检索结果-内蒙古大学图书馆

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Shiluo Jin, Yining Jin, Wei Mu, Ying School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Research Centre for Analytical Instrumentation State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Infrared (IR) spectroscopy is a fundamental technique in analytical chemistry. Recently, deep learning (DL) has drawn great interest as the modeling method of infrared spectral data. However, unlike vision or language tasks, IR spectral data modeling is faced with the problem of calibration transfer and has distinctive characteristics. Introducing the prior knowledge of IR spectroscopy could guide the DL methods to learn representations aligned with the domain-invariant characteristics of spectra, and thus improve the performance. Despite such potential, there is a notable absence of DL methods that incorporate such inductive bias. To this end, we propose Analytical-Chemistry-Informed Transformer (ACT) with two modules informed by the field knowledge in analytical chemistry. First, ACT includes learnable spectral processing inspired by chemometrics, which comprises spectral pre-processing, tokenization, and post-processing. Second, a straightforward yet effective representation learning mechanism, namely spectral-attention, is incorporated into ACT. Spectral-attention utilizes the intra-spectral and inter-spectral correlations to extract spectral representations. Empirical results show that ACT has achieved competitive results in 9 analytical tasks covering applications across pharmacy, chemistry, and agriculture. Compared with existing networks, ACT reduces the root mean square error of prediction (RMSEP) by more than 20% in calibration transfer tasks. These results indicate that DL methods in IR spectroscopy could benefit from the integration of prior knowledge in analytical chemistry. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Outage Performance and Optimal Design of MIMO-NOMA Enhanced Small Cell Networks with Imperfect Channel-State Information

引用

China Communications 2021年第10期18卷 107-128页

作者： Zheng Shi Hong Wang Yaru Fu Guanghua Yang Shaodan Ma Xinrong Ye School of Intelligent Systems Science and Engineering Jinan UniversityZhuhai 519070China State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao 999078China School of Communication and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Science and Technology Hong Kong Metropolitan UniversityHong Kong SARChina School of Physics and Electronic Information Anhui Normal UniversityWuhu 241002China

This paper focuses on boosting the performance of small cell networks(SCNs)by integrating multiple-input multiple-output(MIMO)and nonorthogonal multiple access(NOMA)in consideration of imperfect channel-state information(CSI).The estimation error and the spatial randomness of base stations(BSs)are characterized by using Kronecker model and Poisson point process(PPP),*** outage probabilities of MIMO-NOMA enhanced SCNs are first derived in closed-form by taking into account two grouping policies,including random grouping and distance-based *** is revealed that the average outage probabilities are irrelevant to the intensity of BSs in the interference-limited regime,while the outage performance deteriorates if the intensity is sufficiently ***,as the channel uncertainty lessens,the asymptotic analyses manifest that the target rates must be restricted up to a bound to achieve an arbitrarily low outage probability in the absence of the inter-cell ***,highly correlated estimation error ameliorates the outage performance under a low quality of CSI,otherwise it behaves ***,the goodput is maximized by choosing appropriate precoding matrix,receiver filters and transmission *** the end,the numerical results verify our analysis and corroborate the superiority of our proposed algorithm.

关键词： imperfect channel-state information(CSI) multiple-input multiple-output(MIMO) non-orthogonal multiple access(NOMA) small cell networks(SCNs)

来源：评论

学校读者我要写书评

暂无评论

Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

引用

Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

来源：评论

学校读者我要写书评

暂无评论

Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances

Learning a Simple Low-Light Image Enhancer from Paired Low-L...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhenqi Fu Yan Yang Xiaotong Tu Yue Huang Xinghao Ding Kai-Kuang Ma Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China School of Computer Science and Technology Hangzhou Dianzi University China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in lowlight conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors. To this end, we propose PairLIE, an unsupervised approach that learns adaptive priors from low-light image pairs. First, the network is expected to generate the same clean images as the two inputs share the same image content. To achieve this, we impose the network with the Retinex theory and make the two reflectance components consistent. Second, to assist the Retinex decomposition, we propose to remove inappropriate features in the raw image with a simple self-supervised mechanism. Extensive experiments on public datasets show that the proposed PairLIE achieves comparable performance against the state-of-the-art approaches with a simpler network and fewer handcrafted priors. Code is available at: https://***/zhenqifu/PairLIE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Seven-level Cascade Multilevel Inverter based Minimum Switches for Photovoltaic applications

Seven-level Cascade Multilevel Inverter based Minimum Switch...

引用

IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Ali Abedaljabar Al-Samawi Hafedh Trabelsi Department of Electronic and Communications Engineering College of Engineering Al-Muthanna University Al Muthanna Iraq Department of Electrical Engineering National Engineering School of Sfax (ENIS) Computer and Embedded Systems Laboratory (CES-Lab) Sfax University Sfax Tunisia

To enhance power quality and obtain high power from photovoltaic(PV), and mitigate the impact of the PV system's partial shading condition(PSC) and electrical mismatch power problems. This paper presents a single-phase, simple seven-level multilevel inverter architecture With a lower number of power components connected with four individual group PV systems integration converter DC/DC with independent Maximum Power Point Tracking(MPPT)in each group. The proposed inverter provides a reduced total harmonic distortion (THD) of voltage and unity active power. This reduction in the number of switches generates a decrease in the losses of switching and the number of drive circuits, which in turn causes a decrease in the control complication and decreases the cost and size of the control circuit. We compared this cascaded multilevel topology architecture to evaluate the performance of the suggested inverter, which needed modern cascade multilevel topologies with the same seven levels of voltage. The achievement of the comparison is dependent on several elements, including a set of parameters(diodes and capacitors), the overall harmonic distortion, and the number of switches included inside the inverter. The program(Simulink/Matlab) was used to verify the critical evaluation of the presented topology through simulation results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Game Learning-Based Target Allocation of the UAV Confrontation System

Game Learning-Based Target Allocation of the UAV Confronta...

引用

International Conference on Guidance, Navigation and Control, ICGNC 2024

作者： Hu, Yaxu Zhang, Yanling Xiao, Feng Key Laboratory of Intelligent Bionic Unmanned Systems School of Intelligence Science and Technology University of Science and Technology Beijing Beijing100083 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819622313

Previous studies on the UAV swarm air combat decision-making problem lack consideration of uncertainty, intense confrontation, and high dynamics of aerial combat scenarios. This paper delves into the target allocation decision problem of the UAV attack and defense confrontation in a dynamic aerial combat environment. First, a dynamic game model is proposed to adapt changes in combat resources and adversaries’ behavior throughout multi-stage game processes. Then, the corresponding Nash equilibrium is solved based on a game learning algorithm- the Q-Learning algorithm. Finally, we consider the case where the UAV destructive probability is unknown, an uncertainty model is designed for predicting the UAV destructive probability based on the fuzzy comprehensive evaluation method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： fuzzy comprehensive evaluation game learning target allocation UAV swarm

来源：评论

学校读者我要写书评

暂无评论

Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression

arXiv

引用

arXiv 2024年

作者： Li, Yi-Hsin Knorr, Sebastian Sjöström, Mårten Sikora, Thomas Department of Telecommunication Systems Technical University of Berlin Berlin10587 Germany Department of Computer and Electrical Engineering Mid Sweden University Sundsvall85170 Sweden School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Berlin12459 Germany

Kernel image regression methods have shown to provide excellent efficiency in many image processing task, such as image and light-field compression, Gaussian Splatting, denoising and super-resolution. The estimation of parameters for these methods frequently employ gradient descent iterative optimization, which poses significant computational burden for many applications. In this paper, we introduce a novel adaptive segmentation-based initialization method targeted for optimizing Steered-Mixture-of Experts (SMoE) gating networks and Radial-Basis-Function (RBF) networks with steering kernels. The novel initialization method allocates kernels into pre-calculated image segments. The optimal number of kernels, kernel positions, and steering parameters are derived per segment in an iterative optimization and kernel sparsification procedure. The kernel information from "local"segments is then transferred into a "global" initialization, ready for use in iterative optimization of SMoE, RBF, and related kernel image regression methods. Results show that drastic objective and subjective quality improvements are achievable compared to widely used regular grid initialization, "state-of-the-art" K-Means initialization and previously introduced segmentation-based initialization methods, while also drastically improving the sparsity of the regression models. For same quality, the novel initialization results in models with around 50% reduction of kernels. In addition, a significant reduction of convergence time is achieved, with overall run-time savings of up to 50%. The segmentation-based initialization strategy itself admits heavy parallel computation;in theory, it may be divided into as many tasks as there are segments in the images. By accessing only four parallel GPUs, run-time savings of already 50% for initialization are achievable. Copyright © 2024, The Authors. All rights reserved.

关键词： Radial basis function networks

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of a Web-based Collaborative Manufacturing System Based on Knowledge Graph 4

Design and Implementation of a Web-based Collaborative Manuf...

引用

4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yuan, Meng Wu, Huaiyu Shen, Zhen Han, Yunjun Zhou, Jiehan Yue, Zhijian Xiong, Gang Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266109 China University of Oulu Faculty of Information Technology and Electrical Engineering OuluTS352 Finland

ISBN: (纸本)9798350349252

Collaborative manufacturing is central to agile, intelligent, and cloud-based manufacturing paradigms. This paper presents the design and implementation of a web-based collaborative manufacturing system based on a knowledge graph (KG). The system achieves the full spectrum of knowledge extraction, fusion, and reasoning across the industrial chain, thereby enhancing efficiency and reducing operational costs. The proposed system constructs an industrial chain KG and multi-agent systems, offering comprehensive digital services across the manufacturing value chain. It supports the integration of business processes, data, and standard information for enterprise groups, presenting collaborative manufacturing data of the industrial chain in real-time visualization. The paper further discusses a method for real-time perception of anomalies in the collaborative manufacturing process, utilizing expert knowledge and data-driven insights to construct an operational risk KG. The system's architecture, workflow, and functional design are detailed, along with application cases in visual dynamic management. The conclusion underscores the system's potential to improve supply-demand chain management and operational efficiency in collaborative manufacturing. © 2024 IEEE.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China The School of Computing Kent Interdisciplinary Research Centre in Cyber Security University of Kent CanterburyCT2 7NF United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The Intelligent Transportation Systems Lab Innopolis University Innopolis Russia The School of Computer Science and Technology Xidian University China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. © 2004-2012 IEEE. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Learning-Based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum computing

Learning-Based Sustainable Multi-User Computation Offloading...

引用

IEEE International Conference on Communications (ICC)

作者： Minrui Xu Dusit Niyato Jiawen Kang Zehui Xiong Mingzhe Chen School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA

In this paper, a novel paradigm of mobile edgequantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings.

关键词：

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：