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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Tun Jia, Haipeng Li, Zhihao Li, Chendi Zhang, Yunquan Skl of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Huawei Technologies Co. Ltd Shenzhen China

ISBN: (纸本)9781665494571

According to the traditional multi-dimensional FFT, memory layouts of high-dimensional data are discontinuous. Transposition is introduced to keep high-dimensional data continuous in memory. However, transposition increases memory access and is a hot spot for multi-dimensional FFT. This paper proposes an optimization framework to eliminate explicit transpositions and optimize the three-dimensional (3D) FFT. This framework includes three research points. 1) combines the width-first and breadth-first search to optimize the butterfly network of one-dimensional (1D) FFT;2) adopts a column-order algorithm to eliminate data transposition;3) adopts a blocking algorithm of cache-aware to better use the hardware resources of ARM architecture. Based on this optimized framework, a multi-dimensional FFT library named MDFFT is implemented. The experiments demonstrate that MDFFT generally performs better than FFTW and ARMPL on ARM CPUs. © 2021 IEEE.

关键词： Three-dimensional displays Smart cities Layout Parallel processing Libraries Hardware Optimization

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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Prediction of cognitive scores by joint use of movie-watching fMRI connectivity and eye tracking via Attention-CensNet

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Psychoradiology 2023年第1期3卷 193-202页

作者： Jiaxing Gao Lin Zhao Tianyang Zhong Changhe Li Zhibin He Yaonei Wei Shu Zhang Lei Guo Tianming Liu Junwei Han Xi Jiang Tuo Zhang School of Automation Northwestern Polytechnical UniversityXIi'an 710072China Cortical Architecture Imaging and Discovery Laboratory Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensGA 30602USA School of Computer Science Northwestern Polytechnical UniversityXi'an 710072China School of Life Science and Technology MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China

Background:Brain functional connectivity under the naturalistic paradigm has been shown to be better at predicting individual behaviors than other brain states,such as rest and doing ***,the state-of-the-art methods have found it difcult to achieve desirable results from movie watching paradigm functional magnetic resonance imaging(mfMRI)-induced brain functional connectivity,especially when there are fewer *** other physical measurements into the prediction method may enhance *** tracking,becoming popular due to its portability and lower expense,can provide abundant behavioral features related to the output of human's cognition,and thus might supplement the mfMRI in observing participants'subconscious ***,there are very few studies on how to effectively integrate the multimodal information to strengthen the performance by a unified ***:A fusion approach with mfMRI and eye tracking,based on convolution with edge-node switching in graph neural networks(CensNet),is proposed in this ***:In this graph model,participants are designated as nodes,mfMRI derived functional connectivity as node features,and different eye-tracking features are used to compute similarity between participants to construct heterogeneous graph *** taking multiple graphs as different channels,we introduce squeeze-and-excitation attention module to CensNet(A-CensNet)to integrate graph embeddings from multiple channels into ***:The proposed model outperforms those using a single modality and single channel,and state-of-the-art ***:The results indicate that brain functional activities and eye behaviors might complement each other in interpreting trait-like phenotypes.

关键词： functional connectivity naturalistic stimulus eye movement CensNet attention

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LiDAR Road-Atlas: An Efficient Map Representation for General 3D Urban Environment

Field Robotics

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Field Robotics 2025年 3卷 435-459页

作者： Banghe Wu Chengzhong Xu Hui Kong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China The State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) Department of Computer Science University of Macau Macau The State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) Department of Electromechanical Engineering (EME) University of Macau Macau

In this work, we propose the LiDAR Road-Atlas, a compact and efficient 3D map representation, for autonomous robot or vehicle navigation in a general urban environment. The LiDAR Road-Atlas can be generated by an online mapping framework which incrementally merges local 2D occupancy grid maps (2D-OGMs). Specifically, the contributions of our method are threefold. First, we solve the challenging problem of creating local 2D-OGMs in nonstructured urban scenes based on a real-time delimitation of traversable and curb regions in a LiDAR point cloud. Second, we achieve accurate 3D mapping in multiple-layer urban road scenarios by a probabilistic fusion scheme. Third, we achieve a very efficient 3D map representation of a general environment thanks to the automatic local-OGM-induced traversable-region labeling and a sparse probabilistic local point-cloud encoding. Given the LiDAR Road-Atlas, one can achieve accurate vehicle localization, path planning, and some other tasks. Our map representation is insensitive to dynamic objects which can be filtered out in the resulting map based on a probabilistic fusion. Empirically, we compare our map representation with a couple of popular map representations in robotics society, and our map representation is more favorable in terms of efficiency, scalability, and compactness. Additionally, we also evaluate localization performance given the LiDAR Road-Atlas representations on two public datasets. With a 16-channel LiDAR sensor, our method achieves an average global localization error of 0.26 m (translation) and 1.07° (rotation) on the Apollo dataset, and 0.89 m (translation) and 1.29° (rotation) on the MulRan dataset, respectively, at 10 Hz, which validates its promising performance. The code for this work is open-sourced at https://***/IMRL/Lidar-road-atlas.

关键词： Three-dimensional displays Roads Laser radar Robots Navigation Semantics Location awareness Autonomous vehicles Robot kinematics Cameras

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Efficient Image Enhancement with A Diffusion-Based Frequency Prior

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Yan, Qingsen Hu, Tao Wu, Peng Dai, Duwei Gu, Shuhang Dong, Wei Zhang, Yanning Northwestern Polytechnical University School of Computer Science Xi'an710072 China Xi'an Jiaotong University China University of Electronic Science and Technology of China China Xi'an University of Architecture and Technology China

Due to the lack of appropriate priors, generating the content of dark regions remains a challenge in low-light image enhancement tasks. Currently, diffusion models employ robust image generation capabilities for enhancing low-light images. However, diffusion models require multiple iterations at the image feature level to generate details and content, which limits the speed. Moreover, the diffusion-based methods tend to generate unexpected artifacts in the degraded regions. To address these issues, we propose a Frequency Priors-guided Image Enhancement (FPIE) network, including a frequency prior generation network and an image restoration network. FPIE significantly accelerates inference by learning abstract prior with frequency domain constraints. Concretely, to learn compacted priors at the frequency domain, we introduce a joint training approach for the prior generation and restoration models to constrain the distribution of priors. Furthermore, to better utilize frequency-domain features for enhancing the network's generation capabilities, a wavelet-based transformer block is introduced to produce intricate details and avoid the artifacts of the output. Extensive experimental results on the commonly used benchmarks demonstrate that our approach achieves state-of-the-art performances and well generalization to real-world images. © 1991-2012 IEEE.

关键词： Restoration

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Deep-learning Based Denoising and Enhancement in Video Image of Approach Channel 2022

Deep-learning Based Denoising and Enhancement in Video Image...

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14th International Conference on Machine Learning and Computing, ICMLC 2022

作者： Jiao, Hang Yuan, Peng Liu, Jiaocheng Ren, Xujie Zhang, Jufu School of Computer Science and Artificial Intelligence Wuhan University of Technology China School of Naval Architecture Wuhan University of Technology China

ISBN: (纸本)9781450395700

Due to the ubiquitous video sensor technology, it has become possible to build intelligent information system for the supervision of the approach channel. However, at present, there is still a technical problem to be solved, that is, the low-visibility video images taken in low-light environment have low brightness, low contrast and low signal-to-noise ratio. Meanwhile, they are also mixed with a lot of noise, which makes data extraction difficult. We propose a video image denoising and enhancement network based on deep learning, named DeNet, to improve the quality of low-light video images. DeNet consists of two parts: the first part is a deep residual network, which we named D-Net, to denoise the original video image. In the second part, we connect a D-Net network in parallel and add the normalization layer, which is used to fuse the features of the denoised image with the original image, and eventually realize the denoising and enhancement function. Experimental results on synthetic and real port test data sets show that our proposed method is superior to many advanced methods at present, and can meet the requirements of video denoising and enhancement of the approach channel. © 2022 ACM.

关键词： Image enhancement

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Employment Data Acquiring Method Based on WeChat Scan Code 10

Employment Data Acquiring Method Based on WeChat Scan Code

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Su, Bianping Wang, Yunsong He, Yantao Zhang, Longqing He, Jiayu Lin, Jieming School of Computer Guangdong University of Science & Technology Guangdong Dongguan China Xi'an International Inland Port Investment and Development Group Co. Ltd. Xi'an Chan-Ba International Port Management Committee Xi'an China School of Science Xi'an University of Architecture and Technology Xi'an China

ISBN: (纸本)9798331510138

Based on the current employment situation and methods for obtaining unemployment and employment data in china, a new method for obtaining unemployment and employment data was constructed using the Naive Bayes classification principle in machine learning by WeChat scan data of travelers. This method divides the unemployment or employment status of individuals using prior and posterior probabilities. The evaluation index values of the experimental results on the dataset demonstrate the effectiveness of the method, and the accuracy of determining the employment status of travelers through mobile phone calls has increased by 22.9%. A strategy of data preprocessing combined with this method was proposed for large-scale data and preliminary discussions were conducted. The new method of obtaining unemployment and employment data has opened up new avenues for traditional statistical data acquisition, providing strong intelligent support for the government to dynamically grasp the employment situation in real time. © 2024 IEEE.

关键词： Data accuracy

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Research on the Design Method of Prefabricated Concrete Structure Based on BIM

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computer-Aided Design and Applications 2023年 20卷 148-164页

作者： Yang, Fan Hussain, Saddam Fadhel, Mustafa Abbas Ghafoor, Kayhan Zrar Gannan University of Science and Technology Jiangxi Ganzhou China Department of Civil and Architecture Engineering Kyushu Institute of Technology Fukuoka Kitakyushu8040015 Japan University of Al Muthanna Samawa66001 Iraq Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software & Informatics Engineering Salahaddin University-Erbil Erbil44001 Iraq

With the gradual development in industrialization, the advent of computer and information technology has evolved in the field of engineering and construction industry. BIM technology has become a valuable part of prefabricated concrete engineering with the advancement of modern technology based on 3D modeling simulation. In order to realize the industrialization and automation of prefabricated concrete structures, this paper analyzes the characteristics of fabricated concrete structure and BIM, combining the two techniques. This work applies BIM to fabricated concrete design and chooses finite element analysis and calculation software ETABS to analyze the structure model. The results show that the mass participation ratio of the model studied in this paper reaches more than 90%, the interlayer displacement angle is less than 1/550, and the modal and shell internal force analysis results meet the requirements. In this paper, modal analysis, shell internal force analysis under live load and structural dynamic analysis under response spectrum load conditions were carried out on the ETABS model converted from the Revit model and the model directly established by ETABS. The ETABS model converted from the Revit model by the data interface plug-in can be used for structural analysis. © 2023, CAD Solutions, LLC. All rights reserved.

关键词： Finite element method

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Makespan and Security-Aware Workflow Scheduling for Cloud Service Cost Minimization Using Firefly Optimizer 22nd

Makespan and Security-Aware Workflow Scheduling for Cloud ...

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22nd International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2022

作者： Zhou, Chengliang Wang, Tian Li, Liying Sun, Jin Zhou, Junlong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai200062 China

ISBN: (纸本)9783031226762

As efficient commercial information technology, cloud computing has attracted more and more users to submit their requests to the cloud platform and pay for them based on the amount and quality of services. One of the most important concerns of cloud service providers is to minimize the monetary cost for completing users’ requests while meeting constraints on the quality of services such as makespan and security. To solve this concern, this paper proposes a makespan and security-aware workflow scheduling method based on an improved firefly optimizer that consists of a new solution initialization scheme, a firefly position updating scheme, a firefly-to-solution mapping operator, and two task-to-virtual machine assignment schemes. We carry out extensive simulation experiments on real-world workflows to validate our proposed workflow scheduling method. The results reveal that the monetary cost reduction achieved by our method can be up to 54.0% compared to a baseline and two state-of-the-art approaches. © 2023, Springer Nature Switzerland AG.

关键词： Cloud computing

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Sampling Methods for Efficient Training of Graph Convolutional Networks:A Survey

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 205-234页

作者： Xin Liu Mingyu Yan Lei Deng Guoqi Li Xiaochun Ye Dongrui Fan State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100086 School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049 IEEE Department of Precision Instrument Center for Brain Inspired Computing ResearchTsinghua UniversityBeijing 100084China

Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces *** a GCN model for large-scale graphs in a conventional way requires high computation and storage ***,motivated by an urgent need in terms of efficiency and scalability in training GCN,sampling methods have been proposed and achieved a significant *** this paper,we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of *** highlight the characteristics and differences of sampling methods,we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all ***,we discuss some challenges and future research directions of the sampling methods.

关键词： Efficient training graph convolutional networks(GCNs) graph neural networks(GNNs) sampling method

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