检索结果-内蒙古大学图书馆

Embedded software-define radio for wearable microware detection 1

7th International Conference on Computational Data and Social Networks, CSoNet 2018

作者： Wu, Yizhi Luo, Biao Zhu, Hairui College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Shanghai201620 China

ISBN: (数字)9783030046484

ISBN: (纸本)9783030046477

Microwave imaging (MWI) based breast cancer detection has attracted widespread attentions because it has great advantages of non-ionization and low price. However, the current MWI systems require huge and expensive hardware, and complex signal calibration and present substantial challenges to the application of MWI system. For this reason, the embedded MW imaging (E-MWI) method is proposed for breast cancer detection. Firstly, the Software Define Radio (SDR) software is implemented on an embedded platform to reduce cost and miniaturize system. Secondly, an amplitude-based spatial domain confocal algorithm is used in E-MWI system to simplify the system calibration by avoiding signal phase acquisition. To verify the proposed method, experiments platform is set up using the E-MWI prototype system and a liquid breast phantom. The results show that the system successfully detects the target with different sizes. © Springer Nature Switzerland AG 2018.

关键词： Diseases

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A weighted heteroscedastic Gaussian Process Modelling via particle swarm optimization

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Chemometrics and Intelligent Laboratory Systems 2018年 172卷 129-138页

作者： Hong, Xiaodan Ding, Yongsheng Ren, Lihong Chen, Lei Huang, Biao Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education College of Information Sciences and Technology Donghua University Shanghai 201620 China Department of Chemical and Materials Engineering University of Alberta Canada

In many chemical engineering applications, it is often difficult to get accurate first-principle models because of complexity of modern processes. Even if it is possible to do so, it is often time consuming and computationally expensive. Hence, there is a growing need to develop data-driven models. Gaussian process regression (GPR) model has been extensively applied in data-based modelling due to its good adaptability to deal with high dimensional, small samples, and nonlinear problems. The standard GPR algorithm assumes constant noise power throughout the sampling process. However, in process systems, the observation noise often varies so that different sample points are corrupted by different degrees of noise. Under these circumstances, the standard GPR algorithm may not work properly. To model Gaussian process with heteroscedastic noise, this paper introduces a weighting strategy into the standard GPR algorithm, and proposes three weighted GPR algorithms: the clustered GPR (C-GPR) algorithm, the partial weighted GPR (PW-GPR) algorithm and the weighted GPR (W-GPR) algorithm. Different from the standard GPR algorithm, three weighted algorithms put the weight on sampled data by calculating the noise variance for each data point. In addition, in order to optimize the proposed algorithms, this paper utilizes the particle swarm optimization (PSO) algorithm to estimate hyper-parameters of the GPR model, instead of using the traditional conjugate gradient (CG) method. The effectiveness of the three weighted GPR algorithms is verified by means of two numerical examples and a wet spinning coagulation process. Extensive simulation results demonstrate that the proposed algorithms optimized by the PSO algorithm can improve prediction accuracy of the GPR model. © 2017 Elsevier B.V.

关键词： Clustered GPR Gaussian Process Modelling Heteroscedastic noise Partial weighted GPR PSO algorithm Weighted GPR

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A path planning approach with maximum traffic flow and minimum breakdown probability in complex road network 1

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7th International Conference on Computational Data and Social Networks, CSoNet 2018

作者： Xu, Mengran Li, Demin Zhang, Guanglin Cao, Mengqi Liao, Shuya College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Shanghai201620 China

ISBN: (数字)9783030046484

ISBN: (纸本)9783030046477

In urban scenarios, the issues of traffic congestion keep disturbing governments and individuals, especially in complex road networks. It seems urgent to improve road utilization for alleviating the traffic congestion. In this paper, we first propose an optimization model to improve the road utilization, which not only considers the traffic breakdown probability but also the spontaneous traffic flow. Traffic breakdown occurs during the transition from free flow to spontaneous flow and may probably cause traffic congestion. By considering traffic flow, more drivers can avoid traffic breakdown and the road utilization will be increasing. Secondly, in order to decrease the complexity and redundancy, this paper uses a big traffic flow condition and Taylor series to simplify the objective function and obtain an optimal result with accuracy. Finally, the simulations that use real urban traffic scenario of Songjiang University Town in ShangHai evaluate the proposed algorithm’s performance. Our proposed algorithm outperforms other existing path-planning algorithm. © Springer Nature Switzerland AG 2018.

关键词： Traffic congestion

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An augmented model approach for identification of nonlinear errors-in-variables systems using the em algorithm

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2018年第11期48卷 1968-1978页

作者： Guo, Fan Wu, Ouyang Kodamana, Hariprasad Ding, Yongsheng Huang, Biao Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education College of Information Science and Technology Donghua University Shanghai201620 China Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G2G6 Canada

This paper proposes an augmented model approach for identification of nonlinear errors-in-variables (EIVs) systems. An EIV model accounts for uncertainties in the observations of both inputs and outputs. As the direct identification of nonlinear functions is difficult, we propose to approximate the nonlinear EIV model using multiple ARX models. To estimate the noise-free input signal, we use a collection of particle filters which run in parallel corresponding to each of the multiple ARX models. The parameters of local models are estimated by applying expectation maximization algorithm, under a maximum likelihood framework, using the input-output data of the nonlinear EIV system. Simulated numerical examples and an experiment study on a multitank system are used to illustrate the efficacy of the proposed approach. © 2017 IEEE.

关键词： Maximum likelihood

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Power allocation for uplink cognitive heterogeneous network with nonorthogonal multiple access 4

Power allocation for uplink cognitive heterogeneous network ...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Xu, Woping Qiu, Runhe College of Information Science and Technology Donghua University Shanghai China Ministry of Education Donghua University Engineering Research Center of Digitized Textile Apparel Technology Shanghai China

ISBN: (纸本)9781538683392

In this paper, we propose an optimal power allocation scheme in an uplink two-tier heterogeneous cognitive network with non-orthogonal multiple access (NOMA). Secondary users (SUs) in femtocell tier are allowed to access the licensed frequency band owned by macrocell tier in underlay spectrum sharing mode, which causes severe cross-tier interference. We propose an optimal power allocation scheme by considering the cross-tier interference control from the femtocell tier to the macrocell tier and adopting successive interference cancellation (SIC) to improve the spectrum efficiency within the femtocell tier. The effectiveness of the proposed power allocation scheme on the performance of spectrum efficiency and cross-tier interference mitigation validates by simulation. © 2018 IEEE.

关键词： Convex optimization

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Hybrid attention-based transformer block model for distant supervision relation extraction

arXiv

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arXiv 2020年

作者： Xiao, Yan Jin, Yaochu Cheng, Ran Hao, Kuangrong Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education College of Information Science and Technology Donghua University Shanghai201620 China Department of Computer Science University of Surrey Guildford SurreyGU27XH United Kingdom Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information. As one basic task for natural language processing (NLP), relation extraction aims to extract the semantic relation between entity pairs based on the given text. To avoid manual labeling of datasets, distant supervision relation extraction (DSRE) has been widely used, aiming to utilize knowledge base to automatically annotate datasets. Unfortunately, this method heavily suffers from wrong labelling due to the underlying strong assumptions. To address this issue, we propose a new framework using hybrid attention-based Transformer block with multi-instance learning to perform the DSRE task. More specifically, the Transformer block is firstly used as the sentence encoder to capture syntactic information of sentences, which mainly utilizes multi-head self-attention to extract features from word level. Then, a more concise sentence-level attention mechanism is adopted to constitute the bag representation, aiming to incorporate valid information of each sentence to effectively represent the bag. Experimental results on the public dataset New York Times (NYT) demonstrate that the proposed approach can outperform the state-of-the-art algorithms on the evaluation dataset, which verifies the effectiveness of our model for the DSRE task. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge based systems

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A Gated Recurrent Unit based Echo State Network

A Gated Recurrent Unit based Echo State Network

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International Joint Conference on Neural Networks (IJCNN)

作者： Xinjie Wang Yaochu Jin Kuangrong Hao Engineering Research Center of Digitized Textile and Fashion Technology Ministry of Education Shanghai P. R. China College of Information Sciences and Technology Donghua University Shanghai P. R. China Department of Computing University of Surrey Guildford Surrey United Kingdom

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Echo State Network (ESN) is a fast and efficient recurrent neural network with a sparsely connected reservoir and a simple linear output layer, which has been widely used for real-world prediction problems. However, the capability of the ESN of handling complex nonlinear problems is limited by the relatively simple neuronal dynamics in the reservoir. Although the gated recurrent unit (GRU) model with multiple nonlinear operators has achieved an excellent performance, gradient-based training algorithms usually require intensive computational resources. In this paper, we present a novel ESN model based on GRUs to tackle complex real-world tasks while reducing the computational costs, taking advantage of the characteristics of both the ESN and the GRU models. In the proposed model, the reservoir unit is replaced by the sparsely connected GRU neurons. Experimental results on three regression problems demonstrate that the proposed method performs better than the original ESN and GRU models.

关键词： Computational modeling Reservoirs Training Logic gates Predictive models Task analysis Testing

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Sampled training and node inheritance for fast evolutionary neural architecture search

arXiv

引用

arXiv 2020年

作者： Zhang, Haoyu Jin, Yaochu Cheng, Ran Hao, Kuangrong Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education College of Information Science and Technology Donghua University Shanghai201620 China Department of Computer Science University of Surrey Guildford SurreyGU2 7XH United Kingdom Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The performance of a deep neural network is heavily dependent on its architecture and various neural architecture search strategies have been developed for automated network architecture design. Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms. However, ENAS suffers from extremely high computation costs because a large number of performance evaluations is usually required in evolutionary optimization and training deep neural networks is itself computationally very intensive. To address this issue, this paper proposes a new evolutionary framework for fast ENAS based on directed acyclic graph, in which parents are randomly sampled and trained on each mini-batch of training data. In addition, a node inheritance strategy is adopted to generate offspring individuals and their fitness is directly evaluated without training. To enhance the feature processing capability of the evolved neural networks, we also encode a channel attention mechanism in the search space. We evaluate the proposed algorithm on the widely used datasets, in comparison with 26 state-of-the-art peer algorithms. Our experimental results show the proposed algorithm is not only computationally much more efficiently, but also highly competitive in learning performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A curve model with rapid moving method for multi-robot system 30

A curve model with rapid moving method for multi-robot syste...

引用

30th Chinese Control and Decision Conference, CCDC 2018

作者： Qu, Yunhao Ding, Yongsheng Hao, Kuangrong Wang, Tong Chen, Lei Ren, Lihong Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education!College of Information Science and Technology Donghua University Shanghai 201620 China

ISBN: (纸本)9781538612453;9781538612446

This paper designs a robot rapid moving strategy based on curve model. The virtual target points are introduced into the path planning of the robot so that the robot can complete the task smoothly and quickly. We give the method to solve the curve model in detail. At the same time, the design of state feedback from the robot control model based on the turning radius is used to solve the practical error problem. Simulation experiments show that the design of virtual target points can not only make the robot complete the task faster, but also can be applied to multi-robot formation control. The real experiment shows that the curve model can correct the error through the robot state feedback and finally make the robots reach the target point successfully. © 2018 IEEE.

关键词： Curve Model Multi-robot Rapid Moving State Feedback Virtual Target Point

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A sparse autoencoder compressed sensing method for acquiring the pressure array information of clothing

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Neurocomputing 2018年 275卷 1500-1510页

作者： Han, Tao Hao, Kuangrong Ding, Yongsheng Tang, Xuesong Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education Donghua University Shanghai 201620 China College of Information Sciences and Technology Donghua University 2999 Renmin North Road Songjiang District Shanghai 201620 China

In this paper, we integrate some ideas of sparse autoencoder of deep learning into compressed sensing (CS) theory, and set up a sparse autoencoder compressed sensing (SAECS) model, which can improve the compressed sampling process of CS with compression of sparse autoencoder in deep learning. The original CS theory has no function of autonomic regulation, so we introduce the idea of sparse autoencoder of deep learning to improve CS theory. Then we calculate the error between the recovery output data and the input data. By judging the obtained error and the acceptable error, the SAECS model can choose autonomously the most appropriate sparsity and the most appropriate length of measurement vector. This SAECS model can then reconstruct the original signal that can satisfy the acceptable error requirement with the minimum length of measurement vector in CS theory. We investigate the effectiveness of the proposed method by using sampled pressure data from human body model. Experimental results demonstrate that, in comparison with the state-of-the-art methods for running time, our SACES approach can effectively decrease the running time to find the shortest measurement vector in the case of accepted error. © 2017 Elsevier B.V.

关键词： Compressed sensing Deep learning Human body Neural network Pressure array Sparse autoencoder

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