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Performance of a four deep neural network structure in moiré fringe intelligent analysis

Applied Optics 2025年第9期64卷 2208-2216页

作者： Zeng, Xinyu Chen, Yunyun Cheng, Weihao Xie, Wenzhuo Yang, Cuihong State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion Nanjing University of Information Science & Technology Nanjing210044 China Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere Nanjing University of Information Science & Technology Nanjing210044 China Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science & Technology Nanjing210044 China Nanjing University of Information Science & Technology Nanjing210044 China

Moiré fringe analysis holds significant importance in the assessment of flow fields, while deep learning is advancing rapidly and is frequently employed to handle complex computational challenges. In this paper, the performance of U-net, Unet++, Unet 3+, and ResUnet in moiré fringe analysis is compared and analyzed. The results indicate significant differences among the four neural networks in terms of the training time, prediction speed, prediction accuracy, and generalization capability regarding positional variations. Among the four neural networks, ResUnet exhibits the shortest training times, highest accuracy, and fastest prediction speeds while also exhibiting good generalization capability. Consequently, in practical applications, using ResUnet for moiré fringe analysis is an excellent choice. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Deep neural networks

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AMHF-TP:Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期13卷 127-141页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’anChina Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’anChina Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anChina School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’anChina

Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as extensive training durations,limited sample sizes,and inadequate generalization *** address these issues,we present AMHF-TP,an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance *** AMHF-TP is composed of four key components:a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences;a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures;a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences;and a hierarchical feature extraction module that integrates multimodal peptide sequence *** with leading methods,the proposed AMHF-TP demonstrates superior precision,accuracy,and coverage,underscoring its effectiveness and robustness in MFTP *** comparative analysis of separate hierarchical models and the combined model,as well as with five contemporary models,reveals AMHFTP’s exceptional performance and stability in recognition tasks.

关键词： deep learning hypergraph multifunctional therapeutic peptides multi-granularity hierarchical features

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Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning

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Research in Astronomy and Astrophysics 2025年第3期25卷 236-248页

作者： Zhen-Hong Shang Long Chen Zhen-Ping Qiang Yi Bi Run-Xin Li Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Observatories Chinese Academy of Sciences

Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar *** proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow ***,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow *** apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar *** experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index ***,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity *** code is available at https://***/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.

关键词： methods: data analysis Sun: fundamental parameters techniques: image processing

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Tunable terahertz narrowband filter based on heterostructure waveguides filled with liquid-crystals 2

Tunable terahertz narrowband filter based on heterostructure...

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2nd International Academic Conference on Optics and Photonics, IACOP 2024

作者： Zhang, Ya-Yun Liu, Huan Zhang, Huan Li, Yong Wang, Xiang-Yu Ma, Jing Fan, Ya-Xian Tao, Zhi-Yong Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin541004 China Academy of Marine Information Technology Guilin University of Electronic Technology Beihai536000 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

We propose a tunable waveguide-type terahertz (THz) narrowband filter based on heterostructure waveguide filled with liquid-crystals (LCs). This filter consists of two periodic corrugated waveguide structures with different duty cycles, which produces a narrow linewidth transmission peak within the original stop band. By adjusting the effective refractive index of LC through external fields, the center frequency of the transmission peak can be tuned. The simulated electric field proves that the narrow linewidth transmission peak is mainly attributed to the local resonance occurred at the interface. Furthermore, an increase in the number of periods can greatly reduce bandwidth and improve the Q-factor of the filter. The proposed electrically tunable THz narrowband filter based on the heterostructure waveguide provide an effective method for the design and development of THz narrowband filter. © 2025 SPIE.

关键词： Terahertz waves

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Dual-band tunable terahertz absorber based on all-dielectric InSb metamaterials 2

Dual-band tunable terahertz absorber based on all-dielectric...

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2nd International Academic Conference on Optics and Photonics, IACOP 2024

作者： Zhang, Huan Liu, Huan Ma, Jing Li, Yong Zhang, Ya-Yun Wang, Xiang-Yu Tao, Zhi-Yong Fan, Ya-Xian Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China Academy of Marine Information Technology Guilin University of Electronic Technology Beihai536000 China Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

We propose a dual-band tunable terahertz (THz) absorber based on all-dielectric InSb metamaterials. This absorber consists of a resonant structure at the top and a dielectric substrate at the bottom. The resonant structure layer interacts with THz waves to achieve high-efficiency absorption, while the dielectric substrate layer not only provides support but also effectively prevents THz waves from penetrating the absorber. Simulation results show that under room temperature conditions (T=300 K), the absorber achieves absorption rates of 96.85% and 98.76% at the frequencies of 0.366 THz and 0.991 THz, respectively. As the temperature increases, these two absorption peaks shift towards a higher frequency range. Based on the unique properties of InSb, the frequency shift range of the proposed absorber can be tuned through external thermal excitation, with a maximum of 0.24 THz. Meanwhile, the absorber maintains a high absorption rate of over 85%. The incorporation of InSb metamaterials enables temperature tunability, allowing the absorber to exhibit enhanced performance over a broader temperature range, making it suitable for sensor devices that require precise control over both absorption efficiency and temperature tuning. © 2025 SPIE.

关键词： Terahertz waves

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Terahertz AND-logic switching based on vanadium dioxide micro-ring resonators 2

Terahertz AND-logic switching based on vanadium dioxide micr...

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2nd International Academic Conference on Optics and Photonics, IACOP 2024

作者： Li, Yong Ma, Jing Liu, Huan Zhang, Huan Zhang, Ya-Yun Wang, Xiang-Yu Tao, Zhi-Yong Fan, Ya-Xian Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China Academy of Marine Information Technology Guilin University of Electronic Technology Beihai536000 China Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

The application of optical switches has attracted significant attention in recent years due to their potential in advanced communication systems and integrated photonics. Here, we propose a thermally tunable terahertz (THz) AND-logic switching device based on vanadium dioxide (VO2) micro-ring resonators (MRRs) on a silicon platform. VO2 exhibits an insulator-to-metal transition (IMT) characteristic at relatively low temperatures, and the conductivity can be increased by 4-5 orders of magnitude under external thermal pumps, with the a few picoseconds of rapid phase transition process, which allows for highly efficient modulation of THz waves. This transition shows significant advantages in terms of low-power operation and dynamic control over the THz response. Based on the IMT transition of VO2, the device achieves precise control of THz waves, which enables reliable AND-logic operation. At low temperatures, when VO2 is in the insulating phase, the hybrid VO2 MRR exhibits logic "1". Conversely, at high temperatures, when VO2 transitions to the metallic phase, logic "0" is created. Simulation results demonstrate that when thermal stimulation is applied simultaneously to both micro-rings (A=1, B=1), the transmittance is greater than 0.8 at 1.5142 THz, while for other input combinations, the transmittance close to 0. The modulation depth up to 84%. The simulated electric fields confirm that the AND logic operation function is mainly attributed to the VO2 phase transition and the resonance effect of MRRs. The proposed VO2-based structure demonstrates improved performance compared to conventional materials, including faster switching speeds, lower power consumption, and a higher degree of tunability across the THz spectrum. This work highlights the promise of VO2 as a key material for next-generation tunable THz devices, offering a scalable solution for photonic and communication technologies. © 2025 SPIE.

关键词： Vanadium dioxide

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Optimization algorithm for global k-means algorithm 13

Optimization algorithm for global k-means algorithm

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13th International Conference on intelligent Control and information processing, ICICIP 2025

作者： Li, Sen Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

The k-means method is widely utilized for clustering. Its simplicity, efficacy, and swiftness make it a favored choice among clustering algorithms. It faces the challenge of sensitivity to the initial class center. The enhancement strategy for the k-means algorithm focuses on refining the initial selection of cluster centroids, no longer using its random selection of initial class center points, but starting from the entire dataset, choosing the most distant point to serve as the starting cluster centroid. This can also prevent the local optimization problem of the k-means algorithm. The improved algorithm proposed in this article is an effective method that can select the initial class center point from a global perspective and reduce computation time. The suggested approach has undergone rigorous testing and comparative analysis across multiple datasets, yielding commendable outcomes. © 2025 IEEE.

关键词： Optimization algorithms

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Frame Generation Algorithm for Differentiated Services in Tactical Communication Networks

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Journal of Communications and information Networks 2025年第1期10卷 15-25页

作者： Huaifeng Shi Chengsheng Pan Lishang Qin Yingzhi Wang Huangjie Lu Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China

In response to the complex and multidimensional nature of converged traffic on heterogeneous links in tactical communication networks,which leads to the difficulty in ensuring the quality of service(QoS)requirements for critical services,a frame generation algorithm for differentiated services(DS-FG)is ***-FG deploys an adaptive frame generation algorithm based on deep reinforcement learning(DRL-FG)for timesensitive service,while deploying a high efficient frame generation(HEFG)algorithm for non-time-sensitive ***-FG constructs a reward function by combining the queue status information of time-sensitive service and utilizes deep deterministic policy gradients(DDPG)to train a decision model for adaptive frame generation(AFG)algorithm ***,Gaussian noise sampling and prioritized experience replay strategies are employed to enhance model training efficiency and performance,achieving optimal matching between time-sensitive service QoS requirements and frame generation *** results demonstrate that DS-FG outperforms traditional algorithms,achieving up to 13%improvement in throughput and over 19.7%reduction in average queueing delay for time-sensitive service.

关键词： frame generation algorithm tactical communication network reinforcement learning differentiated services

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A Holistic Evaluation of Piano Sound Quality 10th

A Holistic Evaluation of Piano Sound Quality

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10th Conference on Sound and Music technology, CSMT 2023

作者： Zhou, Monan Wu, Shangda Ji, Shaohua Li, Zijin Li, Wei Central Conservatory of Music Beijing China School of Computer Science and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789819779611

This paper aims to develop a holistic evaluation method for piano sound quality to assist in purchasing decisions. Unlike previous studies that focused on the effect of piano performance techniques on sound quality, this study evaluates the inherent sound quality of different pianos. To derive quality evaluation systems, the study uses subjective questionnaires based on a piano sound quality dataset. The method selects the optimal piano classification models by comparing the fine-tuning results of different pre-training models of Convolutional Neural Networks (CNN). To improve the interpretability of the models, the study applies Equivalent Rectangular Bandwidth (ERB) analysis. The results reveal that musically trained individuals are better able to distinguish between the sound quality differences of different pianos. The best fine-tuned CNN pre-trained backbone achieves a high accuracy of 98.3% as the piano classifier. However, the dataset is limited, and the audio is sliced to increase its quantity, resulting in a lack of diversity and balance, so we use focal loss to reduce the impact of data imbalance. To optimize the method, the dataset will be expanded, or few-shot learning techniques will be employed in future research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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