Journal of Cyber-Physical-Social Intelligence
The Journal of Cyber-Physical-Social Intelligence (JCPSI) is a peer-reviewed, open-access journal published by the Association of Global Intelligent Science and Technology (AGIST). JCPSI provides a focused forum for research on cyber-physical-social systems, artificial intelligence, automation, human-machine collaboration, intelligent infrastructure, and governance for complex intelligent systems. The journal welcomes original research articles, survey and tutorial papers, letters, case studies, datasets, platforms, and application-driven studies that make a clear contribution to theory, methodology, system design, or real-world deployment. Focus and Scope JCPSI publishes research on the theories, methods, systems, and applications that connect cyber, physical, and social spaces. The journal is especially interested in work that integrates AI, automation, human-machine collaboration, social computing, intelligent infrastructure, and real-world cyber-physical-social systems. Core Topics Cyber-physical-social systems and CPSS architectures. Agentic intelligence, autonomous organizations, and parallel systems. Large language models and AI agents for social and physical environments. Digital twins, mixed reality, IoT, robotics, and intelligent transportation. Human-machine collaboration, social intelligence, trust, explainability, and governance. Security, privacy, responsible AI, and ethical design for intelligent systems. Applications Representative application areas include smart cities, intelligent transportation, healthcare, education, energy systems, robotics, industrial automation, environmental sustainability, public safety, and digital society. Peer Review Timeline JCPSI uses double-blind peer review. Editorial screening, including format requirements and technical suitability checks, is normally completed within 3-6 days. The first round of peer review is normally completed within 2-3 weeks. Timelines may vary depending on reviewer availability, manuscript complexity, and revision rounds. Author Resources Submission guidelines Journal policies Start a submission Special Session Proposal
Association of Global Intelligent Science and Technology
Authors retain copyright to their published work. Articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits sharing and adaptation for any lawful purpose provided that appropriate credit is given to the authors and the original publication is cited. Authors grant JCPSI the right of first publication and the right to identify itself as the original publisher of record.
Parallel Organizations for Agentic Societies:From Embodied Intelligence to AutonomousIntelligence for AI Agents and Digital Humans
We are living through an epochal shift in the nature of work, collaboration, and collective action. AI agents, digital humans, and physical robots are no longer auxiliary tools but autonomous, goal-directed participants in organizational life---capable of long-task execution, cross-agent communication, workflow orchestration, and adaptive decision-making at scale. AI systems can now reliably complete tasks of roughly two hours in length, doubling in capability every four months since 2024; up to 57\% of current work hours in the United States are technically automatable, with virtual agents covering 44\% and physical robots 13\%~\cite{ed1}. By 2030, this transformation could unlock approximately \$2.9 trillion in annual economic value~\cite{ed2}. Yet despite this explosive technological progress, organizational theory has failed to keep pace. We continue to rely on frameworks designed for human-only hierarchies~\cite{ed3}, markets~\cite{ed4}, and networks~\cite{ed5}---structures ill-suited to govern hybrid collectives of humans, digital agents, and robotic actors. To situate this challenge, the 2025 issue of the Journal of Cyber-Physical-Social Intelligence (CPSI) brings together a set of articles that collectively reflect several key dimensions of intelligence, coordination, and governance in the emerging agentic society. Building on these contributions, this editorial introduces the conception of \textbf{Parallel Organizations for Agentic Societies (POAS)}: a unified, theoretically grounded, and operationally actionable framework for designing, governing, and evolving organizations in the age of autonomous AI agents and digital humans. POAS is a paradigm built for agentic societies from first principles. It integrates advances in parallel intelligence with the ACP approach~\cite{ed6}: Artificial societies, Computational experiments, Parallel execution, continuous organizational forms, distributed governance, cyber movement mobilization, and embodied-to-autonomous intelligence evolution. In doing so, it resolves four interconnected gaps that have fragmented research and practice: ontology, coordination, governance, and mobilization. Against this backdrop, the core thesis is straightforward: \textit{the future of intelligence is not individual but organizational}. The transition from embodied, task-specific intelligence to generalized, autonomous intelligence in AI agents and digital humans will not be achieved in isolated systems, but through parallel organizations that enable virtual--real coevolution, continuous experimentation, and emergent collective wisdom.
Journal of Cyber-Physical-Social Intelligence 2025
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Parallel Scenarios: A New Paradigm and Evolution Path for Scenario-Based Applications of Artificial Intelligence
Scenario engineering has emerged as a promising approach for deploying artificial intelligence in manufacturing, transportation, healthcare, and other real-world domains. However, its current development still faces several structural limitations, including an overemphasis on models rather than scenarios, on access rather than restructuring, and on capability rather than governance. To address these challenges, this paper proposes a new paradigm of parallel scenarios grounded in the framework of parallel intelligence, namely Artificial Societies, Computational Experiments, and Parallel Execution (ACP). In this paradigm, a parallel scenario is defined as an intelligent application system composed of real scenarios, experimental scenarios, and ideal scenarios. The real scenario represents the physical operational environment, the experimental scenario provides a controllable space for simulation, deduction, and optimization, and the ideal scenario serves as the value-oriented target for system evolution. Through the ACP operating mechanism, these three types of scenarios are dynamically connected through continuous interaction, parallel operation, and iterative tuning, thereby forming a closed-loop mechanism for scenario evolution and intelligent decision-making. On this basis, the paper further develops an operational paradigm driven by parallel scenarios, including scenario sensing, plan simulation, implementation, and feedback iteration, and clarifies the functional role of AI agents in cross-scenario coordination. In addition, model context protocol, the agent-to-agent collaboration protocol, and the ACP communication protocol are introduced to support autonomous collaboration among agents across the three scenario types. This study provides both a theoretical foundation and a practical framework for advancing scenario engineering from isolated technical deployment toward systematic value creation and intelligent governance.
Parallel Exhibition: An ACP-Based Framework for Organization and Management of MICE Systems
The MICE (Meetings, Incentives, Conferences, and Exhibitions) industry serves as a critical infrastructure of the global service economy, combining huge economic scale with an irreplaceable knowledge spillover function. However, the essential characteristics of temporary organizations give rise to multidimensional management challenges, including multi-participant conflicts, spatiotemporal uniqueness, rigid regional constraints, and emergent crowd behavior. Existing project management tools, isolated technologies, and digital twin methods are all insufficient to address these complex challenges. In this paper, we propose a parallel exhibition intelligent management framework based on the ACP (Artificial systems, Computational experiments, Parallel execution) approach. We design a collaborative mechanism among three types of humans---biological humans, digital humans, and robotic humans---to enable virtual-real closed-loop management. The framework aims to achieve coordination among participants, transferable experience accumulation, and real-time dynamic regulation through the ACP approach, addressing systemic management challenges in the MICE industry through a system-level approach and offering a reference for the intelligent management of complex social systems.
Trustworthy Governance of Agentic Societies Based on DePIN and VLA
While Decentralized Autonomous Organizations (DAOs) and Artificial Intelligence are reshaping the governance of academic societies, reliably integrating on-chain decisions with off-chain physical activities remains a critical challenge. The fundamental bottleneck is the difficulty of reliably integrating real-world execution outcomes into the digital decision-making loop. To address this, we propose an endogenous contribution evaluation framework integrating Decentralized Physical Infrastructure Networks (DePIN) and Vision-Language-Action (VLA) models. This approach maps physical entities to on-chain decentralized identities. By leveraging VLA edge nodes to analyze multimodal behavioral data collected via DePIN, the system autonomously generates a verifiable Proof of Real-World Contribution (PoRWC). This proof subsequently drives on-chain incentive distribution through a reputation-weighted consensus mechanism. Consequently, this framework establishes an endogenously trustworthy closed loop from physical processes to digital governance. We demonstrate its feasibility and scalability through a case study of the Chinese Association of Automation (CAA), providing a robust engineering path for the parallel governance of modern academic societies.
Small-Large Model Collaboration in Public Opinion Topic Discovery A Case Study with the Pager Bomb Attack
In September 2024, Lebanon was rocked by an unprecedented cyber-physical attack using Pager bombs. The attack combined advanced cyber warfare techniques with physical destruction, resulting in significant loss of life, infrastructure damage, and geopolitical repercussions. In this paper, we analyze the attitudes on this attack, from both English and Arabic social media users, and investigate impacts on global electronic devices sales and usage. A new topic discovery approach using large models and small models collaboration is proposed. We compare English and Arabic topics generated on social media and find that people in different language spaces share common topics of anxiety on this event. By analyzing market share trends in both China and the United States, an obvious correlation can be found between this event and phone sales. In addition, we discuss the evolution of warfare, and how DAOs(Decentralized Autonomous Organizations) can be utilized to improve the security of electronic devices by secured monitoring of their whole lifecycle.
Cyber-Physical-Social Intelligence for End-to-End Resilience of Next-GenerationPower Systems
We are pleased to present the 2024 issue of the Journal of Cyber-Physical-Social Intelligence (CPSI), titled “Cyber-Physical-Social Intelligence for End-to-End Resilience of Next-Generation Power Systems.” This issue brings together a set of carefully selected regular articles spanning human–robot collaboration, computational stain reduction, brain dynamics identification, and parallel financial systems. Although diverse in application domains, these contributions collectively exemplify the core principles of cyber-physical-social intelligence and reflect the breadth of CPSI research and its relevance to building resilient, interconnected systems across critical infrastructures.
Journal of Cyber-Physical-Social Intelligence 2024
Two-Stage Stain Reduction in Ancient Japanese Manuscripts via Mask-Guided Diffusion Models
Ancient Japanese manuscripts are invaluable cultural heritage, yet their readability is often degraded by stains, bleed-through, and insect damage accumulated over centuries. To address this problem, we propose a two-stage stain reduction method based on Denoising Diffusion Restoration Models (DDRM). The first stage applies binarization to remove stains and extract text regions, while DDRM with noise masking reconstructs character details lost during thresholding. In the second stage, patch-wise restored images are reassembled and refined at the page level to ensure background consistency. Experiments on two representative works, \textit{Tsurezuregusa} (322 pages) and \textit{Isoho Monogatari} (198 pages), demonstrate the effectiveness of the proposed method. For \textit{Tsurezuregusa}, our method has achieved PSNR of 15.97 and SSIM of 0.715, compared with 7.46 and 0.510 for simple binarization. With gamma correction ($\gamma=0.8$), PSNR further improved to 18.03. For \textit{Isoho Monogatari}, we have obtained a PSNR of 18.48 and an SSIM of 0.776, outperforming binarization (9.74, 0.694), and the gamma correction increased PSNR to 20.68. In addition, the proposed method improved contrast (higher brightness variance than ground truth) while keeping kurtosis and entropy in a balanced range. The results demonstrate that our diffusion-based approach achieves both effective stain removal and faithful character preservation, contributing to the long-term accessibility and readability of ancient manuscripts.
Parallel Financial Systems: Towards Governable and Sustainable Intelligent Financial Services
Contemporary financial systems operate in complex environments marked by volatility, uncertainty, complexity, and ambiguity, which elevates the cost of trial and error and exposes the limits of siloed data and centralized control. Considering the complexity challenges for the legacy financial system, we propose the parallel financial systems, which are grounded in the theory of parallel intelligence and utilizes the ACP (Artificial systems, Computational experiments, and Parallel execution) methodology to conduct financial execution and resource coordination through the interaction and co-evolution of real-world and artificial systems. Based on this paradigm, we construct a corresponding technical architecture that integrates cutting-edge intelligent technologies represented by AI agents with decentralized technologies provided by blockchain, smart contracts and decentralized autonomous organizations (DAOs). To further demonstrate the applicability for the legacy enterprise, we detail the system workflow, from individual operation to group coordination, with an illustrative example of how the parallel financial systems are utilized along a project lifecycle. This work serves as a theoretical basis and design roadmap for verifiable, privacy-preserving, and interoperable financial intelligence.
Integrating Cyber-Physical-Social Intelligence for Safer and Smarter Transportation Systems: A Comprehensive Literature Review
The number of fatalities due to human error or mistakes has increased all around the world, including the United States, over the past decades. According to NHTSA, over 42,000 deaths have been recorded all over the United States in 2022, and 90% of which was due to human error or mistakes. Although there was a significant advancement in transportation technologies, the number of crashes has not decreased over the years. The need for integrating new emerging technologies with the physical and social layers is necessary to consider, as human feedback and interaction over the technologies can reduce crashes and improve mobility. Studies show that Cyber-Physical-Social-Intelligent technologies like Vehicle-to-Everything (V2X) communication can reduce traffic fatalities by up to 80%, while human-centered design in automation can significantly improve compliance and lower risky behaviors. Despite their growing importance, there is a lack of proper review to integrate these machine-based autonomous technologies with human feedback input, along with the implementation challenges. This paper provides a comprehensive literature review of CPSI applications in transportation sectors, exploring theoretical models, the potential scopes, and the immense possibilities of integration of CPS-based technologies in the current transportation system in a simple, easy, and fluent way. Real-world applications of these technologies have also been discussed for the smart city framework, wrong-way driving detection, human-machine interaction, and other aspects, along with their potential benefits. Despite these advancements, several technical and institutional challenges, like data security, data management, computational capability, and financial restraints, can impede the widespread deployment of CPSS. By addressing these challenges, this review presents the key gaps in the implementation challenges of CPSI and proposes a roadmap for future development. This research will create scope for future researchers to study this growing field as well as support policymakers, engineers, and researchers in planning and designing transportation systems that are not only intelligent but also adaptive, human-centered, inclusive, and safer for all users, contributing to long-term sustainable goals like vision zero initiatives.
The hidden Markov model reveals the changes in brain dynamics among patients with end-stage renal disease under different dialysis methods
End-stage renal disease patients often exhibit impairments in attention, memory, and executive function, reducing quality of life and increasing mortality risk. Hemodialysis and peritoneal dialysis are the main treatment modalities, yet their effects on brain functional dynamics remain unclear. In this study, resting-state functional magnetic resonance imaging data were collected from healthy controls and end-stage renal disease patients undergoing hemodialysis or peritoneal dialysis. Group differences were then investigated using dynamic functional connectivity, a hidden Markov model, and support vector machine classification to investigate alterations in end-stage renal disease patients. Dynamic analysis showed that hemodialysis patients exhibited more frequent state switching and unstable network patterns, reflecting widespread disruption of brain functional organization. In contrast, peritoneal dialysis patients displayed state occupancy and dwell times closer to healthy controls, indicating relatively preserved dynamic stability, though minor connectivity abnormalities persisted. Functional analysis revealed abnormalities in frontoparietal network and dorsal attention network in hemodialysis patients, while peritoneal dialysis patients showed imbalances between the default mode and other systems. Support vector machine classification further confirmed these patterns, with the highest accuracy in distinguishing healthy controls from hemodialysis patients and intermediate network alterations in peritoneal dialysis patients. These findings demonstrate that dynamic brain network analysis combined with machine learning can reveal modality-specific alterations in end-stage renal disease and provide potential imaging biomarkers for understanding dialysis-related cognitive impairment.
Copyright (c) 2024 Journal of Cyber-Physical-Social Intelligence https://creativecommons.org/licenses/by/4.0
From Vision to Reality: A New Paradigm for Cyber-Physical-Social Education
WITH great enthusiasm, we present the 2023 issueof the Journal of Cyber-Physical-Social Intelligence(CPSI). This issue features four carefully selected regulararticles, each offering valuable insights into the evolvinglandscape of cyber-physical-social systems. In addition, weare proud to showcase an editorial titled “From Vision toReality: A New Paradigm for Cyber-Physical-Social Educa-tion”, which marks a significant milestone in advancing thefield of cyber-physical-social education.
Journal of Cyber-Physical-Social Intelligence 2023
Copyright (c) 2023 Journal of Cyber-Physical-Social Intelligence https://creativecommons.org/licenses/by/4.0
Simulation and calculation of wide frequency harmonic temperature rise based on dry type hollow reactor
With the increasing size of the grid, a large number of non-linear equipment applicable, harmonic pollution is becoming more and more serious. Higher demands on the harmonic operation capability of reactor filtering equipment. In this paper, the dry type hollow reactor was taken as a research object, according to the field-circuit coupling principle, the establishment of finite element two-dimensional wire-turn unit electromagnetic simulation calculation model, solved the coil frequency current superimposed on different harmonic current frequency, content curve. Then calculate the winding loss of each layer, carry out the fluid-heat coupling simulation calculation, and get the temperature rise distribution of the reactor. Study shows that：Elevated harmonic content causes an increase in coil current amplitude and excitation of a stronger magnetic field, resulting in an increase in resistance loss and eddy current loss, leading to a rise in reactor temperature. The increase in harmonic frequency marginalizes the reactor coil current density distribution, increases the coil resistance, and leads to an increase in the local magnetic induction strength, resulting in an increase in coil eddy current losses, leading to an increase in reactor temperature.
Optimization of stray capacitance based on CVT harmonic voltage measurement
With the wide application of nonlinear power electronic devices in power systems, the problem of harmonic pollution is becoming more and more serious, which significantly affects the measurement accuracy of voltage transformers (especially capacitive voltage transformers, CVT). The existing research focuses on the performance optimization of CVT under power frequency conditions, and the research on measurement error under harmonic conditions is insufficient. In particular, the influence of stray capacitance on harmonic transmission characteristics has not been fully paid attention to. Therefore, this paper aims to solve the problem of CVT measurement error in harmonic environment. By proposing an optimization method based on equivalent circuit model, the key stray capacitance parameters are accurately identified and corrected, to improve its measurement accuracy. Firstly, the equivalent circuit model of CVT is established, and the resonance phenomenon data is captured by Simulink simulation technology. The objective function based on the relative error of voltage transfer coefficient is defined, and the Pareto solution set is calculated by optimization algorithm to identify the optimal stray capacitance parameters. After applying this parameter to the circuit model, the voltage transfer coefficient of CVT is corrected, which significantly reduces the measurement error in harmonic environment. This study not only provides a theoretical basis for the high-precision measurement of CVT in power system, but also provides a reference for practical technical application.
TRUE Autonomous Organizations and Operations for Web3
As the key component of the emerging Web3, Decentralized Autonomous Organizations and Operations (DAOs) enables decentralized decision-making and governance mainly driven by blockchain and smart contracts. This paper first provides a comprehensive examination of DAOs’ evolution, tracing their historical development and the progression of their definitions and underlying concepts. Then, it presents their classical governance models founded on token economics and analyzes typical practical cases, subsequently identifying the current dilemmas faced by DAO governance. To address these dilemmas, TRUE Autonomous Organizations and Operations (TAOs) are proposed, which underscores the trustable, reliable, usable, efficient and effective essence of decentralization. Besides, the distinctions between TAOs and DAOs are discussed from perspective of the value systems, governance structures, incentive allocation, decision-making model. Furthermore, it highlights the research issues that need to be addressed to realize the full potential of TAOs.
Multi-scale Group Decision-Making Employing Large Language Model for Sentiment-Oriented Grouping
In contemporary decision-making scenarios involving multiple experts, the expression of opinions through natural language poses significant challenges for traditional analytical frameworks. As online collaboration expands, so does the heterogeneity of expert evaluations, both in scale and form, particularly when assessments are expressed via unstructured textual comments. Conventional Multi-scale Group Decision-Making methods, while effective in managing diverse evaluation metrics, often fall short in processing the semantic complexity of human language. This study proposes an enhanced Multi-scale Group Decision-Making framework that integrates a Large Language Model to classify experts' comments with greater contextual sensitivity. Unlike classical sentiment analysis methods, which rely on rigid lexical heuristics, Large Language Model captures deeper linguistic nuances, such as tone, intent, and implicit meaning, enabling a more precise grouping of experts based on their expressed attitudes. These refined groups contribute to a more coherent consensus process and lead to improved decision outcomes. The proposed approach advances group decision methodologies by bridging structured decision theory with state-of-the-art natural language understanding.
Reinforcement Learning for Optimizing Delivery Paths in Hospital Settings: A Review
Reinforcement learning is a branch of machine learning that facilitates the interaction of autonomous agents with their environments. This is done by “teaching” an agent efficient decision-making through iterative processes of exploration and trial-and-error. This review article focuses on the application of reinforcement learning within the healthcare industry. We review recent publications that address the optimization of the pickup and delivery processes for essential supplies and medications with mobile robots, and the integration of these two key technologies to improve hospital operations efficiency. We also investigate the gap between research results and real-world applications, and point out directions for future work.
YOU ARE IN MY HEART: Enhancing Human-Robot Collaboration-Safety through Human Emotion-Based Perception in Smart Manufacturing Contexts
The development of safe human-robot collaborative systems is crucial to the future advancement of the manufacturing industry. This is particularly relevant to companies that already utilize robots alongside human workers in collaborative tasks such as product co-assembly. These pre-existing large robots allow industrial sectors to create very precise products at a rate that a human could not keep up with or would be very dangerous to attempt. However, as industry standards require workers to continually be in closer proximity to these robots with powerful capabilities, it is vital that workers are kept safe, especially when entering the robot’s envelope or workspace. The proposed approach in this paper allows both the human and robot to make the best use of their respective capabilities, while constantly monitoring the human worker’s emotions in order to prevent accidents. This additional layer of understanding allows the robot to respond appropriately to its human counterpart’s feelings. Having robots take over the tedious, repetitive tasks enables the human workers to focus on controlling the more complicated decision-based operations. This allows industries to reduce the possibility of human error incurred by mind-numbing work, while ensuring the safety and uplifting employee engagement. Allowing robots to have an understanding of the nuances within subjective human emotions allows them to respond to the expression of a given feeling appropriately. This subjectivity of human behavior in human-robot collaborative contexts has not been extensively studied in the field. We explore the potential results of robots appropriately responding to human emotions in a manufacturing setting. To allow for safe human-robot interaction and collaboration, we employ a transfer learning-based method of teaching the robot to discern various human emotional states. This method allows the robot to be able to understand and properly respond to its human partner’s emotional states, while collaborating with a human on a job. Within our approach, the speed of the robot’s actions changes in response to the human’s perceived feeling. For example, detected sadness or discomfort in the human causes the robot to slow down, while perceived happiness and satisfaction result in the robot increasing the pace of its work to match its partner’s attitude. Our method allows for greater understanding between a collaborative human-robot pair, leading to both robot-leveraged efficiency and a reduction in safety risks in the workspace. The results of our experiments show how a robot can correctly detect real-time changes in human expressions and apply this understanding towards safely co-assembling a project with a human. We also discuss future work for our study. The real-world experimental video is available at: https://youtu.be/v488i96LPE8.
Research on Aircraft image recognition based on transfer learning and improved YOLOv5 model
Effective differentiation of aircraft types using images is important for providing military combat information as well as civilian aircraft operations. Aircraft image recognition has many difficulties such as large variations of target scale, complex backgrounds, and difficult data set acquisition, which lead to the low recognition accuracy of existing models. To address the problem of low recognition accuracy caused by the above difficulties. This paper proposes the improved YOLOv5 model for the recognition of aircraft images. First, this paper designs the CSPResNet50dCA network as the backbone of the YOLOv5 model to enhance the feature extraction capability for small target aircraft in images. By introducing the coordinate attention mechanism and the CSP structure, the feature-focusing capability and the computing speed of the model are enhanced. Afterward, we use data enhancement to expand the data set and transfer learning to improve the generalization ability and convergence speed of the model, so as to improve its robustness. The experimental results show that the improved YOLOv5 model has significantly improved the recognition accuracy of aircraft targets， and significantly enhanced feature extraction ability for small target aircraft with good generalization ability.
Journal of Cyber-Physical-Social Intelligence 2022
Copyright (c) 2022 Journal of Cyber-Physical-Social Intelligence
CGAN-DA: A Cross-Modality Domain Adaptation Model for Hand-Vein Biometric-based Authentication
Palm-vein recognition has been the focus of large research efforts over the last years. However, despite the effectiveness of deep learning models, in particular Convolutional Neural Networks (CNNs), in automatically learning robust feature representations, thereby obtaining good accuracy, such good performance is usually obtained at the expense of annotating a large training dataset. Labeling vein images, however, is an expensive and tedious process. Although handcrafted schemes for data augmentation usually increase slightly performance, they are unable to cover complex variations inherently characterizing such images. To overcome this issue, we propose a new unsupervised domain adaptation model, called CycleGAN-based domain adaptation (CGAN-DA), that extracts discriminant representation from the palmvein images, without requiring any image labeling. Our CGAN-DA models allows a conjoint adaptation, at the image and feature levels. Specifically, in order to enhance the extracted features’ domain-invariance, image appearance is transformed across two domains, palm-vein domain and retinal domain. We employ several adversarial losses namely a segmentation loss and a cycle consistence loss to train our model without any annotation from the target domain (palm-vein images). Our experiments on the public CASIA palm-vein dataset demonstrates that our models significantly outperforms the s tart of the art in terms of verification accuracy.
Designing a resilient supply chain through a robust adaptive model predictive control policy under perishable goods and uncertain forecast information
We deal with the inventory level control problem for Supply Chains (SC) whose dynamics is affected by two sources of uncertainties: 1) perishable goods with uncertain deterioration rate, 2) an uncertain future customer demand freely varying inside a given bounded set. The purpose of our contribution is to propose a smooth Replenishment Policy (RP) maximizing the satisfied customer demand and minimizing the inventory level. These requirements should be satisfied despite the above uncertainties and unforeseen customer demand patterns trespassing the "a priori" assumed boundaries. To this purpose, we define a Resilient RP (RRP) using a new Robust Adaptive Model Predictive Control (RAMPC) approach. This requires solving a Minimax Constrained Optimization Problem (MCOP). To reduce the complexity of the solving algorithm, we parametrize the predicted replenishment orders in terms of polynomial B-spline basis functions.
A Diffusion Model with A FFT for Image Inpainting
Diffusion models for image inpainting have been the subject of growing research interest in recent years. However, generating content that is consistent with the original images, especially for complex images with intricate details and structural information, remains a significant challenge. In this paper, we propose a diffusion model with an FFT (FFT-DM) to generate content that matches missing region texture and semantics to inpaint damaged images. Specifically, FFT-DM contains two components: a Denoising Diffusion Probabilistic Model (DDPM) and a Convolutional Neural Network (CNN). The DDPM is used to extract global features and generate image prior while the CNN captures more fine-grained details and predicts the parameters in the reverse process of the diffusion model. Notably, we integrate a Fast Fourier Transform (FFT) into the diffusion model to enhance the perception ability and improve the efficiency of the model. Extensive experiments demonstrate that FFT-DM outperforms current state-of-the-art inpainting approaches in terms of qualitative and quantitative analysis.
An Improved Adaptive Genetic Algorithm for U-shaped Disassembly Line Balancing Problem Subject to Area Resource Constraint
The disassembly, recovery, and reuse of waste products are attracting more and more attention. It not only saves resources and protects the environment but also promotes economic development. In a disassembly process, the disassembly line balancing problem is one of the most important problems.At present, the consideration of the space area of workstations is relatively small, and the relatively large use of the area of workstations can also better reduce costs. Aiming at the balancing problem of u-shaped disassembly line, a single-objective optimization mathematical model with area constraints is established with the goal of maximizing proﬁts. In order to solve this problem, we refer to Adaptive Genetic Algorithm and improve its crossover and mutation operator. We adopt elite strategy to avoid premature convergence and improve the global search ability. Its effectiveness is proved by comparison with the optimization results of CPLEX. Experimental results also verify the feasibility of the proposed model and the superiority of the improved Adaptive Genetic Algorithm when solving large-scale instances over another algorithm. At the same time, the experimental results also verify the superiority and effectiveness of the improved Adaptive Genetic Algorithm algorithm by comparing with Random Search.
Discrete Bat Optimizer for Disassembly Line Balancing Problem
The recycling of end-of-life (EOL) products is the primary link in the remanufacturing process. EOL products rely on disassembly lines to retain valuable parts for remanufacturing. In this work, a disassembly line balancing model is established based on an AND/OR graph. It takes precedence relation, cycle time restriction, failure risk, and time uncertainty into consideration and aims to maximize the dismantling profit and minimize the energy consumption. Then, a multi-objective discrete bat optimizer based on Pareto rules is designed according to the problem model, and a precedence preserving crossover operator, a single point mutation operator, and a 2-optimization operator are used to simulate the flight strategy of bats to satisfy the search of feasible solutions. To speed up the convergence, we propose an elite strategy to maintain the non-dominated solutions in the external files. By decomposing products of different sizes and analyzing the experimental results, the proposed algorithm is evaluated with the existing multi-objective discrete gray wolf optimizer, artificial bee colony optimizer, non-dominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition. The effectiveness of the proposed algorithm in solving this problem is verified.