Multi-objective dynamic flexible job shop scheduling (MO-DFJSS) is a challenging problem that requires finding high-quality schedules for jobs in a dynamic and flexible manufacturing environment, considering multiple ...
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ISBN:
(纸本)9789819983902;9789819983919
Multi-objective dynamic flexible job shop scheduling (MO-DFJSS) is a challenging problem that requires finding high-quality schedules for jobs in a dynamic and flexible manufacturing environment, considering multiple potentially conflicting objectives simultaneously. A good approach to MO-DFJSS is to combine Genetic Programming (GP) with Non-dominated Sorting Genetic Algorithm ii (NSGA-ii), namely NSGP-ii, to evolve a set of non-dominated scheduling heuristics. However, a limitation of NSGPii is that individuals with different genotypes can exhibit the same behaviour, resulting in a loss of population diversity. Semantic genetic programming (SGP) considers individual semantics during the evolutionary process and can enhance population diversity in various domains. However, its application in the domain of MO-DFJSS remains unexplored. Therefore, it is worthy to incorporate semantic information with NSGPii for MO-DFJSS. This study focuses on semantic diversity and semantic similarity. The results demonstrate that NSGPii considering semantic diversity yields better performance compared with the original NSGPii. Moreover, NSGPii incorporating semantic similarity achieves even better performance, highlighting the importance of maintaining a reasonable semantic distance between offspring and their parents. Further analysis reveals that the improved performance achieved by the proposed methods is attributed to the attainment of a more semantically diverse population through effective control of semantic distances between individuals.
process-aware information systems (PAISs) is a special class of information systems intended to support the tasks of initialization, end-to-end management, and completion of business processes. During their operation ...
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process-aware information systems (PAISs) is a special class of information systems intended to support the tasks of initialization, end-to-end management, and completion of business processes. During their operation such systems accumulate a large amount of data that are stored in the form of event logs. Event logs are a valuable source of knowledge about the actual behavior of a system. For example, they include (i) information about the discrepancy between the real and prescribed behavior of the system, (ii) information for identifying the bottlenecks and performance issues, and (iii) information for detecting the antipatterns of building a business system. These problems are studied in the discipline called process mining. The practical application of the process mining methods and practices is carried out using specialized software for data analysts. The subject area of the processanalysis involves the work of an analyst with a large number of graphical models. Such work can be more efficiently with a convenient graphical modeling tool. This paper discusses the principles of designing a graphical tool VTMine for Visio for processmodeling, based on the widespread application Microsoft Visio for business intelligence. The features of the architecture design of the software extension for application in the process mining area are presented along with the features of integration with existing libraries and tools for working with data. The usage of the developed tool for solving various types of tasks in modeling and analysis of processes is demonstrated on a set of experimental schemes.
Industry 4.0 is reshaping manufacturing by seamlessly integrating data acquisition, analysis, and modeling, creating intelligent and interconnected production ecosystems. Driven by cyber-physical systems, the Internet...
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ISBN:
(数字)9783031505836
ISBN:
(纸本)9783031505829;9783031505836
Industry 4.0 is reshaping manufacturing by seamlessly integrating data acquisition, analysis, and modeling, creating intelligent and interconnected production ecosystems. Driven by cyber-physical systems, the Internet of Things (IoT), and advanced analytics, it enables real-time monitoring, predictive maintenance, adaptable production, and enhanced customization. By amalgamating data from sensors, machines, and human inputs, Industry 4.0 provides holistic insights, resulting in heightened efficiency, and optimized resource allocation. Deep Learning (DL), a crucial facet of artificial intelligence, plays a pivotal role in this transformation. This article delves into DL fundamentals, Autoencoders, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) and, Deep Reinforcement Learning discussing their functions and applications. It also elaborates on key DL components: neurons, layers, activation functions, weights, bias, loss functions, and optimizers, contributing to network efficacy. The piece underscores Industry 4.0's principles: interoperability, virtualization, decentralization, real-time capabilities, service orientation, and modularity. It highlights DL's diverse applications within Industry 4.0 domains, including predictive maintenance, quality control, resource optimization, logistics, process enhancement, energy efficiency, and personalized production. Despite transformative potential, implementing DL in manufacturing poses challenges: data quality and quantity, model interpretability, computation demands, and scalability. The article anticipates trends, emphasizing explainable AI, federated learning, edge computing, and collaborative robotics. In conclusion, DL's integration with Industry 4.0 heralds a monumental manufacturing paradigm shift, fostering adaptive, efficient, and data-driven production ecosystems. Despite challenges, a future envisions Industry 4.0 empowered by DL's capabilities, usheri
This article serves as a position paper that explores the complex issue of traffic management in smart cities and the challenges it presents. The problem of urban traffic is particularly relevant in our modern world, ...
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With the in-depth development of global economic integration, the competition in the financial industry at home and abroad has become more and more severe, and the innovation of financial products has gradually become...
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ISBN:
(纸本)9781510664593
With the in-depth development of global economic integration, the competition in the financial industry at home and abroad has become more and more severe, and the innovation of financial products has gradually become a new development trend in the financial industry. For commercial banks, strengthening the innovation of financial products can attract more customers, thus reaping more substantial income and maintaining an advantageous position in the market competition. However, in the process of financial product innovation of commercial banks, the financial risks are also increasing, and how to achieve effective management of financial product innovation risks has become one of the hot topics of concern in the field of commercial banks. This paper takes the study of risk management of technological, financial product innovation of commercial banks as an example, based on methods of big dataanalysis algorithms, analysis of correlation, analysis of effects, analysis of Variance and analysis of regression, through questionnaires and research results, literature review, to explore Commercial Bank’s Risk Management Behavior on the innovation of its scientific and technological financial product(CBRMB) and its influencing factors. Regarding CBRMB, It mainly describes the degree of its risk influence through the following two aspects, including the number of inspectors and research budget, and among the influencing factors research Commercial bank's Risk management (CBRM), Commercial bank's Human Resource management (CBHRM), Commercial Bank's supervision ability on its Financial product innovation (CBSA), Commercial Bank's Internal control system (CBICS), and Commercial Bank's Development of scientific and technological financial products (CBD). According to the regression model operation results, commercial banks can refer to the research recommendations and optimize their scientific and technological innovation and risk management, improve the problems in the risk sys
data science projects often involve various machine learning (ML) methods that depend on data, code, and models. One of the key activities in these projects is the selection of a model or algorithm that is appropriate...
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Today, location-based services have become prevalent in the mobile platform, where mobile apps provide specific services to a user based on his or her location. Unfortunately, mobile apps can aggressively harvest loca...
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This paper proposes a method of hardware implementation of the membrane computing architecture for the control of a mobile robot. The basic idea is to use in the development of control systems the models of functional...
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In the realm of processes modeling and control, the monitoring of processes is crucial for ensuring safety, and a key task in multivariate statistical process monitoring is to extract the operating patterns of the pro...
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ISBN:
(数字)9798350361674
ISBN:
(纸本)9798350361681
In the realm of processes modeling and control, the monitoring of processes is crucial for ensuring safety, and a key task in multivariate statistical process monitoring is to extract the operating patterns of the process. Dynamic strategies, exemplified by dynamic-inner principal components analysis (DIPCA), dynamic-inner partial least squares (DIPLS), and dynamic-inner canonical correlation analysis (DICCA), are employed to tackle temporal correlation and the ever-changing nature of processes using AR structures. Dynamic controlled principal components analysis (DCPCA) additionally considers dynamic controlled characteristics, showcasing remarkable accuracy in both the monitoring of processes and dynamic prediction. Notwithstanding the precision demonstrated by DCPCA, the task of ascertaining optimal control input orders remains intricate, often necessitating reliance on empirical methodologies or trial-and-error approaches. This research introduces an innovative methodology, drawing inspiration from Granger causality analysis, to establish causal relationships between control inputs and patterns. Causal influence (CI) was employed to assess the impact of introducing control inputs and the effects of control inputs at different orders, aiding the model in selecting the optimal control input order. Theoretically supported performance evaluation and order selection, providing assurance for practical applications.
This study addresses the underexplored challenge of inherent dynamics in industrial processes through an innovative attention-based latent variable modeling method. Utilizing attention mechanisms, the method articulat...
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ISBN:
(数字)9789887581598
ISBN:
(纸本)9798331540845
This study addresses the underexplored challenge of inherent dynamics in industrial processes through an innovative attention-based latent variable modeling method. Utilizing attention mechanisms, the method articulates time-variant dynamical relationships among samples. The framework extends attention-based dynamical inner principal component analysis to extract latent dynamical features, integrating them with static features obtained through static principal component analysis. This results in comprehensive monitoring statistics for online applications. Numerical simulations and real-world application in an industrial ethylene oxychlorination process demonstrate the proposed method's efficacy. Comparative analysis highlights its advantages and superior performance over existing methods. This innovative approach provides more accurate insights into complex industrial processes, promising advancements in data-driven modeling within the field.
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