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12th International Conference on Communications and Broadband Networking, ICCBN 2024

作者： Deng, Xingyu Han, Hongyu Zhang, Sheng Xu, Yin College of Computer Science and Visual Computing Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu610066 China The School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798400717109

Multi-target tracking in sensor networks is a challenging problem, especially in scenarios where sensor observations are limited. Conventional centralized Kalman filters and distributed Kalman filters (DKFs) require each sensor to observe all targets, which is often difficult to achieve in practical distributed applications. With this in mind, and inspired by the promising robustness of the maximum correntropy criterion in non-Gaussian noise environments, this paper proposes a novel multi-target distributed maximum correntropy Kalman filter, named MT-DMCKF. The proposed algorithm can operate effectively in distributed multi-target scenarios and provide accurate and consistent tracking performance even when the number of sensors mounted on targets is limited. Within a single period, the algorithm requires only one exchange of information among sensors, thus reducing communication overhead compared to existing distributed Kalman filters. Simulation experiments validate the effectiveness and robustness of the proposed MT-DMCKF. © 2024 ACM.

关键词： Wiener filtering

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Dynamic Semi-structured Data Clustering Based on Frequently Changing Structure 2

Dynamic Semi-structured Data Clustering Based on Frequently ...

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2nd International Conference on Cloud computing, Big Data Application and Software Engineering, CBASE 2023

作者： Yang, Junren Li, Wei Sichuan Normal University College of Computer Science Chengdu610101 China Sichuan Normal University Visual Computing and Visual Reality Key Laboratory of Sichuan Chengdu610068 China

ISBN: (纸本)9798350331448

In order to cluster dynamic semi-structured data documents, a dynamic semi-structured data clustering algorithm based on frequently changing structure is proposed. The algorithm uses dynamic model to store the historical change information of dynamic semi-structured data, builds frequent structure path tree based on the spatial frequent changing structure, uses frequent structure path tree to calculate document similarity, and finally uses DBSCAN algorithm to cluster. Experimental results show that the Jaccard coefficient and Rand index of the proposed method are above 0.99, and the performance consumption and clustering effect are better than the traditional static clustering algorithm. © 2023 IEEE.

关键词： Data mining

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An Optimization Method of Primer Design Based on Attention-BiLSTM 2

An Optimization Method of Primer Design Based on Attention-B...

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2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control, RAIIC 2023

作者： Bai, Binhao Long, Jinyu Yang, Zhibo Li, Junli Wei, Ping Sichuan Normal University College of Computer Science Chengdu China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu China Sichuan Center of Translational Medicine Sichuan Key Laboratory of Translational Medicine of Traditional Chinese Medicine Sichuan Academy of Traditional Chinese Medicine Chengdu China

ISBN: (纸本)9798350328004

In this paper, we propose a method to predict the success of primer amplification based on the relationship existing between the sequence of primer and template, which can optimize the primer design and select the primer with better amplification from the candidate primer set. The double-stranded structure between primer and template nucleotide sequences is represented here by a number of words, each consisting of five characters that form sentences, as the dataset for the experiment, which is learned using an attention-based mechanism of bidirectional long short-term memory neural network model (Attention-BiLSTM), and then predicts primer amplification. The model predicted the results of polymerase chain reaction (PCR) involving specific primers and specific DNA templates with 82% accuracy, an improvement of about 2% over the performance of the LSTM with more stable value. These results show that the model can be used to effectively predict the results of PCR. This is the first paper to optimize primer design by screening the candidate primer set with a neural network model. © 2023 IEEE.

关键词： Neural network models

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Immersive Experience of Movie Scenes Based on Convolutional Neural Network

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Computer-Aided Design and Applications 2024年第S12期21卷 189-204页

作者： Yuan, Jian Peng, Tingting Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu610066 China College of Movie and Media Sichuan Normal University Chengdu610066 China

This paper discusses how to use virtual reality (VR) and computer aided design (CAD) technology to enhance the immersive experience of movie scenes. Firstly, DL technology, especially the model of Convolutional Neural Network (CNN), is used to classify and identify the interactive objects in the virtual movie scene. Then, the deep reinforcement learning algorithm is used to simulate and experiment these objects to determine the best interaction mode. Finally, the best interactive effect is generated by combining the input information of the audience and the results of the deep reinforcement learning algorithm. CNN is obviously superior to the 3D modeling algorithm based on wavelet neural network (WNN) model in accuracy and recall. This improvement shows that CNN can more accurately identify the key elements and details in VR movie scenes, and has better scene layout optimization ability. The focus of the research is to create a highly realistic virtual movie scene by combining deep learning (DL) technology and computer graphics, so that the audience can interact with the VR scene more naturally. This interactive design can improve the immersive experience of the movie scene and enhance the audience's viewing experience. © 2024 U-turn Press LLC.

关键词： virtual reality

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Concurrent Charging with Wave Interference 42

Concurrent Charging with Wave Interference

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Ma, Yuzhuo Wu, Die Ren, Meixuan Peng, Jian Yang, Jilin Liu, Tang Sichuan Normal University College of Computer Science Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Lab Chengdu610068 China Sichuan University College of Computer Science Chengdu610065 China

ISBN: (纸本)9798350334142

To improve the charging performance, employing multiple wireless chargers to charge sensors concurrently is an effective way. In such charging scenarios, the radio waves radiated from multiple chargers will interfere with each other. Though a few work have realized the wave interference, they do not fully utilize the high power caused by constructive interference while avoiding the negative impacts brought by the destructive interference. In this paper, we aim to investigate the power distribution regularity of concurrent charging and take full advantage of the high power to enhance the charging efficiency. Specifically, we formulate a concurrent charGing utility mAxImizatioN (GAIN) problem and build a practical charging model with wave interference. Further, we propose a concurrent charging scheme, which not only can improve the power of interference enhanced regions by deploying chargers, but also find a set of points with the highest power to locate sensors. Finally, we conduct both simulations and field experiments to evaluate the proposed scheme. The results demonstrate that our scheme outperforms the comparison algorithms by 40.48% on average. © 2023 IEEE.

关键词： Inductive power transmission

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Utilizing the Neglected Back Lobe for Mobile Charging 42

Utilizing the Neglected Back Lobe for Mobile Charging

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Ren, Meixuan Wu, Die Xue, Jing Xu, Wenzheng Peng, Jian Liu, Tang Sichuan Normal University College of Computer Science Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Lab Chengdu610068 China Sichuan University College of Computer Science Chengdu610065 China

ISBN: (纸本)9798350334142

Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e., back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by 49.5% and increases the energy usage effectiveness by 10.2% on average as compared with existing algorithms. © 2023 IEEE.

关键词： Wireless sensor networks

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Enhanced Multi-Style Transfer Method based on VGG Network

Enhanced Multi-Style Transfer Method based on VGG Network

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Cloud computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Yong Zheng Juanni Jiao Fange Ye Wei Li College of Computer Science Sichuan Normal University Chengdu Sichuan China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University

In the domain of conventional image style transfer, remarkable progress has been achieved; nonetheless, certain issues persist. A large grayscale image with only one channel of brightness information lacks diverse color and texture features. This limits the variety in style transformation, resulting in unsatisfactory diversity of effects. To overcome this challenge, the present study introduces an enhanced multi-style transfer approach founded upon the VGG network. To achieve a greater variety of style transfers for single-channel grayscale images. This is accomplished by introducing a dual VGG network architecture coupled with the incorporation of the Gram matrix concept. Additionally, a multi-style fusion module is introduced to merge various style transfers for single-channel grayscale images. The experimental outcomes unequivocally demonstrate that the proposed approach effectively accomplishes manifold style transfers within single-channel grayscale images. Furthermore, apart from retaining the inherent content attributes, it bestows images with fresh visual allure. In comparison to conventional methods, the approach showcased in this study achieves significant advancements in both the quality and diversity of style transfer. Consequently, this innovative approach not only expands the scope of image style transfer applications but also paves the way for a novel potential in the realm of style transformation for single-channel grayscale images.

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Few-Shot Classification Based on Feature Enhancement Network

Few-Shot Classification Based on Feature Enhancement Network

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shasha Hu Han Su Ruixuan Gao College of Computer Science Sichuan Normal University Chengdu China Visual Computing and visual reality Key Laboratory of Sichuan Province Chengdu China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Few-shot image classification stands as a pivotal task within the realm of computer vision. However, obtaining accurate class prototypes from limited annotated samples is a challenging problem. In recent years, many methods based on prototype networks have shown excellent performance. Nevertheless, existing methods overlook the discriminative semantic information lost due to sample scarcity and the hidden category information in the query set, failing to address the issue of unreliable prototypes generated from limited annotated samples. In this paper, we propose a feature enhancement network for few-shot classification. To improve the accuracy and robustness of few-shot classification models, we first enhance the support set through learning a weight matrix and then align the enhanced support set prototypes with textual semantics. To avoid being influenced by introduced prior noise, we fuse between semantically aligned prototypes and mean prototypes and ultimately utilize query prototypes for dynamic updating to obtain more accurate class prototypes. Extensive experiments demonstrate that our method achieves competitive performance on miniImageNet and tieredImageNet datasets. Furthermore, it exhibits excellent results in cross-domain few-shot classification.

关键词： Computer vision Accuracy Fuses Semantics Noise Prototypes Robustness Few shot learning Cybernetics Image classification

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Perceptual Evaluation Based on WOA-BP Neural Network

Perceptual Evaluation Based on WOA-BP Neural Network

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Artificial Intelligence and Intelligent Information Processing (AIIIP), International Conference on

作者： Jiao Juanni Zheng Yong Li Wei College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China

In modern society, various patterns play an indispensable role. With the help of computer technology, the efficiency of designers in the field of pattern design has been significantly improved. However, in the process of pattern design, we often only rely on the subjective experience of designers, while ignoring the personal preferences of consumers. In order to explore the relationship between different pattern designs and consumers' perceptual evaluation, the Kansei engineering theory is first employed to capture consumers' perceptual evaluation of the patterns. Subsequently, various pattern designs and consumers' perceptual evaluation were utilized as inputs and outputs for the WOA-BP neural network, thereby constructing an intelligent model capable of predicting consumers' perceptual evaluation of different pattern designs. The experimental results demonstrate that the model has a highly accurate prediction level. With this model, designers can quickly adjust the pattern design to better meet the aesthetic needs of today's consumers.

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A Nested Edge Addition Strategy for Network Controllability Robustness Enhancement

A Nested Edge Addition Strategy for Network Controllability ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chengpei Wu Siyi Xu Zhuoran Yu Junli Li School of computer science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu Sichuan China

Edge rectification is a widely used method to enhance network robustness. However, in some networked systems, edge rectification may be challenging or even infeasible to implement. An edge addition strategy is proposed as an alternative optimization method in this paper. Nested Ring Structure (NRS), whereby each node's edges connect its nearest neighbors along the backbone direction, have exhibited robust controllability against random attacks. Therefore, The Nested Edge Addition (NEA) strategy is proposed, which enhances network controllability by building NRS through edge addition to a given initial network. With a small number of added edges, NEA can rapidly enhance network controllability, allowing the network to be controlled using just one driver node. The more nested edges are added, the stronger the NRS in a network, thus exhibiting better controllability robustness. The effectiveness of NEA is verified by simulations on both synthetic and real-world networks. Extensive experimental results demonstrate that NEA is an efficient strategy for designing network topology and optimizing real-world networks.

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