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5th International Conference on Power and Energy technology, ICPET 2023

作者： Ai, Chuanxian Yang, Jinpeng Chen, Rui Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China The National Computer Network Emergency Response Technical Team Coordination Center of China Yunnan Branch Kunming China Guizhou Power Grid Co. Ltd. China Southern Power Grid Co. Ltd Power Dispatch Control Center Guiyang China

ISBN: (纸本)9798350339673

The variable-frequency drive technology (VFD) can modify the motor speed or torque by varying the frequency and voltage of the AC motor input power according to the load demand as a way to save energy. To analyse the energy saving effect of variable-frequency drive technology applied to data centre heating, ventilation, and air conditioning (HVAC) systems, this paper applies VFD technology to data centre fluorine pump-compressor composite air conditioning systems, implements monitoring and analysis of the operational energy efficiency of this system before and after retrofit, and combines the actual application of this technology. The findings indicate that the VFD technology can substantially reduce the number of compressor starts and stops, reduce the compressor start-up inrush current, and improve the power system's stability. Furthermore, utilising this technology, the HVAC system can effectively synchronise with the load and fluctuations in outdoor temperature and humidity, resulting in accurate temperature regulation and optimal utilisation of outdoor cooling resources. This approach can lead to significant energy conservation of up to 42.95% when compared to extreme hot and cold weather conditions. The findings of the study indicate that the utilisation of VFD technology exhibits commendable adaptability and yields favourable outcomes in regions with varying temperature ranges. The minimum PUE value recorded was 1.228. © 2023 IEEE.

关键词： data center energy saving HVAC system variable-frequency drive technology

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Multi-dimensional classification via stacked dependency exploitation

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Science China(Information Sciences) 2020年第12期63卷 104-117页

作者： Bin-Bin JIA Min-Ling ZHANG School of Computer Science and Engineering Southeast University College of Electrical and Information Engineering Lanzhou University of Technology Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of EducationChina Collaborative Innovation Center of Wireless Communications Technology

Multi-dimensional classification(MDC) aims to build classification models for multiple heterogenous class spaces simultaneously, where each class space characterizes the semantics of an object w.r.t. one specific dimension. Modeling dependencies among class spaces plays a key role in solving MDC tasks, where most approaches work by assuming directed acyclic graph(DAG) structure or random chaining structure over class spaces. Different from existing probabilistic strategies, a deterministic strategy named Seem for dependency modeling is proposed in this paper via stacked dependency exploitation. In the first-level, pairwise dependencies are considered which can be modeled more reliably than modeling full dependencies among all class spaces by DAG or chaining structure. In the second-level, the class label of unseen instance *** class space is determined by adaptively stacking predictive outputs from first-level pairwise *** results show that stacked dependency exploitation leads to superior performance against stateof-the-art MDC approaches.

关键词： machine learning multi-dimensional classification class dependencies deterministic strategy stacked dependency exploitation

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Bi-Causal: Group Activity Recognition via Bidirectional Causality

Bi-Causal: Group Activity Recognition via Bidirectional Caus...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Youliang Zhang Wenxuan Liu Danni Xu Zhuo Zhou Zheng Wang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University of Technology National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Current approaches in Group Activity Recognition (GAR) predominantly emphasize Human Relations (HRs) while often neglecting the impact of Human-Object Inter-actions (HOIs). This study prioritizes the consideration of both HRs and HOIs, emphasizing their interdependence. Notably, employing Granger Causality Tests reveals the presence of bidirectional causality between HRs and HOIs. Leveraging this insight, we propose a Bidirectional-Causal GAR network. This network establishes a causality commu-nication channel while modeling relations and interactions, enabling reciprocal enhancement between human-object interactions and human relations, ensuring their mutual consistency. Additionally, an Interaction Module is devised to effectively capture the dynamic nature of human-object interactions. Comprehensive experiments conducted on two publicly available datasets showcase the superiority of our proposed method over state-of-the-art approaches. Our project page: https://***/***/

关键词： computer vision Computational modeling Cause effect analysis Communication channels Activity recognition

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An Improved iBAT-COOP Protocol for Cooperative Diversity in FANETs

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computers, Materials & Continua 2021年第5期67卷 2527-2546页

作者： Shahzad Hameed Qurratul-Ain Minhas Sheeraz Ahmed Shabana Habib Mohammad Kamrul Hasan Muhammad Islam Sheroz Khan Quaid-i-Azam University IslamabadPakistan Iqra National University PeshawarPakistan Network and Communication Technology Lab Center for Cyber SecurityUKMBangiMalaysia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Electrical Engineering Onaizah College of Engineering&Information TechnologyAl-QassimSaudi Arabia

Flying ad hoc networks(FANETs)present a challenging environment due to the dynamic and highly mobile nature of the *** network topology and uncertain node mobility structure of FANETs do not aim to consider only one path *** different techniques are adopted to address the issues arising in FANETs,from game theory to clustering to channel estimation and other statistical *** approaches mostly employ traditional concepts for problem *** of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired *** Nature-inspired schemes address cooperation and alliance which can be used to ensure connectivity among network *** such species that resembles the dynamicity of FANETs are *** this paper,the biologically inspired metaheuristic technique of the BAT Algorithm is proposed to present a routing protocol called iBATCOOP(Improved BAT Algorithm using Cooperation technique).We opt for the design implementation of the natural posture of bats to handle the necessary flying ***,we envision the concept of cooperative diversity using multiple relays and present an iBAT-COOP routing protocol for *** paper employs cooperation for an optimal route selection and reflects on distance,Signal to Noise Ratio(SNR),and link conditions to an efficient level to deal with FANET’s *** way of simulations,the performance of iBAT-COOP protocol outperforms BAT-FANET protocol and reduces packet loss ratio,end-to-end delay,and transmission loss by 81%,21%,and 82%***,the average link duration is improved by 25%compared to the BAT-FANET protocol.

关键词： Routing protocols UAVs FANETs iBATCOOP BATCOOP

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FISHFUZZ: catch deeper bugs by throwing larger nets 23

FISHFUZZ: catch deeper bugs by throwing larger nets

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Proceedings of the 32nd USENIX Conference on Security Symposium

作者： Han Zheng Jiayuan Zhang Yuhang Huang Zezhong Ren He Wang Chunjie Cao Yuqing Zhang Flavio Toffalini Mathias Payer National Computer Network Intrusion Protection Center University of Chinese Academy of Science and School of Computer and Communication Sciences EPFL National Computer Network Intrusion Protection Center University of Chinese Academy of Science and School of Computer and Communication Lanzhou University of Technology National Computer Network Intrusion Protection Center University of Chinese Academy of Science National Computer Network Intrusion Protection Center University of Chinese Academy of Science and Zhongguancun Laboratory School of Cyber Engineering Xidian University School of Cyberspace Security Hainan University National Computer Network Intrusion Protection Center University of Chinese Academy of Science and Zhongguancun Laboratory and School of Cyberspace Security Hainan University and School of Cyber Engineering Xidian University School of Computer and Communication Sciences EPFL

ISBN: (纸本)9781939133373

Fuzzers effectively explore programs to discover bugs. Greybox fuzzers mutate seed inputs and observe their execution. Whenever a seed reaches new behavior (e.g., new code or higher execution frequency), it is stored for further mutation. Greybox fuzzers directly measure exploration and, by repeating execution of the same targets with large amounts of mutated seeds, passively exploit any lingering bugs. Directed greybox fuzzers (DGFs) narrow the search to a few code locations but so far generalize distance to all targets into a single score and do not prioritize targets *** introduces an input prioritization strategy that builds on three concepts: (i) a novel multi-distance metric whose precision is independent of the number of targets, (ii) a dynamic target ranking to automatically discard exhausted targets, and (iii) a smart queue culling algorithm, based on hyperparameters, that alternates between exploration and exploitation. FISHFUZZ enables fuzzers to seamlessly scale among thousands of targets and prioritize seeds toward interesting locations, thus achieving more comprehensive program testing. To demonstrate generality, we implement FISHFUZZ over two well-established greybox fuzzers (AFL and AFL++). We evaluate FISHFUZZ by leveraging all sanitizer labels as targets. In comparison to modern DGFs and state-of-the-art coverage guided fuzzers, FISHFUZZ reaches higher coverage compared to the direct competitors, finds up to 2.8x more bugs compared with the baseline and reproduces 68.3% existing bugs faster. FISHFUZZ also discovers 56 new bugs (38 CVEs) in 47 programs.

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A hybrid uplink-downlink secure transmission scheme for UAV-aided coordinated multi-point networks

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Digital Communications and networks 2025年第3期11卷 925-936页

作者： Xin Fan Guangkai Li Jianqiang Li Yue Wang Chuanwen Luo Yi Hong Ting Chen Yan Huo School of Information Science and Technology Beijing Forestry University Beijing 100083 China The Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029 China Department of Computer Science Georgia State University Atlanta GA 30303 USA School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing 100044 China

Recognized as a pivotal facet in Beyond Fifth-Generation (B5G) and the upcoming Sixth-Generation (6G) wireless networks, Unmanned Aerial Vehicle (UAV) communications pose challenges due to limited capabilities when serving as mobile base stations, leading to suboptimal service for edge users. To address this, the collaborative formation of Coordinated Multi-Point (CoMP) networks proves instrumental in alleviating the issue of the poor Quality of Service (QoS) at edge users in the network periphery. This paper introduces a groundbreaking solution, the Hybrid Uplink-Downlink Non-Orthogonal Multiple Access (HUD-NOMA) scheme for UAV-aided CoMP networks. Leveraging network coding and NOMA technology, our proposed HUD-NOMA effectively enhances transmission rates for edge users, notwithstanding a minor reduction in signal reception reliability for strong signals. Importantly, the system's overall sum rate is elevated. The proposed HUD-NOMA demonstrates resilience against eavesdroppers by effectively managing intended interferences without the need for additional artificial noise injection. The study employs a stochastic geometry approach to derive the Secrecy Outage Probability (SOP) for the transmissions in the CoMP network, revealing superior performance in transmission rates and lower SOP compared to existing methods through numerical verification. Furthermore, guided by the theoretical SOP derivation, this paper proposes a power allocation strategy to further reduce the system's SOP.

关键词： NOMA UAV CoMP Physical layer security Stochastic geometry approaches Secrecy outage probability

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Remaining Useful Life Prediction of Parts and Components based on BiLSTM Autoencoder with Attention Mechanism and Skip Connection 12

Remaining Useful Life Prediction of Parts and Components bas...

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12th IEEE Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021

作者： He, Mengqi Li, Honghui Duan, Yuhang Bai, Yanhui Beijing Jiaotong University School Of Computer And Information Technology Beijing China China Engineering Research Center Of Network Management Teclinology For High Speed Railway Ministry Of Education China China Engineering Research Center Of Network Management Technology For High Speed Railway Of Moe China

ISBN: (纸本)9781665401302

Remaining useful life (RUL) prediction is one of the main tasks of prognostics and health management (PHM). This paper proposes a novel method for RUL prediction, Attention mechanism and Skip connection are introduced into bidirectional long short-term memory autoencoder (BiLSTM-AS). In the previous RUL prediction scheme based on BiRNN autoencoder, during the encoding process, in the process of embedding vector generation, the multi-dimensional sensor data of each timestep contributes equally, the embedding vector is only used in the first step of decoding. To overcome the above shortcomings, the BiLSTM-AS model is proposed. This model introduces attention mechanism based on the BiLSTM autoencoder, assigns weights to each timestep information and highlights the key timestep information contribution;to relieve the decoding burden of embedding vector, skip connection is introduced in the decoding process. This method evaluated on the datasets of C-MAPSS and compared with the latest prediction methods, the comparison shows that our results are competitive. Subsequently, experiments are carried out on the monitoring data of train wheelset experimental datasets, and the RUL prediction results are obtained, indicating that the model has good generalization capability. © 2021 IEEE.

关键词： Forecasting

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Cross Modal Sentiment Classification of Social Media Based on Meta Heuristic Algorithm

Cross Modal Sentiment Classification of Social Media Based o...

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International Conference on computer Supported Cooperative Work in Design

作者： Wantong Du Wanli Min Yushui Geng Hu Liang Hehu Zhou Ministry of Education Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Synthesis Electronic Technology Co. Ltd

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The evolution of online social media has reshaped public behaviors, and social data infused with emotions provides crucial decision support for sentiment analysis tasks. Conventional multimodal approaches, influenced by redundant information in feature extraction during sentiment analysis, often focus solely on modality interactions without considering the unique content of each modality. This paper introduces the HGB-HFN model, addressing feature redundancy by optimizing extracted features using metaheuristic algorithm techniques, achieving a higher accuracy optimal feature dataset at lower computational costs. To counteract the neglect of modal-independent information, a hybrid fusion approach is adopted. It prioritizes visual features to extract precise semantic and emotional information from textual content, training multiple base classifiers to learn independent and diverse discriminative information. Comparative experiments on streaming datasets MOSI and MOSEI demonstrate the computational approach’s superiority in both accuracy and efficiency over existing methods.

关键词： Training Sentiment analysis Visualization Accuracy Social networking (online) Metaheuristics Semantics

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A SUPERVISED INFORMATION ENHANCED MULTI-GRANULARITY CONTRASTIVE LEARNING FRAMEWORK FOR EEG BASED EMOTION RECOGNITION

arXiv

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arXiv 2024年

作者： Li, Xiang Song, Jian Zhao, Zhigang Wang, Chunxiao Song, Dawei Hu, Bin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study introduces a novel Supervised Info-enhanced Contrastive Learning framework for EEG based Emotion Recognition (SI-CLEER). SI-CLEER employs multi-granularity contrastive learning to create robust EEG contextual representations, potentially improving emotion recognition effectiveness. Unlike existing methods solely guided by classification loss, we propose a joint learning model combining self-supervised contrastive learning loss and supervised classification loss. This model optimizes both loss functions, capturing subtle EEG signal differences specific to emotion detection. Extensive experiments demonstrate SI-CLEER's robustness and superior accuracy on the SEED dataset compared to state-of-the-art methods. Furthermore, we analyze electrode performance, highlighting the significance of central frontal and temporal brain region EEGs in emotion detection. This study offers an universally applicable approach with potential benefits for diverse EEG classification tasks. © 2024, CC0.

关键词： Emotion Recognition

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Multimodal Knowledge Graph Completion Method Based on Integrated Modality Adversarial Training and Relation-Enhanced Attention Mechanism

Multimodal Knowledge Graph Completion Method Based on Integr...

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International Conference on computer Supported Cooperative Work in Design

作者： Ruijia Zhang Zheng Dong Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Negative sampling (NS) is widely used in knowledge graph completion (KGC) to generate negative triples for contrastive learning during training. However, existing NS methods are not suitable when multimodal information is incorporated into KGC models. Due to their complex design, these methods are also inefficient. In this paper, we propose the integration of Modality-Aware Adversarial Training (IMAT) to generate higher-quality negative samples for Multimodal Knowledge Graph Completion (MMKGC), and introduce a Relation-Enhanced Cross-modal Attention (RECA) mechanism to evaluate bidirectional attention weights between multimodal features using relational information, thereby improving the model's ability to identify hard negative samples. Our approach represents a joint design of MMKGC models and training strategies, surpassing 16 recent MMKGC methods and achieving new state-of-the-art results on three public MMKGC benchmarks.

关键词： Training Federated learning Design methodology Computational modeling Semantics Focusing Knowledge graphs Contrastive learning Sampling methods Real-time systems

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