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9th IEEE Smart World Congress, SWC 2023

作者： Yao, Jingwang Lv, Hui Xiao, Yijun Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China

ISBN: (纸本)9798350319804

Biochemical process control is often achieved through feedback control strategies for theoretical analysis and optimization of biochemical reaction processes. Here, the design improves the robustness of the biochemical process by taking advantage of the Smith prediction structure and internal mode control within the chemical reaction network theory framework to intervene against the time delays and perturbations present in the biochemical process. First, an IMC representation of Smith prediction is constructed using the presence of parameter rectification of the control method, and then an IMC-MSP control scheme is proposed. A first-order time-lag controlled object is established in the construction of the IMC-MSP control scheme. Besides, CRNs and kinetic representations are established based on the control structure to facilitate the evaluation of the control system performance. In this paper, CRNs representations of parabolic functions as signal inputs are defined for the first time and optimized for stepping signals to facilitate the design and study of biomolecular circuits. Notably, for the CRNs-based IMC-MSP control scheme, on the one hand, numerical simulation experiments are designed to verify the effectiveness of the whole scheme from multiple perspectives of time delays, external perturbations, and filter tuning parameters;on the other hand, using DNA strand displacement is reflected to verify the feasibility of the whole scheme. © 2023 IEEE.

关键词： Chemical reaction networks (CRNs) DNA strand displacement (DSD) Internal model control (IMC control) Smith predictor

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Synchronization of Hyper-Lorenz System Based on DNA Strand Displacement

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022年第3期19卷 1897-1908页

作者： Zou, Chengye Zhang, Qiang Wei, Xiaopeng School of Mathematics and Statistics of Anyang Normal University Anyang455000 China Dalian University Ministry of Education Key Laboratory of Advanced and Intelligent Computing Dalian116622 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China

Lorenz system is depicted by chemical reaction equations of an ideal formal chemical reaction network, and a series of reversible reactions are added into chemical reaction network in order to construct a cluster of hyper-Lorenz system. DNA as a universal substrate for chemical dynamics can approximate arbitrary dynamical characteristics of ideal formal chemical reaction network through auxiliary DNA strands and displacement reactions. Based on Lyapunov's stableness theory, a novel synchronization strategy is proposed. A 6-dimensional hyper-Lorenz system is taken as examples for simulation and shows that DNA strands displacement reactions can implement the synchronization of ideal formal chemical reaction networks. Numerical simulations indicate that synchronization based on DNA strand displacement is robust to the detection of DNA strand concentration, control of reaction rate, and noise. © 2004-2012 IEEE.

关键词： Substrates

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Linear Multi-Step Prediction and Correction Algorithm of 6-DOF Space Manipulator for Continuous Trajectory Tracking

Linear Multi-Step Prediction and Correction Algorithm of 6-D...

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2014 International Conference on Industrial Engineering and Information Technology

作者： Shuang Liu Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Dalian University

In this paper,the generalized Jacobian matrix and the improved Milne-Hamming linear multi-step prediction and correction algorithm are combined for solving the space manipulator trajectory tracking *** of all,controlling end-effector tracks a known trajectory by the method of pseudo inverse of generalized Jacobian ***,due to the accuracy of the solution of this approach inversely with the step-size of numerical integration,we need larger calculation to get more precise *** increase the accuracy in fixed step-size of calculation,Milne-Hamming linear multi-step prediction and correction algorithm is combined with the methods that mentioned ***,we utilize the RungeKutta method to evaluate the first four solutions of MilneHamming algorithm *** don't have enough parameters to initialize the algorithm until step ***,the validity of the algorithm is verified by computer simulation.

关键词： 6-DOF space manipulator,Continuous trajectory tracking,Milne-Hamming linear multi-step algorithm,Runge-Kutta

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Term translation pair alignment based on a parallel corpus of Chinese historical classics and their english translations

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Journal of Information and Computational Science 2010年第9期7卷 1967-1986页

作者： Li, Xiuying Che, Chao Lin, Hongfei Wang, Rongpei School of Foreign Languages Dalian University of Technology Dalian 116024 China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian 116622 China Department of Computer Science and Technology Dalian University of Technology Dalian 116024 China

We introduce an improved MI-based phrase alignment approach to match Chinese historical terms with their English translations. The proposed approach is motivated by the findings about the inadequacy of popular machine translation systems like Babelfish and Systran in translating Chinese historical terms, and observations about the discrepancies by established translators and Sinologists in handling these historical terms. Modifications to the basic MI alignment approach, including Bridging, Pruning and Head-word Extension, integrate linguistic knowledge of the translation patterns of Chinese historical terms into statistical alignment algorithm. The proposed framework for alignment achieves more than 17 and 10 points increase in percentage respectively above the base line in two individual corpora and detects a number of valid alternative translations for some top-frequency Chinese historical terms. The discovery also alerts us to the potential impact that different corpus may have for an SMT system from the perspective of readability and efficiency. © 2010 Binary Information Press.

关键词： Alignment

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3D motion retrieval measure based on SOM feature and PCA index

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Ruan Jian Xue Bao/Journal of Software 2010年第SUPPL. 1期21卷 173-182页

作者： Li, Yu-Mei Zhang, Qiang Wei, Xiao-Peng Yao, Shu-Lei Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian 116622 China

A novel approach for 3D motion capture data retrieval based on the Self-Organizing Map (SOM) and PCA index is proposed. Firstly, the Self-organizing map topological features are enhanced before learning. Characteristic will be mapped to a main surface after training every motion. Then, principal component analysis (PCA) is applied to deal with the surface characteristics. To improve the retrieval efficiency, an indexing scheme based on the principal eigenvector is constructed. Introduction of SOM avoid directing contact with the raw data, Follow-up work are only based on the main surface. It eliminate the position information influence of different frame length of the motion characteristics. Experimental results show that the algorithm is effective. © by Institute of Software, the Chinese Academy of Sciences.

关键词： Principal component analysis

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Method of age estimation based on ML-GMM feature and double layers estimation model 17

Method of age estimation based on ML-GMM feature and double ...

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2017 International Conference on Advances in Image Processing, ICAIP 2017

作者： Hu, Min Teng, Wendi Zheng, Yaona Zhang, Keke AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information HeFei University of Technology NO.193 Tunxi road Hefei230009 China

ISBN: (纸本)9781450352956

Feature extraction and estimation method are two key components of age estimation. This paper proposes a novel age estimation method based on Multi Levels Gaussian Mixture Model (MLGMM) and double layers estimation model. In the feature extraction phase, ML-GMM is used to construct different GMMs for different level features, which can well reflect the global and local age feature of facial images. In the estimation phase, double layers estimation model based on SVM-KNN is proposed. The first layer roughly divides age groups by using SVM. The second layer adopts KNN theory to find K images of consecutive age which have minimum sum of distance with the testing sample. The specific age is obtained by weighting these K age values. This paper performs a lot of experiments on mixed age database of FG-NET and MORPH-II databases. The mean absolute errors of age estimation are 3.22 years. Experimental results show that the proposed method is more effective than other methods of state-of-the-art for age estimation of facial images. It can extract more rich and complete age feature, improve the generalization ability of age estimation and reduce the mean absolute errors. © 2017 Association for computing Machinery.

关键词： Feature extraction

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A fusion decision method based on the dynamic fuzzy density assignment 17

A fusion decision method based on the dynamic fuzzy density ...

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2017 International Conference on Advances in Image Processing, ICAIP 2017

作者： Li, Yanqiu Ren, Fuji Hu, Min Wang, Xiaohua AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information HeFei University of Technology NO.193 Tunxi road Hefei230009 China

ISBN: (纸本)9781450352956

Fuzzy density is an important part of fuzzy integral, which is used to describe the reliability of classifiers in the process of fusion. Most of the fuzzy density assignment methods are based on the training priori knowledge of the classifier and ignore the difference of the testing samples themselves. To better describe the real-time reliability of the classifier in the fusion process, the dispersion of the classifier is calculated according to the decision information which outputted by the classifier. Then the divisibility of the classifier is obtained through the information entropy of the dispersion. Finally, the divisibility and the priori knowledge are combined to get the fuzzy density which can be dynamically adjusted. Experiments on JAFFE and CK databases show that, compared with traditional fuzzy integral methods, the proposed method can effectively improve the decision performance of fuzzy integral and reduce the interference of unreliable output information to decision. And it is an effective multi-classifier fusion method. © 2017 Association for computing Machinery.

关键词： Face recognition

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Implementation of Multi-compartment Neuron Model with Synaptic Plasticity Using FPGA 21

Implementation of Multi-compartment Neuron Model with Synapt...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： DIng, Yiwen Fu, Wenlong Cao, Lihong Communication University of China State Key Laboratory of Media Convergence and Communication Beijing China Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665432061

Computational neuroscience is interdisciplinary and plays an essential role in facilitating the development of cognitive neuroscience and Artificial General Intelligence (AGI). To explore the computational efficiency of Spiking Neural Network (SNN) on a computational hardware platform, we deploy SNN on the Xilinx Zynq7 Zedboard Field Programmable Gate Array (FPGA). Specifically, we use multi-compartmental Hodgkin-Huxley (HH) neuron model to construct neural circuits via C++ code and adopt a biologically plausible calcium concentration-based plasticity model as the learning rule of SNN. Our experimental results show that the FPGA platform can support parallel computation of the multi-compartment HH model with synaptic plasticity. Compared to other methods, FPGA-based computing has a significant speed advantage, suggesting that FPGA can be a good choice for implementing brain-inspired computation. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Research on 3D Human Body Reconstruction Algorithm of Monocular Image 5

Research on 3D Human Body Reconstruction Algorithm of Monocu...

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5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Liang, Zijian Liang, Huan Fu, Wenlong Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665426305

The three-dimensional human body feature parameters extracted by the neural network method are coupled in the high-dimensional feature space. The fully connected network processes the three-dimensional human pose and shape feature information synchronously, which cannot handle multifactor features well, which makes the model perform poorly. To solve this problem, on the basis of the benchmark network, a decoupling model is added, which processes the three-dimensional human pose and shape information separately, and finally integrates the features through the bi-linear transformation method. Through experimental verification, the algorithm effectively reduces the reconstruction error, and the improved algorithm performs better. © 2021 IEEE.

关键词： Three dimensional computer graphics

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Temporal Attention Convolution Network for Energy Time Series Forecasting 7

Temporal Attention Convolution Network for Energy Time Serie...

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7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering

作者： Hu, Min Xu, Haijun Wang, Xiaohua Li, Liang Li, Hongbo Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei230009 China MediaTek Hefei230009 China

ISBN: (纸本)9781510656437

Energy time series are characterized by stochasticity, short-term periodicity, and long-term periodicity. Many traditional models such as the autoregressive model, Gaussian process regression are unable to capture the complex time relationships of energy time series. Recurrent neural networks (RNNs), which are widely used for energy time series forecasting, are prone to gradient disappearance or gradient explosion when processing a long sequence data. Thus, RNNs are failed to capture the complete information of the whole long sequence data. To extract complex irregular patterns of energy time series and selectively learn spatio-temporal features, a Temporal Attention Convolution Network (TACN) is proposed in this paper. This structure can extract spatial and temporal features of energy time series, which full filled with our demand. Firstly, to solve the problem that RNNs are prone to information loss when processing the long sequence data, a temporal convolutional network (TCN) is introduced. TCN enables the output to capture the complete information of the whole long sequence data. Then, to reinforce the TCN for extracting effective spatio-temporal features, Squeeze-and-Excitation Network (SENet), a module for computing channel attention, was introduced to this TCN structure. By applying this module, the ability of the acquisition of important channel information has been enhanced. Finally, to further improve the robustness of the model, an autoregressive linear module is constructed to capture the linear relationship among the variables. Experiments on the publicly available datasets Electricity and Solar-Energy show that compare with existing deep learning models, TACN achieves more ideal performance. © 2022 SPIE. All rights reserved.

关键词： Time series

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