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检索条件"任意字段=IEEE/SMC International Conference on System of Systems Engineering"

共 216469 条记录，以下是561-570 订阅

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Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Control for Unmanned Surface Vehicles with Uncertainties

Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Con...

引用

2024 ieee international conference on systems, Man, and Cybernetics, smc 2024

作者： Zhao, Huarong Shan, Jinjun Li, Xing Yu, Hongnian School of Internet of Things Engineering Jiangnan University Wuxi214122 China York University Department of Earth and Space Science and Engineering TorontoONM3J 1P3 Canada School of Electrical Engineering and Intelligentization Dong-guan University of Technology Dongguan523000 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110819 China School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom

ISBN: (纸本)9781665410205

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches. © 2024 ieee.

关键词： Sliding mode control

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Controlled Dropout for Uncertainty Estimation

Controlled Dropout for Uncertainty Estimation

引用

2023 ieee international conference on systems, Man, and Cybernetics, smc 2023

作者： Hasan, Mehedi Hossain, Ibrahim Rahman, Ashikur Nahavandi, Saeid Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3220 Australia Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350337020

Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art performance for many applications, they still provide unreliable point predictions, which lack information about uncertainty estimates. Among various methods to enable neural networks to estimate uncertainty, Monte Carlo (MC) dropout has gained much popularity in a short period due to its simplicity. In this study, we present a new version of the traditional dropout layer where we are able to fix the number of dropout configurations. As such, each layer can take and apply the new dropout layer in the MC method to quantify the uncertainty associated with NN predictions. We conduct experiments on both toy and realistic datasets and compare the results with the MC method using the traditional dropout layer. Performance analysis utilizing uncertainty evaluation metrics corroborates that our dropout layer offers better performance in most cases. © 2023 ieee.

关键词： dropout Monte Carlo dropout neural network Uncertainty estimation

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A GCN-Based Trip Recommendation Method Incorporating Reverse Effect

A GCN-Based Trip Recommendation Method Incorporating Reverse...

引用

2024 ieee international conference on systems, Man, and Cybernetics, smc 2024

作者： Luan, Wenjing Wang, Xueyao Qi, Liang Liu, Kun Guo, Xiwang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China

ISBN: (纸本)9781665410205

In location-based services (LBS), trip recommendation accuracy is challenged by diverse user preferences and complex transfer behaviors. Previous studies overlook the reverse effect of following POIs on previously visited ones. To address this, we propose a Graph-based Double-layer Bidirectional Trip Recommendation (GDB-TR) model. This model uses a heterogeneous graph to model user check-in trajectories with spatial and temporal information. Subgraphs are extracted from the heterogeneous graph, and an adjacency matrix is built for each subgraph. These matrices are fused through a neural network to obtain vector representations for POIs and POI categories. GDB-TR's core is a double-layer bidirectional neural network: one layer describes POIs, the other POI categories. Bidirectional computation captures the influence of preceding POIs on following ones and vice versa. Experiments on five real-world datasets demonstrate GDB-TR's superiority over baseline models, measured by F1 and pairs-F1 metrics. © 2024 ieee.

关键词： Undirected graphs

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Time-Efficient Weapon-Target Assignment by Actor-Critic Reinforcement

Time-Efficient Weapon-Target Assignment by Actor-Critic Rein...

引用

2023 ieee international conference on systems, Man, and Cybernetics, smc 2023

作者： Byun, Muhyun Na, Hyungho Moon, IL-Chul Industrial and Systems Engineering Kaist Daejeon Korea Republic of Aerospace Engineering Kaist Daejeon Korea Republic of

ISBN: (纸本)9798350337020

This paper proposes a time-efficient model for solving the Weapon-target assignment (WTA) problem with actor-critic reinforcement learning. While typical heuristic algorithms and recently studied artificial neural network methodologies have shown good performance results, the previous approach has not been time-efficient in large-scale WTA problems. This paper utilizes the actor-critic framework to resolve the WTA problem, and this framework enables retrieving solutions 23 times faster than the previous deep Q-network approach. Additionally, we incorporate a recurrent neural network model of gated recurrent units (GRU) to allow agents to learn the latent state-space of the WTA problem. Our experiments demonstrate the solution quality and the time efficiency compared to traditional heuristic methods as well as recent DQN-based RL models. © 2023 ieee.

关键词： Actor-Critic Framework Gated Recurrent Units (GRU) Reinforcement Learning (RL) Time Efficiency Weapon-Target Assignment (WTA)

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Decrease the Prompt Uncertainty: Adversarial Prompt Learning for Few-shot Text Classification

Decrease the Prompt Uncertainty: Adversarial Prompt Learning...

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2024 ieee international conference on systems, Man, and Cybernetics, smc 2024

作者： Weng, Jinta Zhang, Zhaoguang Yaqi, Jing Niu, Chenxu Huang, Heyan Hu, Yue School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Cyber Engineering Guangzhou University China National Computer Network Emergency Response Technical Team Coordination Center of China China Beijing Institute of Technology School of Computer Science and Technology Beijing China Southeast Academy of Information Technology Putian China

ISBN: (纸本)9781665410205

With few-shot learning abilities, pre-trained language models (PLMs) have achieved remarkable success in classification tasks. However, recent studies have shown that the performance of PLM is vulnerable due to different prompts and the instability of the prompt-based learning process. To address this challenge, we explore appropriate perturbation addition of adversarial training and integrate the global knowledge of the full-parameter fine-tuned pre-trained language model(PLM). Specifically, we propose an adversarial prompt learning model (ATPET) and ATPET with fine-tuning(ATPET-FT), incorporating ATPET with fine-tuning knowledge into the prompt learning process. Through extensive experiments on several few-shot classification tasks and challenging data settings, we demonstrate that our methods consistently improve the robustness while maintaining the effectiveness of PLMs. © 2024 ieee.

关键词： Adversarial machine learning

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Poster: Vault Clinical Operations Management system (CTMS) used in Research Organizations for fast Medicine & Therapies available to the public

Poster: Vault Clinical Operations Management System (CTMS) u...

引用

9th ieee/ACM international conference on Connected Health - Applications, systems and engineering Technologies (CHASE)

作者： Vennapureddy, Srinivas Veeva Syst Inc Release DevOps Platform Engn Pleasanton CA 94588 USA

ISBN: (纸本)9798350345025;9798350345018

Veeva Vault CTMS is the only modern Cloud software application that makes it easy to unify clinical information and processes, streamline trial management and gain visibility across the trial portfolio. Study teams can manage the entire end-to-end clinical trial process and gain a single source for clinical master data and study information. Vault CTMS improves operational efficiency and enables faster higher-quality trial execution. Streamline operations with a flexible agile solution that easily adapts to Research Organization's unique clinical trial needs, study designs and Therapeutic areas. With Vault Clinical operations, study teams enjoy a consistent experience with single sign-on and avoid constantly switching between multiple systems. Submit trial research information and documentation once and leverage it across different systems, sites, and countries. The Single source of truth improves visibility and control and accelerates trial execution for research projects. CTMS behind is supported by two critical applications like CDX (Clinical Exchange) and Artificial Intelligence (AI) Clinical are the Cloud based Software applications.

关键词： Veeva Vault CTMS Veeva CDX and Veeva AI Clinical application Research Organizations

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ANN-smc Control of a Hybrid Energy Storage system for Single-Phase Application 6

ANN-SMC Control of a Hybrid Energy Storage System for Single...

引用

6th ieee international conference on Advanced systems and Emergent Technologies, IC_ASET 2023

作者： Nouri, Achraf Lachheb, Aymen Amraoui, Lilia El University of Carthage Research Laboratory Smart Electricity & Ict LR18ES44 Enicarthage Tunisia

ISBN: (纸本)9798350321029

This paper focuses on the modeling and control of a hybrid energy storage system (HESS) consisting of lithium-ion batteries and supercapacitor designed for a single-phase installation. The proposed control method combines an artificial neural network (ANN) algorithm with a nonlinear sliding mode control (smc) system that considers variations in irradiance and power consumed by the load. The storage systems are separately connected to a DC bus via a DC-DC converter, and the PV installation is connected via a boost converter controlled by an intelligent MPPT using a fuzzy logic algorithm (FLC) to enhance the performance of the PV system. To protect the batteries from overcharging and deep discharge and maintain a constant state of charge for the supercapacitor, two management and protection algorithms are developed. Simulation results demonstrate the efficiency and stability of the proposed ANN-smc control strategy without any oscillations or overshoot during the transient regime until reaching the final value. © 2023 ieee.

关键词： Supercapacitor

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Contextual and Nonstationary Multi-armed Bandits Using the Linear Gaussian State Space Model for the Meta-Recommender system

Contextual and Nonstationary Multi-armed Bandits Using the L...

引用

2023 ieee international conference on systems, Man, and Cybernetics, smc 2023

作者： Miyake, Yusuke Mine, Tsunenori Graduate School of ISEE Department of Advanced Information Technology Kyushu University Fukuoka Japan Pepabo R&D Institute GMO Pepabo Inc. Fukuoka Japan Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798350337020

Selecting an optimal recommendation method is crucial for an electronic commerce (EC) site. However, the effectiveness of recommendation methods cannot be known in advance. Although continuous comparative evaluation in an actual environment is essential, it results in opportunity loss. To overcome this problem, opportunity loss reduction has been studied as a multi-armed bandit (MAB) problem, and adaptive meta-recommender systems (meta-RS) were devised to automatically and continuously select the best recommendation method according to a policy. The following three factors cause opportunity loss: the context of the recommendation method, temporal variation, and response time. However, studies are yet to formulate an MAB policy that considers all three factors. Thus, reducing opportunity loss remains a problem. We propose an MAB policy that considers all three causes of opportunity loss by using a Kalman filter for a linear Gaussian state space model. We conducted extensive experiments to select the best recommendation method using data from a real EC site. The results revealed that the proposed policy is highly effective for reducing opportunity loss of the meta-RS during evaluation and increasing cumulative clicks compared with baseline policies. © 2023 ieee.

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Understanding the Implications of Changes to Build systems 24

Understanding the Implications of Changes to Build Systems

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39th ACM/ieee international conference on Automated Software engineering (ASE)

作者： Nejati, Mahtab Alfadel, Mahmoud McIntosh, Shane Univ Waterloo Software REBELs Waterloo ON Canada Univ Calgary Dept Comp Sci Calgary AB Canada

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

The maintenance of build systems imposes a considerable overhead on software development. Since automated quality assurance methods are rarely applied to build specifications, the importance of the role peer code review plays in the maintenance of build systems is amplified. Yet prior work shows that the review process for build systems suffers from a lack of build experts and effective tooling. To support the understanding of changes to build specifications (a key stage in the review process), we propose BCIA-an approach to summarize the impact of changes to build specifications across the build configuration space. BCIA traverses the paths through which data and control flow in the prior and updated versions of the build system to generate an Impact Knowledge Graph (IKG), which describes the impact of the change across the build configuration space. We develop BuiScout-a prototype implementation of BCIA for CMake-based build systems. We use BuiScout to evaluate our approach through an empirical study of 10,000 change sets that we mine from ten large open-source projects that span a total of 28 development years. Our findings indicate that BuiScout can detect an impact that propagates to unmodified parts of the build system in 77.37% of the studied change sets. These changes impact a median of 14 unmodified commands, with a median of 95.55% of the impacted commands per change set appearing in unmodified files. Our study suggests that dedicated approaches, such as BCIA, have the potential to alleviate the challenges developers face when assessing the impact of changes to build systems.

关键词： Build systems Build system maintenance Impact analysis

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Enhanced Spatial-Temporal Analysis for EEG-Based Microsleep Detection: Integrating Kalman Filtering with Voronoi Tessellation and Adaptive Coverage Control

Enhanced Spatial-Temporal Analysis for EEG-Based Microsleep ...

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2024 ieee international conference on systems, Man, and Cybernetics, smc 2024

作者： Biro, Attila Cuesta-Vargas, Antonio Ignacio Szilagyi, Laszlo George Emil Palade University of Medicine Pharmacy Science and Technology of Targu Mures Department of Electrical Engineering and Information Technology Targu Mures540088 Romania University of Malaga Department of Physiotherapy Malaga29071 Spain Malaga29590 Spain School of Clinical Science Queensland University Technology Faculty of Health Science Brisbane4000 Australia Sapientia Hungarian University of Transylvania Computational Intelligence Research Group Targu Mures540485 Romania Physiological Controls Research Center Obuda University Budapest1034 Hungary

ISBN: (纸本)9781665410205

Detecting microsleep in real time is crucial to facilitating the transition from semi-autonomous systems to completely autonomous driving technologies. Integrating sophisticated detection algorithms with vehicle control systems enables the provision of prompt corrective measures, such as driver alerts or temporary vehicle control. Minimizing the probability of accidents caused by driver fatigue not only improves the safety of users, but also promotes the overall security of the road network. By integrating Kalman filtering, Voronoi tessellation, and adaptive coverage control algorithms, the study seeks to find a feasible and sophisticated methodology to enhance the spatial and temporal resolution of EEG data analysis, leading to more robust and reliable detection of microsleep episodes. The paper presents a framework as a significant advancement in sleep technology, offering a new method to diagnose and understand microsleeps, characteristics, and patterns of brain activity and sleep disorders. © 2024 ieee.

关键词： Kalman filters

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