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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16443 条记录，以下是1091-1100 订阅

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AN ADAPTIVE SCHEDULING ALGORITHM FOR MULTI-TARGET ISAR IMAGING RADAR BASED ON GREEDY ALGORITHM

AN ADAPTIVE SCHEDULING ALGORITHM FOR MULTI-TARGET ISAR IMAGI...

引用

IET International Radar conference 2023, IRC 2023

作者： Yao, Huan Lou, Hou Wang, Dan Chen, Yijun School of Information Engineering Engineering University of the People's Armed Police Xi'an China Institute of Information and Navigation Air Force Engineering University Xi'an China The National Lab of Radar Signal Processing Xidian University Xi'an China

ISBN: (纸本)9781839539954

With the increasing number of airborne targets and the shift towards clustered combat modes, radar systems are facing the challenge of dealing with a large number of targets within limited time. Effectively utilizing time resources to image as many targets as possible has become an urgent problem to address. This paper firstly investigates the impact of target motion on the quality of ISAR imaging. Subsequently, an adaptive scheduling algorithm for multi-target ISAR imaging radar based on greedy algorithm is *** algorithm begins by analyzing the changing trends in the equivalent rotational angular velocity of multiple targets relative to the radar system. This information is then used to determine the optimal observation period for each target. By considering target characteristics in conjunction with the optimal observation period, the greedy algorithm is applied to achieve optimal scheduling of multi-target imaging by the radar *** results demonstrate that the algorithm proposed in this paper can improve the success rate of radar imaging on multiple targets while ensuring imaging quality. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

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Training Transformers with 4-bit Integers 37

Training Transformers with 4-bit Integers

引用

37th conference on Neural Information processing systems (NeurIPS)

作者： xi, Haocheng Li, Changhao Chen, Jianfei Zhu, Jun Tsinghua Univ Dept Comp Sci & Tech Inst AI BNRist CtrTHBI LabTsinghua Bosch Joint ML Ctr Beijing Peoples R China Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing Peoples R China

ISBN: (纸本)9781713899921

Quantizing the activation, weight, and gradient to 4-bit is promising to accelerate neural network training. However, existing 4-bit training methods require custom numerical formats which are not supported by contemporary hardware. In this work, we propose a training method for transformers with all matrix multiplications implemented with the INT4 arithmetic. Training with an ultra-low INT4 precision is challenging. To achieve this, we carefully analyze the specific structures of activation and gradients in transformers to propose dedicated quantizers for them. For forward propagation, we identify the challenge of outliers and propose a Hadamard quantizer to suppress the outliers. For backpropagation, we leverage the structural sparsity of gradients by proposing bit splitting and leverage score sampling techniques to quantize gradients accurately. Our algorithm achieves competitive accuracy on a wide range of tasks including natural language understanding, machine translation, and image classification. Unlike previous 4-bit training methods, our algorithm can be implemented on the current generation of GPUs. Our prototypical linear operator implementation is up to 2.2 times faster than the FP16 counterparts and speeds up the training by 17.8% on average for sufficiently large models. Our code is available at https://***/xijiu9/Train_Transformers_with_INT4.

关键词： Distribution transformers

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VIBRATION-BASED BRIDGE DAMAGE DETECTION USING image-BASED PRE-TRAINED DEEP LEARNING NETWORK

VIBRATION-BASED BRIDGE DAMAGE DETECTION USING IMAGE-BASED PR...

引用

15th ASME conference on Smart Materials, Adaptive Structures and Intelligent systems (SMASIS)

作者： Song, xi Dyogi, Joshua Cho, Chunhee Univ Hawaii Manoa Honolulu HI 96822 USA

ISBN: (纸本)9780791886274

Vibration-based damage detection has become one of the principal practices to prevent structural collapses in civil, mechanical, and other engineering disciplines. Meanwhile, with the advancement of computing technology, various machine learning (ML) approaches have been applied toward structural damage detection through the application of post-processing algorithms. To accurately predict damages with ML, large amounts of structural response data are collected from a series of sensors attached to the structure. Therefore, the damage diagnosis requires high computational efforts. To address such an issue, this paper presents a revolutionary approach utilizing an image-based pre-trained convolutional neural network (CNN) to detect bridge damage locations and severities. Our research adopted scalograms from wavelet transform to convert structure acceleration data into image data. Compared with the traditional frequency analysis derived from the Fourier transform, the new method maintains both spatial and temporal information from the original structural behaviors. To generate CNN learning features, six channels of acceleration data are gathered from six strategically selected points of a finite element (FE) bridge model. Two pre-trained CNN, AlexNet and Resnet, are selected to conduct transfer machine learning for higher training efficiency. The performances of the proposed method are assessed with various damage scenarios. The prediction accuracies of AlexNet and Resnet are 98% and 100%, respectively.

关键词： Structural Damage Detection Machine Learning Deep Learning Transfer Learning Wavelet Transform Scalogram

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Female or male observers ? - Gender differences in subjective image quality assessment 23

Female or male observers ? - Gender differences in subjectiv...

引用

6th International conference on Networking, Intelligent systems and Security, NISS 2023

作者： El Mayel, Fatima Ezzahra Dahmouni, Abdellatif Ait Abdelouahad, Abdelkaher Laroseri Laboratory Faculty of Sciences Chouaib Doukkali University ELjadida Morocco

ISBN: (纸本)9798400700194

Objective image quality assessment measures were extensively used to evaluate the performance of different image processing and analysis algorithms. However, they are application-driven. In contrast, subjective assessment measures are general-purpose and considered as the ground truth. Several Subjective image Quality Assessment (SIQA) methods have been proposed in the literature. These measures require normalized environment and standardized protocols and are mainly based on the visual judgments of image quality. Nevertheless, these judgments are strongly influenced by human factors which might skew the precision of the assessment. Consequently, when selecting observers panel, researchers take into account several criteria such as age, sex, ethnic origin, socio-cultural and professional. Following an in-depth analysis of recent state-of-the-art work, we found that no one has investigated a gender difference in human judgment. In this paper, we aim to highlight the effect of this property in the subjective evaluation tests that are conducted on the TID2008 database. Obtained results show that men and women judge the same distorted images differently, with high precision in favor of women, outperforming in several cases the results provided by the TID2008 database. © 2023 ACM.

关键词： image quality

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Advances in Database Technology - Proceedings: 27th International conference on Extending Database Technology, EDBT 2024

Advances in Database Technology - Proceedings: 27th Internat...

引用

27th International conference on Extending Database Technology, EDBT 2024

ISBN: (纸本)9783893180950

The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;Data-CASE: grounding data regulations for compliant data processing systems;data coverage for detecting representation bias in image datasets: a crowdsourcing approach;balancing utility and fairness in submodular maximization;stateful entities: object-oriented cloud applications as distributed dataflows;learning over sets for databases;a new PET for data collection via forms with data minimization, full accuracy and informed consent;adaptive compression for databases;analysis of open government datasets from a data design and integration perspective;fine-grained geo-obfuscation to protect workers’ location privacy in time-sensitive spatial crowdsourcing;and a framework to evaluate early time-series classification algorithms.

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17th CIRP conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2023

17th CIRP Conference on Intelligent Computation in Manufactu...

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17th CIRP conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2023

The proceedings contain 180 papers. The topics discussed include: analysis of medical data and machine-learning algorithms from the perspective of public-goods models of data-provision decision making;price competition for service provision with different bargaining abilities;survey on stakeholder cooperative behavior for designing voluntary medical data provision motivation mechanisms;analysis of excellent service systems from co-creation and emergent synthesis perspective;meta-heuristic scheduling auction applying distributed genetic algorithm;the impact of characteristic function of Shapley value mechanism in distributed machine learning environment for equipment diagnosis;automatic measurement of timber diameter using image processing;intelligent scheduling based on discrete-time simulation using machine learning;and a fuzzy synthesis approach for hierarchical decision analysis to select optimum repair technique.

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Optimal Scale Selection in Multi-Scale Interval-Set Decision Tables

Optimal Scale Selection in Multi-Scale Interval-Set Decision...

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2023 International conference on Machine Learning and Cybernetics, ICMLC 2023

作者： xie, Zhen-Huang Wu, Wei-Zhi Wang, Lei-xi School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang316022 China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan Zhejiang316022 China

ISBN: (纸本)9798350303780

In traditional interval-set information systems (ISISs), each attribute is single-scale. However, processing and analyzing data at different scales is often necessary for practical applications. This paper introduces the granular information transformation into multi-scale interval-set information systems (MISISs), defines the similarity relation between objects in MISISs, and defines the rough approximation and correlation properties at different scales studied. Then, we propose the multi-scale interval-set decision table (MISDT) concept and discuss the monotonicity of scale variation of the positive region, generalized decision and positive complementarity conditional entropy. Then, we give three criteria for determining the optimal scales and discuss the relationship among the optimal scales of MISDTs. Finally, give heuristic algorithms and examples. © 2023 IEEE.

关键词： Decision tables

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Urban Visual Pollution Detection in Public Realm Using Artificial Intelligence: A Case Study of Riyadh Area, Saudi Arabia

Urban Visual Pollution Detection in Public Realm Using Artif...

引用

Intelligent systems conference

作者： Alzahrani, Mohammed Ali R. Almutlaq, Fahad Saleh Taif Univ Comp & ICT Fac Informat Technol Dept Taif Saudi Arabia Imam Abdulrahman Bin Faisal Univ Urban & Reg Planning Dept Coll Architecture & Planning Dammam Saudi Arabia

ISBN: (纸本)9783031664304;9783031664311

Visual pollution is a significant obstacle in the modern era, where the world is advancing towards increasingly diverse inventions. These inventions require a suitable environment to achieve accurate outcomes. Artificial intelligence has already permeated all fields and interests of life;similarly, visual pollution also needs to be addressed properly. Visual pollution often creates obstacles in performing various tasks. To mitigate these issues, an artificial intelligence-based model will play a vital role. This work deals with detecting visual pollution using an artificial intelligence-based algorithm to apply practical solutions that enhance urban public scenery. In the first step, a dataset is chosen from an authorized organization;specifically, the data is sourced from Mendeley, named the Saudi Arabia Public Roads Visual Pollution Dataset 2023. The second step involves data scaling and background removal from training images to facilitate learning in AI models. In the third step, the dataset is processed using Random Forest and support vector machine algorithms to visualize the model's accuracy results. The support vector machine demonstrates better performance compared to the Random Forest.

关键词： Artificial intelligence image processing Visual pollution Support vector machine Random forest

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A Novel Saliency-Noise Multi-Feature Fusion for Music Signal Recognition Algorithm

A Novel Saliency-Noise Multi-Feature Fusion for Music Signal...

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2025 International conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Zhang, TingTing School of Music Shaanxi Vocational Academy of Art Xi'an710054 China

ISBN: (纸本)9798331509828

The rapid development of signal processing (SP) technology, especially the continuous innovation of audio signal recognition and analysis algorithms, has led to a wide variety of application areas. Under this background, this study proposes the novel saliency-noise multi-feature fusion framework for music signal recognition algorithm, which aims to improve the recognition performance of music signals. Initially, the proposed framework extracts the emotional features of music from low-level and mid-level emotional features as the basis for overall modeling. This framework innovatively uses the eGeMAPS feature set as the low-level features, and combines the MIRToolbox tool to extract mid-and high-level music emotion features as a strong support for overall feature extraction. Subsequently, a new MFCC is designed and proposed for musical spectral modeling and generates Hilbert spectrogram through Hilbert-Huang transform. Subsequently, audio saliency detection is performed. During the detection process, the adaptive adjustment to different input signals is achieved through a two-step architecture of dynamic saliency prediction network and channel-level weighting module. Finally, the music rhythm detection is realized by constructing a histogram based on the beat position. In terms of model implementation, the sparse representation classification model is used to realize the classification and recognition of music signals. In order to test the effectiveness of the proposed algorithm, this study used a self-collected data set to experimentally verify the model. The proposed algorithm is superior to the existing Transformer and CNN models in terms of accuracy, precision, recall, and F1 value. The comparative experiment verifies the effectiveness of the proposed algorithm. © 2025 IEEE.

关键词： Hilbert-Huang transform

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Real-time UAV Localization and Tracking in Multi-Weather Conditions using Multispectral image Analysis*

Real-time UAV Localization and Tracking in Multi-Weather Con...

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2023 IEEE International conference on Real-Time Computing and Robotics, RCAR 2023

作者： Lin, Yuxiang Peng, xiaojiang Yu, Jiahe Chen, Wei Wu, Yan Liu, Hu Shenzhen Technology University Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Technology University Shenzhen518000 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Xi'an Institute of Applied Optics Xi'an710065 China

ISBN: (纸本)9798350327182

With the increasing availability of unmanned aerial vehicles (UAV), their potential misuse has become a serious concern, posing a threat to public security. Existing tracking methods have limitations in detecting UAV effectively due to their small size, high speed, complex flight patterns, and complicated flying background. To address this problem, we propose a UAV localization and tracking method that uses multispectral images captured by specific hardware, which enhances the detection process by allowing for greater visibility in challenging weather conditions. The proposed method combines the YOLOv5 detection algorithm with the KCF tracking algorithm to provide a reliable solution for preventing potential misuse of UAV and enhancing public *** results demonstrate that the proposed method provides a reliable solution for UAV localization and tracking in various weather conditions. The method was found to improve the inference speed compared to a single YOLOv5 model, demonstrating its potential for real-time UAV tracking and control. By combining multispectral image analysis, detection algorithms, and tracking algorithms, the proposed method provides an effective solution for preventing the potential misuse of UAV and enhancing public safety. This research presents a promising direction for future studies on UAV tracking and control. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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