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4th International Conference on Electronic Information engineering and Computer Science, EIECS 2024

作者： Feng, Haiwen Shi, Yunxiao School of Software Engineering Shenyang University of Technology Shenyang Key Laboratory of Intelligent Manufacturing of High-end Industrial Equipment Liaoning Shenyang China School of Software Engineering Shenyang University of Technology ShenganpLiaoning China

ISBN: (纸本)9798331531409

Aiming at the problem that the acoustic input network of Conformer encoder is insufficient for Fbank speech feature extraction, an end-to-end speech recognition modeling method of DM-Conformer is proposed. Firstly, the Dilated convolution is utilized in the downsampling module to increase the receptive field, which captures more speech feature information without adding extra model parameters, while ensuring that the height and width of the original input feature map remain unchanged. Then, the Mish activation function is introduced to alleviate the problem of slow convergence of the ReLU activation function due to its negative value. Finally, experiments are carried out on the public dataset AISHELL-1, and the speech recognition model with the improved downsampling module proposed in this paper reduces the word error rate by 5.6% compared with the Conformer baseline model on the test set, and the model finally achieves a word error rate of 6.15%. © 2024 IEEE.

关键词： Speech enhancement

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Highly oriented MXene/polyvinyl alcohol films prepared by scalable layer-by-layer blade coating for efficient electromagnetic interference shielding and infrared stealth

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Nano Research 2024年第6期17卷 5651-5660页

作者： Jingyu Dong Zhaoyang Li Congqi Liu Bing Zhou Chuntai Liu uezhan Feng State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China Shangqiu Normal University Shangqiu 476000China

Controlling the orientation of two-dimensional MXene within layered films is essential to optimize or tune their mechanical properties and electromagnetic interference shielding(EMI)performance,but achieving the high orientation MXene layers on an industrial scale remains a challenging *** this paper,a scalable layer-by-layer blade coating(LbLBC)method was employed to fabricate highly oriented MXene/polyvinyl alcohol(PVA)*** the LbLBC process,MXene/PVA colloid suffered a strong shearing effect,which induced the ordered alignment of MXene nanosheets along the direction of the blade *** orientation of MXene can be effectively adjusted by changing the scraping gap of LbLBC,achieving a maximum Herman orientation factor f of *** a result,the mechanical properties and EMI performance of the as-prepared MXene/PVA films are in direct proportion to their orientation,with the optimal values of tensile strength of 145.5 MPa,fracture strain of 19.6%,toughness of 17.7 MJ·m^(−3),and EMI shielding effectiveness of 36.7 ***,the inherently low mid-infrared(mid-IR)emissivity of MXene,combined with the densely oriented structure affords the composite films with IR stealth,resulting in a substantial decrease from 150 to 66.1℃in the radiative temperature of a ***,these scalable MXene/PVA films exhibit remarkable potential for integration into the next generation of multifunctional protective camouflage materials.

关键词： MXene nanosheets densely oriented structure layer-by-layer blade coating electromagnetic interference shielding infrared stealth

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Compressible thermoplastic polyurethane/silver nanorod foams for absorption dominant electromagnetic interference shielding

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Journal of Materials Science & technology 2024年第30期197卷 9-16页

作者： Zhaoyang Li Yong Shen Yang Zhou Bing Zhou Chuntai Liu Yuezhan Feng State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China Shangqiu Normal University Shangqiu 476000China

Conductive polymer composites(CPCs)have attracted significant interest in the field of flexible electro-magnetic protection,but the challenge of balancing high electromagnetic interference shielding effec-tiveness(EMI SE)and low reflection losses still ***,thermoplastic polyurethane/silver nanorod(TPU/AgNR)composite foams have been successfully prepared using both the salt template and vacuum-assisted thermal compression *** varying the AgNRs content and employing a layer-by-layer bonding approach,a gradient structure with optimized impedance matching is ***“absorb-reflect-reabsorb”EM attenuation mechanismofthe asymmetricgradientEMI shieldingin theinternal structure is exploited,resulting in TPU/AgNR foam(TAF)with high EMI SE and significantly reduced EM ***,the three-layer foams exhibit an average shielding efficacy of 35.5 dB and a reflected power coefficient(R)of 0.085 in the X-band,thereby substantially mitigating secondary EM wave ***,these foams demonstrate exemplary compressive resilience,with the sample main-taining excellent EMI shielding stability even after undergoing 100 compression cycles at 50%***,a straightforward approach is employed to fabricate materials with high EMI SE and low reflectivity,offering the potential for use in EM shielding applications of next-generation flexible elec-tronic devices.

关键词： Thermoplastic polyurethane foam Asymmetric gradient structure EMI shielding EM wave absorption Mechanical properties

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Audio2AB:Audio-driven collaborative generation of virtual character animation

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虚拟现实与智能硬件（中英文） 2024年第1期6卷 56-70页

作者： Lichao NIU Wenjun XIE Dong WANG Zhongrui CAO Xiaoping LIU School of Computer Science and Information Engineering Hefei University of TechnologyHeifei 230009China School of Software Hefei University of TechnologyHeifei 230009China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of TechnologyHefei 230601China

Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of ***,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient *** Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body *** parameterization method can be used to drive full-body animations of virtual characters and improve their *** the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial *** Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio ***,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss *** By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape ***,different 3D virtual character animations could be created through *** The experimental results showed that the proposed method could generate significant gestures and facial animations.

关键词： Audio-driven Virtual character Full-body animation Audio2AB Blendshape GAN-GF

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Super-resolution reconstruction of single image for latent features

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Computational Visual Media 2024年第6期10卷 1219-1239页

作者： Xin Wang Jing-Ke Yan Jing-Ye Cai Jian-Hua Deng Qin Qin Yao Cheng School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu 610000China School of Marine Engineering Guilin University of Electronic TechnologyGuilin 541000China School of Computer and Information Security Guilin University of Electronic TechnologyGuilin 541000China

Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)***,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture *** challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model *** address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed *** then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising *** so boosts the generative modeling capabilities within a minimal number of sampling *** comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.

关键词： image superresolution reconstruction denoising diffusion probabilistic model normalized flow adversarial neural network variational auto-encoder

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Organoids revealed:morphological analysis of the profound next generation in-vitro model with artificial intelligence

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Bio-Design and Manufacturing 2023年第3期6卷 319-339页

作者： Xuan Du Zaozao Chen Qiwei Li Sheng Yang Lincao Jiang Yi Yang Yanhui Li Zhongze Gu State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210008China Key Laboratory of Environmental Medicine Engineering Ministry of EducationSchool of Public HealthSoutheast UniversityNanjing 210009China

In modern terminology,“organoids”refer to cells that grow in a specific three-dimensional(3D)environment in vitro,sharing similar structures with their source organs or *** themorphology or growth characteristics of organoids through a microscope is a commonly used method of organoid ***,it is difficult,time-consuming,and inaccurate to screen and analyze organoids only manually,a problem which cannot be easily solved with traditional *** intelligence(AI)technology has proven to be effective in many biological and medical research fields,especially in the analysis of single-cell or hematoxylin/eosin stained tissue *** used to analyze organoids,AI should also provide more efficient,quantitative,accurate,and fast *** this review,we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis ***,we will summarize the development from machine learning to deep learning and the advantages of the latter,and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and ***,we will discuss the limitations of current AI used in organoid research,as well as opportunities and future research directions.

关键词： Artificial intelligence Organoids Morphology Growth characteristics Deep learning Convolutional neural network

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An Evaluation System for Large Language Models based on Open-Ended Questions 11

An Evaluation System for Large Language Models based on Open...

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11th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2024

作者： Cao, Zhiyuan Ma, Zeyu Chen, Mingang Computer and Information Engineering College Shanghai Polytechnic University Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Mathematical and Science College Shanghai Normal University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer software Technology Shanghai China

ISBN: (纸本)9798350376982

We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with evaluation reports. Currently, the evaluation of large-scale language models often exists with two prominent limitations: (1) The evaluation methods are often single-minded, resulting in less credible results. (2) Most evaluations are based on datasets with closed-ended questions, treating generative large language models as discriminative models, which fails to adequately reflect the high output flexibility characteristic of these models. For these two limitations, we proposed an evaluation system for LLMs based on open-ended questions. Our experiments on the adapted open-source datasets demonstrated the effectiveness of this system. © 2024 IEEE.

关键词： Natural language processing systems

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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A Reduced State Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri Nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model checking method traverses the full state space, which suffers from the state space explosion problem. A reduced state space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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Keyword extraction method based on complex network

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International Journal of Information and Communication technology 2024年第4期25卷 323-335页

作者： Yang, Zhen Gong, Huajian Ma, Weiyun Yin, Chunlin Li, Jie Liu, Siyang Zhu, Hua Zhao, Na Electric Power Research Institute of Yunnan Power Grid Kunming650217 China School of Computer Science and Technology Xidian University Xian710000 China Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650091 China

Keyword extraction has a wide range of applications in the field of natural language processing. Many research results on keyword extraction at present. Among them, keyword extraction methods are based on complex networks do not require a lot of data training in advance and are simple to implement. This paper analyses and realises the application of complex network theory in the Chinese news keyword extraction task. We collected and analysed 2,061 news texts from Sina website as experimental data, and found that the number of keywords extracted affected the performance of keyword extraction method. We also compared the performance of our algorithm with other keyword extraction algorithms. The experiments verify the accuracy and effectiveness of complex networks in keyword extraction of Chinese news texts. Based on PyQt and TextRank, a news text keyword analysis platform is constructed to realise the visualisation of text network and the extraction of Chinese news keywords Copyright © 2024 Inderscience Enterprises Ltd.

关键词： complex network keyword extraction text network TextRank

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