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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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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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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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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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Utilizing Multipath Effects for Mobile Charging

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IEEE Transactions on Mobile computing 2025年

作者： Gao, Jing Wu, Die Zhang, Linglin Li, Jingwen Yang, Jin Peng, Jian Liu, Tang Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University College of Computer Science Chengdu610065 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China

Recently, Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution to address the energy limitations of wireless sensor networks. In practical applications of WRSNs, environmental objects are ubiquitous, reflecting radio waves and causing them to reach sensors via multiple paths. These multipath effects significantly impact the power intensity received by sensors. In this paper, we study a fundamental issue of charGing schEduling with mulTipath effectS (GETS), that is, how to schedule a mobile charger by comprehensively considering the multipath effects to maximize the overall charging utility. To this end, we first establish a charging model with environmental objects to investigate the impact of multipath effects on power distribution. Then, we propose a charging scheduling scheme that not only selects a series of sojourn locations for the MC (Mobile Charger) to maximize the total power received by nearby sensors but also construct a charging path that avoids environmental objects. We conduct extensive simulations as well as indoor and outdoor field experiments to evaluate the performance of our scheme. The results demonstrate that, on average, our scheme outperforms baseline algorithms by 48.87%. © 2002-2012 IEEE.

关键词： Charging stations

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ReconGait: Giant Panda Gait Recognition Based on Spatio-Temporal Feature Reconstruction

ReconGait: Giant Panda Gait Recognition Based on Spatio-Temp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peng Min Han Su Mengnan He Rong Hou Pinjia Que Peng Chen School of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu Research Base of Giant Panda Breeding Chengdu China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

As an endangered species, protecting and identifying individual giant pandas has always been a focus. However, traditional methods based on feces and molecular biology have inherent limitations. With the progress of deep learning, image analysis methods can deliver good results, but they rely on high-quality frontal facial images, which are challenging to acquire in the wild. Furthermore, these approaches have neglected the fact that a substantial portion of data from the wild is predominantly in the form of video, and have inadequately exploited the temporal information. Therefore, we apply gait recognition to identify individual giant pandas. By examining the unique walking patterns of individuals in the video, gait recognition allows accurate identification from various angles, long ranges, and without subject cooperation. This makes it ideal to identify wild individual giant pandas, but it also introduces new challenges, such as quadrupeds having more complex body gestures compared to humans, and the generally lower image quality taken by cameras in the wild. To tackle issues in natural environments, we design ReconGait, a giant panda gait recognition model capable of aligning multi-view images and restoring low-quality regions effectively. Experiments shows state-of-the-art results, proving the effectiveness of using gait analysis for individual giant panda identification. Through our work, we strive to contribute significantly to the preservation of this rare species and set a precedent for the integration of gait recognition in wildlife conservation efforts.

关键词： Endangered species Wildlife Neural networks Image restoration Quadrupedal robots Object recognition Environmental management

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Age Estimation of Giant Pandas Based on Narrowed Window Ordered Regression

Age Estimation of Giant Pandas Based on Narrowed Window Orde...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zihao Zhang Han Su Rong Hou Pengcheng Wu Ping Xu Peng Chen School of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu Research Base of Giant Panda Breeding Chengdu China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The age estimation of giant pandas is a crucial aspect of panda conservation efforts. Traditional methods for giant panda age estimation are time-consuming and expensive. Previous research has explored computer vision approaches for this task, primarily focusing on standardized frontal images of giant pandas. However, capturing clear frontal photos in the wild is challenging. In this study, we proposed a method based on Deep Convolutional Neural Networks (DCNN) for age estimation using multi-angle facial images of giant pandas. We expanded the original giant panda frontal age dataset into a multi-angle dataset, consisting of 13,524 facial images from 285 pandas. To enhance the model’s ability to fit multi-angle data, we incorporated deformable convolutions during feature extraction. We introduced a narrowed window ordered regression (NWR), and comparisons across multiple images to reduce spatial distances between feature vectors from different angles. The final model achieved a Mean Absolute Error (MAE) of 3.38. demonstrating superior performance even on frontal data. The results indicate the effectiveness of our approach in utilizing multi-angle facial data for wild giant panda age estimation. Computer vision technologies showcase their potential for meaningful contributions to wildlife conservation efforts.

关键词： Deformable models Computer vision Wildlife Neural networks Estimation Focusing Feature extraction

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Fast Collection of Target Tags Information for Large-Scale RFID Systems 20

Fast Collection of Target Tags Information for Large-Scale R...

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20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chu, Chu Fu, Ning Xu, Rui College of Computer Science Sichuan Normal University Chengdu610101 China The Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610101 China School of Network and Communication Engineering Chengdu Technological University Chengdu611730 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Radio Frequency Identification (RFID) technology is greatly supporting a variety of life and industry. Enhanced by sensors, RFID tags can monitor real-time information about attached objects and the environment, making efficient collection of tag information an important task. However, most works focus on collecting all tags but lack effective mechanisms to avoid interfering from irrelevant tags when the application only cares about a part of the tags in some cases. Besides, in more common scenarios with multiple RFID readers, existing works fail to effectively determine the surrounding tags for each reader, increasing the execution time. To conquer these defects, we propose a new solution called the efficient Vector-based target tag Information Collection (VIC) protocol to accelerate the collection rate. First, we introduce a composite vector to silence irrelevant tags and determine the local tags. During this process, the expected empty slots are skipped and the useless information transmitted by tags is reduced. Second, we use an allocation vector to collect tag information by resolving tag collision, thus improving the time efficiency. Finally, we provide theoretical analyses to optimize the parameter settings. Extensive experiments prove that VIC outperforms existing solutions by up to 32% increase in time efficiency. © 2023 IEEE.

关键词： Efficiency

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