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arXiv 2022年

作者： Song, Jie Yi, Huawei Xu, Wenqian Li, Xiaohui Li, Bo Liu, Yuanyuan School of Electronics and Information Engineering Liaoning University of Technology Jinzhou121001 China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116622 China

The proposal of perceptual loss solves the problem that per-pixel difference loss function causes the reconstructed image to be overly-smooth, which acquires a significant progress in the field of single image super-resolution reconstruction. Furthermore, the generative adversarial networks (GAN) is applied to the super-resolution field, which effectively improves the visual quality of the reconstructed image. However, under the condtion of high upscaling factors, the excessive abnormal reasoning of the network produces some distorted structures, so that there is a certain deviation between the reconstructed image and the ground-truth image. In order to fundamentally improve the quality of reconstructed images, this paper proposes a effective method called Dual Perceptual Loss (DP Loss), which is used to replace the original perceptual loss to solve the problem of single image super-resolution reconstruction. Due to the complementary property between the VGG features and the ResNet features, the proposed DP Loss considers the advantages of learning two features simultaneously, which significantly improves the reconstruction effect of images. The qualitative and quantitative analysis on benchmark datasets demonstrates the superiority of our proposed method over state-of-the-art super-resolution methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Methodology for optimization of preload in a bolted-flange connection based on Markov theory 9

Methodology for optimization of preload in a bolted-flange c...

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9th International Conference on advanced Manufacturing Technology and Materials Science, AMTMS 2021

作者： Wang, Yuezhen Liu, Zhifeng Li, Ying Yang, Congbin Institute of Advanced Manufacturing and Intelligent Technology Beijing University of Technology Beijing Beijing100124 China Key Laboratory of CNC Equipment Reliability Ministry of Education Jilin University Jilin Changchun130012 China Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Beijing University of Technology Beijing Beijing100124 China

The bolted-flange connection is one of the most popular detachable mechanical joints in mechanical flange connection. It is well known that bolted-flange joints need to be tightened to meet the assembly performance in practical engineering applications. However, the dispersion of the pre-tightening force caused by the elastic interaction between the bolts has a serious impact on the sealing and safety of the product, and therefore the optimization of bolt preload has always been a hot spot in the assembly research field. Combining mathematical theory and experimental data, this paper proposes an optimization model for uniformly distributed preload based on the Markov theory. The torque method was utilized to fasten the bolt-flange joint. The bolt pre-tightening force in the final state was collected and used as sample data. A state transition model was constructed to determine the state transition matrix. Ultimately, an iterative model of the preload was deduced, and the rationality of the model was verified through experimentation. The proposed optimization method of bolt preload can be applied to determine the initial preload of each bolt. This method improves the assembly efficiency and accuracy, which provides a theoretical approach to effectively guide the tightening work of bolted-flange connections. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Bolts

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Speech Emotion Recognition using Channel Attention Mechanism

Speech Emotion Recognition using Channel Attention Mechanism

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Ruifeng Zhu Caixia Sun Xiaopeng Wei Lasheng Zhao Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education (Dalian University) Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

In order to improve the accuracy of speech emotion recognition, this paper proposes a speech emotion recognition method based on the channel attention mechanism. Firstly, Mel Frequency Ceptral Coefficient(MFCC), speech spectrograms and spectral envelopes are selected as the initial input features; then, multiple depth network models are used to extract feature maps from different angles in parallel; then, weights are assigned and fused to the feature maps output from each sub-depth network model by the channel attention mechanism; finally, the fused feature maps are used to predict emotion categories. The experimental results on CASIA, Emo-DB, and SAVEE emotion datasets show that the method achieves 88.3 % , 85.1%, and 64.5% recognition accuracy, respectively, with better recognition performance compared to recent comparative literature models.

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Visual synchronization of two 3-variable Lotka-Volterra oscillators based on DNA strand displacement

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RSC advances 2018年第37期8卷 20941-20951页

作者： Chengye Zou Xiaopeng Wei Qiang Zhang School of Computer Science and Engineering Dalian University of Technology Dalian 116024 China zhangq@***. Key Laboratory of Advanced and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China.

DNA strand displacement as a theoretic foundation is helpful in constructing reaction networks and DNA circuits. Research on chemical kinetics is significant to exploit the inherent potential property of biomolecular systems. In this study, we investigated two typical reactions and designed DNA strands with a fluorophore and dark quencher for reaction networks using a 3-variable Lotka-Volterra oscillator system as an example to show the convenience of and superiority for observation of dynamic trajectory using our design, and took advantage of the synchronization reaction module to synchronize two 3-variable Lotka-Volterra oscillators. The classical theory of chemical reaction networks can be used to represent biological processes for mathematical modeling. We used this method to simulate the nonlinear kinetics of a 3-variable Lotka-Volterra oscillator system.

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A Weighting Possibilistic Fuzzy C-Means Algorithm for Interval Granularity 17th

A Weighting Possibilistic Fuzzy C-Means Algorithm for Interv...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Tang, Yiming Xi, Lei Wu, Wenbin Wu, Xi Li, Shujie Chen, Rui Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Anhui Hefei230601 China Engineering Research Center of Safety Critical Industry Measure and Control Technology Ministry of Education Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789819923847

Granular clustering is an emerging branch in the field of clustering. However, the existing granular clustering algorithms are still immature in terms of weight setting of granular data and noise resistance. In this study, a weighting possibilistic fuzzy c-means algorithm for interval granularity (WPFCM-IG) is proposed. To begin with, a new weight setting method for interval granular data is given. The principle of justifiable granularity is used as the evaluation criterion of granular data, and a weight is assigned to each granular data from two perspectives of coverage and specificity to measure the quality of the granular data. In addition, the idea of possibilistic clustering is introduced, which is helpful to improve the noise resistance. And, with the proposed weights of interval granular data, the influence of data with smaller weights on the clustering results can be reduced during the clustering process. Based upon these ideas, the WPFCM-IG algorithm is put forward, and its core idea, formula derivation and implementation process are described. Finally, the performance of the proposed algorithm is verified by comparison experiments on the artificial and UCI datasets. The experimental results show that the WPFCM-IG algorithm is better than other advanced algorithms in this field in terms of reconstruction error. Next, the WPFCM-IG algorithm is smoother than other algorithms on the collaborative relationship curve between the fuzzy coefficient and the reconstruction error, so WPFCM-IG can better optimize the fuzzy coefficient. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy clustering

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Missing Data Recovery for Human Mocap Data Based on A-LSTM and LS Constraint

Missing Data Recovery for Human Mocap Data Based on A-LSTM a...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Lu Ji Rui Liu Dongsheng Zhou Qiang Zhang Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

Human mocap (motion capture) data has been widely used in many fields. However, influenced by the occlusion and falling, missing of markers is inevitable in optical motion capture system. According to the temporal and spatial characteristics of mocap data, a missing data recovery method based on attention-based LSTM network (A-LSTM) and least-squares (LS) constraint is proposed in this paper. First, the LSTM is chosen as the basic network to estimate the position of the missing markers, in which an attention mechanism is introduced to learn the temporal continuity of human mocap data. Then, a space affine transformation based on least-squares fitting is adopted as the spatial constraint to refine the estimated positions, after which the restored error can be reduced significantly. Experiments show that the proposed A-LSTM+LS method is robust to the length of missing gap and the number of missing markers. Under the same conditions, our method achieves smaller restore error than the state of the art.

关键词： Legged locomotion Deep learning Image recognition Correlation Fitting Neural networks Image restoration

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On Interval Perturbation of the ɑ-Symmetric Implicational Algorithm 2nd

On Interval Perturbation of the ɑ-Symmetric Implicational A...

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2nd International Conference on Artificial Intelligence Logic and Applications, AILA 2022

作者： Tang, Yiming Han, Jianghui Bao, Guangqing Chen, Rui Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Anhui Hefei230601 China Engineering Research Center of Safety Critical Industry Measure and Control Technology Ministry of Education Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789811975097

Perturbation is an important property of fuzzy reasoning algorithms. It is an important task to estimate the upper and lower bounds of the output after a perturbation (such as an interval perturbation) is given to the input. In this study, the interval perturbation problem of a fuzzy reasoning algorithm, i.e., the ɑ-symmetric implicational algorithm, is researched systematically. For FMP (Fuzzy Modus Ponens) and FMT (Fuzzy Modus Tollens) problems of fuzzy reasoning, aiming at the case of a single rule, the estimation for the upper and lower bounds are obtained for the input interval disturbance, in which R-implications and (S, N)-implications are mainly employed. Then the stability is verified on account of the upper and lower bounds. Following that, for the multi-rule case, the upper and lower bounds of the output are also given, and the corresponding stability is validated. In conclusion, it is found that the ɑ-symmetric implicational algorithm has good stability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy inference

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MFN: Explainable DNA triple helixes Stabilized design based on mCGR and flow network

MFN: Explainable DNA triple helixes Stabilized Design based ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoru Wen Lijun Sun Lei Xie Yanfen Zheng Ben Cao Bin Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

DNA triple helix structure, as a highly specific gene targeting tool, enable gene regulation by precisely identifying and binding to target DNA sequences. However, the limits of design quality and efficiency affect their wide application in gene therapy. Therefore, in this paper, we propose an antiparallel DNA triple helixes design method-MFN based on matrix chaotic game representation (mCGR) and flow network. Leveraging the structural characteristics of DNA sequences, this method employs the mCGR algorithm to construct an initial matrix, generating a set of DNA sequences that conform to foundational constraints. Then, these sequences are mapped as stream network nodes to screen crosstalk structures by path search, and the whole process is observable and interpretable. Experimental results show that MFN significantly improves the design efficiency of triple helix structure and reduces crosstalk phenomenon. Wet experiments further verify the effectiveness of the method. In summary, MFN achieves an efficient and high-quality design of DNA triple helixes and provides a new idea for targeted gene therapy.

关键词： Scalability design methodology DNA Crosstalk Genomics Games Reliability engineering Regulation Gene therapy Bioinformatics

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Positive-Unlabeled Learning with Field of View Consistency for Histology Image Segmentation 1

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13th International Conference on Frontier computing, FC 2023

作者： Jia, Xiaoqi Fu, Chong Hou, Jiaxin Qin, Wenjian School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

ISBN: (数字)9789819993420

ISBN: (纸本)9789819993413

Histology image annotation is costly and time-consuming. Utilizing Positive and Unlabeled (PU) data for model training offers a more resource-efficient alternative. However, previous methods for PU learning suffer from the noise arising from label assignment to unlabeled data. We observe that predictions on noisy data lack consistency under data augmentation. In this paper, we present Field of View (FoV) consistency regularization for PU segmentation in histology images, which effectively reduces the noise influence by promoting consistent predictions across varying FoVs. Using only 20% of positive labels on the Glas Dataset, our approach outperforms previous methods, achieving a Dice score of 90.69%-almost reaching the fully supervised result of 93.30%. Source code is available at: https://***/lzaya/PU_with_FoV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image segmentation

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Compactness and Separateness Driven Fuzzy Clustering Validity Index Called TLW 18th

Compactness and Separateness Driven Fuzzy Clustering Validit...

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18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Tang, Yiming Wang, Xiang Li, Bing Hu, Xianghui Xie, Wenjun Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Anhui Hefei230601 China Engineering Research Center of Safety Critical Industry Measure and Control Technology Ministry of Education Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789819996391

The design of validity index of fuzzy clustering has always been a historical problem in fuzzy clustering field. When the distribution of cluster centers is very close, it is difficult for the existing fuzzy clustering validity indexes to obtain a reasonable cluster number, and the separation mechanism of these indexes is too simple. In order to solve the above problems, we propose a novel fuzzy clustering validity index called TLW (Tang-Li-Wang) index. Firstly, compactness is expressed as the ratio of the membership weighted distance value to the sample variance of the dataset. Secondly, the sum of the maximum distance between cluster centers and the mean distance is used in separateness, and the sample variance of cluster centers is introduced, and the two are multiplied to describe the separateness. Thirdly, on the basis of considering compactness and separateness, the introduction of cluster number can alleviate the phenomenon that the index value may change monotonically with the increase of cluster number. Finally, the classical FCM (Fuzzy C-Mean) algorithm is used to conduct experiments on indexes. Comparative experiments and analyses were carried out on 17 typical datasets and 12 clustering validity indexes. From the experimental results of normal simple datasets and high-dimensional difficult datasets, the proposed index shows some advantages. All in all, these results verify that the proposed TLW index has better accuracy and stronger stability. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy clustering

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