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Randomness-oriented Multi-dimensional Cloud-based belief rule Base approach for complex system modeling

EXPERT systemS WITH APPLICATIONS 2022年 203卷

作者： Chang, Leilei Zhang, Limao Xu, Xiaobin Hangzhou Dianzi Univ Dept Automat Hangzhou Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

Many complex system modeling problems have to face two major challenges, randomness and interpretability, which are the motives and major contributions of this study. For the randomness challenge, the cloud model is adopted which reveals randomness by assuming the points within an interval following a certain distribution. For the interpretability challenge, the Belief Rule Base (BRB) is adopted which is essentially a white-box with direct access to experts and decision-makers. Therefore, a new approach is proposed to meet the two challenges by combining the cloud model and BRB, namely Cloud-BRB. In the new Cloud-BRB approach, new rule activation and matching degree calculation procedures have been proposed to better accommodate the cloud model into BRB. Moreover, new weight calculation procedures have also been proposed based on the multi-dimensional cloud model which connects the multiple attributes in practical problems and multiple attributes in BRB. A practical case of risk assessment on tunnel-induced damages to pipes is studied. Case study results show the consistency with previous approaches, which validate the efficiency of the proposed Cloud-BRB approach. As the belief distribution and randomness have been included in the Cloud-BRB approach, the results are more informative for decision-makers than previous approaches. Furthermore, Pipe No. 10 has been used to demonstrate the key influential factors identification process by the Cloud-BRB approach.

关键词： Cloud model Belief Rule Base (BRB) complex system modeling Randomness Interpretability

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Belief Rule Base Structure and Parameter Joint Optimization Under Disjunctive Assumption for Nonlinear complex system modeling

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2018年第9期48卷 1542-1554页

作者： Chang, Lei-Lei Zhou, Zhi-Jie Chen, Yu-Wang Liao, Tian-Jun Hu, Yu Yang, Long-Hao High Tech Inst Xian Xian 710025 Shaanxi Peoples R China Univ Manchester Manchester Business Sch Decis & Cognit Sci Res Ctr Manchester M15 6PB Lancs England Beijing Inst Syst Engn State Key Lab Complex Syst Simulat Beijing 100101 Peoples R China Fuzhou Univ Dept Management Fuzhou 350116 Fujian Peoples R China

Nonlinear complex system modeling has drawn attention from diverse fields and many approaches have been developed. Among those approaches, the advantages of the belief rule base (BRB) expert system have been shown for managing multiple types of information under uncertainty and modeling the nonlinearity present in many theoretical and practical complex systems. However, two challenges still need to be addressed. First, BRB needs to be downsized to conserve modeling and computational effort. For this challenge, a new disjunctive assumption is applied, which can significantly downsize BRB while maintaining its completeness. Second, the structure and parameters of BRB need to be jointly optimized. For this challenge, a new Akaike information criterion (AIC)-based optimization objective is derived to represent both modeling accuracy and modeling complexity. Moreover, a joint bi-level optimization model with an AIC-based objective is constructed for the BRB structure and parameters, and a bi-level optimization algorithm is proposed. Three evolutionary algorithms, namely, the genetic algorithm, particle swarm optimization algorithm, and differential evolutionary algorithm, are tested in a comparative fashion to determine the best fit for the optimization engine. The results of two practical case studies show that the joint optimization approach can identify an optimal configuration for both its structure and parameters, which is referred to as the best decision structure in this paper.

关键词： Belief rule base (BRB) complex system modeling disjunctive assumption joint optimization

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Akaike Information Criterion-based conjunctive belief rule base learning for complex system modeling

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KNOWLEDGE-BASED systemS 2018年第Dec.1期161卷 47-64页

作者： Chang Leilei Zhou Zhijie Chen Yuwang Xu Xiaobin Sun Jianbin Liao Tianjun Tan Xu Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Zhejiang Peoples R China Shenzhen Inst Informat Technol Sch Software Engn Shenzhen 518172 Peoples R China High Tech Inst Xian Xian 710025 Shaanxi Peoples R China Univ Manchester Manchester Business Sch Decis & Cognit Sci Res Ctr Manchester M15 6PB Lancs England Natl Univ Def Technol Sch Informat Syst & Management Changsha 410073 Hunan Peoples R China Beijing Inst Syst Engn State Key Lab Complex Syst Simulat 10 An Xiang Bei Li Rd Beijing Peoples R China

Nonlinear complex system modeling has become the basis of many theoretical and practical problems, which requires balancing the correlations between the modeling accuracy and the modeling complexity. However, the two objectives may not be consistent with each other under many practical conditions, especially for complex systems with multiple influential factors. The belief rule base (BRB) has shown advantages in nonlinear complex system modeling under uncertainty. However, most of current works on BRB has focused only on the modeling accuracy. As such, an Akaike Information Criterion (AIC)-based objective, AIC(BRB), is deduced to represent both the modeling accuracy (denoted by the Mean Square Error (MSE)) and the modeling complexity (denoted by the number of the parameters). Based on the proposed AIC(BRB), a bi-level optimization model and a corresponding bi-level optimization algorithm are developed. Moreover, an empirical optimization path search strategy is proposed for upper-level optimization. The optimization path is comprised of multiple solutions with optimal performance. After the BRB learning process, both the structure and the parameters of BRB are optimized, which identifies the best decision structure of BRB. A numerical multi-extreme function case and a practical pipeline leak detection case are studied. The results show that an optimization path could be identified with a series of optimal solutions. With AIC(BRB) as the objective, the jointly optimized BRB in the best decision structure can be obtained with an improved modeling accuracy as well as reduced modeling complexity.

关键词： Conjunctive belief rule base Akaike Information Criterion complex system modeling Optimization path

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Automatic building energy model development and debugging using large language models agentic workflow

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ENERGY AND BUILDINGS 2025年 327卷

作者： Zhang, Liang Ford, Vitaly Chen, Zhelun Chen, Jianli Univ Arizona Tucson AZ 85721 USA Natl Renewable Energy Lab Golden CO 80401 USA Arcadia Univ Glenside PA USA Drexel Univ Philadelphia PA USA Tongji Univ Shanghai Peoples R China Univ Utah Salt Lake City UT USA

Building energy modeling (BEM) is a complex process that demands significant time and expertise, limiting its broader application in building design and operations. While Large Language Models (LLMs) agentic workflow have facilitated complex engineering processes, their application in BEM has not been specifically explored. This paper investigates the feasibility of automating BEM using LLM agentic workflow. We developed a generic LLMplanning-based workflow that takes a building description as input and generates an error-free EnergyPlus building energy model. Our robust workflow includes four core agents: 1) Building Description Pre-Processing, 2) IDF Object Information Extraction, 3) Single IDF Object Generator Suite, and 4) IDF Debugging Agent. These agents divide the complex tasks into manageable sub-steps, enabling LLMs to generate accurate and reliable results at each stage. The case study demonstrates the successful translation of a building description into an error-free EnergyPlus model for the iUnit modular building at the National Renewable Energy Laboratory. The effectiveness of our workflow surpasses: 1) naive prompt engineering, 2) other LLM-based workflows, and 3) manual modeling, in terms of accuracy, reliability, and time efficiency. The paper concludes with a discussion on the interplay between foundational models and LLM agent planning design, advocating for the use of fine-tuned, specialized models to advance this field.

关键词： Building energy modeling complex system modeling Large language model Generative artificial intelligence Agentic workflow

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Data driven modeling for self-similar dynamics

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PHYSICA D-NONLINEAR PHENOMENA 2025年 472卷

作者： Tao, Ruyi Tao, Ningning You, Yi-zhuang Zhang, Jiang Beijing Normal Univ Sch Syst Sci Beijing Peoples R China Swarma Res Beijing Peoples R China Univ Calif San Diego Dept Phys San Dieg CA USA

Multiscale modeling of complex systems is crucial for understanding their intricacies. In recent years, data- driven multiscale modeling has emerged as a promising approach to tackle challenges associated with complex systems. Still, at present,this field is more focused on the prediction or control problems in specific fields, and there is no suitable framework to help us promote the establishment of complex system modeling theory. On the other hand, self-similarity is prevalent in complex systems, hinting that large-scale complex systems can be modeled at a reduced cost. In this paper, we introduce a multiscale neural network framework that incorporates self-similarity as prior knowledge, facilitating the modeling of self-similar dynamical systems. Our framework can discern whether the dynamics are self-similar to deterministic dynamics. For uncertain dynamics, it not only can judge whether it is self-similar or not, but also can compare and determine which parameter set is closer to self-similarity. The framework allows us to extract scale-invariant kernels from the dynamics for modeling at any scale. Moreover, our method can identify the power-law exponents in self-similar systems, providing valuable insights for the establishment of complex system modeling theory.

关键词： Self-similarity Machine learning Dynamics complex system modeling

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ETHICA: Designing Human Digital Twins-A systematic Review and Proposed Methodology

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IEEE ACCESS 2024年 12卷 86947-86973页

作者： Lauer-Schmaltz, Martin Wolfgang Cash, Philip Rivera, David Gabriel Tacdeo Tech Univ Denmark Dept Technol Management & Econ DK-2800 Lyngby Denmark Northumbria Univ Sch Design Newcastle Upon Tyne NE1 8ST England

Human Digital Twins (HDTs) have emerged as a key technology in digitalizing human-centered applications, able to enhance decision-making, personalize healthcare, and optimize workplace environments. However, despite a growing research body, there exists a critical lack of guidance for designing sophisticated, user-centric HDTs. In addressing this gap, this paper reports a systematic literature review of 44 papers in the field of HDTs (published between 2022 and 2023) to distill key design process insights and identify gaps in the current understanding and approaches to HDT design. Based on insights from this review, we introduce ETHICA, a systematic methodology that advocates for an iterative development process, emphasizing stakeholder collaboration and focusing on ethical and user-centric principles throughout the design life cycle. The resulting ETHICA methodology offers a robust blueprint for constructing sophisticated HDTs that harmonizes technological advancements with human-centered design and ethical considerations, encouraging a shift towards more holistic and human-centric approaches in the emerging field of Digital Twin technologies.

关键词： Agile artificial intelligence big data complex system modeling design development process digital twin ethical design framework guideline human digital twins human-centered technology Industry 5.0 information systems iterative design methodology personalized healthcare technology user-centric design

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A system Approach to Detect Medical Errors in Operational Data in Hospitals 20

A System Approach to Detect Medical Errors in Operational Da...

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20th ACS/IEEE International Conference on Computer systems and Applications (AICCSA)

作者： Aldoihi, Saad Alblalaihid, Khalid Alzemaia, Fozah Almoajel, Alia Hammami, Omar Alwablely, Shatha King Abdulaziz City Sci & Technol Inst Earth & Space Sci Riyadh Saudi Arabia King Saud Univ Coll Educ Riyadh Saudi Arabia King Saud Univ Coll Appl Med Sci Riyadh Saudi Arabia Inst Polytech Paris Comp Sci & Syst Engn Dept Palaiseau France

ISBN: (纸本)9798350319439

Medical errors represent a significant challenge in healthcare systems worldwide, leading to increased patient morbidity, mortality, and healthcare costs. Early detection and prevention of such errors in hospital operational data can significantly improve patient safety and overall healthcare quality. This paper proposes a novel, data-driven approach to model a healthcare system for detecting medical errors using advanced machine learning techniques. We leverage electronic health records (EHR) and other hospital operational data sources to develop a comprehensive framework that can automatically identify potential errors in real-time. The model aims to identify patterns and anomalies in the data to detect potential errors and provide insights for process improvement. The proposed model can help healthcare providers to proactively monitor and address medical errors, thereby reducing the risk of harm to patients.

关键词： Healthcare system Modelling Healthcare Artificial Intelligence complex system modeling Data Mining Medical Error Detection

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A MBSE Framework for the Design and Analysis of complex Automotive systems Using SysML and PCE 21

A MBSE Framework for the Design and Analysis of Complex Auto...

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21st International Conference on Software Architecture (ICSA)

作者： Kaloor, Tirtha Barosan, Ion Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350366266;9798350366259

The development of automotive systems has become more complex due to the growing demand for sophisticated features, the necessity for smooth integration of technology, and faster production schedules, leading to numerous challenges in their development. To tackle some of these challenges, this paper introduces a practical method designed to simplify the early design and assessment stages of complex automotive systems through the use of sophisticated systems engineering methodologies. It details the combination of systems modeling Language (SysML) and IBM Rational Rhapsody's Parametric Constraint Evaluator (PCE) within an improved Model-Based systems Engineering (MBSE) framework aimed at the development of complex systems. This fusion enables the virtual evaluation of parametric constraints and the analysis of engineering performance for different system configurations, thus enhancing the process of engineering analysis. We demonstrate the practicality and effectiveness of this approach using a generic automotive powertrain case study. This involves modeling a system based on specific requirements and evaluating its performance across predetermined configurations. Furthermore, the versatility of the MBSE framework is highlighted, showing its applicability beyond the automotive industry to encompass a wide range of engineering domains. This study aims to pave the way for more efficient and manageable development processes in the face of growing system complexity.

关键词： Model-Based systems Engineering complex system modeling SysML PCE Parametric Evaluation Automotive

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A Survey of Belief Rule-Base Expert system

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2021年第8期51卷 4944-4958页

作者： Zhou, Zhi-Jie Hu, Guan-Yu Hu, Chang-Hua Wen, Cheng-Lin Chang, Lei-Lei Rocket Force Univ Engn Sch Missile Engn Xian 710025 Peoples R China Hainan Normal Univ Sch Informat Sci & Technol Haikou 571158 Hainan Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou 310000 Peoples R China

The belief rule-base (BRB) model is a new intelligent expert system with the characteristics of both expert system and data-driven model. In BRB there are many if-then rules which use belief degrees to express various types of uncertain information, including fuzziness, randomness, and ignorance. As a semi-quantitative modeling tool for complex systems, BRB has the superiorities of dealing both numerical quantitative data and linguistic qualitative knowledge that are derived from heterogeneous sources. Moreover, it is also a white box approach which can provide direct access and transparency to decision makers and stakeholders. Currently, BRB has been widely applied in many fields, such as decision making, reliability evaluation, network security situation awareness, fault diagnosis, and so on. To fully demonstrate the progress of BRB, the original BRB, and some evolution forms are introduced in this article.

关键词： Expert systems Erbium Cognition Object oriented modeling Reliability complex systems Linguistics Artificial intelligence belief rule-base (BRB) model complex system modeling expert system machine learning semi-quantitative information

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REDUCING PAPR IN OTFS 6G WAVEFORMS USING PARTICLE SWARM OPTIMIZATION-BASED PTS AND SLM TECHNIQUES WITH 64, 256, AND 512 SUB-CARRIERS IN RICIAN AND RAYLEIGH CHANNELS

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FRACTALS-complex GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2024年第9N10期32卷

作者： Alanazi, Meshari H. Kumar, Arun Aljebreen, Mohammed Alzaben, Nada Nanthaamornphong, Aziz Maray, Mohammed Sorour, Shaymaa Alzahrani, Yazeed Northern Border Univ Coll Sci Dept Comp Sci Ar Ar Saudi Arabia New Horizon Coll Engn Dept Elect & Commun Engn Bengaluru India King Saud Univ Community Coll Dept Comp Sci POB 28095 Riyadh 11437 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Prince Songkla Univ Coll Comp Phuket Thailand King Khalid Univ Coll Comp Sci Dept Informat Syst Abha Saudi Arabia King Faisal Univ Coll Business Adm Dept Management Informat Syst Al Hasa 31982 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawasir Dept Comp Engn Al Kharj Saudi Arabia

The search complexity for partial transmit sequence (PTS) and selective mapping (SLM) techniques increases exponentially with the number of sub-blocks, necessitating a comprehensive search over all possible combinations of phase-weighting variables. This paper proposes a novel complex system modeling approach for PTS and SLM in an Orthogonal Time Frequency Space (OTFS) system, utilizing phase factors and a sub-block partition scheme. We describe an OTFS system that achieves low computational complexity in identifying optimal phase-weighting factors and reducing the peak-to-average power ratio (PAPR) using sub-optimal PTS and SLM based on the particle swarm optimization (PSO) algorithm. Parameters such as PAPR, bit error rate (BER), and power spectral density (PSD) were analyzed for 64, 256, and 512 sub-carriers in Rayleigh and Rician channels. The experimental outcome reveals that the proposed approaches can effectively regulate the optimal phase-weighting factors, substantially lessening PAPR with modest complexity. Fractals enhance complex modeling by optimizing PAPR reduction in OTFS 6G waveforms using fractal-influenced PSO for sub-carrier efficiency. The proposed method incorporates fractal modeling to enhance the optimization process in complex environments. Fractals, known for their intricate patterns and self-similarity, provide a robust framework for exploring vast and complex search spaces, crucial in PSO. This approach improves the efficiency of the framework.

关键词： Fractal Particle Swarm Optimization complex system modeling OTFS 5G Fractal PAPR complex PTS-SLM modeling

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