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IEEE Electron Device Letters 2025年第5期46卷 813-816页

作者： Sun, Na Zhang, Zhengliang Zhou, Feng Wang, Tianqi Ren, Fang-Fang Gu, Shulin Lu, Hai Zhang, Rong Ye, Jiandong Nanjing University School of Electronic Science and Engineering Nanjing210093 China Harbin Institute of Technology Harbin150001 China

Single event burnout (SEB) caused by heavy ion irradiation in space environments poses a significant threat to aerospace power electronic devices. This work demonstrates irradiation-hardened -Ga2O3 heterojunction barrier Schottky (HJBS) diodes with exceptional SEB capability. The device design incorporates micron-scale deep trenches filled by p-type nickel oxide (NiO) and high-k BaTiO3 field-plate (FP) edge termination. This architecture efficiently extracts radiation-induced positive charges (holes) during single-event irradiation through the trenched-embedded Ni/p-NiO with low Ohmic contact resistance, significantly alleviating charge aggregation while minimizing non-uniform field distributions through strategically engineered charge drainage pathways. As a result, the HJBS device achieves a SEB voltage exceeding 1.4 kV and a SEB degradation rate of only 9.6%. This is the first demonstration of kilovolt-class radiation-hardened diodes, and its performance metrics are the best reported among SiC, GaN, Ga2O3 and Si power diodes to date. This work underscores the great potential of Ga2O3 power diodes for irradiation power applications. © 1980-2012 IEEE.

关键词： Nickel oxide

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Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting

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Neural Computing and Applications 2025年 1-46页

作者： Purohit, Sourav Kumar Panigrahi, Sibarama Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Burla India Department of Computer Science and Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India

Crude oil prices (COP) profoundly influence global economic stability, with fluctuations reverberating across various sectors. Accurate forecasting of COP is indispensable for governments, policymakers, and stakeholders to make well-informed decisions and effectively mitigate risks. The decomposition-based hybrid models have been showing promising COP forecasting accuracy than other time series forecasting methods. Despite this fact, no systematic study has been conducted to evaluate the true potential of different decomposition-based hybrid methods employing different forecasting models to forecast the COP. Therefore, a hybrid modeling framework is developed by combining efficient decomposition techniques, namely empirical mode decomposition (EMD), ensemble EMD (EEMD), complete EEMD with adaptive noise (CEEMDAN), and variational mode decomposition (VMD) with seven statistical models, fourteen machine learning (ML) models, and six deep learning (DL) models. Further, a systematic study is conducted on the resulting decomposition-based hybrid models to find the best hybrid model for COP forecasting. Three distinct train-test data splits are employed to ensure a reliable evaluation of the models using four performance metrics. Extensive statistical analysis is conducted to identify the optimal combination of the decomposition technique and forecasting model for precise COP prediction. The results demonstrate that the proposed decomposition-based hybrid model employing VMD and Huber Regression is statistically the best method among all alternatives to forecast monthly COP. The proposed hybrid method VMD-Huber Regression improves the root mean square error (RMSE) by 21% than CEEMDAN-ARIMA, 58.31% than EEMD-Theta, 13.18% than EMD-Random Walk, and 49.44% than VMD-TBATS hybrid methods in 60–40 Train-Test split ratio. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Mean square error

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Composite spectral spatial pixel CNN for land-use hyperspectral image classification with hybrid activation function

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Multimedia Tools and Applications 2025年第12期84卷 10527-10550页

作者： Banerjee, Anasua Swain, Satyajit Rout, Minakhi Bandyopadhyay, Mainak School of Computer Engineering Kalinga Institute of Industrial Technology-Deemed-to-be-University Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Silchar India

Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging which examines the pattern of light in a target and recognizes objects based on varying spectral properties. However, in remote sensing, detecting surface material via HSI analysis is a critical and difficult task. The performance of spectral-spatial data exploitation is well established to outperform typical spectral pixel-wise techniques. Because of its great feature extraction ability, convolutional neural networks (CNN) have emerged as a potent deep learning approach. CNN translates the input features of an image into an equivalent CNN feature map, in addition to naturally combining spectral and spatial information. However, spectral-spatial properties when combined with pixel-wise extraction can learn more minute details of objects present on the earth’s terrain. In this paper, a noble Composite Spectral Spatial Pixel CNN model for the classification of hyperspectral data is presented which is an amalgamation of 3D-2D-1D CNN. While the 3D and 2D CNN exploit the spectral-spatial features effectively, 1D CNN works on pixel-wise feature extraction. Further, to optimize the classification performance of the proposed model, a new hybrid activation function Flatten-T Swish is also used which is the combination of ReLU and Swish function. The proposed model is compared with other state-of-the-art models based on three popular HSI datasets, and it is found that the proposed model performs better among others in terms of classification and computation time, giving 98.87% accuracy for Indian Pines, 99.92% accuracy for Pavia university, and 99.99% accuracy for Salinas Valley dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Improving Multimodal Large Language Models through Combining Resampler and MLP Projections

Improving Multimodal Large Language Models through Combining...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, Zixing Bai, Yuting School of Computer Science and Technology Fudan University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

Current multimodal large language models (LLMs) achieve impressive performance through connecting visual encoders and LLMs by resampler or MLP projections. Although the MLP projections are effective and widely used in recent works when compared to resampler projections, it has the flaw of the inability to change the number of tokens, which limits the application scenarios. In this paper, we propose a new projection structure that inherits the efficiency of traditional MLP projections and has the ability to flexibly change the number of tokens. Furthermore, we propose a new multimodal LLM with the proposed projection applied. To validate the performance, we evaluate the proposed model on the first large-scale multimodal science question dataset scienceQA, and the first multimodal LLM evaluation benchmark MME. Our model achieves a new state-of-the-art (SoTA) result of 94.27% on scienceQA, which is higher than the previous SoTA result of 92.53% achieved by the LLaVA+GPT-4 judge model. On the MME benchmark, our model achieves better performance when compared to representative chatbot models BLIP-2 and InstructBLIP, with significantly lower training costs. © 2025 IEEE.

关键词： large language models Multimodal vision and language

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A Speech Spoofing Detection Method Based on Feature Fusion and Residual Attention 24

A Speech Spoofing Detection Method Based on Feature Fusion a...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Lan, Haiyan Dong, Linfan Wang, Yuhua Li, Yike Jiang, Dongxu Han, Zhiying College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China

ISBN: (纸本)9798400710865

The emergence of various types of deception attacks presents a substantial risk to Automatic Speaker Verification (ASV) systems, and an increasing number of experts and scholars are focusing on the research of voice deception detection. This paper introduces an innovative approach for detecting voice deception that addresses the accuracy and generalization limitations of traditional methods. The introduced method is based on spectral-temporal feature fusion, which complements the acoustic features captured by different channels to obtain more comprehensive speaker information. It then utilizes multi-channel fused spectrogram features, applying a residual attention learning mechanism to assign varying levels of attention to the spectrogram features from different channels. The method further leverages an improved ResNet network to learn more refined acoustic information for classification. The method attained an EER of 3.60 and a t-DCF score of 0.0931 on the LA logical access corpus of ASVspoof 2019, which represent improvements of 55.5% and 56.0%, respectively, over the best performing baseline system. © 2024 Copyright held by the owner/author(s).

关键词： Spectrographs

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Domain generalization with semi-supervised learning for people-centric activity recognition

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science China(Information sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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MM-UKAN++: A Novel Kolmogorov–Arnold Network-Based U-Shaped Network for Ultrasound Image Segmentation

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IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2025年第4期72卷 498-514页

作者： Zhang, Boheng Huang, Haorui Shen, Yi Sun, Mingjian Harbin Institute of Technology Department of Control Science and Engineering Heilongjiang Harbin150001 China Pohang University of Science and Technology Department of Convergence IT Engineering Pohang37673 Korea Republic of Harbin Institute of Technology Suzhou Research Institute Jiangsu Suzhou215000 China Harbin Institute of Technology Department of Information Science and Engineering Shandong Weihai264200 China Harbin Institute of Technology Shandong Weihai264200 China Shandong Laboratory of Advanced Biomaterials and Medical Devices Shandong Weihai264209 China

Ultrasound (US) imaging is an important and commonly used medical imaging modality. Accurate and fast automatic segmentation of regions of interest (ROIs) in US images is essential for enhancing the efficiency of clinical and robot-assisted diagnosis. However, US images suffer from low contrast, fuzzy boundaries, and significant scale variations in ROIs. Existing convolutional neural network (CNN)-based and transformer-based methods struggle with model efficiency and explainability. To address these challenges, we introduce MM-UKAN++, a novel U-shaped network based on Kolmogorov-Arnold networks (KANs). MM-UKAN++ leverages multilevel KAN layers as the encoder and decoder within the U-network architecture and incorporates an innovative multidimensional attention mechanism to refine skip connections by weighting features from frequency-channel and spatial perspectives. In addition, the network effectively integrates multiscale information, fusing outputs from different scale decoders to generate precise segmentation predictions. MM-UKAN++ achieves higher segmentation accuracy with lower computational cost and outperforms other mainstream methods on several open-source datasets for US image segmentation tasks, including achieving 69.42% IoU, 81.30% Dice, and 3.31 mm HD in the BUSI dataset with 3.17 G floating point of operations (FLOPs) and 9.90 M parameters. The excellent performance on our automatic carotid artery US scanning and diagnostic system further proves the speed and accuracy of MM-UKAN++. Besides, the good performance in other medical image segmentation tasks reveals the promising applications of MM-UKAN++. The code is available on GitHub. © 1986-2012 IEEE.

关键词： Image segmentation

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Progress of crystalline carbon nitride in synthesis,atomic structure characterization and photocatalysis

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Nano Research 2025年第1期18卷 455-476页

作者： Yueyang Tan Zhongyan Zhang Yayun Pu Zongzhao Sun Limin Huang School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemistry Southern University of Science and TechnologyShenzhen 518055China School of Traffic and Environment Shenzhen Institute of Information TechnologyShenzhen 518172China College of Chemistry and Chemical Engineering Linyi UniversityLinyi 276000China School of Optoelectronic Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China

Crystalline carbon nitride(CCN)has emerged as a highly promising semiconductor photocatalyst with unique properties,such as enhanced charge migration rate,reduced carrier recombination probability,narrow band gap and improved light-harvesting efficiency,which are suitable for a wide range of applications in solar-to-chemical conversion,energy storage,therapeutic and environmental pollution *** the past few years,there has been an increasing number of reviews on CCN ***,most of these reviews mainly focus on synthesis methods,modification and applications,with less emphasis on the relationship between structures and properties,as well as on indepth exploration of the crystalline *** electronic instability of CCN presents challenges for conventional characterization techniques to directly and thoroughly investigate the relationship between its intrinsic atom structure and photocatalytic *** mini-review not only highlights the progress in CCN-based photocatalysts,with a focus on molten-salt synthesis(including solid-salt-induced crystallization),but also emphasizes the atomic structure characterization by specifically introducing the differential phase contrast(DPC)scanning transmission electron microscopy(STEM)technique,which is essential for enhancing our understanding of the crystal structure and photocatalytic mechanisms of ***,the review outlines the photocatalytic performance and puts forward potential challenges on CCN *** review will provide a clearer understanding of the relationship in developing precise customization strategies for CCN materials and ultimately explain the regularity and specificity of the enhanced performance in targeted photocatalytic systems.

关键词： crystalline carbon nitride synthesis characterization photocatalysis

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Plasma-assisted fabrication of multiscale materials for electrochemical energy conversion and storage

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Carbon Energy 2025年第2期7卷 144-171页

作者： Chen Li Tengfei Zhang Zhong Qiu Beirong Ye Xinqi Liang Xin Liu Minghua Chen Xinhui Xia Chen Wang Wangjun Wan Yongqi Zhang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengduChina Key Laboratory of Engineering Dielectric and Applications(Ministry of Education) School of Electrical and Electronic EngineeringHarbin University of Science and TechnologyHarbinChina State Key Laboratory of New Textile Materials and Advanced Processing Technology Wuhan Textile UniversityWuhanChina College of Materials Science and Engineering Zhejiang University of TechnologyHangzhouChina School of Materials Science and Engineering Zhejiang UniversityHangzhouChina Zhejiang Academy of Science and Technology for Inspection and Quarantine HangzhouChina

Plasma,the fourth state of matter,is characterized by the presence of charged particles,including ions and *** has been shown to induce unique physical and chemical ***,there have been increased applications of plasma technology in the field of multiscale functional materials'preparation,with a number of interesting *** review will begin by introducing the basic knowledge of plasma,including the definition,typical parameters,and classification of plasma *** this,we will provide a comprehensive review and summary of the applications(phase conversion,doping,deposition,etching,exfoliation,and surface treatment)of plasma in common energy conversion and storage systems,such as electrocatalytic conversion of small molecules,batteries,fuel cells,and *** article summarizes the structure-performance relationships of electrochemical energy conversion and storage materials(ECSMs)that have been prepared or modified by *** also provides an overview of the challenges and perspectives of plasma technology,which could lead to a new approach for designing and modifying electrode materials in ECSMs.

关键词： electrocatalysts energy conversion and storage nanomaterials plasma

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ACbot:an IIoT platform for industrial robots

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Frontiers of computer science 2025年第4期19卷 13-27页

作者： Rui WANG Xudong MOU Tianyu WO Mingyang ZHANG Yuxin LIU Tiejun WANG Pin LIU Jihong YAN Xudong LIU School of Computer Science and Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100194China School of Software Beihang UniversityBeijing 100191China School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Engineering China University of GeosciencesBeijing 100083China

As the application of Industrial Robots(IRs)scales and related participants increase,the demands for intelligent Operation and Maintenance(O&M)and multi-tenant collaboration *** methods could no longer cover the requirements,while the Industrial Internet of Things(IIoT)has been considered a promising ***,there’s a lack of IIoT platforms dedicated to IR O&M,including IR maintenance,process optimization,and knowledge *** this context,this paper puts forward the multi-tenant-oriented ACbot platform,which attempts to provide the first holistic IIoT-based solution for O&M of *** on an information model designed for the IR field,ACbot has implemented an application architecture with resource and microservice management across the cloud and multiple *** this basis,we develop four vital applications including real-time monitoring,health management,process optimization,and knowledge *** have deployed the ACbot platform in real-world scenarios that contain various participants,types of IRs,and *** date,ACbot has been accessed by 10 organizations and managed 60 industrial robots,demonstrating that the platform fulfills our ***,the application results also showcase its robustness,versatility,and adaptability for developing and hosting intelligent robot applications.

关键词： IIoT platform industrial robots cloud-edge collaboration intelligent applications

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