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Complex integrity constraint discovery: measuring trust in modern intelligent railroad systems

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Journal of zhejiang University-Science A(Applied Physics & Engineering) 2022年第10期23卷 832-837页

作者： Wen-tao HU Da-wei JIANG Sai WU Ke CHEN Gang CHEN key lab of intelligent computing based big data of zhejiang province Zhejiang UniversityHangzhou 310027China

1Introduction data are at the heart of intelligent rail systems in the high-speed transportation sector(Zhou et al.,2020;Ho et al.,2021;Hu et al.,2021;Chen et al.,2022).The core of modern intelligent railroad systems typically includes rail transportation and equipment monitoring models learned from large datasets,which are often optimized for specific data and workloads(Zhu et al.,2019;Tan et al.,2020).While these intelligent railroad systems have been widely adopted and successful,their reliability and proper function will change as the data used *** the data used(on which the system operates)deviates from the fundamental constraints of the initial data(on which the system is trained)then,in that case,the system performance degrades,and the results inferred by the system model become unreliable,so the system model must be retrained and redeployed to re-store reliable inference results(Sharma and Chandel,2013).The mechanism for assessing the trustworthiness of intelligent rail system inferences is of paramount importance,especially for rail systems performing safety-critical or high-impact operations.

关键词： constraint intelligent operations

Reliability Analysis of Heterogeneous Sensor-Cloud Systems against Targeted Attacks

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China Communications 2022年第8期19卷 181-197页

作者： Hao Peng Zhen Qian Guangquan Xu Kejie Mao Kangtian Li Dandan Zhao Department of Computer Science and Engineering Zhejiang Normal UniversityJinhua Zhejiang 321004China key laboratory of intelligent Education Technology and Application of zhejiang province Zhejiang Normal UniversityJinhua Zhejiang 321004China The big data School Qingdao Huanghai UniversityQingdao Shandong 266427China College of Intelligence and computing Tianjin UniversityTianjin 300072China

based on the wide application of cloud computing and wireless sensor networks in various fields,the Sensor-Cloud System(SCS)plays an indispensable role between the physical world and the network ***,due to the close connection and interdependence between the physical resource network and computing resource network,there are security problems such as cascading failures between systems in the *** this paper,we propose a model with two interdependent networks to represent a sensor-cloud ***,based on the percolation theory,we have carried out a formulaic theoretical analysis of the whole process of cascading *** the system’s subnetwork presents a steady state where there is no further collapse,we can obtain the largest remaining connected subgroup components and the penetration ***,this result is the critical maximum that the coupled SCS can *** verify the correctness of the theoretical results,we further carried out actual simulation *** results show that a scale-free network priority attack’s percolation threshold is always less than that of ER network which is priority ***,when the scale-free network is attacked first,adding the power law exponentλcan be more intuitive and more effective to improve the network’s reliability.

关键词： sensor-cloud systems targeted attacks heterogeneous networks reliability analysis system security

Uncertainty-aware complementary label queries for active learning

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Frontiers of Information Technology & Electronic Engineering 2023年第10期24卷 1497-1503页

作者： Shengyuan LIU Ke CHEN Tianlei HU Yunqing MAO key lab of intelligent computing based big data of zhejiang province Zhejiang UniversityHangzhou 310027China State key laboratory of Blockchain and data Security Zhejiang UniversityHangzhou 310027China City Cloud Technology(China)Co. Ltd.Hangzhou 310000China

Many active learning methods assume that a learner can simply ask for the full annotations of some training data from *** methods mainly try to cut the annotation costs by minimizing the number of annotation ***,annotating instances exactly in many realworld classification tasks is still *** reduce the cost of a single annotation action,we try to tackle a novel active learning setting,named active learning with complementary labels(ALCL).ALCL learners ask only yes/no questions in some *** receiving answers from annotators,ALCL learners obtain a few supervised instances and more training instances with complementary labels,which specify only one of the classes to which the pattern does not *** are two challenging issues in ALCL:one is how to sample instances to be queried,and the other is how to learn from these complementary labels and ordinary accurate *** the first issue,we propose an uncertainty-based sampling strategy under this novel setup.

关键词： complementary instance ordinary

HAM:a deep collaborative ranking method incorporating textual information

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Frontiers of Information Technology & Electronic Engineering 2020年第8期21卷 1206-1216页

作者： Cheng-wei WANG Teng-fei ZHOU Chen CHEN Tian-lei HU Gang CHEN The key laboratory of big data intelligent computing of zhejiang province Hangzhou 310027China State key lab of CAD&CG Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

The recommendation task with a textual corpus aims to model customer preferences from both user feedback and item textual *** is highly desirable to explore a very deep neural network to capture the complicated nonlinear ***,training a deeper recommender is not as effortless as simply adding layers.A deeper recommender suffers from the gradient vanishing/exploding issue and cannot be easily trained by gradient-based ***,textual descriptions probably contain noisy word *** extracting feature vectors from them can harm the recommender’s *** overcome these difficulties,we propose a new recommendation method named the HighwAy reco Mmender(HAM).HAM explores a highway mechanism to make gradient-based training methods stable.A multi-head attention mechanism is devised to automatically denoise textual ***,a block coordinate descent method is devised to train a deep neural *** studies show that the proposed method outperforms state-of-the-art methods significantly in terms of accuracy.

关键词： Deep learning Recommendation system Highway network Block coordinate descent

Toward a Neurophysiological Measure of Image Quality Perception based on Algebraic Topology Analysis

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China Communications 2022年第2期19卷 31-38页

作者： Chang Liu Xiaoyu Ma Yijie Zhou Jiaojiao Wang Dingguo Yu Institute of intelligent Media Technology Communication University of ZhejiangZhejiang 310018China key lab of Film and TV Media Technology of zhejiang province Communication University of ZhejiangZhejiang 310018China College of Media Engineering Communication University of ZhejiangZhejiang 310018China

The bandwidth of internet connections is still a bottleneck when transmitting large amounts of images,making the image quality assessment *** assessment of image quality has highlight advantages for it does not interfere with natural viewing ***,in JPEG compression,the previous study is hard to tell the difference between the electroencephalogram(EEG)evoked by different quality *** this paper,we propose an EEG analysis approach based on algebraic topology analysis,and the result shows that the difference between Euler characteristics of EEG evoked by different distortion images is striking both in the alpha and beta ***,we further discuss the relationship between the images and the EEG signals,and the results implied that the algebraic topological properties of images are consistent with that of brain perception,which is possible to give birth to braininspired image compression based on algebraic topological *** general,an algebraic topologybased approach was proposed in this paper to analyze the perceptual characteristics of image quality,which will be beneficial to provide a reliable score for data compression in the network and improve the network transmission capacity.

关键词： image quality assessment electroencephalogram algebraic topology analysis Euler characteristic

Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning

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National Science Review 2024年第5期11卷 29-45页

作者： De Ma Xiaofei Jin Shichun Sun Yitao Li Xundong Wu Youneng Hu Fangchao Yang Huajin Tang Xiaolei Zhu Peng Lin Gang Pan College of Computer Science and Technology Zhejiang University Research Center for intelligent computing Hardware Zhejiang Lab The State key lab of Brain-Machine Intelligence Zhejiang University MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University College of Micro-Nano College of Micro-Nano Electronics Zhejiang University

Spiking neural networks(SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs,neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.

关键词： neuromorphic computing spiking neural network instruction set architecture connectivity compression

Hierarchical Covering Algorithm

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Tsinghua Science and Technology 2014年第1期19卷 76-81页

作者： Jie Chen Shu Zhao Yanping Zhang Department of Computer Science and Technology and key lab of intelligent computing and Signal Processing Anhui University

The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA） method to determine the levels of a hierarchical structure based on the Covering Algorithm （CA）. The CA constructs neural networks based on samples＇ own characteristics, and can effectively handle multi-category classification and large-scale data. Further, we abstract characters based on the CA to automatically embody the feature of a deep structure. We apply CA to construct hidden nodes at the lower level, and define a fuzzy equivalence relation R on upper spaces to form a hierarchical architecture based on fuzzy quotient space theory. The covering tree naturally becomes from R. HCA experiments performed on MNIST dataset show that the covering tree embodies the deep architecture of the problem, and the effects of a deep structure are shown to be better than having a single level.

关键词： deep architecture hierarchy fuzzy equivalence relation covering tree MNIST dataset

Image smoothing based on global sparsity decomposition and a variable parameter

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Computational Visual Media 2021年第4期7卷 483-497页

作者： Xiang Ma Xuemei Li Yuanfeng Zhou Caiming Zhang Shandong University Jinan 250101China Shandong Co-Innovation Center of Future intelligent computing Yantai 264025China Digital Media Technology key lab of Shandong province Jinan 250014China

Smoothing images,especially with rich texture,is an important problem in computer *** an ideal result is difficult due to complexity,irregularity,and anisotropicity of the ***,some properties are shared by the texture and the structure in an *** is a hard compromise to retain structure and simultaneously remove *** create an ideal algorithm for image smoothing,we face three *** images with rich textures,the smoothing effect should be *** should overcome inconsistency of smoothing results in different parts of the *** is necessary to create a method to evaluate the smoothing *** apply texture pre-removal based on global sparse decomposition with a variable smoothing parameter to solve the first two problems.A parametric surface constructed by an improved Bessel method is used to determine the smoothing *** evaluation measures:edge integrity rate,texture removal rate,and gradient value distribution are proposed to cope with the third *** use the alternating direction method of multipliers to complete the whole algorithm and obtain the *** show that our algorithm is better than existing algorithms both visually and *** also demonstrate our method’s ability in other applications such as clip-art compression artifact removal and content-aware image manipulation.

关键词： image smoothing texture removal global sparse decomposition Bessel method

Dynamic Modeling and Sensitivity Analysis for an MEA-based CO_(2) Capture Absorber

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intelligent Automation & Soft computing 2023年第6期36卷 3535-3550页

作者： Hongwei Guan Lingjian Ye Yurun Wang Feifan Shen Yuchen He Ningbo University of Finance and Economics Ningbo315175China Huzhou University Huzhou313000China key laboratory of intelligent Manufacturing Quality big data Tracing and Analysis of zhejiang province China Jiliang UniversityHangzhou310018China Ningbotech University Ningbo315100China

The absorber is the key unit in the post-combustion monoethanolamine(MEA)-based carbon dioxide(CO_(2))capture process.A rate-based dynamic model for the absorber is developed and validated using steady-state experimental data reported in open *** analysis is performed with respect to important model parameters associated with the reaction,mass transport and phy-sical property ***,a singular value decomposition(SVD)-based subspace parameter estimation method is proposed to improve the model ***,dynamic simulations are carried out to investigate the effects of the feed rate of lean MEA solution and the flue inlet *** results indicate that the established dynamic model can reasonably reflect the physical behavior of the *** new insights are gained from the simulation results.

关键词： CO_(2)capture dynamic modeling sensitivity analysis model validation