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Modulating perovskite crystallization and band alignment using coplanar molecules for high-performance indoor photovoltaics

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Journal of Energy Chemistry 2024年第11期98卷 383-390页

作者： Qu Yang Shuhan Fan Haozhe Zhang Zhenhuang Su Xingyu Gao Hui Shen Mingkui Wang Xiu Gong College of Physics &Guizhou Province Key Laboratory for Photoelectrics Technology and ApplicationGuizhou UniversityGuiyang 550025GuizhouChina State Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025GuizhouChina Shanghai Synchrotron Radiation Facility(SSRF) Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201204China Wuhan National Laboratory for Optoelectronics School of Optical and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074HubeiChina

The proper bandgap and exceptional photostability enable CsPbI_(3) as a potential candidate for indoor photovoltaics(IPVs),but indoor power conversion efficiency(PCE) is impeded by serious nonradiative recombination stemming from challenges in incomplete DMAPbI_(3) conversion and lattice structure ***,the coplanar symmetric structu re of hexyl sulfide(HS) is employed to functionalize the CsPbI_(3) layer for fabricating highly efficient *** hydrogen bond between HS and DMAI promotes the conversion of DMAPbI_(3) to CsPbI_(3),while the copianar symmetric structure enhances crystalline ***,surface sulfidation during HS-induced growth results in the in situ formation of PbS,spontaneously creating a CsPbI_(3) N-P homojunction to enhance band alignment and carrier *** a result,the CsPbI_(3)&HS devices achieve an impressive indoor PCE of 39.90%(P_(in):334.6 μW cm^(-2),P_(out):133.5 μW cm^(-2)) under LED@2968 K,1062 lux,and maintain over 90% initial PCE for 800 h at ^(3)0% air ambient humidity.

关键词： Perovskite indoor photovoltaics CsPbI_(3) Coplanar symmetric structure molecules Crystallization kinetics Hydrogen bond N-P homojunction

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Research on Home Energy Consumption Optimization Based on User Habit Analysis

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 839-855页

作者： Sung, Tien-Wen Huang, Zeming Liang, Qiaoxin Lin, Tao Kong, Lingping College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science VSB-Technical University of Ostrava Czech Republic

With the development of smart electricity technology and demand response, optimization of household electricity consumption behavior has become an important research element for energy saving in residential buildings. In the study of smart electricity consumption in households, the differences in users’ lifestyles and their preferences for the use of various appliances can have a great impact on the results. And many existing methods need to rely on users’ awareness, which does not meet the popular demand. In this paper, we propose a new method for residential load scheduling that takes into account the load characteristics of appliances and electricity consumption habits. By analyzing the household electricity consumption data set and mining the personalized needs and us-age preferences of this user for various appliances, we establish an optimization model for electricity consumption behavior that combines the minimization of electricity expenses and user comfort. Finally, an improved artificial bee colony algorithm is proposed for solving the optimization model and generating a personalized dispatching strategy combined with real-time electricity pricing (RTEP) tariff. The proposed improved artificial swarm algorithm is compared with other classical algorithms, including GA, PSO, ABC, and QABC, and the analysis of cases shows that the model can effectively reduce the electricity consumption cost and ensure the customer satisfaction, and the proposed improved ABC-based algorithm outperforms other algorithms in terms of cost and user comfort. © 2023, Journal of Network Intelligence.

关键词： Customer satisfaction

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph Networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

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computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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CPAKE: Dynamic Batch Authenticated key Exchange with Conditional Privacy 24th

CPAKE: Dynamic Batch Authenticated Key Exchange with Condit...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xiang, Axin Tian, Youliang Xiong, Jinbo Ying, Zuobin Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guiyang550025 China Industrial School of Joint Innovation Quanzhou Vocational and Technical University Quanzhou362268 China Faculty of Data Science City University of Macau 999078 China

ISBN: (纸本)9789819615247

Authenticated key exchange (AKE) needs to be designed for realizing point-to-multipoint secure communications in blockchain networks (BNet). However, since BNet is open, untrusted and decentralized, traditional certificateless AKE schemes are difficult to overcome two technical bottlenecks, i.e., private distribution of partial keys and accurate identification of anonymous traitors. This paper proposes a conditionally private and dynamic batch AKE scheme (CPAKE) in BNet. We first develop a privacy-preserving certificateless key generation algorithm to prevent the disclosure of nodes’ privacy during partial key distribution. Then, we design a simple anonymous traitor tracing mechanism against Byzantine adversaries to accurately identify and trace the traitor’s identity. Finally, we build a dynamic batch authentication protocol with decentralized node management to ensure the final validity of honest nodes. Moreover, performance analysis indicates that our scheme reduces the signing and batch-verifying time by approximately 193.10 ms and 10.93 ms respectively over state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

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computer Modeling in Engineering & sciences 2024年第2期138卷 1695-1712页

作者： Xiaodong Yang Runze Diao Tao Liu Haoqi Wen Caifen Wang College of Computer Science and Engineering Northwest Normal UniversityLanzhou730070China Institute of China TelecomWanwei China TelecomWanwei Information Technology Co.Ltd.Lanzhou730030China College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Key Laboratory of Cryptography and Data Analytics Northwest Normal UniversityLanzhou730070China

The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading *** signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity ***,most certificateless signatures still suffer fromvarious security *** present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature *** ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and *** scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota *** addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing *** results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.

关键词： Electricity carbon trading certificateless signature blockchain forgery attack carbon quota

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation 17

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Ye, Chen Cheng, Xinyu Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350387384

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma. © 2024 IEEE.

关键词： Semantic Segmentation

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Secrecy Performance Analysis of Artificial Noise Assisted Secure Transmission for MISO systems with Multiple Eavesdroppers

Secrecy Performance Analysis of Artificial Noise Assisted Se...

引用

2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Xu, Panpan Tan, Weijie Niu, Kun Li, Zhen Peng, Changgen School of Mathematics and Statistics Guizhou University Guiyang China Key Laboratory of Advanced Manufacturing Technology of Ministry of Education Guizhou China Guizhou Big Data Academy Guizhou University Guiyang China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China College of Big Data and Information Engineering Guizhou University Guiyang China

ISBN: (纸本)9798350316728

In the paper, we investigate the secure communication of multiple-input single-output (MISO) systems with multiple eavesdroppers. We jointly design the beamforming (BF) and the artificial noise (AN) in MISO systems with multiple eavesdroppers to enhance the secrecy rate. Furthermore, under the secrecy interrupt constraint, we obtain the calculation formulas of the secrecy interrupt probability and the optimal secrecy rate. The simulation results demonstrate that AN with a higher dimension can effectively obstruct eavesdroppers and enhance the system's optimal secrecy rate. In addition, it is proved that the 1/4 full-dimension AN's secrecy rate close to that of the full-dimension AN in the system with multiple eavesdroppers, allowing the trade-off between system secrecy performance and artificial noise design complexity to be realized. © 2023 IEEE.

关键词： Economic and social effects

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EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suitable for Natural

EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suit...

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

作者： Wang, Yuzhe Chen, Jiahao Duan, Xiaodong Li, Zhuohui College of Mechanical and Electronic Engineering Dalian China College of Computer Science and Engineering Dalian China Seac Key Laboratory of Big Data Applied Technology Dalian China

ISBN: (纸本)9781665410205

Weed detection is crucial for the healthy growth of crops, yet existing detection models struggle to perform high-accuracy real-time detection of weeds in natural environments on edge computing devices. This paper introduces EMPA-YOLO, a model designed for rapid, accurate, real-time weed detection on low-performance edge computing devices. It incorporates an efficient multi-scale convolutional structure, C3EMSC, and a lightweight, adaptive weight subsampling layer, LAWDS, into YOLOv5s. Additionally, a logical distillation algorithm, AlignSoftTarget, is proposed for knowledge distillation. Validation on a mixed dataset of crops and weeds showed that EMPA-YOLO improved mAP50 by 11.2%, reduced parameter count by 4.8M, decreased computational load by 8.7GFLOPs, and increased inference frame rate by 58% compared to the original YOLOv5s algorithm. When compared to YOLOv3, YOLOv5s, YOLOv6, YOLOv8s, and RT-DETR, inference speed improved by 89.2%, 60.8%, 77.5%, 76.5%, and 94.7%, respectively, with mean accuracy enhancements of 7.3%, 11.2%, 16.7%, 0.9%, and 1.2%. Real-world testing on edge computing devices met real-time detection requirements, proving its efficacy and practicality in weed detection. © 2024 IEEE.

关键词： edge computing knowledge distillation lightweight model model compress ion YOLO

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