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2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)

作者： Hanna He Fang Fang Wei Wang Hefei University of Technology School of Computer Science and Information Engineering Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine

Mapping of IP(Intellectual Property) cores onto NoC(Network-on-Chip) architectures is a key step in NoCbased designs. Energy is the key parameter to measure the designs. Therefore, we propose an Improved Simulated Annealing Genetic Alogrithm, abbreviated as ISAGA. The algorithm combines the parallelism of Genetic Algorithm(GA) and the local search ability of Simulated Annealing(SA). We improve the initial population selection of GA to get the lower power consumption mapping scheme. The experimental results show that compared with the GA, ISAGA has good convergence and can search the optimal solution quickly, which can effectively reduce the power consumption of the system. In the case of 124 IP cores, the average power consumption of the ISAGA is reduced by 32.0% compared with the GA.

关键词： NTD Improved Simulated Annealing Genetic Algorithm based Low power mapping for 3D NoC

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Phasor Domain Transmission Line Fault Locating with Three Phase Distributed Parameter Modeling

Phasor Domain Transmission Line Fault Locating with Three Ph...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Liu, Yu Tan, Zhenyu Xie, Jiahao Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology School of Electronic Science & Applied Physics Hefei University of Technology Hefei Anhui China

ISBN: (纸本)9781538677032

Accurate fault locating reduces power outage time and operational costs. In this paper, a novel phasor domain transmission line fault locating algorithm is proposed. Best implementation requires GPS synchronized measurements at both terminals of the line. First, the model of the transmission line with fault is constructed using three phase distributed parameter modeling with neutral conductors and grounding representation. The model is without the assumption that the transmission line is geometrically balanced. Next, the state estimation algorithm is utilized to solve the model of the transmission line with fault, where the fault location is included as a state. Numerical experiments demonstrate that the method has higher accuracy than traditional fault locating methods, independent of fault types, locations and impedances.

关键词： Three Phase Distributed Parameter Modeling State Estimation (SE) Fault Locating

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Syntactically constrained bidirectional-asynchronous approach for emotional conversation generation

arXiv

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arXiv 2018年

作者： Li, Jingyuan Sun, Xiao Anhui Province Key Laboratory of Affective Computing Advanced Intelligent Machine Hefei University of Technology Hefei

Traditional neural language models tend to generate generic replies with poor logic and no emotion. In this paper, a syntactically constrained bidirectional-asynchronous approach for emotional conversation generation (ESCBA) is proposed to address this issue. In our model, pre-generated emotion keywords and topic keywords are asynchronously introduced into the process of decoding. It is much different from most existing methods which generate replies from the first word to the last. Through experiments, the results indicate that our approach not only improves the diversity of replies, but gains a boost on both logic and emotion compared with baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Computer circuits

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Research of ac motor vector control system voltage model adaptive internal model control

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Journal of Computers (Taiwan) 2018年第2期29卷 86-95页

作者： Hu, Bo Xie, Lun Zhang, Lei Ren, Fuji Li, Dan Wang, Zhi-liang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Affective Computing and Advanced Intelligent Machines Key Laboratory HeFei University of Technology Hefei230009 China

In latest motor control area, traditional voltage model is affected by variation of stator resistance and pure integration element of back electromotive force under the circumstance of low speed, whose effects would easily cause observation errors and lower motor speed control precision. Adaptive internal model control (AIMC for short) structure can adapt to stator resistance parameters variation and improve control precision. Cascaded Low pass filter (LPF for short) with compensation structure was proposed to revise amplitude and phase of observed flux linkage. The paper proposed the theory of AIMC and LPC, the structure would improve the accuracy of flux observer. At the same time, disturbance eliminating with compensation structure was put forward to improve the system anti-interference ability. Finally, PSIM *** used to build and simulate AC-DC-AC synchronous machine vector control system. Experiment results showed that the improved voltage model can effectively restrain the effects of stator resistance parameters variation. At the same time, precision of flux observer was effectively improved. Anti-interference ability of system was enhanced with nice tracking performance and robustness. © 2018 Computer Society of the Republic of China. All Rights Reserved.

关键词： Low pass filters

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Video Facial Expression Recognition Based on Multi-angle Feature Fusion

Video Facial Expression Recognition Based on Multi-angle Fea...

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作者： Mingxing Jiang Min Hu Xiaohua Wang Haowen Wang School of Computer and Information of Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Anhui Institute of International Business

In view of the problems of high computational complexity and low recognition rate of video facial expression recognition,this paper proposes a new method of video facial texture representation and facial expression recognition,which is based on multi-angle feature fusion algorithm of local Gabor adaptive ternary orientation pattern(LGATOP).Firstly,in order to obtain an enhanced Gabor amplitude spectrum,Gabor wavelet transform with different scales and directions is applied to each face image normalized in the video frame *** the LGATOP features of the video texture are extracted by using the local three-value model on three orthogonal ***,the SVM is used to complete the emotion classification of the texture and motion information after *** the cross experiment on CK+ and MMI databases,it has been proved that the proposed method has better robustness to mination change,expression changes and so *** experiments with other related methods have also verified the superiority of the method.

关键词： Video face expression recognition local Gabor adaptive ternary orientation local three-value model

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Investigating Lstm with k-Max Pooling for Text Classification

Investigating Lstm with k-Max Pooling for Text Classificatio...

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International Conference on intelligent Computation Technology and Automation, ICICTA

作者： Bo Shu Fuji Ren Yanwei Bao School of Computer and Information Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei China University of Tokushima Graduate School of Advanced Technology & Science Tokushima Japan

For most deep learning practitioners, text classification is synonymous with recurrent networks and is tackled at word level. The presented study is demonstrated that word-level inputs are better than character-level inputs. We describe the proposed model that utilizes Spatial-Dropout1D, LSTM and k-max pool. Because the word of these representations is measured in a word similarity task, so SpatialDropout can reduce information redundancy; LSTM keep track of information over longer periods of time. k-max pooling keep more useful information. The proposed model is better than the strong baseline methods on six benchmark datasets for sentiment classification and topic categorization. The code will be open.

关键词： Handheld computers Conferences Automation XML

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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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Extension of the non-parametric cluster-based time-frequency statistics to the full time windows and to single condition tests

arXiv

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arXiv 2018年

作者： Beste, Christian Kaping, Daniel Tzvetanov, Tzvetomir Cognitive Neurophysiology Department of Child and Adolescent Psychiatry Faculty of Medicine TU Dresden Germany National Institute of Mental Health Klecany Czech Republic CAS Key Laboratory of Brain Function and Disease School of Life Sciences University of Science and Technology of China Hefei Anhui230027 China Anhui Key Laboratory for Affective Computing and Advanced Intelligent Machines School of Computer and Information Hefei University of Technology Hefei Anhui230009 China

Oscillatory processes are central for the understanding of the neural bases of cognition and behaviour. To analyse these processes, time-frequency (TF) decomposition methods are applied and non-parametric cluster-based statistical procedure are used for comparing two or more conditions. While this combination is a powerful method, it has two drawbacks. One the unreliable estimation of signals outside the cone-of-influence and the second relates to the length of the time frequency window used for the analysis. Both impose constrains on the non-parametric statistical procedure for inferring an effect in the TF *** we extend the method to reliably infer oscillatory differences within the full TF mapand to test single *** show that it can be applied in small time windowsirrespective of the cone-of-influence and we further develop its application to single-condition case for testing the hypothesis of the presence or not of time-varying *** present tests of this new method on real EEG and behavioural data and show thatits sensitivity to single-condition testsis at least as good as classic Fourier *** inference in the full TF map is available and efficient in detecting differences between conditions as well as the presence of time-varying signal in single condition. Copyright © 2018, The Authors. All rights reserved.

关键词： Fourier analysis

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Method of Age Estimation Based on ML-GMM Feature and Double Layers Estimation Model

Method of Age Estimation Based on ML-GMM Feature and Double ...

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作者： Min Hu Wendi Teng Yaona Zheng Keke Zhang AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information He Fei University of Technology

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.

关键词： Multi Levels Gaussian Mixture Model SVM KNN Age Estimation

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