Leeds United Kingdom Beijing Municipal Engineering Research Institute Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Since the safe and reliable behavior of a Railway Point Machine (RPM) is definitely pivotal for rail transportation, scholars and engineers have studied a considerable number of algorithms for RPM fault diagnosis via many kinds of sensor. However, few scholars have considered the possibility that sensors can also experience faults, which cannot be ignored in practical railway applications. To fill the gap, we propose an end-to-end deep learning network named Simultaneous Fault Diagnosis Network (SFDNet) for the simultaneous fault diagnosis for sensor and RPM. Our approach includes Information Aggregate and Update Module (IAUM) and Fault Diagnosis Module (FDM). The IAUM module leverages the concept of Spatial Attention Mechanism (SAM) and Channel Attention Mechanism (CAM) to effectively filter and aggregate information from diverse sensors. It enhances the salience of normal sensor observations while attenuating the influence of anomalous signals, thereby enabling the resultant features to more accurately capture the operational state of the RPMs. The features yielded by SAM are directly employed for sensor fault diagnosis. The FDM integrates Bidirectional Gated Recurrent Units (BiGRUs) and Time-series Feature Pyramid Network (TFPN) to respectively extract and integrate long-term dependencies and multi-scale features, enhancing fault diagnosis in RPM by capturing comprehensive temporal patterns and correlations. Lastly, ablation experiments and comparison studies shows the superiority of our SFDNet.

关键词： Fault diagnosis Deep learning Rails Attention mechanisms Aggregates Feature extraction Reliability engineering Rail transportation Frequency division multiplexing Intelligent transportation systems

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Resource-Efficient sensor Fusion via System-Wide Dynamic Gated Neural Networks

Resource-Efficient Sensor Fusion via System-Wide Dynamic Gat...

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IEEE Communications Society conference on sensor and Ad Hoc Communications and Networks (SECON)

作者： C. Singhal Y. Wu F. Malandrino S. Ladron de Guevara Contreras M. Levorato C. F. Chiasserini INRIA France UC Irvine USA CNR-IEIIT Italy CNIT Italy Politecnico di Torino Italy Chalmers University of Technology Sweden

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

Mobile systems will have to support multiple AI-based applications, each leveraging heterogeneous data sources through DNN architectures collaboratively executed within the network. To minimize the cost of the AI inference task subject to requirements on latency, quality, and - crucially - reliability of the inference process, it is vital to optimize (i) the set of sensors/data sources and (ii) the DNN architecture, (iii) the network nodes executing sections of the DNN, and (iv) the resources to use. To this end, we leverage dynamic gated neural networks with branches, and propose a novel algorithmic strategy called Quantile-constrained Inference (QIC), based upon quantile-Constrained policy optimization. QIC makes joint, high-quality, swift decisions on all the above aspects of the system, with the aim to minimize inference energy cost. We remark that this is the first contribution connecting gated dynamic DNNs with infrastructure-level decision making. We evaluate QIC using a dynamic gated DNN with stems and branches for optimal sensor fusion and inference, trained on the RADIATE dataset offering Radar, LiDAR, and Camera data, and real-world wireless measurements. Our results confirm that QIC matches the optimum and outperforms its alternatives by over 80%.

关键词： Wireless communication Wireless sensor networks Costs Soft sensors Heuristic algorithms Neural networks Logic gates sensor fusion Dynamic scheduling Time measurement

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Enhanced Cluster-Assisted Routing Protocol for Improved Energy Efficiency of Wireless sensor Network

Enhanced Cluster-Assisted Routing Protocol for Improved Ener...

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Smart systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), International conference on

作者： R. Krishnamoorthy Kazuaki Tanaka M. Amina Begum Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Kyushu Institute of Technology Japan Oxford College of Engineering Venmani Tamilnadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

Dynamic Source Routing (DSR) has been one of the most prominent WSN technologies over the last many decades. Researchers have developed several clustering protocol variants of the DSR protocol based on diverse application circumstances. However, existing clustering protocols often involve sensor nodes expending a great deal of energy propagating messages on a wide scale in order to build clusters and find the best path between clusters. The majority of sensor nodes in WSN s rely on low-power batteries, which severely hampers the network's performance, reliability, and durability. This led to the development of several clustering techniques that aim to improve WSN energy efficiency. With the advent of Fifth Generation transmissions, several Internet of Things applications have begun to use Multiple Input Multiple Output (MIMO) multiple antennas in order to increase capacity in multipath spectrum environments. In this paper, we provide Enhanced Node Energy and Lifetime Improvement Protocol, a novel cluster-assisted routing protocol designed to improve Wireless sensor Networks' energy efficiency. All four sections of the proposed ENELIP must be read in order. Partitioning a large-scale WSN to smaller sub-networks using a size-balanced approach is the first step in using 5G infrastructure. Second, minimization of energy consumption during centralized sub-network information maintenance is the goal of an estimation-based technique. The third step is to reduce the massive amount of energy needed to cluster nodes worldwide by implementing centralized query-driven clustering algorithms in each sub-network to group the query targets. Finally, a system is created for inter-cluster routing that is multi-sub networked, energy-efficient, and load-balanced. After conducting extensive research, it was shown that ENELIP outperforms other WSN protocols in terms of energy efficiency, resulting in an increase in the network lifetime.

关键词： Wireless sensor networks Energy consumption Power demand Computational modeling Switches Routing Throughput

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SmokerBeacon: Hybrid Real-Time Smoking Detection Using Object Detection and Gas sensor

SmokerBeacon: Hybrid Real-Time Smoking Detection Using Objec...

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IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： S Harshavardhan Patri Upender N Ganesh Sharon Giftsy School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Enforcing smoking bans in public areas remains a challenge due to limitations in conventional monitoring methods. This paper introduces SmokerBeacon, an innovative system for real-time detection of smoking activities in restricted zones. SmokerBeacon uses gas sensors and advanced image classification - YOLO to identify smoking activities. The system catego-rizes detection scenarios into three types: (i) instances where smoking actions are detected by YOLO without corresponding signals from gas sensors, (ii) cases where YOLO fails to detect smoking actions despite gas sensors detecting smoking-related substances, and (iii) situations where both smoking activities and corroborating signals from gas sensors confirm smoking. Each scenario triggers specific actions to improve enforcement and foster healthier environments. By generating timely alerts and enabling proactive intervention, SmokerBeacon mitigates risks associated with smoking in prohibited areas, leveraging IoT technology.

关键词： YOLO Computer science Real-time systems Internet of Things Gas detectors Monitoring Image classification

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Design and Development of an IoT based Intelligent Energy Meter using Less Expensive Controller Unit

Design and Development of an IoT based Intelligent Energy Me...

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Intelligent systems for Cybersecurity (ISCS), International conference on

作者： M. Balasubramani B. Gohin J. Jayashankari V. Samuthira Pandi Kiran Chand Ravi Deepak Arumugam Department of Computer Science and Engineering V.S.B Engineering College Karur Tamil Nadu India Department of Computer Applications Swarnandhra College of Engineering and Technolnogy Seetharamapuram Andhra Pradesh India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad India Department of Electrical and Electronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

With an eye towards improving the efficacy of energy monitoring and management, this research offers a thorough approach for the creation of an Intelligent Energy Metre that is based on the Internet of Things. A key component of the system is the use of specialized sensors to continuously measure electrical characteristics like voltage and current. These sensors generate analog signals, which are conditioned, digitized, and processed by a microcontroller. Algorithms within the firmware/software calculate energy consumption metrics, which are transmitted wirelessly to a central server or cloud platform using secure communication protocols. The collected data is then stored and analyzed to provide insights into energy usage patterns, facilitating informed decision-making for optimizing energy efficiency and reducing costs. The methodology encompasses various stages, including market research, hardware and software development, integration, testing, deployment, and maintenance. Through the implementation of this methodology, a robust and scalable IoT-based energy monitoring system can be developed, empowering users to make informed decisions for sustainable energy management.

关键词： Energy consumption Protocols Voltage measurement sensor phenomena and characterization sensor systems Real-time systems Internet of Things

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Evolutionary fuzzy rules for intrusion detection in wireless sensor networks 12th

Evolutionary fuzzy rules for intrusion detection in wireless...

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12th International conference on Intelligent Networking and Collaborative systems, INCoS 2020

作者： Batiha, Tarek Krömer, Pavel VSB - Technical University of Ostrava 17. listopadu 2172/15 Ostrava Czech Republic

ISBN: (纸本)9783030577957

Next–generation digital services and applications often rely on large numbers of devices connected to a common communication backbone. The security of such massively distributed systems is a major issue and advanced methods to improve their ability to detect and counter cybernetic attacks are needed. Evolutionary algorithms can automatically evolve and optimize sophisticated intrusion detection models, suitable for different applications. In this work, a hybrid evolutionary–fuzzy classification and regression algorithm is used to evolve detectors for several types of intrusions in a wireless sensor network. The ability of genetic programming and differential evolution to construct and optimize intrusion detectors for wireless sensor networks is evaluated on a recent intrusion detection data set capturing malicious activity in a wireless sensor network. © Springer Nature Switzerland AG 2021.

关键词： Intrusion detection

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Context-Aware Remote Patient Monitoring System with IoT and Digital Security

Context-Aware Remote Patient Monitoring System with IoT and ...

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IEEE International conference on System, Computation, Automation and Networking (ICSCAN)

作者： S. Geetha P. Karthikeyan Yughesh N Prasanth D Vishva V Department of Banking Technology Pondicherry University Puducherry India Department of Computer Science and Engineering Sri Manakula Vinayagar Engineering College Puducherry India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The “Context Awar Remote Patient Monitoring System with IoT and Digital Security” revolutionizes continuous healthcare by integrating IoT technology, sensors, and cloud computing to address the growing need for effective at-home patient monitoring. This system ensures vital health data is continuously recorded, analyzed, and made available to healthcare professionals in real-time. At its core is the Node MCU microcontroller, which connects key sensors to a cloud-based platform. The SPO2 sensor measures blood oxygen saturation to detect issues like hypoxemia, while the body temperature sensor tracks core temperature, signaling possible infections or fevers. An environmental temperature sensor monitors ambient conditions, crucial for patients with chronic illnesses or weakened immunity. Data is securely transmitted to the cloud for analysis using machine learning algorithms. These tools cross-reference vital and environmental data to detect health anomalies and predict early illness onset. For example, a drop in oxygen levels combined with a fever triggers alerts for potential respiratory infections, empowering healthcare providers to intervene promptly and prevent complications. This system's predictive capabilities and real-time alerts ensure timely responses to critical thresholds, enhancing patient safety and reducing hospital admissions. A user-friendly interface allows patients and caregivers to monitor health trends via mobile and web applications. By enabling continuous, remote monitoring, this solution benefits patients with chronic conditions, the elderly, and post-surgery individuals, providing peace of mind and reducing the workload on healthcare facilities. It promotes high-quality care from the comfort of patients' homes, aligning with modern telehealth needs. In conclusion, the “Context Awar Remote Patient Monitoring System with IoT and Digital Security” is a transformative tool for remote care, combining advanced sensor technology, real-time analytics,

关键词： Temperature sensors Temperature measurement Patient monitoring Cloud computing Medical services Real-time systems sensor systems sensors Security Biomedical monitoring

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An Efficient and Secure Framework for Smart Healthcare Using IoT and Machine Learning

An Efficient and Secure Framework for Smart Healthcare Using...

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Decision Aid Sciences and Application (DASA), International conference on

作者： Vivek Kumar Pandey Shiv Prakash Tarun Kumar Gupta Vandana Rathore Abhishek Singh Tiansheng Yang Rajkumar Singh Rathore Department of Electronics and Communication University of Allahabad Prayagraj India Department of Electronics and Communication University of Allahabad Prayagraj Uttar Pradesh India Department of Computer Science Miranda House University of Delhi Delhi India Institute of Engineering & Technology Dr. A.P. J. Abdul Kalam Technical University Lucknow India University of South Wales Pontypridd United Kingdom Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The healthcare systems worldwide are challenged with the increased demand for rapid and efficient patient care. In this context, an advanced solution is required to meet the growing needs. This paper presents an intelligent healthcare framework integrating IoT devices with machine learning algorithms to enable real-time predictive patient monitoring. It comprises a four-layer architecture: data acquisition from IoT sensors, data preprocessing, analytics for predictive modeling, and an application layer for user interactions. Validated with data from the MIMIC-iii and PhysioNet datasets along with 12 months of IoT sensor data from a university medical center, the framework achieved 96% prediction accuracy with precision, recall, and ROC AUC scores of 0.95, 0.96, and 0.98, respectively. The system had a great response time of 200 milliseconds. It has greatly improved early detection by 36.9% and reduced false alarms by 68%. False alarms decreased from 25% to 8%, and response time was reduced from 45 to 12 minutes, or 73.3 %. Resource utilization analysis reflects optimal performance since the analytics layer hit a spike at 45% CPU utilization, thereby pointing out optimal load management. Patient satisfaction also rose to 22.7%, increasing from 75% to 92%, reflecting the framework's impact on care quality. All these results render the framework an effective and scalable solution that optimizes healthcare resources while improving care for the patient.

关键词： Patient monitoring Accuracy Machine learning algorithms Medical services Predictive models Real-time systems Internet of Things Time factors Resource management Random forests

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Intelligent Paving and Rolling Construction Technology of Asphalt Pavement

Intelligent Paving and Rolling Construction Technology of As...

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2021 International conference on advanced Technologies and applications of Modern Industry, ATAMI 2021

作者： Yuan, Taiping Wang, Zheng Hong, Qiaozhang Chen, Jing Lei, Jiechao Meng, Yongjun China Southern Power Grid Energy Development Research Institute China College of Civil Engineering and Architecture Guangxi University Nanning China Key Laboratory of Disaster Prevention and Engineering Safety Ministry of Education Nanning China National High- Performance Computing Center Nanning Branch Nanning China

The intelligent construction system of asphalt pavement consists of an intelligent paver, driverless roller, mobile base station, intelligent control program, etc. By installing GPS(BDS)+ GNS satellite receiver, 5G communication chip, microwave communication host, radio signal receiving antenna on the construction equipment and connecting with the processor, switch, program software, and other components set on the equipment, integrate the laser obstacle avoidance radar, infrared temperature detection device, laser ranging sensor and other terminal equipment set on the equipment to realize the intellectualization of equipment use function;By running the intelligent control system, mobile 5G communication base station system and construction area acquisition system, command the linkage operation of multiple intelligent types of equipment to realize the intelligent control of pavement construction and meet the requirements of asphalt pavement design specifications. © Published under licence by IOP Publishing Ltd.

关键词： 5G mobile communication systems

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