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Universal firefighter sensor for dangerous road tunnel environment

Transportation Research Procedia 2021年 55卷 1019-1025页

作者： Marek Bujňák Rastislav Pirník Dušan Nemec Marián Hruboš Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Univerzitná 8215/1 010 26 Žilina Slovakia

This article deals with the issue of detecting and measuring the concentration of dangerous substances in the road tunnel during an accident, in order to minimize the risk to the firefighter during the intervention. The article also includes the design and construction of a mobile sensor system together with the processing of data and a suitable presentation of measured data. The gases are measured by specific detectors of the MQ gas sensors series. NTC type thermistors were used for temperature measuring. The control unit for the mentioned universal sensor is the development board D1 mini Pro, which has an integrated Wi-Fi chip. The universal sensor was designed especially for firefighters intervening during an accident in road tunnels. The measured data are processed and then sent via Wi-Fi network to an external database, from which they are then conveniently presented on the website. The website is then viewed on-line data from sensor for firefighters, whose are intervening during an accident in the tunnel.

关键词： tunnel gas detection dangerous substances MQ sensors universal sensor

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On-line identification methods as part of the tunnel ventilation control algorithm

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Transportation Research Procedia 2021年 55卷 961-968页

作者： Roman Michalík Jozef Hrbček Vojtech Šimák University of Žilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Univerzitná 8215/1 010 26 Žilina Slovakia

The paper presents algorithms of on-line methods of discrete parametric identification of pollutant concentrations in a tunnel tube and comparison of their properties. It describes the characteristics of an individual methods and a detailed evaluation in order to select the most suitable method for the identification of dynamical systems with a significant stochastic component and changing parameters. The aim of the paper is to propose discrete parametric identification methods for systems that have variable parameters. It is a choice of model structure and design of an algorithm that is able to monitor changes in system parameters. The article also describes ways to implement identification algorithms in PLC (Programmable Logic controller) by programming the target device directly from the simulation environment MATLAB Simulink using the toolbox "B&R automation Studio Target for Simulink". From a practical point of view, these algorithms serve to create an apparatus for optimizing the ventilation control in the tunnel and thus ensure the safe passage of vehicles through the tunnel.

关键词： PLC identification algorithm implementation ventilation control in the tunnel

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Two-wheel electric chassis with automatic balancing

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Transportation Research Procedia 2021年 55卷 1026-1033页

作者： Michal Mihálik Marek Bujňák Vojtech Šimák Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Univerzitná 8215/1 010 26 Žilina Slovakia

This work deals with the construction of an electric vehicle. This vehicle is designed to transport one person for shorter distances in urban areas. The main control member is a microcontroller from Atmel corporation. The aim was to construct vehicle in the best possible ratio of price and quality. The device consists of a mechanical part, a drive part and a control part. The whole vehicle is powered by a battery. A PID controller is used to balance the vehicle.

关键词： Segway microcontroller PID Atmel

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Reputation-Based Resilient Consensus with Differential Privacy Guarantees

SSRN

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SSRN 2024年

作者： Ramos, Guilherme Pequito, Sérgio Silvestre, Daniel Departamento de Informática Instituto Superior Técnico Universidade de Lisboa Portugal Instituto de Telecomunicações Lisbon1049-001 Portugal Division of Systems and Control Department of Information Technology Uppsala University Sweden Department of Electrical and Computer Engineering Faculty of Sciences and Technology NOVA University of Lisbon Portugal

Reputation-based consensus methods have gained significant attention in distributed systems to mitigate the impact of malicious agents and ensure reliable decision-making. However, privacy concerns arise when sensitive information is shared among participants. In this paper, we propose a reputation-based consensus method that incorporates resilience and differential privacy guarantees, providing a balance between preserving participants' privacy and maintaining the accuracy of the resilient consensus process. Our approach leverages the notion of differential privacy to add noise to the agents' states, ensuring that individual contributions are masked while still enabling the detection of malicious behavior. We present a detailed analysis of the privacy-accuracy trade-off and demonstrate the effectiveness of our method through simulations. The results show that our reputation-based consensus method with differential privacy guarantees offers robustness against attacks while preserving the privacy of participants, making it a promising solution for privacy-conscious resilient distributed systems. © 2024, The Authors. All rights reserved.

关键词： Decision making

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SHARM: Segmented Head Anatomical Reference Models

arXiv

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arXiv 2023年

作者： Rashed, Essam A. Al-Shatouri, Mohammad Laakso, Ilkka Hirata, Akimasa Graduate School of Information Science University of Hyogo Kobe650-0047 Japan Radiology Department Faculty of Medicine Suez Canal University Ismailia41522 Egypt Department of Electrical Engineering and Automation Aalto University Espoo Finland Aalto neuroimaging Aalto University Espoo Finland Department of Electrical and Mechanical Engineering Nagoya Institute of Technology Nagoya466-8555 Japan Center of Biomedical Physics and Information Technology Nagoya Institute of Technology Nagoya466-8555 Japan

Reliable segmentation of anatomical tissues of human head is a major step in several clinical applications such as brain mapping, surgery planning and associated computational simulation studies. Segmentation is based on identifying different anatomical structures through labeling different tissues through medical imaging modalities. The segmentation of brain structures is commonly feasible with several remarkable contributions mainly for medical perspective;however, non-brain tissues are of less interest due to anatomical complexity and difficulties to be observed using standard medical imaging protocols. The lack of whole head segmentation methods and unavailability of large human head segmented datasets limiting the variability studies, especially in the computational evaluation of electrical brain stimulation (neuromodulation), human protection from electromagnetic field, and electroencephalography where non-brain tissues are of great importance. To fill this gap, this study provides an open-access Segmented Head Anatomical Reference Models (SHARM) that consists of 196 subjects. These models are segmented into 15 different tissues;skin, fat, muscle, skull cancellous bone, skull cortical bone, brain white matter, brain gray matter, cerebellum white matter, cerebellum gray matter, cerebrospinal fluid, dura, vitreous humor, lens, mucous tissue and blood vessels. The segmented head models are generated using open-access IXI MRI dataset through convolutional neural network structure named ForkNet+. Results indicate a high consistency in statistical characteristics of different tissue distribution in age scale with real measurements. SHARM is expected to be a useful benchmark not only for electromagnetic dosimetry studies but also for different human head segmentation applications. © 2023, CC BY.

关键词： Convolution

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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Semi-Global Leaderless Consensus of Linear Multi-agent Systems with Actuator and Communication Constraints

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Journal of Systems Science & Complexity 2020年第4期33卷 882-902页

作者： WANG Jian SHI Liangren GUAN Xinping Department of Automation and Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Shanghai Key Laboratory of Navigation and Location-based Services and Academy of Information Technology and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

This paper studies the leaderless consensus problems of multi-agent systems with input saturation and intermittent communication over directed networks. Both the state feedback and the output feedback consensus algorithms are developed based on low gain feedback approach. The convergence of the trajectories of all agents can be achieved by these proposed algorithms, if the communication topology has a directed spanning tree, and the intermittent communication period T and time rate ρ are larger than their associated threshold values. Simulation examples are provided to verify the theoretical results.

关键词： agent directed communication

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods

Detection of Detonators in Baggage Using Deep CNN Architectu...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Oulhissane Lynda Merah Mostefa Simona Moldovanu Luminita Moraru Laboratory of Signals and Systems LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Mostaganem Algeria MSlab Department of Computer Science and Information Technology Modelling & Simulation Laboratory Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania MSlab Department of Chemistry Physics & Environment Modelling & Simulation Laboratory Faculty of Sciences and Environment Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL

关键词： Wavelet transforms Radiography Visualization Gray-scale Inspection Feature extraction Libraries

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A Novel Transfer Learning Approach for Detecting Partial Shading in Photovoltaic Systems

A Novel Transfer Learning Approach for Detecting Partial Sha...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Ali Teta Maissa Medkour Ahmed Chennana Ammar Chouchane Yassine Himeur Shadi Atalla Wathiq Mansoor El Ouanas Belabbaci Applied Automation and Industrial Diagnostics Laboratory Department of Electrical Engineering University of Djelfa Djelfa Algeria LI3C Laboratory University of Biskra Biskra Algeria University Center of Barika Batna Algeria College of Engineering and Information Technology University of Dubai Dubai UAE Laboratory of Medical Informatics (LIMED) Faculty of Technology University of Bejaia Bejaia Algeria

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

In Photovoltaic Systems, detecting partial shading is critical for optimizing energy output and ensuring system reliability. This paper presents a novel method for partial shading detection in photovoltaic systems, leveraging transfer learning to improve accuracy and efficiency. By utilizing a pre-trained InceptionV3 model, discriminative features are extracted from time series signals. To align with the architectural requirements of InceptionV3, these time series signals are transformed into 2D pixel-mapped images. The proposed model is rigorously validated using both balanced and unbalanced scenarios on the Grid-connected PV System Faults (GPVS-Faults) dataset, achieving remarkable accuracies of 96.73% and 94.59% in the respective scenarios. This approach represents a significant advancement in accurately identifying partial shading, thus enhancing the performance and reliability of solar energy systems.

关键词： Photovoltaic systems Accuracy Scalability Transfer learning Time series analysis Solar energy Big Data Feature extraction Distance measurement Reliability

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