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Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

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computers, Materials & Continua 2024年第7期80卷 1543-1561页

作者： Bing Li Liangyu Wang Xia Liu Hongbin Fan Bo Wang Shoudi Tong School of Automation Harbin University of Science and TechnologyHarbin150080China Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration Harbin150080China School of Computer Engineering Technology Guangdong Institute of Science and TechnologyZhuhai519090China

Nuclearmagnetic resonance imaging of breasts often presents complex *** tumors exhibit varying sizes,uneven intensity,and indistinct *** characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor ***,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention ***,the breast region of interest is extracted to isolate the breast area from surrounding tissues and ***,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor *** incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion ***,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel ***,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional *** was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the *** results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

关键词： Dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI) breast tumor segmentation multi-scale dilated convolution boundary attention the hybrid loss function with boundary weight

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Effect of Ti_(p) Content on the Work Hardening and Softening Behavior of Mg-Zn-Ca Alloy

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Acta Metallurgica Sinica(English Letters) 2024年第3期37卷 551-560页

作者： Jin-Kai Zhang Cui-Ju Wang Yi-Dan Fan Chao Xu Kai-Bo Nie Kun-Kun Deng Shanxi Key Laboratory of Advanced Magnesium-Based Materials College of Materials Science and EngineeringTaiyuan University of TechnologyTaiyuan 030024China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China

The Ti_(p)/ZX60 composites with different Ti_(p) contents were prepared by semi-solid stirring *** extrusion,the microstructure,work hardening and softening behavior of the Ti_(p)/ZX60 composites were analyzed compared with the ZX60(Mg-6Zn-0.2Ca)*** results showed that the addition of Ti_(p) could not only promote the nucleation of dynamic recrystallized(DRXed)grains,but also be propitious to the refinement of DRXed *** increasing Ti_(p) content,the size of DRXed grains decreased accompanied with increasing volume fraction of DRXed *** the Ti_(p) content increased to 15 vol.%,the average size and volume fraction of DRXed grains reached to~0.32μm and 93.2%,***,both the strength and elongation were improved by the addition of Ti_(p).With increasing content of Ti_(p),a substantial increase in the strength was achieved with little change in the ***,the elongation decreased sharply when the Ti_(p) content further increased to 15 vol.%.The addition of Ti_(p) led to an increase in the work hardening rate,which gradually increased with increasing Ti_(p) ***,the softening rate did not demonstrate the same tendency with increasing Ti_(p) *** the conventional ceramic particles,the Ti_(p) can be deformed in coordination with the matrix alloy,which imparted a higher softening rate to the matrix *** though the softening rate improved as the Ti_(p) content increased from 5 to 10 vol.%,it dropped deeply as the Ti_(p) content increased to 15 vol.%owing to the fracture of Ti_(p) during extrusion.

关键词： Titanium particle content Particle reinforced magnesium matrix composites Work hardening Softening behavior Mechanical properties

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Microstructure and Mechanical Properties of Al_(2)O_(3p)/AZ91 Magnesium Matrix Laminated Material Adjusted by Freezing Temperature

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Acta Metallurgica Sinica(English Letters) 2024年第11期37卷 1819-1829页

作者： Ze-Xin Bai Kun-Kun Deng Ze-Qi Du Kai-Bo Nie Chao Xu Quan-Xin Shi Shanxi Key Laboratory of Advanced Magnesium-based Materials College of Materials Science and EngineeringTaiyuan University of TechnologyTaiyuan 030024China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China

The Al_(2)O_(3)laminated preforms with different layers thickness were prepared by freezing casting in present ***,the Al_(2)O_(3p)/AZ91 magnesium matrix laminated materials were obtained by infiltrating the AZ91 alloy melt into the Al_(2)O_(3)laminated preform based on pressure infiltration ***,the influence of freezing temperature on the microstructure,mechanical properties and fracture behavior of magnesium-based laminates was *** results indicated that with the decrease of freezing temperature,the thickness of Al_(2)O_(3)layers decreases gradually,the number of layers increases obviously,and the interlayers spacing *** with the decrease of interlayers spacing,the size of Mg17Al12 phase precipitated in the AZ91 alloy layers was refined,and the compression strength and strain were both improved *** micro-cracks initiated in Al_(2)O_(3)layers during loading process,while the AZ91 layers could effectively suppress the initiation and propagation of ***,the changing layers structure influenced by the decrease of freezing temperature had significant inhibiting effect on the initiation and propagation of micro-cracks,which endowed the Al_(2)O_(3p)/AZ91 magnesium matrix laminated materials with better strength and ***,the best compression properties of Al_(2)O_(3p)/AZ91 magnesium matrix laminated materials could be obtained at the freezing temperature of−50℃,the compression strength and elastic modulus of which were the 160%and 250%of monolithic AZ91 alloy,respectively.

关键词： Bio-inspired composites Freeze-casting Mechanical properties Metal matrix composites

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A self-learning human-machine cooperative control method based on driver intention recognition

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CAAI Transactions on Intelligence technology 2024年第5期9卷 1101-1115页

作者： Yan Jiang Yuyan Ding Xinglong Zhang Xin Xu Junwen Huang College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina Department of Computer Science Technical University of MunichGarchingGermany

Human-machine cooperative control has become an important area of intelligent driving,where driver intention recognition and dynamic control authority allocation are key factors for improving the performance of cooperative decision-making and *** this paper,an online learning method is proposed for human-machine cooperative control,which introduces a priority control parameter in the reward function to achieve optimal allocation of control authority under different driver intentions and driving safety ***,a two-layer LSTM-based sequence prediction algorithm is proposed to recognise the driver's lane change(LC)intention for human-machine cooperative steering ***,an online reinforcement learning method is developed for optimising the steering authority to reduce driver workload and improve driving *** driver-in-the-loop simulation results show that our method can accurately predict the driver's LC intention in cooperative driving and effectively compensate for the driver's non-optimal driving *** experimental results on a real intelligent vehicle further demonstrate the online optimisation capability of the proposed RL-based control authority allocation algorithm and its effectiveness in improving driving safety.

关键词： Self-leaming Control Hmman-machine Cooperation Intelligent Vehicles Reinfocement Learning

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Coarse-to-fine lightweight meta-embedding for ID-based recommendations

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science China(Information sciences) 2025年第4期68卷 82-97页

作者： Yang WANG Haipeng LIU Zeqian YI Biao QIAN Meng WANG School of Computer Science and Information Engineering Hefei University of Technology College of Information and Intelligence Hunan Agricultural University

State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage. However, these approaches consider only the coarse-grained aspects of embeddings, overlooking subtle semantic nuances. This limitation results in an adversarial degradation of meta-embedding performance, impeding the system's ability to capture intricate relationships between users and items, leading to suboptimal recommendations. To address this, we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts. Specifically, we introduce a recommender system based on a graph neural network, where each user and item is represented as a node. These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes, facilitating learning of multi-grained semantics. Fine-grained semantics are captured through sparse meta-embeddings, which dynamically balance embedding uniqueness and memory constraints. To ensure their sparseness, we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function. Moreover, we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items. Comprehensive experiments demonstrate that our method outperforms existing baselines. The code of our proposal is available at https://***/htyjers/C2F-MetaEmbed.

关键词： lightweight meta-embedding coarse-to-fine learning ID-based recommendations

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LaERC-S: Improving LLM-based Emotion Recognition in Conversation with Speaker Characteristics 31

LaERC-S: Improving LLM-based Emotion Recognition in Conversa...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Fu, Yumeng Wu, Junjie Wang, Zhongjie Zhang, Meishan Shan, Lili Wu, Yulin Liu, Bingquan School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Computer Science and Technology Soochow University Suzhou China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798891761964

Emotion recognition in conversation (ERC), the task of discerning human emotions for each utterance within a conversation, has garnered significant attention in human-computer interaction systems. Previous ERC studies focus on speaker-specific information that predominantly stems from relationships among utterances, which lacks sufficient information around conversations. Recent research in ERC has sought to exploit pre-trained large language models (LLMs) with speaker modelling to comprehend emotional states. Although these methods have achieved encouraging results, the extracted speaker-specific information struggles to indicate emotional dynamics. In this paper, motivated by the fact that speaker characteristics play a crucial role and LLMs have rich world knowledge, we present LaERC-S, a novel framework that stimulates LLMs to explore speaker characteristics involving the mental state and behavior of interlocutors, for accurate emotion predictions. To endow LLMs with this knowledge information, we adopt the two-stage learning to make the models reason speaker characteristics and track the emotion of the speaker in complex conversation scenarios. Extensive experiments on three benchmark datasets demonstrate the superiority of LaERC-S, reaching the new state-of-the-art. © 2025 Association for Computational Linguistics.

关键词： Emotion Recognition

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Brain Tumor Classification in MRI Images Using VGG19 with Type-2 Fuzzy Logic

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 163-174页

作者： Dihin, Rasha Ali Hamza, Nesreen Readh Department of Computer Science College of Education for Women University of Kufa Najaf Iraq Department of Computer Science College of Computer Science and Information Technology University of Al-Qadisiyah Diwania

A Brain Tumors are highly dangerous illnesses that significantly reduce the life expectancy of patients. The classification of brain tumors plays a crucial role in clinical diagnosis and effective treatment. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients Precisely diagnosing brain tumors is of utmost importance for devising suitable treatment plans that can effectively cure and improve the quality of life for patients afflicted with this condition. To tackle this challenge, present a framework that harnesses deep convolutional layers to automatically extract crucial and resilient features from the input data. Systems that use computers and with the help of convolutional neural networks have provided huge success stories in early detection of tumors. In our framework, utilize VGG19 model combined with fuzzy logic type-2 where used fuzzy logic type-2 that applied to enhancement the images brain where Type-2 fuzzy logic better handles uncertainty in medical images, improving the interpretability of image enhancement by managing noise and subtle differences with greater precision than Type-1 fuzzy logic for MRI images often contain ambiguous or low-contrast areas where noise, lighting conditions different and greatly improve accuracy. while used the VGG19 architecture to feature extraction and classify Tumor and non- Tumor. This approach enhances the accuracy of tumors classification, aiding in the development of targeted treatment strategies for patients. The method is trained on the Br35H dataset, resulting in a training accuracy of 0.9983 % and Train loss of 0.2118 while the validation accuracy of 0.9953 % validation loss of 0.2264. This demonstrates effective pattern learning and generalization capabilities. The model achieves outstanding accuracy, with a best accuracy for the model of 0.9983 %, While the test accuracy of the model reached of 99 %, and both of sensitivity and specificity at 0.9967

关键词： Convolutional neural networks

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Modified filter for mean elements estimation with state jumping

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Journal of Systems Engineering and Electronics 2024年第4期35卷 999-1012页

作者： YU Yanjun YUE Chengfei LI Huayi WU Yunhua CHEN Xueqin Research Center of Satellite Technology Harbin Institute of TechnologyHarbin 150001China Institute of Space Science and Applied Technology Harbin Institute of Technology(Shenzhen)Shenzhen 518055China College of Astronautics Nanjing University of Aeronautics and AstronauticsNanjing 211106China

To investigate the real-time mean orbital elements(MOEs)estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit trans-fer,a modified augmented square-root unscented Kalman filter(MASUKF)is *** MASUKF is composed of sigma points calculation,time update,modified state jumping detec-tion,and measurement *** with the filters used in the existing literature on MOEs estimation,it has three main ***,the state vector is augmented from six to nine by the added thrust acceleration terms,which makes the fil-ter additionally give the state-jumping-thrust-acceleration ***,the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low ***,when sate jumping is detected,the covariance matrix inflation will be done,and then an extra time update process will be con-ducted at this time instance before measurement *** this way,the relatively large estimation error at the detection moment can significantly ***,typical simulations are per-formed to illustrated the effectiveness of the method.

关键词： unscented Kalman filter mean orbital elements(MOEs)estimation state jumping detection nonlinear system

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Hybrid tri-memristor hyperchaotic map and application in Wasserstein Generative Adversarial Nets

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science China(Technological sciences) 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213159China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic *** investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations,*** the HTMH mapping structure with hybrid parallel/cascade operation as an example,this map possesses a spatial invariant set whose stability is closely related to the initial states of the *** distributions and bifurcation behaviours dependent on the control parameters are explored with numerical ***,the memristor initial offset-boosting mechanism is theoretically demonstrated,and memristor initial offset-boosting behaviours are numerically *** results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite *** addition,an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high ***,the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs)to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

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Collision-free parking recommendation based on multi-agent reinforcement learning in vehicular crowdsensing

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Digital Communications and Networks 2024年第3期10卷 609-619页

作者： Xin Li Xinghua Lei Xiuwen Liu Hang Xiao College of Computer Science and Technology China University of Petroleum(East China)QingDao266580China

The recent proliferation of Fifth-Generation(5G)networks and Sixth-Generation(6G)networks has given rise to Vehicular Crowd Sensing(VCS)systems which solve parking collisions by effectively incentivizing vehicle ***,instead of being an isolated module,the incentive mechanism usually interacts with other *** on this,we capture this synergy and propose a Collision-free Parking Recommendation(CPR),a novel VCS system framework that integrates an incentive mechanism,a non-cooperative VCS game,and a multi-agent reinforcement learning algorithm,to derive an optimal parking strategy in real ***,we utilize an LSTM method to predict parking areas roughly for recommendations *** incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network *** order to cope with stochastic parking collisions,its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking *** its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently,but also proves that the optimal parking strategy for each vehicle is ***,numerical results demonstrate that CPR can accomplish parking tasks at a 99.7%accuracy compared with other baselines,efficiently recommending parking spaces.

关键词： Incentive mechanism Non-cooperative VCS game Multi-agent reinforcement learning Collision-free parking strategy Vehicular crowdsensing

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