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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Yuxin Ma, Jing Wang, Wei Wu, Zhihao Dong, Suyu Luo, Gongning Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology Faculty of Computing Harbin China

ISBN: (纸本)9798350386226

Cardiovascular diseases are a major global health challenge, with electrocardiography (ECG) being critical for diagnosis and monitoring. As artificial intelligence and automated ECG diagnostic technologies rapidly advance, the demand for large-scale ECG databases continues to grow. Generative ECG has become a mainstream method to enhance database size and diversity. However, existing methods typically generate ECG randomly or focus on limited physiological categories, lacking the ability to synthesize ECG with varying physiological features and cardiac cycles, which is crucial for various practical applications. In response to this need, we propose a novel approach introducing a diffusion model called DIFF-ECG to generate precisely customized ECG that accurately reflect diverse cardiac conditions. Segmentation-based quality assessments confirmed that the synthesized ECG accurately followed the specified cardiac cycle information, with our model significantly outperforming baseline diffusion and GAN-based methods. Therefore, our approach addresses the critical need for generating clinically relevant and customizable ECG, contributing significantly to the field of automated cardiac disease diagnosis. By enabling fine-tuning of cardiac cycle phases, our method significantly expands the application range of generative ECG, potentially improving the diagnostic accuracy for rare diseases and advancing personalized medicine. © 2024 IEEE.

关键词： Electrocardiography

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A Self-Attention-Based Stock Prediction Method Using Long Short-Term Memory Network Architecture 9th

A Self-Attention-Based Stock Prediction Method Using Long Sh...

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9th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Ye, Xiaojun Ning, Beixi Bian, Pengyuan Feng, Xiaoning College of Computer Science and Technology Harbin Engineering University Harbin China College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9789819959679

The ability to analyze the trend of the stock market has always been paid high attention to. A large number of machine learning technologies have been used for stock analysis and prediction. The traditional time series prediction models, including RNN, LSTM and their deformed bodies, show the problems of gradient disappearance and low efficiency in long-span prediction. This paper proposes a long-term and short-term memory network architecture, which based on Encoder and Decoder Stacks and self-attention mechanism, replacing the feature extraction part of traditional LSTM through self-attention mechanism and provides interpretable insights into the dynamics of time. Through the results of simulation experiments, this paper shows the comparison of stock prediction effects through using RNN, Bi-LSTM and Encoder and Decoder-Attention-LSTM models. The experimental task shows that the prediction accuracy of this model is improved by an order of magnitude compared with the traditional LSTM-like model, and can achieve high accuracy when the epoch is small. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Achieving high strength-ductility synergy in TiB_(w)/near α-Ti composites by ultrafine grains and nanosilicides via low-temperature severe plastic deformation

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Journal of Materials science & technology 2024年第35期202卷 253-266页

作者： Changjiang Zhang Xiaojian Liang Yonggang Sun Shuzhi Zhang Ruipeng Guo Hong Feng Fantao Kong Peng Cao College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China School of Materials Science and Engineering Yanshan UniversityQinhuangdao 66004China Department of Chemical and Materials Engineering The University of AucklandPrivate Bag 92019Auckland 1142New Zealand

The common problem of low strength-ductility matching prevails in near-αhigh-temperature titanium matrix composites(TMCs).In this work,the design strategy of ultrafine grains and dispersed(Ti,Zr)_(6)Si_(3) nanoprecipitates in the microstructure of TiB_(w)/nearα-Ti composites via low-temperature isothermal mul-tidirectional forging(IMDF),is expected to break the trade-offdilemma between strength and *** results show that with the decrease in the temperature of IMDF,the grain scale decreased from 0.98 to 0.59μm,and the location of silicide precipitation shifted from phase boundaries and grain boundaries toα-grain boundaries and intracrystalline *** experiments confirm that the local segregation of Si and the temperature of thermomechanical deformation are the key factors affecting the precipitation behavior of *** the decrease of the deformation temperature,the precipitation mechanism of silicides changes from a single diffusion-controlled precipitation to the coupling of two mechanisms,namely,elemental diffusion and dislocation-assisted nucleation,which facilitates the successive precip-itation of nanometer-sized silicides at the grain boundaries and in the inner *** ultimate ten-sile strength(UTS)and elongation of the composites were substantially increased after IMDF at 950 and 800℃,especially the excellent performance at 800℃,where the strength reached 1320.3 MPa and the elongation was 5.8%.The room and high-temperature strengthening and failure mechanisms of the com-posites are analyzed and discussed,and the yield strength(YS)increments provided by various strength-ening mechanisms at room temperature are quantified,aiming to provide a potential preparation strategy for the synergistic strengthening of near-αTMCs with ultrafine grains and nanoparticles.

关键词： Titanium matrix composites High temperature titanium alloy Severe plastic deformation Ultrafine grain Silicides Strengthening mechanisms

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Unraveling the role of NiSnPH@OOH/CC perovskite hydroxide for efficient electrocatalytic oxidation of methanol to formate

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Nano Research 2024年第4期17卷 2388-2399页

作者： Jing Shao Yusheng Fang Xiaobing Wu Muhammad Imran Abdullah Youkun Tao College of Chemistry and Environmental Engineering Shenzhen UniversityShenzhen 518060China School of Science Harbin Institute of TechnologyShenzhen 518055China

The sluggish kinetics of oxygen evolution reaction(OER)is the key tailback for hydrogen production from the water *** OER with thermodynamically auspicious methanol oxidation reaction(MOR)can significantly boost the H_(2) and value-added products ***,it is currently challenging to achieve a synergistic manipulation of product selectivity and performance for MOR ***,we report NiSnPH@OOH/CC(CC=carbon cloth)perovskite hydroxide nanosphere as an efficient MOR electrocatalyst with high activity,stability,and selectivity towards methanol oxidation to formate.A surface amorphous layer of defect rich NiOOH was generated in operando by selective Sn leaching with stable perovskite hydroxide bulk structure,which mitigates the oxidative power and optimizes the local coordination environment of the active NiOOH *** situ Raman combined with electrochemical studies further confirm the key active species,NiOOH,generated in operando enhance the MOR and blocking the over oxidation of methanol to CO_(2).As a result,NiSnPH@OOH/CC effectively masks the OER and attains>99%selectivity with 100%Faradic efficiency for *** results of this study show the advances of NiSnPH@OOH/CC as an efficient electrocatalyst for MOR and also suggest its potential applications for various small organic molecules oxidation.

关键词： perovskite hydroxide small organic molecules nickel oxy(hydroxide) value-added products methanol oxidation reaction

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Underwater Biological Target Detection Algorithm and Research Based on YOLOv7 Algorithm

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第6期51卷 594-601页

作者： Zhuang, Hongwei Liu, Weisheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China

Underwater target detection is an important method for detecting marine organisms. However, due to the image occlusion of underwater targets, blurred water quality, poor lighting conditions, small targets, and complex backgrounds, the detection of underwater biological targets has posed significant challenges. In the intricate underwater environment, the conventional feature extraction method has a few drawbacks, including imprecise feature extraction, sluggish detection speed, and inadequate robustness. Consequently, an underwater target detection method based on the enhanced You Only Look Once 7 (YOLOv7) is proposed in this study. The network architecture is reconstructed, and the Deformable Convolutional Network (DCN) modules replace some 3×3 convolutional blocks in the ELAN structure to offset sampling points and reduce background interference. Skip connections and 1× 1 convolutional architecture are added to the DCN module to improve the model’s perception of image details. In addition, Contextual Transformer 3 (COT3) is also incorporated to improve visual performance. Finally, to improve the detection efficiency of small objects, the CIoU loss function is finally replaced by the Normalized Wasserstein Distance (NWD) algorithm. The mAP of DCCN-YOLOv7 on the URPC dataset is 80.4%, according to the experimental results, 2.8% higher than the YOLOv7 network model that is used as a baseline. Furthermore, in contrast to the original YOLOv7 algorithm, the detection speed and accuracy are higher, making it more appropriate for target recognition underwater. © (2024), (International Association of Engineers). All rights reserved.

关键词： Feature extraction

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Multi-scale feature fusion for facial expression recognition

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Neural Computing and Applications 2025年第17期37卷 11399-11420页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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Ultra-high temperature shape memory in high-Hf content NiTiHf alloys

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材料科学技术（英文版） 2025年第6期209卷 124-127页

作者： A.V.Shuitcev Q.Z.Li M.G.Khomutov L Li Y.X.Tong International Joint Laboratory of Advanced Nanomaterials of Heilongjiang Province College of Materials Science and Chemical EngineeringHarbin Engineering UniversityHarbin150001China National University of Science and Technology"MISIS" Moscow119049Russia

The active development of space industry necessitates the cre-ation of novel materials with unique properties,including shape memory alloys(SMAs).The development of ultra-high temperature SMAs(UHTSMAs)with operating temperatures above 400 ℃ is a significant challenge[1-3].It is known that reversible thermoelas-tic martensitic transformation(MT)is the basis for shape mem-ory behavior[4].Currently,there are several systems in which MT temperatures meet the above requirements,for example,RuNb[5],HfPd[6],TiPd[7],TiPt[8],TiNiPd[9],TiNiPt[10],NiTiHf[11],*** them,NiTiHf alloys are of particular interest due to the following ***,Hf significantly increases MT tempera-tures of NiTi-based alloys[2].Secondly,these alloys exhibit high dimensional stability and high work output(up to～250 ℃)[12].Thirdly,they are relatively inexpensive in comparison with other *** operating temperatures may be an unfavorable fac-tor influencing the reversible strain *** thermoelastic na-ture of MT requires the elastic stresses that arise during the crys-tallographic rearrangements of high-temperature austenite phase into low-temperature martensite to be *** relax-ation processes at elevated temperatures can be particularly active,which may suppress strain recovery.

关键词： temperature content memory shape alloys high-hf nitihf ultra-high

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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Energy leakage in OFDM sparse channel estimation:The drawback of OMP and the application of image deblurring

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Digital Communications and Networks 2024年第5期10卷 1280-1288页

作者： Gang Qiao Xizhu Qiang Lei Wan Hanbo Jia Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001China Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001China College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China School of Informatics Xiamen UniversityXiamen 361005China Key Laboratory of Underwater Acoustic Communication and Marine Information Technology(Xiamen University) Ministry of EducationXiamen 361005China

In this paper,in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing(OFDM)systems,we systematically have analyzed the optimal locations of atoms with discrete delays for each path reconstruction from the perspective of linear fitting ***,we have investigated the adverse effects of the non-ideal inner product function on the iteration in one of the most widely used channel estimation method,Orthogonal Matching Pursuit(OMP).The study shows that the distance between the selected atoms for each path in OMP can be larger than the sampling interval,which prevents OMP-based methods from achieving better *** overcome this drawback,the image deblurring-based channel estimation method,in which the channel estimation problem is analogized to one-dimensional image deblurring,was proposed to improve the large compensation distance of traditional *** advantage of the proposed method was validated by the results of numerical simulation and sea trial data decoding.

关键词： Sparse channel estimation Orthogonal matching pursuit(OMP) Orthogonal frequency division multiplexing (OFDM) Linear fitting Image deblurring

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