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检索条件"丛书名=Communications in Computer and Information Science"

共 43063 条记录，以下是601-610 订阅

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Modified TD3 Reinforcement Learning-Based Path Following Control for an Autonomous Underwater Vehicle 5th

Modified TD3 Reinforcement Learning-Based Path Following Con...

引用

5th China Intelligent Robotics Academic Conference

作者： Jiang, Yuze Li, Ziying Qu, Xingru Long, Feifei Zhang, Rubo Dalian Minzu Univ Sch Mech & Elect Engn Dalian 116600 Peoples R China PLA Dalian Naval Acad Dalian 116000 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

This article aims to investigate the path following issue associated with the underactuated autonomous underwater vehicle (AUV), particularly in the presence of model uncertainties and disturbances from marine environments. A modified twin delayed deep deterministic policy gradient (TD3) algorithm is proposed to design a deep reinforcement learning controller. The prioritized experience replay mechanism is developed by calculating the sample priority and sampling probability. Double experience replay buffers using the successful and failed experience are constructed, and the adaptive proportionality coefficient is designed to adjust the composition structure of each mini-batch replay data. Besides, the long short-term memory layer is added to the deep network structure to handle the historical state of path following, thereby enhancing the training efficiency of the proposed modified TD3 algorithm. Simulation validations and comparative analyses demonstrate the significant efficacy and predominance of the proposed control method.

关键词： Autonomous Underwater Vehicle Path Following Control Twin Delayed Deep Deterministic Policy Gradient Prioritized Experience Replay Long Short-Term Memory Network

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Optimizing the Performance Characteristics of SBS - Geopolymer Modified Bitumen: A Data-Driven Approach with ANN and Design Expert Software 2nd

Optimizing the Performance Characteristics of SBS - Geopolym...

引用

2nd International Conference on information Technologies and Their Applications

作者： Ahmedzade, Perviz Katanalp, Burak Yigit Tastan, Murat Turkyilmaz, Cigdem Canbay Turkyilmaz, Emrah Samarkand State Architectural & Civil Engn Univ Samarkand 140147 Uzbekistan Cukurova Univ TR-01100 Adana Turkiye Ege Univ TR-35100 Izmir Turkiye

ISBN: (纸本)9783031734199;9783031734205

In this study, the optimization of rutting and fatigue performance characteristics in SBS and geopolymer composite-modified bitumen was achieved by using an experimental design approach and an Artificial Neural Network (ANN) model. The Design Expert 10.0.3 software was employed to construct an optimal mixture design, taking into account the ratios of SBS and Geopolymer within the bitumen matrix. A reference binder of B 50/70 penetration grade bitumen was used, and modified binders were prepared considering various ratios of SBS and Geopolymer. The factors are defined as SBS and Geopolymer concentrations, and the responses include SHRP rutting (G*/sin delta) and fatigue (G*sin delta) factors, non-recoverable creep compliance (Jnr), and number of cycles to failure (Nf), which were measured through Dynamic Shear Rheometer (DSR), Multiple Stress Creep Recovery (MSCR), and Linear Amplitude Sweep (LAS) tests. In experimental design, four central composite design (CCD) models were constructed, and variance analysis was performed to investigate the statistical significance of the model results. In ANN model, the multilayer perceptron was employed. Findings indicated that a higher percentage of Geopolymer in SBS modified bitumen is considered a promising option for reducing the SBS content, thus offering a potential pathway for the enhancement of the performance characteristics of the modified bitumen.

关键词： SBS Geopolymer Central Composite Design ANN MSCR LAS

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Factors Affecting Contract Farming Enforcement in Rice Production in Vietnam 5th

Factors Affecting Contract Farming Enforcement in Rice Produ...

引用

5th 5th Olympus - International Conference on Supply Chain

作者： Tuyen, Mai Chiem Hung, Pham Xuan Hue Univ Univ Econ Fac Econ & Dev Studies Hue 530000 Vietnam

ISBN: (纸本)9783031693502;9783031693519

Contract farming (CF) is considered a relevant measure to supply chain governance. Empirical studies indicate that CF positively affected economic performance of rice production and household welfare. However, there was a low rate of farmers' CF participation, the CF breaks and breaches still happened regularly. While a main stand of literature investigates factors affecting farmers' participation in CF, a few focusses on CF enforcement. This study utilized household survey data by multistage stratified random sampling to address this research gap. Data were analyzed by the Tobit model to determine factors affecting CF enforcement of farmers in rice production in Vietnam. The results highlight that CF enforcement was affected by the characteristics of household heads and households, some contract attributes, and farmers' evaluation on transparency and mutual benefits of CF agreement. Therefore, the relevant design of contract attributes and their levels, improving the transparency and mutual benefits of CF agreements among the stakeholders need to be considered.

关键词： Contract Farming Enforcement Rice Production Vietnam

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LGFNet: A Remote Sensing Change Detection Network with Local-Global Semantic Feature Fusion 5th

LGFNet: A Remote Sensing Change Detection Network with Local...

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5th China Intelligent Robotics Academic Conference

作者： Xue, Mingliang Liang, Xuan Lu, Yao Han, Xu Dalian Minzu Univ Sch Comp Sci & Engn SEAC Key Lab Big Data Appl Technol Dalian 116650 Peoples R China Beijing Inst Remote Sensing Informat Beijing 100010 Peoples R China Liaoning Normal Univ Sch Management Dalian 116081 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Change detection (CD) is crucial for accurately obtaining change information from geographic surfaces across different time periods. Recent advances in deep learning have made significant achievements in this field. However, there are still several challenges remaining: (1) Existing methods for change feature fusion are not comprehensive and accurate enough to capture and integrate change information;(2) Traditional CD models often confine themselves to local information and lack the ability to model the global context, which somewhat limits their performance in CD tasks. Therefore, we propose a localglobal semantic feature fusion network (LGFNet). This network effectively leverages the long-range modeling capabilities of Transformers and the local perception strengths of CNNs by integrating the global and local features they extract, resulting in efficient feature representation. Additionally, to enhance the change features and reduce redundant pseudo-changes, we designed a feature difference enhancement module. On several benchmark datasets, our method outperforms several stateof-the-art approaches. The experimental results demonstrate that this method detects changes with high accuracy.

关键词： Change Detection Remote Sensing Image Feature Fusion NetWork

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Requirements Engineering (RE) in Artificial Intelligence (AI) Systems Implementation: The Need to Emphasize Non-Functional Requirements (NFRs) for Ethical AI 5th

Requirements Engineering (RE) in Artificial Intelligence (AI...

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5th Southern African Conference for Artificial Intelligence Research

作者： Liywalii, Eddie Univ Pretoria UP Dept Philosophy Fac Humananities Pretoria South Africa Eth AI Res Grp Ctr Artificial Intelligence Res CAIR Pretoria South Africa

ISBN: (纸本)9783031782541;9783031782558

In traditional approaches to develop software systems that do not have an Artificial Intelligence (AI) or Machine Learning (ML) component, requirements determination activities - also called Requirements Engineering (RE) - are well-established and researched. When it comes to building software systems with one or more AI/ML components, the process is dependent heavily on data with limited or in some cases no insight into the non-functional requirements of the system's internal workings such as fairness, accountability and transparency, inter alia. In this paper, I review literature to probe how an emphasis on nonfunctional requirements during requirements determination can help address ethical requirements when implementing AI systems. The results show that currently technical experts often emphasize functional requirements more than non-functional requirements. Where they do, the emphasis is still on system-oriented non-functional requirements such as portability, maintainability, opacity, inter alia. However, AI being an interdisciplinary subject, encompassed in a bracket of sociotechnical systems, there is need to revisit this tendency by technical experts. I propose a framework for classifying ethical requirements under non-functional requirements in order to ensure equal emphasis on environment-oriented non-functional requirements such as privacy, fairness, eco-friendly among others. I also argue that the use of a value sensitive design (VSD) approach will help in the implementation of ethical AI systems in order to address ethical concerns from stakeholders. In addition, existing technical literature has focused more on using AI to manage RE activities (AI in RE), yet very limited research has been done on RE for AI systems (RE4AI). Requirements engineering is generally accepted as the most critical and complex process within the development of sociotechnical systems. The literature also confirms that during requirements determination for AI systems (RE4AI), a

关键词： Ethical Requirements Requirements Engineering (RE) Non-functional requirements (NFRs) ethical AI

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Wire Rope Damage Detection Method Based on Support Vector Machine Wavelet Kernel Function Algorithm 5th

Wire Rope Damage Detection Method Based on Support Vector Ma...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Jin, Zusong Wane, Tao Luo, Zhiheng Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250000 Shandong Peoples R China

ISBN: (纸本)9789819770069;9789819770076

In response to the challenges of low detection accuracy and poor stability in traditional wire rope damage detection techniques, we propose a novel approach based on the Support Vector Machine (SVM) wavelet kernel algorithm. This method leverages the similarity between kernel functions and wavelets in decomposition and synthesis processes, integrating kernel functions with wavelet denoising to effectively suppress interference noise. To achieve non-destructive inspection of wire ropes, we designed a magnetic leakage detection device. This device undergoes three analytical processing steps: trend removal of leakage magnetic signals, intervention-based signal denoising, and normalization of feature values for model training. This facilitates the detection, extraction, and identification of leakage magnetic signals. Through comparative analysis using simulated data, our results demonstrate superior detection performance of the proposed fault detection method.

关键词： Wire Rope Inspection Wavelet Kernel Algorithm Magnetic Leakage Detection

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Design and Implementation of a Multi-metric Performance Analysis Tool for Android System and Its Applications 7th

Design and Implementation of a Multi-metric Performance Anal...

引用

8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Yang, Zhenyu Shanghai Jiao Tong Univ 800 Dong Chuan Rd Shanghai Peoples R China

ISBN: (纸本)9789819600540;9789819600557

Android is a dominant smartphone OS known for its Linux kernel foundation and open-source nature, offering flexibility and customization via its multi-layer design. Understanding system performance under different application loads is essential to improve user experience and optimize devices. However, existing profiling tools fail to combine information from multiple system layers. The paper introduces PolyMetrica, a framework designed for Android system developers to measure and analyze performance across various layers. PolyMetrica stands out for its extensive data collection, adaptability, and customizability. It collects data through a monitoring module that interfaces with different system layers and allows for the addition of new metrics. PolyMetrica's testing has proven effective in reflecting Android's performance metrics and providing depth through cross-level data analysis. Its customizable nature enables easy addition and customization of metrics and load scenarios, making it a valuable tool for Android system development and optimization.

关键词： Android System Language Runtime Performance Evaluation Performance Analysis Profiling Tools

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A Triple-Branch Frequency-Aware Network for Image Manipulation Detection 42nd

A Triple-Branch Frequency-Aware Network for Image Manipulati...

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42nd National Conference on Theoretical computer science

作者： Pan, Wenyan Xia, Zhihua Qin, Jiaohua Nanjing Univ Informat Sci & Technol Sch Comp Sci Nanjing 210044 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Cent South Univ Forestry & Technol Coll Comp Sci & Informat Technol Changsha 410004 Peoples R China

ISBN: (纸本)9789819614899;9789819614905

The goal of Image Manipulation Detection (IMD) is to identify and locate manipulated regions within images. Recent approaches have primarily designed sophisticated neural networks to capture highf-requency information for IMD tasks. However, these methods often overemphasize high-frequency information while overlooking the important role of low-frequency information in IMD tasks. To address this issue, we propose a Triple-Branch Frequency-Aware Network (TFNet), which includes an information Separation Module (ISM), a Main Steam Branch (MSB), a Low-Frequency Learning Branch (LFL), a High-Frequency Learning Branch (HFL), and an Adaptive Aggregate Module (AAM) within a unified framework. Specifically, TFNet initially employs FSM to separate the manipulated image into RGB, high-frequency, and low-frequency components. Then, MSB, LFB, and HFB take the above components as input to learn the distinct features. Furthermore, the HFB is supervised with the boundary information to encourage the network to focus on the high-frequency information. Finally, the outputs of MSB, LFB, and HFB are sent to the ABM to adaptively aggregate features learned from the MSB, LFB, and HFB. Experiments on the CASIA, NIST, and Coverage datasets demonstrate the effectiveness of our TFNet.

关键词： Image manipulation detection frequency information

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An ABMS COVID-19 Propagation Model for Hospital Emergency Departments 12th

An ABMS COVID-19 Propagation Model for Hospital Emergency De...

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12th Conference on Cloud Computing, Big Data and Emerging Topics

作者： Ansari Dogaheh, Morteza Taboada, Manel Epelde, Francisco Luque, Emilio Rexachs, Dolores Wong, Alvaro Univ Autonoma Barcelona Comp Architecture & Operating Syst Dept Bellaterra Spain Escuelas Univ Gimbernat Barcelona Spain Univ Autonoma Barcelona Sch Comp Sci Barcelona Spain Hosp Univ Parc Tauli Dept Med Barcelona Spain

ISBN: (纸本)9783031708060;9783031708077

The spread of COVID-19 between different agents in a hospital emergency department can be simulated by modeling the interactions between the agents and the environment. In this research, we use Agent Based Modeling and Simulation techniques to build a model of COVID-19 propagation based on an Emergency Department Simulator which has been tested and validated previously. The benefits of ABM include its ability to simulate complex systems, its flexibility, and its ability to model the interactions between different agents in the system. The obtained model will allow us to build a propagation simulator that enables us to build virtual environments with the aim of analyzing how the interactions between agents influence the rate of virus transmission. The model can be used to study the effectiveness of different interventions, such as social distancing, wearing masks, and vaccination, in reducing the spread of COVID-19.

关键词： Emergency Department Simulator Agent Based Modeling and Simulation COVID-19 Propagation

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Neural Agents with Continual Learning Capacities 12th

Neural Agents with Continual Learning Capacities

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12th Ecuadorian Conference on information and Communication Technologies

作者： Zhinin-Vera, Luis Pretel, Elena Moya, Alejandro Jimenez-Ruescas, Javier Astudillo, Jaime Univ Castilla La Mancha LoUISE Res Grp Albacete 02071 Spain Yachay Tech Univ Sch Math & Computat Sci Urcuqui 100650 Ecuador MIND Res Grp Model Intelligent Networks Dev Urcuqui Ecuador

ISBN: (纸本)9783031754302;9783031754319

The contemporary Artificial Neural Networks (ANNs) often suffer from catastrophic forgetting, where learned parameters are overwritten by new tasks. This paper presents a novel approach using a Reinforcement Learning (RL) agent with Continual Learning (CL) capabilities to navigate a visual robotic structure, achieving advanced proficiency in Tic-Tac-Toe. The system integrates a webcam for environmental perception, specialized neural blocks for feature extraction, and a communication bus linking self-taught agents with advisors. A knowledge protection mechanism prevents the loss of acquired parameters during new learning iterations. The methodology was validated on a physical robot, implemented with C++ and OpenCV, demonstrating its ability to retain knowledge and enhance gameplay, effectively emulating intelligent children's learning strategies. The proposed system was tested in a real-world setting, achieving an average accuracy of 92% in task completion and demonstrating a 15% improvement in task retention over traditional methods.

关键词： continual learning neural agents reinforcement learning

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