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Research on the Performance of a Mooring Platform Under the Collision of Icebergs

China Ocean engineering 2025年第1期39卷 43-57页

作者： ZHANG Yan ZHANG Song-bao WANG Yi-tao ZHAO Wei-dong GUI Hong-bin School of Ocean Engineering Harbin Institute of TechnologyWeihaiWeihai 264209China China Ship Scientific Research Center(CSSRC) Wuxi 214082China College of Shipbuilding Engineering Harbin Engineering UniversityHarbin 150001China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China

In the process of developing oil and gas resources in the Arctic,the impact of icebergs can pose a considerable threat to the structural safety of semi-submersible mooring platforms in ice *** the basis of the arbitrary Lagrangian Eulerian(ALE)algorithm,a numerical model for the interaction between an iceberg and a semi-submersible mooring platform is built in this ***,a mooring system with a link element is designed and *** ice material model for the target iceberg is built and validated.A numerical model for the interaction between an iceberg and a semi-submersible mooring platform is then built.A parametric study(cable angle,tension angle and number of cables)is carried out to study the performance of the mooring *** collision process between the semi-submersible mooring platform and the iceberg in the polar marine environment can be predicted by the present numerical model,and then the optimal mooring arrangement scheme can be *** research results in this work can provide a reference for the design of mooring systems.

关键词： ALE algorithm semi-submersible platform iceberg collision mooring system

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Recurrent ConFormer for WiFi Activity Recognition

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1491-1493页

作者： Miao Shang Xiaopeng Hong the College of Software Engineering Xi’an Jiaotong UniversityXi’an 710049China Harbin Institute of Technology Harbin 150001China

Dear Editor, Human activity recognition(HAR) using WiFi signals has been a significant task due to its potential applications in for example,healthcare services and smart homes. This letter deals with the WiFi channel state information(CSI)-based HAR task. To capture the dynamics of human activities well from CSI without using a huge number of training samples.

关键词： WiFi channel CSI

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DNAS: Depth-First Neural Architecture Search

DNAS: Depth-First Neural Architecture Search

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhou, Jianjun Chen, Junying Cai, Yi School of Software Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)9781665410205

The key challenge of neural architecture search (NAS) methods lies in efficiently exploring search spaces. To solve this problem, Breadth-First Search (BFS) method uses two trees to represent a search space, and performs bi-level BFS on these trees to find the optimal network architecture. However, the BFS method did not discover the best network in NAS-Bench-201, and its search efficiency is still not high enough. In this work, we propose a one-shot method called Depth-first Neural Architecture Search (DNAS) to efficiently explore the best architecture. Given a search space with N-candidate operations, we represent it as a single N-ary tree and employ depth-first search on this tree to explore high-performance network architectures while significantly reducing the resource consumption during the exploration. While the BFS method explores multiple networks simultaneously in each exploration, DNAS efficiently explores along only one direction at a time, eliminating the need to synchronize the search processes of multiple networks. The proposed DNAS, performed on a single RTX 2080Ti GPU, finds the optimal architectures for CIFAR-10 and ImageNet16-120 in 0.3 hour on NAS-Bench-201, and in 1.0 hour on CIFAR-100. While finding the best network, the search time of the DNAS method has been reduced by around 2 to 8 GPU hours as compared to other well-performed methods, especially reducing the search time by 91.67 % when compared to the BFS method. The best searched network was further transferred to medical image classification tasks and achieved high classification accuracy across multiple datasets. In addition, the results of ablation experiments substantiate the effectiveness and efficiency of the proposed method. Our source code is available at: https://***/Bob5090/DNAS. © 2024 IEEE.

关键词： Trees (mathematics)

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MLANet: A Multi-Level Attention-Based Change Detection Method for Remote Sensing Images 2

MLANet: A Multi-Level Attention-Based Change Detection Metho...

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2023 2nd International Conference on Environmental Remote Sensing and Geographic Information technology, ERSGIT 2023

作者： Zhang, Yuping Lu, Wangze Lv, Wenchao Gao, Ai Xie, Runbo Yang, Guang Software Engineering Technology Research Center Institute of Disaster Prevention Sanhe China

ISBN: (纸本)9781510672949

Change detection of dual-temporal remote sensing images is widely employed in natural disaster monitoring and land resource planning. Recently, most change detection methods aim at combining the complementary features at different scales to enhance the detection accuracy. However, it is challenging to obtain the complete change regions due to the general differences between semantic concept information and location representation in different feature layers. In this letter, we propose a multi-level attention-based network for change detection of high-resolution optical satellite imagery, namely MLANet. It combines siamese network in the encoding structure and integrates multi-level attention mechanism (MLAM) in the decoding structure to improve the UNet++. MLANet focuses on strengthening the natural aggregation and spatial matching in multi-layer feature information fusion, and utilizes MLAM to enhance the effective change features and alleviate the impact of interference factors to refine the feature mapping information. Experimental results on two representative change detection datasets demonstrate that the proposed method outperforms most of the state-of-the-art change detection methods. © 2024 SPIE.

关键词： Change detection

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Color coherence-based scene-change detection for frame rate up-conversion

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Multimedia Tools and Applications 2025年第2期84卷 547-569页

作者： Lee, Ho Sub Cho, Sung In Department of Electronic Engineering at Kumoh National Institute of Technology Gumi Korea Republic of Division of AI Software Convergence Dongguk University Seoul Korea Republic of

Existing scene-change detection methods usually use the differences in luminance values between consecutive frames to detect scene changes. Therefore, they can have difficulty detecting scene changes for various video content because luminance values cannot properly represent the region characteristics. To solve this problem, this paper proposes a new scene-change detection method that uses the color coherence values for frame rate up-conversion. We define the patterns of the distribution of color features—the so-called color coherence patterns—as our feature to determine whether a given frame is a scene-change. The proposed method converts the color coherence pattern in the corresponding regions into bit codes and then uses both difference between the converted bit codes and the average luminance values from previous and current frames to locally determine whether the regions are the scene change or not. In this process, local scene changes are determined by calculating the region where the scene change occurred in the divided block, and if the number of blocks in the entire scene is greater than a certain threshold, it is determined that global scene changes have occurred. In addition, it uses a refinement process to enhance the detection accuracy. Using these detection processes, the proposed can further improve the detection accuracy. The experimental results showed that the proposed method enhanced the average F1 score to 0.5398 compared to benchmark methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Motion estimation

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Knowledge and Task-Driven Multimodal Adaptive Transfer Through LLMs with Limited Data

Knowledge and Task-Driven Multimodal Adaptive Transfer Throu...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Xu Chen, Zhikui Ren, Hao Tian, Yang The Dalian University of Technology School of Software Technology International School of Information Science and Engineering Dalian China

ISBN: (纸本)9798350386226

The scarcity of annotated data and the challenge of generative models that meet the authenticity requirements collectively constrain the modeling of highly specialized deep medical models, making data remain a central issue in model training. The emergence of large-scale models has facilitated access to knowledge and data references for various tasks, even those with high specialization. Therefore, in the absence of data sources, latent knowledge and model structure can be considered for reuse instead of directly utilizing samples, and generalizing specific medical models based on large-scale models is expected to be competitive in the future. However, due to significant differences between fine-grained medical features and tasks, generalization based on large language models (LLMs) is not entirely satisfactory, it requires a more targeted inheritance of knowledge and important structures to achieve effective generalization. This paper presents a novel multi-modal medical model training approach designed to circumvent the need for extensive real data preparation, leveraging the capabilities of LLMs to generalize downstream tasks. Initially, knowledge threads are extracted to facilitate multi-modal diagnostic knowledge retrieval from LLMs without relying on massive datasets. These threads are then utilized to generate multimodal task instructions, guiding LLMs in providing feedback and further fine-tuning them for direct adaptation to downstream tasks by encoding samples into a unified feature representation, thereby simplifying the search and traversal process. Finally, an adaptive knowledge transfer strategy is proposed, implementing collaborative modeling with key patterns anchored under the joint influence of knowledge threads and task instructions, leading to the generation of an equivalent and concise initialization model guided by LLMs. The experimental design validates the efficacy of the approach in rapid generalizing specific models, demonstrating high accu

关键词： Deep learning

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A novel thermally-stable single-phase phosphor Ca_(2)InTaO_(6):Dy^(3+)for NUV-excited white LEDs

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Journal of Rare Earths 2024年第10期42卷 1855-1864页

作者： Jian Hu Ruirui Cui Jun Zhang Peng Linghu Youzhen Shi Xiang Guo Key Laboratory of Micro-Nano-Electronics and Software Technology of Guizhou Province College of Big Data and Information EngineeringGuizhou UniversityGuiyang550025China College of Computer and Information Engineering Guizhou University of CommerceGuiyang550014China

The work focuses on the utilization of the conventional solid-state sintering procedure to synthesize white phosphors Ca_(2)InTaO_(6):xDy^(3+)(0.02≤x≤0.12).Utilizing X-ray diffraction,the phase structure of samples was examined,and the crystal structure was refined using the Rietveld method.A scanning electron microscope was used to analyze the microstructure of ***-principles calculations confirm that the indirect bandgap of Ca_(2)InTaO_(6)is 3.786 eV,The luminous properties and energy transfer mechanism of Ca_(2)InTaO_(6):xDy^(3+)were studied using photoluminescence ***^(4)F_(9/2)→^(6)H_(13/2)transition of Dy^(3+)ions is responsible for the greatest emission peak,which was measured at 575 *** to research,the lifespan falls as the concentration of Dy^(3+)doping amount rises because of frequent interaction and ene rgy transfer between Dy^(3+)*** correlated color temperature of the WLEDs packaged with Ca_(2)InTaO_(6):0.08Dy^(3+)is 4677 K and CIE 1931 chromaticity coordinates are(0.3578,0.3831).Meantime,the phosphor also shows outstanding te mperature stability property,which maintains 83.8%of its initial emission intensity at 450 K(activation energy of 0.1467 eV).The W-LEDs retain their performance for 100 min when powered at 3.4 V voltage and 600 mA current,demonstrating the packed W-LEDs'sustaine d operation at high temperatures.

关键词： Ca_(2)InTaO_(6):Dy^(3+)phosphors Thermal stability Photoluminescence W-LEDs Thermal imaging Rare earths

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Enhancing Emotion Detection in Chatbots for Improved Mental Health Support: Addressing Limitations in Mental Disorders and Emotion Detection 17

Enhancing Emotion Detection in Chatbots for Improved Mental ...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Krabashini, Nagendran Pillai Guo, Jiandong Chen, Junzuo Han, Bolin Dai, Zihan School of Information & Software Engineering University of Electronic Science & Technology of China China

ISBN: (纸本)9798331507398

The increase in the adoption of AI-driven chatbots in mental health support, accurately detecting and responding to users' emotions is crucial for effective communication. This paper proposes a novel framework that enhances emotion detection in chatbots by leveraging BERT (Bidirectional Encoder Representations from Transformers) and ensemble learning techniques. Our approach addresses the inherent limitations in existing emotion detection systems, particularly when dealing with complex emotional states and mental health disorders. Using an array of tests, we show that our suggested model works better than conventional approaches, providing enhanced precision, resilience, and the ability to more effectively assist mental health therapies. This research contributes to the ongoing development of AI in mental health, providing a more nuanced and effective approach to emotion detection. © 2024 IEEE.

关键词： Electronic health record

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Scalable Motion Estimation and Temporal Context Reinforcement for Video Compression Using RGB Sensors

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IEEE Sensors Journal 2025年第10期25卷 18323-18333页

作者： Huang, Qian Lu, Hao Liu, Wenting Wang, Yiming Hohai University College of Computer Science and Software Engineering Nanjing211100 China Jiangsu Engineering Research Center of Digital Twinning Technology for Key Equipment in Petrochemical Process Changzhou213164 China

We propose a novel deep learned video compression technique, named scalable motion estimation (SME), which is designed for video data generated by sensor systems in smart devices. These devices face unique challenges due to limited bandwidth, constrained computational resources, and the complexity of real-world scenarios. Existing learned video compression methods primarily rely on optical flow for motion estimation;however, optical flow often suffers from inaccuracies and displacement errors, resulting in imprecise motion vectors, and the increasing complexity of models further limits their deployment in resource-constrained environments. To address these issues, the SME framework introduces a hierarchical motion vector enhancement strategy that progressively optimizes motion information in video captured by sensors. In addition, we propose the temporal context reinforcement (TCR) module, which partitions high-dimensional contextual information into three components. Compared to direct context encoding methods, the TCR module optimizes bit allocation, achieving a more balanced distribution of high- and low-frequency information. Extensive experiments on benchmark datasets demonstrate that the SME framework substantially enhances the efficiency and performance of video compression systems on RGB sensor-captured videos, making it particularly suitable for video applications. © 2001-2012 IEEE.

关键词： Optical flows

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Incorporating Contextual Cues for Image Recognition: A Multi-Modal Semantic Fusion Model Sensitive to Key Information

Incorporating Contextual Cues for Image Recognition: A Multi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Xu Chen, Zhikui Ren, Hao Jin, Shan Tian, Yang The Dalian University of Technology School of Software Technology International School of Information Science and Engineering Dalian China

ISBN: (纸本)9798350386226

Multi-modal data feature fusion can effectively improve the accuracy of primary modal pattern recognition and address the issue of missing data through multi-modal collaboration. To some extent, supplementing multi-view information can alleviate the conflict between medical images' reliance on professional annotations and the limited number of annotations. However, there are still several challenges in engineering multimodal feature perception and fusion for medical images, including imbalanced patterns that make it challenging to establish a weighted allocation criterion suitable for different tasks, misleading factors not positively correlated with knowledge richness, and complexities associated with integrating modalities having diverse formats for text and time series data into overall feature engineering and similarity. This paper introduces a multi-modal semantic fusion model that is sensitive to key information represented by contextual cues. Furthermore, it incorporates a dynamic feature engineering scheme capable of acquiring and integrating important insights from three perspectives with an optimized structure. Additionally, it presents a novel similarity evaluation criteria and fusion mode that adaptively adjust weights based on their contributions, facilitating the measurement of target object category likelihoods across various modalities for weighted analyses. The system combines LSTM and CNN models to builda sequence and local information sensitive structure designed to integrate heterogeneous features while embedding the multimodal similarity fusion. Emphasizing small yet compelling pieces of information such as contextual cues that prompt medical representation assignment to the target category, our system places significant importance on specificity removal and accuracy enhancement, exemplified by introducing contextual information demonstrating our sensitivity to contextual cues despite our non-specifically structured approach. © 2024 IEEE.

关键词： Image annotation

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