Peer-To-Peer Energy-Carbon Management Method Of Multiple Integrated Energy Systems Considering Multi-Agent Interaction Strategy,
2024
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China
Peer-To-Peer Energy-Carbon Management Method Of Multiple Integrated Energy Systems Considering Multi-Agent Interaction Strategy, Yudong Wang, Junjie Hu
Journal of System Simulation
Abstract: To explore a new energy management model of P2P transaction of electricity, heat and carbon among IES with the participation of ESP, a P2P energy-carbon management method of IES considering multi-agent interaction strategy is proposed. A two-layer energy management framework with the multiagent participation of involving ESP and IES is established. A two-layer electricity-heat-carbon energy management model is constructed in which the upper model is constructed based on reinforcement learning framework to optimize the energy management strategy between ESP and IES cooperative alliance and the lower model is based on Nash negotiation game theory to optimize the cooperative operation …
Solubility Characterization Of Organic Molecules For Aqueous Organic Redox Flow Batteries,
2024
California Polytechnic State University, San Luis Obispo
Solubility Characterization Of Organic Molecules For Aqueous Organic Redox Flow Batteries, Anthony W. Ferrell, Harkeerith K. Vij, Seamus D. Jones
College of Engineering Summer Undergraduate Research Program
The major obstacle to renewable energy sources is a lack of long-term energy storage capabilities. Energy produced during the day dissipates, leaving insufficient electricity for at night. The goal of the project is to design an Aqueous Organic Redox Flow Battery (AORFB) to act as long-term energy storage. Work has been done using machine learning to identify suitable compounds for the batteries. In this work there was no indication as to the aqueous solubility of the molecules; this controls the device’s energy storage capabilities. We used a machine learning model to determine the aqueous solubility of slightly more than 3000 …
Leveraging Tradespace-Exploration For A Senior Project Team Formation Application,
2024
California Polytechnic State University, San Luis Obispo
Leveraging Tradespace-Exploration For A Senior Project Team Formation Application, Miguel Saenz
College of Engineering Summer Undergraduate Research Program
This project revolves around the development of an app in MATLAB that leverages the VASSAR rule-based system and a genetic algorithm to form groups of teams for the Mechanical Engineering Senior Design project class. We leveraged the iterative design process to eventually attain a functional app with a reasonable runtime that works provided correctly formatted rulesheets describing student project preference and member preference.
Joint Weakly Supervised Image Emotion Analysis Based On Interclass Discrimination And Intraclass Correlation,
2024
Singapore Management University
Joint Weakly Supervised Image Emotion Analysis Based On Interclass Discrimination And Intraclass Correlation, Xinyue Zhang, Zhaoxia Wang, Guitao Cao, Seng-Beng Ho
Research Collection School Of Computing and Information Systems
Regional information-based image emotion analysis has recently garnered significant attention. However, existing methods often focus on identifying region proposals through layered steps or merely rely on visual saliency. These approaches may lead to an underestimation of emotional categories and a lack of comprehensive interclass discrimination perception and emotional intraclass contextual mining. To address these limitations, we propose a novel approach named InterIntraIEA, which combines interclass discrimination and intraclass correlation joint learning capabilities for image emotion analysis. The proposed method not only employs category-specific dictionary learning for class adaptation, but also models intraclass contextual relationships and perceives correlations at the channel …
Data Provenance Via Differential Auditing,
2024
Peng Cheng Laboratory
Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu
Research Collection School Of Computing and Information Systems
With the rising awareness of data assets, data governance, which is to understand where data comes from, how it is collected, and how it is used, has been assuming evergrowing importance. One critical component of data governance gaining increasing attention is auditing machine learning models to determine if specific data has been used for training. Existing auditing techniques, like shadow auditing methods, have shown feasibility under specific conditions such as having access to label information and knowledge of training protocols. However, these conditions are often not met in most real-world applications. In this paper, we introduce a practical framework for …
Transformer-Based Joint Learning Approach For Text Normalization In Vietnamese Automatic Speech Recognition Systems,
2024
Singapore Management University
Transformer-Based Joint Learning Approach For Text Normalization In Vietnamese Automatic Speech Recognition Systems, The Viet Bui, Tho Chi Luong, Oanh Thi Tran
Research Collection School Of Computing and Information Systems
In this article, we investigate the task of normalizing transcribed texts in Vietnamese Automatic Speech Recognition (ASR) systems in order to improve user readability and the performance of downstream tasks. This task usually consists of two main sub-tasks: predicting and inserting punctuation (i.e., period, comma); and detecting and standardizing named entities (i.e., numbers, person names) from spoken forms to their appropriate written forms. To achieve these goals, we introduce a complete corpus including of 87,700 sentences and investigate conditional joint learning approaches which globally optimize two sub-tasks simultaneously. The experimental results are quite promising. Overall, the proposed architecture outperformed the …
Large-Scale Graph Label Propagation On Gpus,
2024
Singapore Management University
Large-Scale Graph Label Propagation On Gpus, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun
Research Collection School Of Computing and Information Systems
Graph label propagation (LP) is a core component in many downstream applications such as fraud detection, recommendation and image segmentation. In this paper, we propose GLP, a GPU-based framework to enable efficient LP processing on large-scale graphs. By investigating the data processing pipeline in a large e-commerce platform, we have identified two key challenges on integrating GPU-accelerated LP processing to the pipeline: (1) programmability for evolving application logics; (2) demand for real-time performance. Motivated by these challenges, we offer a set of expressive APIs that data engineers can customize and deploy efficient LP algorithms on GPUs with ease. To achieve …
Does Ceo Agreeableness Personality Mitigate Real Earnings Management?,
2024
Singapore Management University
Does Ceo Agreeableness Personality Mitigate Real Earnings Management?, Shan Liu, Xingying Wu, Nan Hu
Research Collection School Of Computing and Information Systems
Despite efforts to mitigate aggressive financial reporting, earnings management remains challenging to parties interested in inhibiting its dysfunctional effects. Using linguistic algorithms to assess CEO agreeableness personality from their unscripted texts in conference calls, we find that it is a determinant that mitigates a firm's real earnings management. Furthermore, such an effect is more pronounced when firms confront intensive market competition and financial distress and have weaker managerial entrenchment or when CEOs face stronger internal governance. Our findings persist even after we utilize several alternative real earnings management metrics and control other confounding personalities in prior earnings management studies. The …
Motif Graph Neural Network,
2024
Guangdong University of Technology
Motif Graph Neural Network, Xuexin Chen, Ruicui Cai, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao
Research Collection School Of Computing and Information Systems
Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing high-order graph structures as opposed to low-order structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, the existing motif-based GNNs still often suffer from less discriminative power on high-order …
Efficient Cascaded Multiscale Adaptive Network For Image Restoration,
2024
Singapore Management University
Efficient Cascaded Multiscale Adaptive Network For Image Restoration, Yichen Zhou, Pan Zhou, Teck Khim Ng
Research Collection School Of Computing and Information Systems
Image restoration, encompassing tasks such as deblurring, denoising, and super-resolution, remains a pivotal area in computer vision. However, efficiently addressing the spatially varying artifacts of various low-quality images with local adaptiveness and handling their degradations at different scales poses significant challenges. To efficiently tackle these issues, we propose the novel Efficient Cascaded Multiscale Adaptive (ECMA) Network. ECMA employs Local Adaptive Module, LAM, which dynamically adjusts convolution kernels across local image regions to efficiently handle varying artifacts. Thus, LAM addresses the local adaptiveness challenge more efficiently than costlier mechanisms like self-attention, due to its less computationally intensive convolutions. To construct a …
Latent Representation Learning For Geospatial Entities,
2024
Singapore Management University
Latent Representation Learning For Geospatial Entities, Ween Jiann Lee, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons, and networked structures. However, existing approaches often fall short of explicitly capturing both semantic and spatial information, relying on proxies and synthetic features. This article presents GeoNN, a novel graph neural network-based model designed to learn spatially-aware embeddings for geospatial entities. GeoNN leverages edge features generated from geodesic functions, dynamically selecting relevant features based on relative locations. It introduces …
Research On Digital Twin System Of Rockshaft Hoist,
2024
Institute of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Research On Digital Twin System Of Rockshaft Hoist, Baiting Zhao, Jianguo Shi, Xiaofen Jia
Journal of System Simulation
Abstract: In order to solve the problem of low intelligence and digitization of the current mine hoisting system, a method based on DT for digital modeling, 3D visualization, and virtual real interaction of shaft hoisting machines is proposed. Aiming at the rockshaft hoist system, based on the digital twin five dimensional model framework, we analyze the operating mechanism of the equipment, and model the fully physical digital system of the rockshaft hoist. By constructing multidimensional multi-scale models and multidimensional heterogeneous data models, twin digital scenes are constructed, and virtual real mapping technology is combined to achieve dynamic mapping and virtual …
A Multimodal Residual Spatial-Temporal Fusion Model Based On Automatic Sleep Classification,
2024
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; School of Electronics and Information Engineering, Wuxi University, Wuxi 214105, China
A Multimodal Residual Spatial-Temporal Fusion Model Based On Automatic Sleep Classification, Yecai Guo, Shuang Tong
Journal of System Simulation
Abstract: Highly accurate sleep staging plays a crucial role in correctly assessing sleep conditions. Aiming at the problem that the existing convolutional network cannot obtain the topological characteristics of physiological signals, a sleep staging algorithm based on multi-modal residual spatio-temporal fusion is proposed. Time-frequency images and spatio-temporal images are obtained using short-time Fourier transform and adaptive map convolution, which are converted into high-dimensional feature vectors; lightweight interaction of feature information flow is realized through time-frequency feature and spatiotemporal feature extraction modules; the feature enhancement fusion module fuses feature information to outputs sleep staging results. The results show that the model …
Research On Simulation Resource Management Based On Graph Association Organization,
2024
Beijing Simulation Center, Beijing 100854, China
Research On Simulation Resource Management Based On Graph Association Organization, Zewei Liu, Yishan Ding, Tingyu Lin, Mingxing Ke, Liqing Guo, Yingying Xiao, Zhilong Zhao, Yan Li, Xuan Lü
Journal of System Simulation
Abstract: The simulation test and evaluation of intelligent system of systems, systems and single equipment is a complex system engineering, which requires effective management of multi-source, heterogeneous and distributed massive simulation resources scattered in cloud test centers and test sites of various units; and good control of dynamically generated tasks, assumptions, configurations, results, evaluations and other data and files. The traditional way of managing and querying simulation resources by category is inefficient and difficult to meet the requirements of large-scale intelligent simulation test and evaluation activities. An overall framework for simulation resource management based on graph association organization, defines a …
Indicator Transfer Learning Based On Cloud Model And Maximum Mean Discrepancy,
2024
National Innovation Institute of Defense Technology, Academy of Military Science, Beijing 100071, China
Indicator Transfer Learning Based On Cloud Model And Maximum Mean Discrepancy, Lixia Xu, Jilong Zhong, Shaoshi Wu, Yishan Ding, Xiaoyu Zhai, Shizhao Chen, Yizhe Wang, Xue Wen, Juanfang Zeng, Xinwen Hou
Journal of System Simulation
Abstract: In response to the problem of rare data samples in application experiment scenarios, this paper proposes an indicator transfer learning method based on cloud models and Maximum Mean Discrepancy (MMD), which transfers the indicator calculation model from typical simulation experiment scenarios to application experiment scenarios to meet the needs across platform and domain simulation evaluation. Using the maximum mean difference method to align the indicator distribution in the typical simulation experiment scenario to the indicator distribution in the application experiment scenario, thereby achieves indicator transfer, and by using cloud models based on a small number of examples for modeling …
Adversarial Simulation Testing Algorithm For Svm Based On Multi-Objective Evolutionary Optimization,
2024
Chinese Flight Test Establishment, Xi'an 710089, China
Adversarial Simulation Testing Algorithm For Svm Based On Multi-Objective Evolutionary Optimization, Feixing Li, Lining Xing, Yu Zhou
Journal of System Simulation
Abstract: Machine learning typically mines underlying patterns and rules from data, making it susceptible to phenomena such as overfitting and underfitting, which in turn affects the generalization and robustness of learning models. This paper explores the potential fragility and instability of SVM from the perspective of adversarial simulation testing. The adversarial simulation strategy employed involves selectively contaminating training sample labels to simulate an attack on the SVM classifier, thereby degrading its performance and testing its dependency on training samples. To explore the ceiling of performance degradation of an SVM classifier under the combination attack of different samples, the contradictory objectives …
Simulation Study Of Personnel Evacuation In Fire Scenarios Of Old School Buildings,
2024
Institute of Earthquake Protection and Disaster Mitigation, Lanzhou University of Technology, Lanzhou 730050, China
Simulation Study Of Personnel Evacuation In Fire Scenarios Of Old School Buildings, Qiankun Zhu, Jiwu Li, Yongfeng Du
Journal of System Simulation
Abstract: In order to improve the emergency evacuation capability of an old school building under fire scenarios, a fire evacuation model of an old school building is developed. The PyroSim software is used to build a fire dispersion model to simulate and analyse the changes of smoke visibility, temperature and CO at the safety exit of the fire floor in the school building under the condition of mechanical smoke exhaust, automatic sprinkler and whether the windows of the fire room are open or not. The simulation of the exit status and evacuation of people has been carried out in conjunction …
Research On Orb-Slam Algorithm Based On Windowed Matching Estimation,
2024
Department of Automation, North China Electric Power University (Baoding), Baoding 071003, China
Research On Orb-Slam Algorithm Based On Windowed Matching Estimation, Wanye Yao, Zewei Pang, Peijie Sun, Zhu Wang
Journal of System Simulation
Abstract: To address unstability of location accuracy of ORB-SLAM system caused by randomness of camera pose solution method, an improved pose solution method based on feature point windowed matching and analytical ICP is proposed, and the mobile robot ORB-SLAM system is constructed. The extracted feature points are windowed to improve matching efficiency while ensuring good feature point matching, the analytical ICP algorithm is used to solve the camera pose for avoiding iteration, and the windowed pose solution with the smallest error is selected for bundle adjustment to reduce the pose errors caused by local information loss or mismatching. The results …
Edge Surveillance Task Offloading And Resource Allocation Algorithm Based On Drl,
2024
School of Computer Science, Hubei University of Technology, Wuhan 430000, China
Edge Surveillance Task Offloading And Resource Allocation Algorithm Based On Drl, Chao Li, Jiabao Li, Caichang Ding, Zhiwei Ye, Fangwei Zuo
Journal of System Simulation
Abstract: For the resource limitation of intensive surveillance tasks in edge computing, a surveillance task offloading and resource allocation algorithm based on DRL is proposed. With the optimization objectives of surveillance task delay and recognition accuracy, the joint decision objective optimization solution of task offloading, wireless channel allocation, and image compression rate was modeled as a Markov decision process. To address the problem of slow and unstable algorithm convergence due to the high volatility of training samples caused by the dynamic nature of wireless channels and the randomness of surveillance tasks, an attention mechanism is used to jointly encode channel …
Modeling And Simulation Of Pipeline Cable Inspection Robot Based On Omnidirectional Wheel,
2024
Department of Automation, North China Electric Power University, Baoding 071003, China
Modeling And Simulation Of Pipeline Cable Inspection Robot Based On Omnidirectional Wheel, Chao Yuan, Yao Zhang, Yadong Zhao, Dawei Xu, Jing Yuan, Yongjie Zhai
Journal of System Simulation
Abstract: Aiming at the problem that the inner space of underground pipeline cable is narrow and closed, which cannot be inspected by humans, and the existing pipeline robot cannot adapt to the special environment of pipeline cable, a miniaturized, compact pipeline cable inspection robot is designed. This robot is capable of operating within the underground pipeline where cables have already been laid to inspect the inner wall of the pipeline and the working condition of the cables. According to the requirements of the working conditions, the whole three-dimensional model of the robot has been established. The mapping relationship between the …
