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International Conference of the TC Electrimacs Committee, ELECTRIMACS 2024

作者： Carrasco-González, David Sarrias-Mena, Raúl Horrillo-Quintero, Pablo Llorens-Iborra, Francisco Fernández-Ramírez, Luis M. Cadiz Spain SURET Research Group Department of Engineering in Automation Electronics and Computer Architecture and Networks ETSI Algeciras Cadiz Spain

ISBN: (纸本)9783031739200

Nowadays, hybrid AC/DC microgrids (HMG) are growing in popularity because of human needs and their benefits. They are composed of the interconnection of a DC microgrid (MG) and an AC MG, and each MG is constituted by generation, energy storage, and consumption technologies. This study develops a new control strategy for an HMG consisting of a DC MG and an AC MG interconnected through a bidirectional power converter. The AC MG is composed of AC loads, a photovoltaic (PV) generator and a battery bank. The DC MG is made up of a hydrogen system, DC loads, wind turbine (WT) and ultracapacitor (UC). The HMG is evaluated under different conditions in an experimental setup constituted by an OPAL-RT 4512 unit and a Raspberry Pi microcontroller. This experimental setup permits to execute the HMG in real time and to validate the results obtained. Finally, the control system developed for this HMG presents a satisfactory behaviour under different working conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wind turbines

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Accelerating Blockchain Application Development: Integrating Blockchain as a Service Within Low-Code Platforms 7th

Accelerating Blockchain Application Development: Integrating...

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7th International Conference on Blockchain, ICBC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： He, Sheng Huang, Qinglin Jiao, Shaoshuai Lin, Zepeng Lin, Jinxuan Ren, Jun Xiong, Dengbin Zhang, Liang-Jie National Engineering Research Center for Supporting Software of Enterprise Internet Services Shenzhen518057 China Kingdee Research Kingdee International Software Group Co. Ltd. Shenzhen518057 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783031770944

The integration of Blockchain as a Service (BaaS) within the Low-Code Development Platforms (LCDPs) is expected to revolutionize the speed and accessibility of blockchain application development. This paper explores the synergy between BaaS and the low-code or no-code development environments in Platform as a Service (PaaS) frameworks, examining opportunities, challenges, and implications for developers and organizations. LCDPs have gained popularity for simplifying application creation, empowering a broad spectrum of developers from citizen developers to experienced professionals. Concurrently, blockchain technology offers enhanced security and transparency but traditionally requires specialized skills for implementation. By embedding BaaS capabilities within LCDPs, developers can leverage blockchain benefits without extensive expertise, streamlining development processes and accelerating time-to-market for blockchain applications. Additionally, it addresses challenges related to security, scalability, and the complexity of managing blockchain operations within simplified development environments. By presenting best practices and identifying opportunities, this paper aims to contribute to the advancement of accessible and efficient blockchain application development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Platform as a Service (PaaS)

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A Paradigm Shift to Causal Model-Driven Decision-Making With Generative AI 13th

A Paradigm Shift to Causal Model-Driven Decision-Making Wit...

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13th International Conference on AI and Multimodal Services, AIMS 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： He, Sheng Ning, Yishuang Zhang, Liang-Jie Lei, Kai National Engineering Research Center for Supporting Software of Enterprise Internet Services Shenzhen518057 China Kingdee Research Kingdee International Software Group Co. Ltd. Shenzhen518057 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Peking University Shenzhen518055 China

ISBN: (纸本)9783031776809

In recent years, the rise of big data has popularized data-driven decision-making. However, the interpretability shortcomings of artificial intelligence (AI) models limit their reliability for critical decisions. This paper proposes a paradigm shift from conventional data-driven to causal model-driven decision-making, leveraging advancements in causal inference and large language models (LLMs). By applying the capabilities of generative AI, this paradigm shift enables the fusion of extensive domain knowledge, facilitating the development of causal models that capture the complexity of real-world systems and problems. The model-driven approach provides a better understanding of the causal mechanisms, relationships, and dynamics compared to correlational data-driven methods. Moreover, we introduce the concept of a composable business model based on modular causal components. We present a methodology for constructing robust causal model-driven decision frameworks, emphasizing the comprehensive utilization of generative AI to incorporate domain knowledge with causal inferences. Through an in-depth analysis of case studies across multiple domains, this model-driven approach empirically showcases its potential to improve decision quality, optimize resource allocation, and enhance process efficiency. Additionally, we critically discuss future research directions and challenges in the evolutionary trajectory of causal model-driven decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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COSIS: An AI-Enabled Digital Transformation Framework Integrating Large Language Models and Key Performance Indicators 21st

COSIS: An AI-Enabled Digital Transformation Framework Integr...

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21st International Conference on Services Computing, SCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Zhang, Liang-Jie Chen, Huan He, Sheng Li, Changhu Chen, Junyang Zhang, Haodi Du, Wenfeng Center for AI Services Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen China SF Technology Co. Ltd. Shenzhen China Kingdee Research Kingdee International Software Group Co. Ltd. Shenzhen China

ISBN: (纸本)9783031769993

The rapid advancement of Artificial Intelligence (AI) and Large Language Models (LLMs) has significantly transformed various sectors of society, compelling enterprises to undertake comprehensive digital transformations to remain competitive in the AI era. This paper introduces COSIS, an AI-enabled digital trans-formation framework comprising five key dimensions: Culture, Operation, Strategy, Innovation, and Service. The COSIS framework provides a structured approach for enterprises to navigate the complexities of digital transformation by integrating AI and LLM technologies into their core functions. For each dimension, the paper identifies specific key performance indicators (KPIs) to measure the effectiveness of AI-enabled transformations. These KPIs enable enterprises to quantitatively assess progress and outcomes, ensuring alignment with strategic objectives and facilitating continuous improvement. Additionally, the paper discusses the implications of AI and LLM technologies on enterprise management team building in the AI era. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Human resource management

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research on Entity Recognition Method for Quality Standards of Concrete Prefabricated Components 1

Research on Entity Recognition Method for Quality Standards ...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

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Universal Construction for Linearizable but Not Strongly Linearizable Concurrent Objects 10th

Universal Construction for Linearizable but Not Strongly L...

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10th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2024

作者： Wang, Chao Wu, Peng Petri, Gustavo Jia, Qiaowen He, Youlin Lv, Yi Liu, Zhiming Centre for Research and Innovation in Software Engineering College of Computer and Information Science Southwest University Chongqing China and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Arm Research Cambridge United Kingdom China Great Wall Technology Group Co. Ltd. Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819606016

Strong linearizability is a variant of linearizability and is more suitable for verification. In this paper we investigate the following two problems: (1) for which deterministic sequential specifications there exist linearizable but not strongly linearizable objects;(2) can we capture any violation scheme of strong linearizability for practical objects that are linearizable but not strongly linearizable. To deal with the first problem, we propose two classes of deterministic sequential specifications called inclusively-permutative sequential specifications and exclusively-permutative sequential specifications. The first class of sequential specifications contains sequential specifications of counter, register, max-register, snapshot, set, queue, stack and priority queue. The second class of sequential specifications contains sequential specifications of blocking queue, blocking stack and blocking priority queue. To establish these results, we propose a uniform methodology to construct a wait-free, linearizable but not strongly linearizable object for these two classes of sequential specifications. Our universal construction is based on the classical universal construction, and it additionally permits each operation to change the "take-effect-order" of previous operations. To deal with the second problem, we use bi-branch distinguishable executions to represent violations of strong linearizability. We investigate eight objects that are linearizable but not strongly linearizable, and we show that for each object, it has a pair of bi-branch distinguishable executions such that our universal construction can generate another pair of bi-branch distinguishable executions with the same set of histories. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Two dimensional DOA estimation of coherent signals based on covariance matrix element transformation 24

Two dimensional DOA estimation of coherent signals based on ...

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8th International Conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Qu, Zhiyu Wu, Xu Geng, Zhenya College of Information Science and Engineering Hohai University Jiangsu Nanjing China College of Computer Science and Software Engineering Hohai University Jiangsu Nanjing China The 49th Research Institute of China Electronics Technology Group Corporation Heilongjiang Harbin China

ISBN: (纸本)9798400710094

Traditional Direction of Arrival (DOA) estimation algorithms for coherent signals in uniform circular array (UCA), such as mode space transformation, allow for the adaptation of advanced algorithms initially developed for uniform linear arrays to be applied effectively in UCA. However, after mode space transformation, the array will encounter aperture loss and will also be unable to perform two-dimensional DOA estimation effectively. In this paper, we propose a new method that that integrates covariance matrix element transformation with the Multiple Signal Classification (MUSIC) to estimate the DOA of coherent signals in various symmetric antenna arrays, including UCA. The algorithm leverages the phase difference symmetry of the antenna array to decorrelate the incoming signals, effectively restoring the full rank of the covariance matrix. Subsequently, it employs two-dimensional MUSIC for DOA estimation. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Covariance matrix

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Elastic Spectrum Sensing: An Adaptive Sensing Method for Non-Terrestrial Communication Under Highly Dynamic Channels

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3868-3881页

作者： Xu, Tianheng Xu, Yinjun Zhou, Ting Zhang, Haijun Hu, Honglin Leung, Victor C. M. Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Shanghai Frontier Innovation Research Institute Shanghai201100 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China Shanghai University School of Microelectronics Shanghai200444 China Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

With the rapid development of space technology, the role of satellite communications has become progressively significant. Non-terrestrial communication is deemed a critical scenario in the sixth generation (6G) communication systems, which showcases seamless connectivity, minimal geographic constraints and substantial communication capacity. Simultaneously, satellites and terminals spring up, spatial density increases, which further emphasizes the scarcity of spectrum resources. Consequently, to improve spectrum utilization for non-terrestrial communication is a significant concern. Spectrum sensing, which allows dynamic resource reuse, plays an important role in 6G. However, high mobility in non-terrestrial scenarios poses great challenges, such as fast time-varying channels, Doppler effect, etc., which seriously affect sensing accuracy and cannot well support optimal spectrum utilization. Motivated by such circumstances, this paper proposes an elastic sensing method for the downlink non-terrestrial communication scenario. Firstly, we design the system architecture and sensing workflow. To overcome the negative effects raised by high mobility, we propose the elastic sensing criterion and multi-area dividing scheme for the sensing zone. Thresholds affected by the elastic sensing are derived for different areas. Finally, the numerical results show that from -10 dB to -5 dB, the proposed method can improve the total performance by an average of 28.3% while stabilizing the false alarm probability around 0.1 typical level and demonstrating higher constancy compared with traditional technologies from −10 dB to −5 dB. © 2002-2012 IEEE.

关键词： Satellite communication systems

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Intelligent Spatial Anomaly Activity Recognition Method Based on Ontology Matching

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computers, Materials and Continua 2025年第3期83卷 4447-4476页

作者： Longgang Zhao Seok-Won Lee The Knowledge-intensive Software Engineering (NiSE) Research Group Department of Artificial Intelligence Ajou University Suwon City 16499 Republic of Korea Department of Software and Computer Engineering Ajou University Suwon City 16499 Republic of Korea

This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities, and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency. The method in this paper adopts the event sequence segmentation technique, combines location awareness with time interval reasoning, and improves human activity recognition through ontology reasoning. Compared with the existing methods, the framework performs better when dealing with uncertain data and complex scenes, and the experimental results show that its recognition accuracy is improved by 15.6% and processing time is reduced by 22.4%. In addition, it is found that with the increase of context complexity, the traditional ontology inference model has limitations in abnormal behavior recognition, especially in the case of high data redundancy, which tends to lead to a decrease in recognition accuracy. This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.

关键词： Context awareness activity recognition ontological reasoning complex context anomaly detection

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Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

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