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International Conference on Bio-Inspired computing: Theories and Applications, BICTA

作者： Xing Wang Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

DNA computing theories can be applied in cryptography and it is a very hopeful direction. Though still in primitive level, it applies in many fields and solved some hard problems successfully. The art of cryptography security is to make anybody can not read it by encoding message. This paper use a new way to show how cryptography works with DNA computing, it can transmit message securely and effectively. The RSA algorithm belongs to asymmetric key cryptography, it is used in this paper connecting with DNA computing technique to encrypt message.

关键词： DNA computing Cryptography Encoding Arithmetic Turing machines Chemicals Art Security Computational modeling Qualifications

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A multilevel image encryption algorithm based on chaos and DNA coding

A multilevel image encryption algorithm based on chaos and D...

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International Conference on Bio-Inspired computing: Theories and Applications, BICTA

作者： Qian Wang Qiang Zhang Changjun Zhou Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

Chaos has been attracted much attention in current research of image encryption. In this paper, we change the location of pixels and pixel values through the combination of chaos with DNA coding. First of all, we transform the digital image into DNA coding matrix based on DNA code rules. Secondly, we divided this matrix into four sub matrixes, and disorder these sub matrixes through logistic chaotic sequence. Then, we scramble the sub matrixes by adding new logistic chaotic sequences and get the new sub matrixes. Finally, we join all scrambled DNA sub-matrixes in a DNA sequence and decode the scramble DNA matrix to image matrix. The experiment results demonstrate that the proposed scheme is effective, and it has wide secret key space, strong sensibility, high security and good ability of resisting statistic attack.

关键词： Cryptography Chaos DNA Image coding Sequences Logistics Digital images Decoding Security Statistics

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GP-Net: Global Perceptual Network with Adaptive Gradient Clipping for Emotional Image Classification 8

GP-Net: Global Perceptual Network with Adaptive Gradient Cli...

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8th International Conference on intelligent computing and Signal Processing, ICSP 2023

作者： Wang, Xiaohua Yang, Jie Hu, Min Wu, Xia Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350302455

Traditional image emotion recognition focuses only on the emotion information embedded in the subject or part of the image, while ignoring the global emotion information. In this paper, we propose a new network based on Vision Transformer: a global perception network, containing a main module and a background module. The main module enhances the weighting of objects by showing the interdependencies between modelled channels. The background module further searches for emotional information in the background. In addition, we employ multi-task learning and Adaptive Gradient Clipping to improve the network. Extensive experiments show that our proposed method outperforms the state-of-the-art methods for image sentiment analysis. © 2023 IEEE.

关键词： Image classification

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Cross-Stage Mutual Distillation for Replay Attack Detection

Cross-Stage Mutual Distillation for Replay Attack Detection

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6th International Conference on intelligent computing and Signal Processing (ICSP)

作者： Yinqing Cheng Lasheng Zhao Ling Wang Han Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Automated speaker verification systems face a higher risk of replay attacks in practice. However, the existing studies face the problem of limited detection capabilities and insufficient use of shallow fine-grained information. To address these issues, we propose the cross-stage mutual distillation(CS-MD) framework, which involves two models learning from a deep network output of each other in different stages of training. This mutual learning approach enhances the ability of shallow networks to capture fine-grained speech information. Additionally, we use an attentional feature fusion module to integrate shallow information more effectively. The multi-scale attention mechanisms in this module can combine local and global speech features while preserving detailed information. Experimental results on the ASVspoof 2019 physical access dataset demonstrate that our proposed method outperforms state-of-the-art methods in terms of EER and min t-DCF metrics, validating the effectiveness of our CS-MD framework.

关键词： Training Voice activity detection Measurement Knowledge engineering Attention mechanisms Speech enhancement Signal processing Feature extraction Faces

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Image encryption algorithm based on DNA biological properties and chaotic systems

Image encryption algorithm based on DNA biological propertie...

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International Conference on Bio-Inspired computing: Theories and Applications, BICTA

作者： Qian Wang Qiang Zhang Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian university Dalian China

With the rapid development of DNA computing, people began to use DNA technology to encrypt information. In this paper, we combine the biological characteristics of DNA and the improved sequence of Logistic mapping method, encrypt the image. The biological characteristics of DNA can better scramble image pixel, and the improved sequence can better change the pixel values. The encryption effect of this paper is better than traditional encryption, and the correlation of adjacent pixels is very low.

关键词： Biomedical imaging Cryptography Clocks Biology

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Semi-Supervised Semantic Segmentation Based on Class Balanced Framework

Semi-Supervised Semantic Segmentation Based on Class Balance...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Jiacheng Yang Jing Dong Pengfei Yi Rui Liu Ling Wang The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Semi-supervised semantic segmentation is a task that utilizes a small amount of labeled data and a large amount of unlabeled data to achieve pixel-level annotation. Due to the common long tail distribution phenomenon in semi-supervised semantic segmentation datasets and the fixed threshold strategy commonly used in filtering pseudo labels, the model tends to favor the head classes when generating pseudo labels, resulting in poor segmentation performance on tail data. This article proposes a semi-supervised semantic segmentation method based on a class- balanced framework, which improves the model's recognition ability for minority classes and the quality of pseudo labels through tail classes oversampling and dynamic threshold adjustment. This method first constructs a Class Prediction Sampling-based Self-balancing Module (CPS 2 M). It identifies tail classes through Intersection-over-Union (IoU) analysis and constrains the random oversampling process based on the tail classes data distribution degree. Subsequently, the sampled labeled data is mixed with unlabeled samples to transfer prior knowledge, increase training frequency for tail classes, and solve the experience mismatch problem. Finally, we design a Dynamic Threshold Updating module (DTU) based on a non-linear function. The confidence threshold can dynamically change according to the model's learning ability to avoid discarding correct tail classes with low confidence, which alleviates the class imbalance problem of generating pseudo-labels. Based on the DeepLabV3+ network framework and ResNet-101 pre-training model, this method achieves 82.1 mIoU under 1464 labeled partition protocols on the Classic Pascal VOC 2012 dataset, demonstrating the most advanced performance compared to existing methods.

关键词： Training Protocols Filtering Annotations Semantic segmentation Semantics Tail

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Temporal Attention Based TCN-BIGRU Model for Energy Time Series Forecasting

Temporal Attention Based TCN-BIGRU Model for Energy Time Ser...

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2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering, CSAIEE 2021

作者： Li, Liang Hu, Min Ren, Fuji Xu, Haijun Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine HeFei China Graduate School of Advanced Technology Science University of Tokushima Tokushima Japan

ISBN: (纸本)9781665422048

Over the years, energy time series forecasting has been widely studied and has played an important role in various fields, such as electric energy forecasting, solar energy forecasting, etc. In energy time series forecasting, it is crucial to building forecasting models for long series in order to obtain accurate forecasting results. Since the use of long series can cause the accuracy of the model to decrease. In this paper, we propose a deep learning model (TCNTA-BiGRU) based on a bi-directional gated cyclic unit (BiGRU) with a temporal attention mechanism to address the problem of accuracy degradation in long sequence tasks. First, in order to capture long-term dependencies, this paper divide the dataset and input it into a temporal convolutional network (TCN) to transform long sequences into multiple short sequences, which not only solves the problem that to cause gradient explosion or disappearance when processing long sequences, but also reduces the spatial complexity. Then, BiGRU is used to learn historical and future information and capture more short-term dependencies. Moreover, in order to enhance the model's ability to focus on data periodicity, a temporal attention mechanism is introduced. Additionally the autoregressive module is used to increase the linear fitting ability of the model. The model proposed in this paper is applied to the Electricity and Solar Energy datasets and the results show a better performance relate to existing deep learning models. © 2021 IEEE.

关键词： Solar energy

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Robust keyword Spotting Based on Long-Term Feature Fusion and Band-Weighted Normalization

Robust Keyword Spotting Based on Long-Term Feature Fusion an...

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Computer Information Science and Application Technology (CISAT), International Conference on

作者： Zepeng Qiu Lasheng Zhao Ling Wang Tingting Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Currently, convolution-based models for keyword spotting focus predominantly on research conducted in clean speech environments. However, their recognition accuracy is lower in low signal-to-noise ratio conditions. To enhance model robustness, we propose the long-term feature fusion module. By extracting and fusing long-term features in both the time and frequency domains, the model’s perception of long-term features is strengthened, improving its recognition performance in noisy environments. At the same time, we propose a band-weighted normalization method, allowing the model to adjust the importance of different frequency bands during the normalization process, further enhancing the robustness of the model. Experimental results indicate that, on the Google Speech Command V2 dataset, the proposed model achieves higher recognition accuracy under various signal-to-noise ratios compared to the comparative models, with a lower parameter count. Moreover, the proposed model exhibits superior generalization to signal-to-noise ratios not covered during the training phase.

关键词： Training Time-frequency analysis Accuracy Convolution Speech recognition Feature extraction Robustness Internet Noise measurement Signal to noise ratio

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AN IMPROVED MODAL ANALYSIS FOR THREE-DIMENSIONAL PROBLEMS USING FACE-BASED SMOOTHED FINITE ELEMENT METHOD

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Acta Mechanica Solida Sinica 2013年第2期26卷 140-150页

作者： Zhicheng He Guangyao Li Zhihua Zhong Aiguo Cheng Guiyong Zhang Eric Li State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Intelligent Systems for Medicine Laboratory School of Mechanical and Chemical EngineeringThe University of Western Australia35 Stirling HighwayCrawley WA 6009Australia Department of Mechanical Engineering National University of Singapore9 Engineering Drive 1117576Singapore

In this work, we further extended the face-based smoothed finite element method （FS-FEM） for modal analysis of three-dimensional solids using four-node tetrahedron elements. The FS-FEM is formulated based on the smoothed Calerkin weak form which employs smoothed strains obtained using the gradient smoothing operation on face-based smoothing domains. This strain smoothing operation can provide softening effect to the system stiffness and make the FSFEM provide more accurate eigenfrequency prediction than the FEM does. Numerical studies have verified this attractive property of FS-FEM as well as its ability and effectiveness on providing reliable eigenfrequency and eigenmode prediction in practical engineering application.

关键词： numerical method meshfree method modal analysis face-based smoothed finiteelement method （FS-FEM）

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An improved collision detection algorithm based on OBB

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Computer Modelling and New Technologies 2014年第1期18卷 71-79页

作者： Man, Ranran Zhou, Dongsheng Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China

In this paper, we present an effective algorithm based on the Oriented Bounding Box (OBB). Particularly, the article focus on how to reduce the amount of the time that the intersection of bounding boxes are detected for increasing the efficiency of collision detection algorithm through the following three steps. Firstly, the detection time of bounding box intersection is decreased by a pre-treatment method. Secondly, we optimize the traversal approach of the bounding box tree and reduce the depth of the two fork tree under take into account the temporal and spatial correlation of motion of virtual environment. Then, we reduce the breadth of tree result from this algorithm compares the distance between the bounding boxes and traverse the node of distance that near the tree. At last, the validity of the algorithm is verified by programming simulations.

关键词： Virtual reality

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