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Solar evaporator coupled with strong/weak convection and its cascade utilization

Science Bulletin 2025年第8期70卷 1264-1274页

作者： Zhengtong Li Jia-Han Zhang Jing Zhang Peng Yan Tao Yang Chao Yang Peng Lin Taotao Meng Meng An Yingtang Zhou Xingtao Xu The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentState Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringHohai UniversityNanjing 210098China Electronic-Photonic Smart Sensing Device R&D Team Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal ProcessingSchool of Electronic Information EngineeringInner Mongolia UniversityHohhot 010021China Ningbo Key Laboratory of Green Petrochemical Carbon Emission Reduction Technology and Equipment Zhejiang Institute of Tianjin UniversityNingbo 315201China Department of Mechanical Engineering The University of TokyoTokyo113-8656Japan Marine Science and Technology College Zhejiang Ocean UniversityZhoushan 316022China College of Material Science and Engineering Nanjing Forestry UniversityNanjing 210037China

Modulating the macro/nanoarchitecture of evaporators to effectively harness diverse renewable energy sources is of paramount importance for optimizing the performance of solar-driven interfacial *** by the geometric structure of a windmill,we designed an innovative solar evaporator that expertly harnesses both strong and weak *** the purification of heavy metal wastewater,the maximum evaporation rate can reach 4.95 kg m^(−2)h^(−1)under one sun irradiation by introducing an ultralow wind flow(0.1 m s^(−1)),yielding an evaporation rate that is twice that of traditional ***,the gradual deposition of inorganic salt sediments on the evaporator surface is clearly *** address this issue,we present several innovative proof-of-concept cascade treatments that significantly extend the evaporator’s operational *** innovative design and exceptional performance of this solar evaporator open avenues for advancements in sustainable water treatment,energy generation,and environmental remediation.

关键词： Interfacial evaporation Petiole-based solar evaporators Secondary flow Cascade utilization Wastewater treatment

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Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5208-5222页

作者： Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets - unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR) - reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Heart

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SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection

arXiv

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arXiv 2022年

作者： Liu, Zhengyi Tan, Yacheng He, Qian Xiao, Yun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China

Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model, SwinNet, for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity ***://***/liuzywen/SwinNet © 2022, CC0.

关键词： Convolutional neural networks

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A Survey on Audio-Driven Talking Face Generation

A Survey on Audio-Driven Talking Face Generation

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Xue Bai Xiangzhen He Mengdi Ma Xiang Wang Wenhao Jiang Tingting Du Zijian Huang Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Key Laboratory of Linguistic and Cultural Computing Ministry of Education Gansu Province (Northwest Minzu University) Lanzhou China

ISBN: (数字)9798331522285

ISBN: (纸本)9798331522292

In recent years, advancements in deep learning have driven the development of audio-driven talking face generation. This technology has widespread applications in virtual avatars, intelligent assistants, film and television special effects, remote education, digital human live streaming, and more. Audio-driven talking face generation methods synthesize synchronized talking videos based on input speech signals, ensuring that the mouth movements, facial expressions, and head poses of virtual characters naturally align with the speech content. This paper provides an overview of the major research progress in this field and analyzes the advantages and limitations of different techniques. Additionally, it introduces evaluation metrics used to assess model performance in detail. Finally, the paper summarizes current challenges and potential future research directions.

关键词： Surveys Seminars Measurement TV Education Mouth Synchronization Information technology Faces Videos

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CSCL-DTI: Predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: Predicting drug-target interaction through cross-v...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xuan Zhang, Xi Yu, Zu-Guo Long, Yahui Zeng, Xiangxiang Yu, Philip S. Xiangtan University College of Computer Science China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing Ministry of Education China Singapore Hunan University College of Information Science and Engineering China University of Illinois Chicago Department of Computer Science United States

ISBN: (纸本)9798350386226

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI1 outperforms six state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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A Decentralized Threshold Credential Management with Fine-Grained Authentication for VANETs

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5818-5831页

作者： Zhang, Jing Wang, Xin Cui, Jie Li, Ru Zhong, Hong Anhui University School of Computer Science and Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei 230039 China Anhui University Anhui Engineering Laboratory of IoT Security Technologies Hefei 230039 China Anhui University School of Computer Science and Technology Hefei 230039 China

In Vehicular Ad-hoc Networks (VANETs), vehicles must authenticate their identities before accessing services. However, existing authentication schemes based on anonymous credentials still face single-point failure in multi-authority scenarios. In addition, in traditional anonymous credential schemes, the public key of credential authority is used directly to verify the credential, which may increase the risk of vehicle privacy being misused. To address these issues, we propose a decentralized threshold credential management system with fine-grained authentication for VANETs. The decentralized credential management architecture is proposed for VANETs with multiple credential authorities, each credential authority consists of multiple credential managers who issue credentials using the threshold mechanism, effectively solving the single-point failure. Based on this architecture, we design a fine grained, privacy-preserving authentication scheme that allows vehicles to autonomously perform selective attribute disclosure, credential aggregation, and randomization before requesting verification from the Cloud Service Provider, thereby achieving a balance between privacy preservation and authentication efficiency. The security proofs and analysis show that our scheme satisfies the target security properties. Performance evaluations indicate that our scheme enables efficient, flexible credential management and authentication in VANETs while ensuring privacy preservation. © 2005-2012 IEEE.

关键词： anonymous credentials authentication decentralization privacy preservation VANETs

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Emotional Knowledge Self-Distillation in Dialogue

Emotional Knowledge Self-Distillation in Dialogue

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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Jian, Zhongquan Wang, Yancheng Wu, Weichao Yao, Junfeng Wang, Meihong Wu, Qingqiang School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Mechanics and Civil Engineering China University of Mining & Technology Beijing China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen China

ISBN: (纸本)9798350368741

Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in Natural Language processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of emotional features in emotion recognition. To address this gap, we focus on the role of emotional features in ERC and propose a novel method, Emotional Knowledge Self-Distillation (EmoKSD1), to enhance the model's emotional sensitivity. In EmoKSD, utterances are enriched with implicit 〈mask〉 tokens to represent conveyed emotions, allowing the distillation of emotional knowledge from explicit emotional tokens to implicit 〈mask〉 tokens, thereby enhancing the model's ability to perceive subtle emotions within the dialogue. Through thorough evaluations on two public ERC datasets (i.e., IEMOCAP and MELD) using proposed coarse-grained utterance distillation and fine-grained token distillation techniques, EmoKSD demonstrates superior performance compared to existing methods, highlighting the significance of emotional features in ERC. © 2025 IEEE.

关键词： Dialogue System Emotion Recognition in Conversation Knowledge Distillation Natural Language processing

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Complex-valued cross-domain few-shot learning network for PolSAR image classification

Complex-valued cross-domain few-shot learning network for Po...

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IET International Radar Conference (IRC 2023)

作者： Yice Cao Dayi Zhu Zhenhua Wu Jie Chen Zhixiang Huang Lixia Yang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei 230601 People's Republic of China

ISBN: (数字)9781837240982

Gathering reliable labeled samples for polarimetric synthetic aperture (PolSAR) image classification is laborious. Moreover, applying a trained classifier to new domains often leads to noticeable performance degradation due to domain disparities. Therefore, this paper proposes the novel complex-valued cross-domain (CD) few-shot learning classification (CCFSLC) method for PolSAR images to address these issues. Firstly, the transferrable knowledge learning module (TKLM) with a complex-valued feature encoder (CVFE) is trained using source data with sufficient labeled samples. Then, the deep few-shot learning module (DFSLM), constructed using the pre-trained CVFE, is trained by episodes in both source and target domains, with only minimal target labeled samples. Meanwhile, the adversarial domain adaptation module (ADAM) is employed to eliminate domain shift. The proposed CCFSLC mainly focuses on exploring discriminative information from raw PolSAR data, while reducing the domain gap to recognize novel categories in unseen domains with only a few annotated samples. Experiments on typical PolSAR datasets validate the effectiveness of the proposed method.

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Affine-Projection Maximum Asymmetric Correntropy Criterion Algorithm Under System Identifications

Affine-Projection Maximum Asymmetric Correntropy Criterion A...

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Yan Peng Xinqi Huang Canping Yu Qi Mao Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

The Affine-Projection Maximum Asymmetric Correntropy Criterion (APMACC) is constructed, drawing upon the fundamental principles of the maximum asymmetric correntropy criterion and an affine-projection scheme. The APMACC algorithm incorporates the asymmetric Gaussian model as a kernel function within the Affine-Projection (AP) algorithm framework, thereby endowing it with robustness against asymmetrically distributed noise. Furthermore, the bound for step-size is established in the literature. Simulation results demonstrate that the APMACC has fast convergence and low steady-state.

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A RIS-Aided MIMO Secure Communication Scheme Based on Subarray Partition

A RIS-Aided MIMO Secure Communication Scheme Based on Subarr...

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International Conference on Computer and Communications (ICCC)

作者： Suzhen Wang Hui Zhi Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Electronic and Information Engineering Anhui University Hefei China

Subarray partition of reconfigurable intelligent surface (RIS) can significantly reduce the computational complexity of solving optimal reflection coefficients. However, there is no research about the RIS subarray partition of RIS-aided multiple-input multiple-output (MIMO) communication for the eavesdropper scenario. In this paper, we intend to solve the optimization subarray partition problem for the eavesdropper scenario, design a RIS-aided MIMO secure communication scheme based on subarray partition (RSC-SP). We consider minimizing the number of subarrays while satisfying secrecy rate requirements. First, this problem is described as a nonconvex combinatorial optimization problem, then we solve it by combining alternating optimization and bisection. In this scheme, we derive the closed expressions of the optimal transmit covariance matrix and the optimal reflection coefficients of RIS. The alternating optimization algorithm is used to jointly optimize the transmit covariance matrix and the reflection coefficients of RIS, the bisection method is used to calculate the minimum number of subarrays. Simulation results show that compared with the traditional scheme without subarray partition, RSC-SP can significantly reduce the computational complexity while meeting secrecy rate requirements.

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