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International Joint Conference on Neural Networks (IJCNN)

作者： Jiasen Li Jianxin Zhang Qiule Sun Hengbo Zhang Jing Dong Chao Che Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China School of Computer Science and Engineering Dalian Minzu University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

With the breakthrough performance in a variety of computer vision and medical image analysis problems, convolutional neural networks (CNNs) have been successfully introduced for the classification task of breast cancer histopathological images in recent years. Nevertheless, existing breast cancer histopathological image classification networks mainly utilize the first-order statistic information of deep features to represent histopathological images, failing to characterize the complex global feature distribution of breast cancer histopathological images. To address the problem, this work makes a first attempt to explore global second-order statistics of deep features for the above task. More specifically, we propose a novel deep second-order pooling network (DSoPN) for breast cancer histopatho-logical image classification, in which a robust global covariance pooling module based on matrix power normalization (MPN) is embedded into a simple yet effective CNN architecture. The given DSoPN model can capture richer second-order statistical information of deep convolutional features and produce more informative global representations for breast cancer histopatho-logical images. Experimental results on the public BreakHis dataset illuminate the promising performance of the second-order pooling for breast cancer histopathological image classification. Besides, our DSoPN achieves very competitive performance compared to the state-of-the-art methods.

关键词： Breast cancer Covariance matrices Task analysis Feature extraction Computer architecture Pathology

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Improving sustainability in the value chain of the apparel industry empowered with social manufacturing

Improving sustainability in the value chain of the apparel i...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Babak Mohajeri Ilkka Kauranen Timo Nyberg Elina Ilen Mark Nelson Gang Xiong Aalto University Helsinki Finland School of Arts Design and Architecture Aalto University Helsinki Finland Human Sciences and Technologies Advanced Research Institute (H-STAR) Stanford University USA Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation & Cloud Computing Center Chinese Academy of Sciences China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

One of the major contributions of social manufacturing is in the realm of sustainability. The apparel industry is a good example to assess contribution of social manufacturing to improve sustainability in practice. Value chains in the apparel industry are faced with various challenges regarding sustainability issues. Apparel companies pay higher attention to economic sustainability issues, and environmental and social sustainability issues of the apparel industry are often underrated. We realize that the apparel brand owners have the highest impact on improving the sustainability of the apparel industry. Thus, we design a collaborative business model empowered with social manufacturing to join the forces among the brand owners for improving sustainability of the apparel industry throughout the value chain. We chose a case study of shifting from conventional screen-printing to more environmentally sustainable digital textile printing. We suggest that this shift can be accelerated if the brand owners join their forces together to shift from conventional printing to digital printing technology in the apparel industry.

关键词： Industries Industrial electronics Economics Manufacturing Sustainable development Textiles Digital printing

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Logic Operation Model of the Complementer Based on Two-domain DNA Strand Displacement

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Fundamenta Informaticae 2019年第2-3期164卷 277-288页

作者： Xie, Wendan Zhou, Changjun Lv, Hui Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University of Technology ChinaMinistry of Education Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China.

DNA strand replacement technology has the advantages of simple operation which makes it becomes a common method of DNA computing. A four bit binary number Complementer based on two-domain DNA strand displacement is proposed in this paper. It implements the function of converting binary code into complement code. Simulation experiment based on Visual DSD software is carried out. The simulation results show the correctness and feasibility of the logic model of the Complementer, and it makes useful exploration for further expanding the application of molecular logic circuit.

关键词： Two-Domain DNA Strand Displacement Complementer Logic Circuit

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An IGS algorithm applied to DNA sequence design 17

An IGS algorithm applied to DNA sequence design

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17th IEEE International Conference on Dependable, Autonomic and Secure computing, IEEE 17th International Conference on Pervasive Intelligence and computing, IEEE 5th International Conference on Cloud and Big Data computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

作者： Liu, Kaiqiang Wang, Bin Liu, Yuan Wu, Ranfeng Zhang, Xun Liu, Xin Zhang, Qiang Yin, Zhixiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China School of Mathematics and Big Data Anhui University of Science and Technology Dalian China

ISBN: (纸本)9781728130255;9781728130248

American scientist Adelman first used DNA molecules to solve the NP-complete problem, thus creating a new research field. As an advanced interdisciplinary subject, it has attracted many scholars because of its high parallelism, great storage density and low energy consumption. DNA sequence design is critical in the field of DNA computing. The success of DNA computing depends on the quality of DNA sequence. In this paper, we proposed an improved gravitational search algorithm for DNA sequence design. We improved the gravitational coefficient and introduced chaotic system and Gaussian mutation to the position update of the particles. We compared the result from our improved algorithm with the result obtained by other algorithms. The result shows that our proposed algorithm can generate high quality DNA sequence for DNA computing. © 2019 IEEE.

关键词： DNA computing DNA sequence design Gravitational search algorithm NP-complete problem

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Speech Emotion Recognition Based on Image Enhancement

Speech Emotion Recognition Based on Image Enhancement

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Dongyan Wang Jing Dong Dongsheng Zhou Xiaopeng Wei Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9781728123486

ISBN: (纸本)9781728123493

The performance of an emotion recognition system is determined by the quality of emotional features. In this paper, we propose a feature optimization algorithm based on image enhancement and present a convolutional recurrent model to realize emotional recognition of natural speech. For three-dimensional (3-D) log-Mel spectrum and 3-D spectrogram features, the fast gamma transformation with an adaptive threshold is adopted for feature enhancement to make full use of the dynamic characteristics of non-stationary speech signals. Meanwhile, the model combining Convolutional Neural Network (CNN) with the rectangular kernels and Long Short-Term Memory (LSTM) is used to complete speech emotion recognition tasks. Experiments are carried out on two public emotional datasets, and results demonstrate the good generalization ability and recognition performance of our proposed model.

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Image Super-resolution Reconstruction Based on Deep Residual Network

Image Super-resolution Reconstruction Based on Deep Residual...

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2018 2nd International Conference on Artificial Intelligence: Technologies and Applications（ICAITA2018）

作者： Bin Sun Jian Lu Xiaopeng Wei Laboratory of Advanced Design and Intelligent Computing Dalian University

Having powerful ability to learn and represent features, deep convolutional neural networks(CNN) can get better results in image super-resolution reconstruction. However, deep networks also exist some problems. For example, the gradient will gradually vanish through the network, which makes the training difficult to converge. To address these problems, this paper presents a deep network model that is suitable for superresolution reconstruction. By applying residual network modules, the model realizes the transmission of information across one or more layers. This residual network structure can not only avoid the loss of effective information in the network, but also speed up the training convergence. Compared with the previous classical methods, the proposed model converges faster, and the subjective and objective evaluations have been improved to a certain extent.

关键词： super-resolution reconstruction deep learning residual network

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Ladle Furnace Temperature Prediction Model Based on Large-scale Data With Random Forest

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 770-774页

作者： Xiaojun Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of EducationDalian UniversityDalian 116622 China

In ladle furnace, the prediction of the liquid steel temperature is always a hot topic for the researchers. The most of the existing temperature prediction models use small sample set. Today, the precision of them can not satisfy practical production. Fortunately, the large sample set is accumulated from the practical production process. However, a large sample set makes it difficult to build a liquid steel temperature model. To deal with the issue, the random forest method is preferred in this paper, which is a powerful regression method with low complexity and can be designed very quickly. It is with the parallel ensemble structure,uses sample subsets,and employs a simple learning algorithm of sub-models. Then, the random forest method is applied to establish a temperature model by using the data sampled from the production process. The experiments show that the random forest temperature model is more precise than other temperature models.

关键词： Ladle furnace random forest regression tree temperature prediction

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Fused behavior recognition model based on attention mechanism

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Visual computing for industry, biomedicine, and art 2020年第1期3卷 7页

作者： Lei Chen Rui Liu Dongsheng Zhou Xin Yang Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Dalian University Dalian 116622 China. Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Dalian University Dalian 116622 China. liurui@***. School of Computer Science and Technology Dalian University of Technology Dalian 116024 China.

With the rapid development of deep learning technology, behavior recognition based on video streams has made great progress in recent years. However, there are also some problems that must be solved: (1) In order to improve behavior recognition performance, the models have tended to become deeper, wider, and more complex. However, some new problems have been introduced also, such as that their real-time performance decreases; (2) Some actions in existing datasets are so similar that they are difficult to distinguish. To solve these problems, the ResNet34-3DRes18 model, which is a lightweight and efficient two-dimensional (2D) and three-dimensional (3D) fused model, is constructed in this study. The model used 2D convolutional neural network (2DCNN) to obtain the feature maps of input images and 3D convolutional neural network (3DCNN) to process the temporal relationships between frames, which made the model not only make use of 3DCNN's advantages on video temporal modeling but reduced model complexity. Compared with state-of-the-art models, this method has shown excellent performance at a faster speed. Furthermore, to distinguish between similar motions in the datasets, an attention gate mechanism is added, and a Res34-SE-IM-Net attention recognition model is constructed. The Res34-SE-IM-Net achieved 71.85%, 92.196%, and 36.5% top-1 accuracy (The predicting label obtained from model is the largest one in the output probability vector. If the label is the same as the target label of the motion, the classification is correct.) respectively on the test sets of the HMDB51, UCF101, and Something-Something v1 datasets.

关键词： Action recognition Attention mechanism Res34-SE-IM-net ResNet34-3DRes18

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Chinese Historical Term Translation Pairs Extraction Using Modern Chinese As a Pivot Language

Chinese Historical Term Translation Pairs Extraction Using M...

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第十八届中国计算语言学大会暨中国中文信息学会2019学术年会

作者： Xiaoting Wu Hanyu Zhao Lei Jing Chao Che State Grid Info & Telecom Group Beijing China-Power Information Technology Co. LTDBeijingChina Key Laboratory of Advanced Design and Intelligent Computing Ministry of EducationDalian University Chiping Vocational Education School LiaochengChina

ISBN: (纸本)9783030323806

Term translation of Chinese historical classics is very difficult and time-consuming work,and using term alignment methods to extract term translation pairs is of great help for historical term ***,the limited bilingual corpora resources of historical classics and special morphology of the ancient Chinese result in poor performance of term *** this end,this paper proposes a historical term alignment method using modern Chinese as a pivot *** method first identifies English terms by rules,then aligns them from English to modern Chinese and then from modern Chinese to ancient *** use of English-modern Chinese corpus and modern-ancient Chinese corpus instead of English-ancient Chinese corpus solves the shortage problem of the parallel ***,using modern Chinese as a pivot language effectively reduces the alignment errors caused by the abbreviations and the interchangeable characters of ancient *** the term alignment experiment on Shiji,our method outperformed the direct alignment method significantly,which proves the validity of our method.

关键词： Chinese Historical Classics Term Alignment Pivot Language

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Constructing DNA Barcode Sets Based on Particle Swarm Optimization

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IEEE/ACM transactions on computational biology and bioinformatics 2018年第3期15卷 999-1002页

作者： Bin Wang Xuedong Zheng Shihua Zhou Changjun Zhou Xiaopeng Wei Qiang Zhang Ziqi Wei the Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China Department of Computing Science University of Alberta Edmonton AB Canada

Following the completion of the human genome project, a large amount of high-throughput bio-data was generated. To analyze these data, massively parallel sequencing, namely next-generation sequencing, was rapidly developed. DNA barcodes are used to identify the ownership between sequences and samples when they are attached at the beginning or end of sequencing reads. Constructing DNA barcode sets provides the candidate DNA barcodes for this application. To increase the accuracy of DNA barcode sets, a particle swarm optimization (PSO) algorithm has been modified and used to construct the DNA barcode sets in this paper. Compared with the extant results, some lower bounds of DNA barcode sets are improved. The results show that the proposed algorithm is effective in constructing DNA barcode sets.

关键词： Bioinformatics DNA Particle Swarm Optimisation Particle Swarm Optimization Next Generation Sequencing DNA Barcode Sets DNA Sequential Analysis Algorithm design And Analysis Next Generation Networking Particle Swarm Optimization Hamming Distance Filtering Algorithms DNA Barcodes Edit Distance Particle Swarm Optimization PSO Lower Bound

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