检索结果-内蒙古大学图书馆

ieee symposium Series on computational intelligence (ieee SSCI)

作者： Karim, Hassan Rawat, Danda B. Howard Univ Dept Elect Engn & Comp Sci DoD Ctr Excellence Artificial Intelligence & Mach Washington DC 20059 USA

ISBN: (纸本)9781728190488

The most widely used method to prevent adversaries from eavesdropping on sensitive sensor, robot, and war fighter communications is mathematically strong cryptographic algorithms. However, prevailing cryptographic protocol mandates are often made without consideration of resource constraints of devices in the internet of Battlefield Things (IoBT). In this article, we address the challenges of IoBT sensor data exchange in contested environments. Battlefield IoT (Internet of Things) devices need to exchange data and receive feedback from other devices such as tanks and command and control infrastructure for analysis, tracking, and real-time engagement. Since data in IoBT systems may be massive or sparse, we introduced a machine learning classifier to determine what type of data to transmit under what conditions. We compared Support Vector Machine, Bayes Point Match, Boosted Decision Trees, Decision Forests, and Decision Jungles on their abilities to recommend the optimal confidentiality preserving data and transmission path considering dynamic threats. We created a synthesized dataset that simulates platoon maneuvers and IED detection components. We found Decision Jungles to produce the most accurate results while requiring the least resources during training to produce those results. We also introduced the JointField blockchain network for joint and allied force data sharing. With our classifier, strategists, and system designers will be able to enable adaptive responses to threats while engaged in real-time field conflict.

关键词： Machine Learning Homomorphic Encryption IoBT V2X Blockchain Confidentiality Internet of Battlefield Things Privacy Preservation

来源：评论

学校读者我要写书评

暂无评论

Mathematical Formalism of Sequential Automaton with Parallel Branches used in Industrial automation 14

Mathematical Formalism of Sequential Automaton with Parallel...

引用

ieee 14th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Ungureanu-Anghel, Dan Robu, Raul Prostean, Octavian Politehn Univ Timisoara Dept Automat & Appl Informat Fac Automat & Comp Timisoara Romania

ISBN: (数字)9781728173771

ISBN: (纸本)9781728173764

Sequential automata have been used for quite a while in the control of industrial systems, mainly in the automotive industry. A series of modelling and analysis techniques have been used. Sequential automata models are at the basis for implementing control algorithms of assembly processes in the automotive industry The paper aims to to establish a mathematical definition for deterministic automatons with parallel branches, with direct application in control of industrial systems. Starting from classical definition of deterministic automaton, a new definition for a deterministic automaton with parallel branches is established.

关键词： sequential automata deterministic automaton parallel branches

来源：评论

学校读者我要写书评

暂无评论

Analysis of time series data for anomaly detection

Analysis of time series data for anomaly detection

引用

International symposium on computational intelligence and Informatics

作者： Katalin Ferencz József Domokos Levente Kovács Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Faculty of Technical and Human Sciences Târgu Mure. Romania Sapientia Hungarian University of Transylvania Faculty of Technical and Human Sciences Târgu Mure Sapientia Hungarian University of Transylvania Târgu Mure. Romania John Von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (纸本)9798350398830

The integration of sensors in our everyday lives and in industry presents a serious challenge to data analysis professionals. Since the use of smart devices has exponentially increased the amount of data collected in all areas, we must not only store these data, but also extract valuable information from those using some data analysis method. In many cases, the increased amount of data also causes problems for data analysis algorithms, so we need to be continuously updated and specialized for fundamental purposes. In the article, we will present some data analysis techniques, starting from the simplest statistical methods to more complex techniques using machine learning and presenting one of their possible applications. In our study, we will use the KMeans clustering algorithm and examine its effectiveness in time series sensor data analysis.

关键词： Support vector machines Analytical models Data analysis Machine learning algorithms computational modeling Time series analysis Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Reinforcement Learning With Multi Discount Factors In A Differential Game

Hierarchical Reinforcement Learning With Multi Discount Fact...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Amirhossein Asgharnia Howard M. Schwartz Mohamed Atia Departmant of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9781665487696

Reinforcement learning (RL) is an approach to solving differential games, especially pursuit-evasion games. Re-inforcement learning, treats the model and the environment as black boxes. It is shown that using multi-agent reinforcement learning is a suitable tool to address pursuit-evasion games. This paper proposes a new approach based on hierarchical reinforcement learning to find an instantaneous reward function for the game of guarding a territory, which is a more complex version of pursuit-evasion games. To have greater control over the generated path, a novel modification to the fuzzy actor-critic learning algorithm is proposed, so the augmented algorithm is able to handle several reward functions with different time horizons. The proposed approach has shown to have a suitable control over the combination of a terminal reward function and an instantaneous reward.

关键词： Reinforcement learning Games Differential games computational intelligence

来源：评论

学校读者我要写书评

暂无评论

Bidirectional Attention Network for Monocular Depth Estimation

Bidirectional Attention Network for Monocular Depth Estimati...

引用

ieee International Conference on Robotics and automation (ICRA)

作者： Aich, Shubhra Vianney, Jean Marie Uwabeza Islam, Md Amirul Kaur, Mannat Liu, Bingbing Huawei Technol Noahs Ark Lab Markham ON L3R 5Y1 Canada

ISBN: (纸本)9781728190778

In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation (MDE) that addresses the limitation of effectively integrating local and global information in convolutional neural networks. The structure of this mechanism derives from a strong conceptual foundation of neural machine translation, and presents a light-weight mechanism for adaptive control of computation similar to the dynamic nature of recurrent neural networks. We introduce bidirectional attention modules that utilize the feed-forward feature maps and incorporate the global context to filter out ambiguity. Extensive experiments reveal the high degree of capability of this bidirectional attention model over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets - KITTI and DIODE. We show that our proposed approach either outperforms or performs at least on a par with the state-of-the-art monocular depth estimation methods with less memory and computational complexity.

关键词： Recurrent neural networks Conferences computational modeling Memory management Estimation Machine translation Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Monitoring system of plant growth environment temperature based on LabVIEW and BP neural network

Monitoring system of plant growth environment temperature ba...

引用

ieee Information Technology, Networking, Electronic and automation control Conference

作者： Xudong Cui Liguo Tian Meng Li Han Han Shidong Hou Chunhu Shang College of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin China

In the current agricultural intelligence, the monitoring of plant growth environment parameters needs to use a variety of sensors which also leads to the existing intelligent control system being generally large. The electrical signal is another important signal in plants which contains abundant information besides hydraulic and chemical signals. Plant electrical signals can not only regulate plant growth but also reflect the environment to a certain extent. The collection of Aloe Vera electric signals at different temperatures (15, 19, 23, 27, 31, and 35 ℃) is accomplished by using LabVIEW software and hardware. The signal is denoised by using the module in the signal processing toolkit. The BP network is trained repeatedly by extracting the eigenvalues from the signal and the model with the smallest mean variance is selected as the optimal model. Deploy the model to the computer and use mixed programming with MATLAB to invoke the model. Use the powerful graphical programming and interface development capabilities of LabVIEW to ultimately build a monitoring system for plant growth environment temperature. Making the future agricultural intelligent control system lightweight and portable.

关键词： Temperature sensors computational modeling Plants (biology) Neural networks Programming Mathematical models Eigenvalues and eigenfunctions

来源：评论

学校读者我要写书评

暂无评论

Inertial sensor-based movement classification with dimension reduction based on feature aggregation

Inertial sensor-based movement classification with dimension...

引用

International symposium on computational intelligence and Informatics

作者： Peter Sarcevic József Sárosi Dominik Csík Ákos Odry Department of Mechatronics and Automation University of Szeged Szeged Hungary

ISBN: (纸本)9798350398830

In this paper, a novel feature aggregation method is proposed for dimension reduction in inertial sensor-based pattern recognition applications. The method is utilized in a movement classification algorithm, which applies measurements of wearable sensor units attached to the wrists of the subjects. The feature aggregation is realized separately for each feature type by using a linear combination of the feature values extracted for each sensor axis. The weights are computed using genetic algorithm-based optimization, which uses the classification efficiency provided by the k-nearest neighbor (kNN) method to compute the fitness value. After the weights are determined for all feature types and the aggregated inputs are computed for both the training and validation data, a multi-layer perceptron (MLP) classifier is trained, which can be used during real-time classification. The algorithm was tested with multiple datasets based on features extracted using different processing window sizes and used sensor combinations. The achieved results show that the proposed weighted aggregation provides significantly, mostly 5-6%, higher recognition rates on validation data than when equal coefficients are utilized during aggregation. The method in the case of some datasets with the same configuration gives even higher classification efficiencies than when using the features extracted for the sensor axes separately.

关键词： Dimensionality reduction Training Wrist Feature extraction Robot sensing systems Pattern recognition Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

引用

ieee Transactions on Circuits and Systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 ieee.

关键词： Memristors

来源：评论

学校读者我要写书评

暂无评论

Adaptation of XAI to Auto-tuning for Numerical Libraries

Adaptation of XAI to Auto-tuning for Numerical Libraries

引用

ieee International symposium on Embedded Multicore Socs (MCSoC)

作者： Shota Aoki Takahiro Katagiri Satoshi Ohshima Masatoshi Kawai Toru Nagai Tetsuya Hoshino Graduate School of Informatics Nagoya University Japan Information Technology Center Nagoya University Japan Research Institute for Information Technology Kyushu University Fukuoka Japan

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

The unregulated utilization of Artificial intelligence (AI) outputs, potentially leading to various societal issues, has received considerable attention. While humans routinely validate information, manually inspecting the vast volumes of AI-generated results is impractical. Therefore, automation and visualization are imperative. In this context, Explainable AI (XAI) technology is gaining prominence, aiming to streamline AI model development and alleviate the burden of explaining AI outputs to users. Simultaneously, software Auto-Tuning (AT) technology has emerged for reducing the man-hours required for performance tuning in numerical calculations. AT is a potent tool for cost reduction during parameter optimization and high-performance programming for numerical computing. The synergy between AT mechanisms and AI technology is noteworthy, with AI finding extensive applications in AT. However, applying AI to AT mechanisms introduces challenges in AI model explainability. This study focuses on XAI for AI models when integrated into two different processes for practical numerical computations: performance parameter tuning of accuracy-guaranteed numerical calculations and sparse iterative algorithm.

关键词： Adaptation models Visualization Explainable AI computational modeling Libraries Numerical models Iterative methods Sparse matrices Tuning Optimization

来源：评论

学校读者我要写书评

暂无评论

Safe Stabilization with Model Uncertainties: A Universal Formula with Gaussian Process Learning

Safe Stabilization with Model Uncertainties: A Universal For...

引用

International Conference on control and automation (ICCA)

作者： Ming Li Zhiyong Sun Department of Electrical Engineering Eindhoven University of Technology and also with the Eindhoven Artificial Intelligence Systems Institute Eindhoven The Netherlands

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

A combination of control Lyapunov functions (CLFs) and control barrier functions (CBFs) forms an efficient framework for addressing control challenges in safe stabilization. Developing an analytical control strategy, known as the universal formula approach, which integrates CLF and CBF conditions, is recognized as a computationally efficient method for achieving safe stabilization. However, successful implementation of this universal formula relies on an accurate model, as any mismatch between the model and the actual system can compromise stability and safety. In this paper, we propose a new universal formula that leverages Gaussian processes (GPs) learning to address the safe stabilization problem in the presence of model uncertainty. By utilizing the results related to bounded learning errors, we achieve a high probability of stability and safety guarantees with the proposed universal formula. Additionally, we introduce a probabilistic compatibility condition to evaluate conflicts between the modified CLF and CBF conditions with GP learning results. In cases where compatibility assumptions fail, we propose a modified universal formula that relaxes stability constraints. We illustrate the effectiveness of our approach through a simulation of adaptive cruise control (ACC), highlighting its potential for practical applications in real-world scenarios.

关键词： Adaptation models Uncertainty computational modeling Gaussian processes Probabilistic logic Stability analysis Safety

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：