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Effi-MAOT: A Communication-Efficient Multi-Camera Active Object Tracking

Effi-MAOT: A Communication-Efficient Multi-Camera Active Obj...

引用

international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Maolong Yin Zhuo Sun Bin Guo Zhiwen Yu School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Multi-camera Active Object Tracking (AOT) has become a promising technique to continuously track the moving target in the intelligent surveillance system. the communication among cameras provides an essential way to share the information and improve the tracking performance. To achieve the efficient collaboration among cameras, we propose a novel communication-efficient multi-camera AOT system in this paper, where one camera cooperates with other cameras to realize the active object tracking by sharing its visual observations when necessary. In the proposed system, we design a binary classified neural network based switch to determine whether to communicate. Moreover, we propose an attention based communication group construction method, which allows the camera to decide who to communicate. through the control of whether to communicate and who to communicate, many communication connections of less importance are pruned. We construct a high-fidelity environment to mimic the real-world scenario. Experimental results demonstrate that compared to the existing systems, the proposed system can significantly reduce the occupied bandwidth and achieve an improved tracking performance, especially for the high-speed moving target.

关键词： Visualization Target tracking Surveillance Neural networks Collaboration Switches Bandwidth

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Towards a Greener Future: Leveraging Blockchain for Sustainable Demand Response and Supplier Baseline Assessment

Towards a Greener Future: Leveraging Blockchain for Sustaina...

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international conference on Emerging Technologies, ICET

作者： Muhammad Athar Bin Li Zaid Bin Tariq Baig Ali Muqtadir Shiming Tian Songsong Chen School of Electrical and Electronics Engineering North China China electric power university Beijing China Department of Control and Computer Science North China Electric Power University Beijing China

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

this paper will explore the possibilities of leveraging the underpinning blockchain technology to drive sustainability in an energy management system using the areas of demand response and supplier baseline assessment as a case study. Rising global energy needs, and an unwavering commitment to environmental sustainability necessitate the need for innovative solutions to improve energy efficiency and transparency. Blockchain also showed that in a pilot project at a local municipal electric utility for a small to mid-sized city that utilizing blockchain technology, which would be completely decentralised and secure, would improve targeted KPI performance: 20% reduction in peak load power consumption; 91% accuracy in supplier verification; increased transaction throughput and overall system reliability. these results suggest that blockchain not only supports efficient energy management but also promotes a sustainable and transparent energy sector, with scalability indicating potential broader applications for smart grid systems and renewable energy sources.

关键词： Renewable energy sources Scalability Green products Urban areas throughput Demand response Blockchains Smart grids Reliability Sustainable development

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Metrics to Enhance the Resilience of Cyber-Physical systems

Metrics to Enhance the Resilience of Cyber-Physical Systems

引用

IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Michel Barbeau Frédéric Cuppens Nora Cuppens Romain Dagnas Joaquin Garcia-Alfaro School of Computer Science Carleton University Ottawa ON Canada École Polytechnique de Montréal Montréal QC Canada Institut de Recherche Technologique SystemX France Institut Polytechnique de Paris SAMOVAR Telecom SudParis France

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

We focus on resilience towards covert attacks on Cyber-Physical systems (CPS). We define the new k-steerability and l-monitorability control-theoretic concepts. k-steerability reflects the ability to act on every individual plant state variable with at least k different groups of functionally diverse input signals. l-monitorability indicates the ability to monitor every individual plant state variable with £ different groups of functionally diverse output signals. A CPS with k-steerability and l-monitorability is said to be (k, l)-resilient. k and l, when both greater than one, provide the capability to mitigate the impact of covert attacks when some signals, but not all, are compromised. We analyze the influence of k and l on the resilience of a system and the ability to recover its state when attacks are perpetrated. We argue that the values of k and l can be augmented by combining redundancy and diversity in hardware and software techniques that apply the moving target paradigm.

关键词： Diversity reception Redundancy Cyber-physical systems Software Hardware Monitoring Resilience

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Approach for Smartwatch-Based Complex Human Activity Recognition in Internal Logistics systems

Approach for Smartwatch-Based Complex Human Activity Recogni...

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international computer sciences and Information Technologies (CSIT)

作者： Myroslav Mishchuk Olena Pavliuk Mykola Medykovskyy Department of Automated Control Systems Lviv Polytechnic National University Lviv Ukraine Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

As the domain evolves, studies in Human Activity Recognition (HAR) are pivoting from the classification of simple, singular actions to the real-time identification of complex, multi-step activities. this study proposes an approach for real-time multi-step activity recognition, specifically demonstrating its application in internal logistics systems based on autonomous guided vehicles through intelligent human activity analysis. the approach leverages such techniques as ensemble learning with classifier stacking, transfer learning, and continuous wavelet transform (CWT). To support the aim of this study, a smartwatch-based data gathering system was developed. the data were collected from human staff representatives engaged in the continuous execution of one of two predefined aggregates that were decomposed into four basic activities. A scalogram-pre-trained DenseNet121 model was utilized as the base classifier, achieving an F1 score of 91.01% for basic activity classification. For complex activities, a convolutional neural network was used as the meta-classifier, achieving an F1 score of 87.44% on the best-performing dataset split strategy. the resulting performance, considering the challenges faced and the method's adaptability, indicates the considerable potential of the proposed method. the proposed approach can be integrated to create a robust framework for HAR, providing a positive impact on intelligent enterprise management.

关键词： Continuous wavelet transforms Aggregates Transfer learning Stacking Real-time systems Human activity recognition Ensemble learning Convolutional neural networks Long short term memory Logistics

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TSFCC: the Two-Stage Fast Congestion control Algorithm Based on Software Defined Networking

TSFCC: The Two-Stage Fast Congestion Control Algorithm Based...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Hongxiang Wang Yifei Lu Zhen Wang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Currently, data center networks (DCNs) face a significant challenge known as the TCP incast problem, resulting from many-to-one communication. this problem leads to reduced throughput and increased flow completion time, greatly impacting application performance. Researchers have explored various methods like ECN, latency, and SDN to address this problem. However, these approaches suffer from issues such as inaccurate congestion detection, untimely congestion feedback, and imprecise congestion adjustment. To tackle these concerns, this paper proposes a two-stage fast congestion control algorithm (TSFCC) that leverages SDN’s global perspective. TSFCC leverages the programmability and global view of SDN to accurately predict the occurrence of concurrent flows and proactively control the sending rate, effectively mitigating TCP incast problem. Subsequently, TSFCC monitors network congestion in real-time and employs SDN technology to provide congestion feedback directly to switches nearest to senders, ensuring more timely feedback. Finally, the SDN controller differentiates between mouse flows and elephant flows, allocating more bandwidth precisely to mouse flows to accommodate more bursty flows and further alleviate TCP incast problem. To assess the performance of TSFCC, we conduct a custom experimental testbed using Mininet and OpenFlow to validate and demonstrate the effectiveness of our algorithm. the results demonstrate that TSFCC can significantly improve mouse flow completion time, tolerate a higher number of concurrent flows, and achieve more stable queue lengths in large-scale networks.

关键词： Data centers Software algorithms Bandwidth throughput Prediction algorithms Real-time systems Sensors

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Position Sensorless for IPMSM with Heavy Magnetic Saturation based on Pattern Matching Method

Position Sensorless for IPMSM with Heavy Magnetic Saturation...

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international conference on Electrical Machines and systems

作者： Hamin Song Shinji Doki Department of Electrical Engineering and Computer Science Nagoya University Japan

ISBN: (纸本)9781467388634

A pattern matching method for position sensorless control of IPMSM with heavy magnetic saturation in standstill and very low speed region has been proposed. the rotor position is estimated by comparing the feature values measured in a real-time with in beforehand. To measure feature values, high frequency signal must be injected. the operation region of pattern matching method is limited by amplitude of injectable signal. this paper presents the operating region that could utilize pattern matching method. Also to expand the operating region, a new signal injection method and matching method are proposed. By using the proposed method, the same sampling time interval for measuring feature values could be obtained even smaller signal is injected.

关键词： IPMSM Pattern matching method Position sensorless control Signal injection method

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Autonomous Bimanual Functional Regrasping of Novel Object Class Instances

Autonomous Bimanual Functional Regrasping of Novel Object Cl...

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IEEE-RAS international conference on Humanoid Robots

作者： Dmytro Pavlichenko Diego Rodriguez Christian Lenz Max Schwarz Sven Behnke Autonomous Intelligent Systems (AIS) Group Computer Science Institute VI University of Bonn Germany

ISBN: (数字)9781538676301

ISBN: (纸本)9781538676318

In human-made scenarios, robots need to be able to fully operate objects in their surroundings, i.e., objects are required to be functionally grasped rather than only picked. this imposes very strict constraints on the object pose such that a direct grasp can be performed. Inspired by the anthropomorphic nature of humanoid robots, we propose an approach that first grasps an object with one hand, obtaining full control over its pose, and performs the functional grasp with the second hand subsequently. thus, we develop a fully autonomous pipeline for dual-arm functional regrasping of novel familiar objects, i.e., objects never seen before that belong to a known object category, e.g., spray bottles. this process involves semantic segmentation, object pose estimation, non-rigid mesh registration, grasp sampling, handover pose generation and in-hand pose refinement. the latter is used to compensate for the unpredictable object movement during the first grasp. the approach is applied to a human-like upper body. To the best knowledge of the authors, this is the first system that exhibits autonomous bimanual functional regrasping capabilities. We demonstrate that our system yields reliable success rates and can be applied on-line to real-world tasks using only one off-the-shelf RGB-D sensor.

关键词： Pose estimation Robot sensing systems Handover Pipelines Grasping

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All Your PLCs Belong to Me: ICS Ransomware Is Realistic

All Your PLCs Belong to Me: ICS Ransomware Is Realistic

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yipeng Zhang Zhonghao Sun Liqun Yang Zhoujun Li Qiang Zeng Yueying He Xiaoming Zhang State Key Lab of Software Development Environment Beihang University Beijing China CNCERT/CC Beijing China Department of Computer Science and Engineering University of South Carolina City United States

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Ransomware is a new business model for cybercrime which mainly targets individual users and machines. Many events have shown how profitable the technique can be. Industrial control systems (ICS) are becoming the next domain. More and more researchers and attackers have become the focus on this field and presented some ICS ransomware. But existing ICS ransomware is theoretically feasible and has a limited effect on real ICS. In this work, we present ICS-BROCK, a full-fledged ICS ransomware that can compromise a real-world. To demonstrate the capability of ICS-BROCK, we use SIEMENS S7-300 PLC, one of the most widely used devices in ICSs, to build a real water treatment environment. the results empirically demonstrate the feasibility of launching ICS ransomware attacks in a practical setting. In the end, we give some suggestions on ICS ransomware to aid in future study and defenses.

关键词： Integrated circuits Protocols Weapons Stability analysis Safety Ransomware Security

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Modeling neuronal interactivity using Dynamic Bayesian Networks 05

Modeling neuronal interactivity using Dynamic Bayesian Netwo...

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Proceedings of the 19th international conference on Neural Information Processing systems

作者： Lei Zhang Dimitris Samaras Nelly Alia-Klein Nora Volkow Rita Goldstein Computer Science Department SUNY at Stony Brook Stony Brook NY and Medical Department Brookhaven National Laboratory Upton NY Computer Science Department SUNY at Stony Brook Stony Brook NY Medical Department Brookhaven National Laboratory Upton NY

Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active brain. However, interactivity between functional brain regions, is still little studied. In this paper, we contribute a novel framework for modeling the interactions between multiple active brain regions, using Dynamic Bayesian Networks (DBNs) as generative models for brain activation patterns. this framework is applied to modeling of neuronal circuits associated with reward. the novelty of our framework from a Machine Learning perspective lies in the use of DBNs to reveal the brain connectivity and interactivity. Such interactivity models which are derived from fMRI data are then validated through a group classification task. We employ and compare four different types of DBNs: Parallel Hidden Markov Models, Coupled Hidden Markov Models, Fully-linked Hidden Markov Models and Dynamically Multi-Linked HMMs (DML-HMM). Moreover, we propose and compare two schemes of learning DML-HMMs. Experimental results show that by using DBNs, group classification can be performed even if the DBNs are constructed from as few as 5 brain regions. We also demonstrate that, by using the proposed learning algorithms, different DBN structures characterize drug addicted subjects vs. control subjects. this finding provides an independent test for the effect of psychopathology on brain function. In general, we demonstrate that incorporation of computer science principles into functional neuroimaging clinical studies provides a novel approach for probing human brain function.

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Study on the Classification of Performance Shaping Factors in Digital control System of Nuclear Power Plants

Study on the Classification of Performance Shaping Factors i...

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2012 IEEE 19th international conference on Industrial Engineering and Engineering Management(IE&EM 2012)

作者： Peng-cheng Li Li Zhang Guo-hua Chen Li-cao Dai Ming Zhao Human Factor Institute University of south China Hengyang China Institute of Safety Science and Engineering South China University of Technology Hunan Institute of Technology

In order to identify the performance shaping factors (PSFs) which influencing operator's reliability in digital control system of nuclear power plants (NPPs), the comparative analysis of the representative contextual characteristics between traditional analog control system and digital control system is carried out by means of literature research, field observation and interviews in this paper, and identify the typical PSFs in terms of technical system, human-computer interface, procedure system, task, and team communication and cooperation. the classification of PSFs has been collected from existing PSF taxonomies of human reliability analysis (HRA), human error analysis (HEA), and organizational factors classification and performance evaluation methods, and based on the established classification criterion and classification framework (the conceptual causal model of human error), the relative comprehensive classification system of PSFs is built to provide the reference to HEA and HRA in digital control systems of NPPs.

关键词： Digital control system, human reliability, nuclear power plant, performance shaping factors

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