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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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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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An Effective Deployment Scheme for Elimination of Phase Cancellation in Backscatter-based WPCN

An Effective Deployment Scheme for Elimination of Phase Canc...

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IEEE Conference on Wireless Communications and Networking

作者： Qiang Wang Yuzhuo Ma Chenglong Zhang Tang Liu Jilin Yang Dié Wu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China

Without the need for batteries, backscatter-based Wireless Powered Communication Network (WPCN) has been envisioned as a promising alternative to conventional wireless networks. Unfortunately, the unique phase cancellation problem in backscatter-based WPCN is essentially a phenomenon that severely affects connectivity and reliability of the network. Many arts have tried to tackle this issue either by using multiple antennas to employ the signal diversity, which increases the size and is not cost-efficient, or by making a repetition of the same information with different load impedances, which significantly decreases the throughput of network. In our paper, we propose an effective deployment scheme, aiming to fundamentally eliminate the phase cancellation problem. Specifically, we first build a practical communication model seeking the blind areas caused by phase cancellation. Then, a greedy algorithm and a minimum-weight graph based algorithm are proposed to elaborate topology of the network to ensure the connectivity. Finally, extensive experiments are carried out to evaluate the performance.

关键词： Greedy algorithms Network topology Wireless networks Throughput Topology Reliability Communication networks

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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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What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 575-579页

作者： Fei-Yue Wang Qinghai Miao Xuan Li Xingxia Wang Yilun Lin School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China Urban Computing Lab Department of Applied ResearchShanghai AI LaboratoryShanghai 200232China IEEE

THE well-known ancient Chinese philosopher Lao Tzu(老子)or Laozi(6th~4th century BC during the Spring and Autumn period)started his classic Tao Teh Ching《道德经》or Dao De Jing(see Fig.1)with six Chinese characters:"道(Dao)可(Ke)道(Dao)非(Fei)常(Chang)道(Dao)",which has been traditionally interpreted as“道可道,非常道”or"The Dao that can be spoken is not the eternal Dao".

关键词： DAO Intelligence Spring

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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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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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Predicting Robustness Performance with Noises in Network Representation

Predicting Robustness Performance with Noises in Network Rep...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chengpei Wu Siyi Xu 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

The connectivity and controllability of complex networks play an important role in ensuring the proper functioning of network systems. Robustness of connectivity and controllability is the ability of a network to maintain its basic functions against various malicious attacks. Convolutional neural network (CNN)-based approaches provide an efficient framework to approximate the network robustness, which significantly reduces computation time compared to attack simulations. In this paper, the performance of CNN-based prediction for connectivity and controllability robustness is investigated, when there are noises in the network input representation. Two CNN-based predictors are compared and investigated, 1) convolutional neural network-based robustness predictor (CNN-RP), and 2) spatial pyramid pooling-based convolutional neural network (CNN-SPP). Two aspects of network information noises are considered and investigated, 1) the random node information noises (RNIN), and 2) the random edge information noises (REIN). The following main conclusions are obtained from extensive experimental studies on synthetic networks: 1) CNN-RP is more tolerant than CNN-SPP to network noises, 2) The characteristics of small-world and scale-free networks make them have a favorable anti-noise ability, and 3) RNIN has less impact on CNN-based prediction performance than REIN, RNIN and REIN show opposite effects on prediction performance when the size of the predicted network is out of the training network size range.

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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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