Operations Research, Systems Engineering and Industrial Engineering Commons^™

State Of Industry 5.0—Analysis And Identification Of Current Research Trends, Aditya Akundi, Daniel Euresti, Sergio Luna, Wilma Ankobiah, Amit Lopes, Immanuel Edinbarough

Manufacturing & Industrial Engineering Faculty Publications

The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing …

Enterprise Engineering And Intellectual Technologies For Lifecycle Management Of Industrial Production, Nodirbek Rustambekovich Yusupbekov, Valery Borisovich Tarasov, Shukhrat Manapovich Gulyamov, Fahritdin Raupovich Abdurasulov

Chemical Technology, Control and Management

The fundamental scientific problem of the development of the mathematical foundations of engineering for industrial enterprises and the development of mathematical methods of production management, as well as the creation of intelligent systems for coordinated management of the life cycles of products and production in the network of enterprises are discussed. The issues in demand in the development of a vast interdisciplinary field of enterprise engineering and the development of modern network enterprises and intelligent production using mathematical modeling methods are discussed.

Analysis Of Consistency Of Similarity Predicates To Measures Of Similarity And Advantages Of Their Application In Digital Systems For Solving Intellectual Problems, Makhmudjon Abdullaev, Nodira Batirdjanovna Alimova

Chemical Technology, Control and Management

The results of the analysis of the correspondence of similarity predicates to similarity measures used in various metric methods used for signal classification and the feasibility of using similarity predicates in the construction of digital systems for solving intellectual problems, for which the simplification of computational operations is of no small importance, are presented. Some general and distinctive features of the similarity predicate are considered in comparison with the euclidean metric in relation to one-dimensional and two-dimensional spaces, and generalized to the case of n-dimensional metric spaces. The expediency of using methods based on the calculation of similarity predicates, which …

Application Of Analogical Reasoning For Use In Visual Knowledge Extraction, Kara Lian Combs

Browse all Theses and Dissertations

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task because of its inability to learn beyond its current comprehension. Analogical reasoning (AR) has been proposed as one method to achieve this goal. Current literature lacks a technical comparison on psychologically-inspired and natural-language-processing-produced AR algorithms with consistent metrics on multiple-choice word-based analogy problems. Assessment is based on “correctness” and “goodness” metrics. There is not a one-size-fits-all algorithm for all textual problems. As contribution in visual AR, a convolutional neural network (CNN) is integrated with the AR vector space model, Global …

Application Of Analogical Reasoning For Use In Visual Knowledge Extraction, Kara Lian Combs

Browse all Theses and Dissertations

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task because of its inability to learn beyond its current comprehension. Analogical reasoning (AR) has been proposed as one method to achieve this goal. Current literature lacks a technical comparison on psychologically-inspired and natural-language-processing-produced AR algorithms with consistent metrics on multiple-choice word-based analogy problems. Assessment is based on “correctness” and “goodness” metrics. There is not a one-size-fits-all algorithm for all textual problems. As contribution in visual AR, a convolutional neural network (CNN) is integrated with the AR vector space model, Global …

Research On Geographical Battlefield Environment Model Facing Autonomous Platform, You Xiong, Jiangpeng Tian

Journal of System Simulation

Abstract: Battlefield environment model is an abstraction and description of the complex battlefield environment for specific needs. It supports the research and application of the nature and evolution of the battlefield environment. However, the existing battlefield environment model is mainly oriented to human war activities to describe the battlefield environment,and lacks the design for unmanned autonomous platforms. A multi-level battlefield environment model structure which couples the advantages of humans and machines is proposed, which can give full play to the machine's rapid numerical calculation capabilities at the geometric and feature levels, as well as human cognitive experience at the element, …

Electronic Spam Filtering Based On Neural Networks, Tulkun Fayzievich Bekmuratov, Fayzullajon Bakhtiyorovich Botirov, Elshod Dilshod Ugli Haydarov

Chemical Technology, Control and Management

This article analyzes the problem of filtering spam messages and addressing spam messages, Bayesian theorems based on artificial intelligence, LVQ algorithms (LVQ learning vector quantization) and a filtering scheme for systems based on neural networks. The direct construction of an effective neural network model of spam filtering using database recognition technology is considered. The parameters of access to the neural network how to include predefined statistical and non-statistical attributes of messages are given. The structure of neural network technology for classifying emails is also considered. The procedure for analyzing incoming data using the tool included in the analytical platform Deductor …

Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé

Theses and Dissertations

A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework" uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. Algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of …

Research On Interaction Model Of Hand Tracking Based On Cognitive Theory, Shaobai Zhang, Zhang Teng

Journal of System Simulation

Abstract: This paper aims to solve the problems in a vector integration to endpoint (VITE) model of human reaching and grasping under perturbations of object size, distance and orientation. We discuss how to reduce the numbers of disturbances of three main kinds of components: hand/wrist transport, grip aperture and hand orientation. Based on the achievements of cognitive psychology, and a tracking and cognitive model for operational 3D gestures, this paper proposes a new divide-and-conquer model that is used for indicating current grasping status and to trigger three main kinds of methods of when to start or stop working. The model …

From Situation Cognition Stepped Into Situation Intelligent Cognition, Zhu Feng, Xiaofeng Hu, Wu Lin, Xiaoyuan He, Xuezhi Lü, Liao Ying

Journal of System Simulation

Abstract: Aimed at operational situation cognition and some relevant problems under the background of the Joint-Tactical, some deep researches are carried out in this paper. The concept models of combat situation cognition and situation intelligent cognition are proposed respectively, and some related concepts are clarified. The situation intelligent cognition technology framework is proposed, and five key problems which should be solved are analyzed, and the possible technical routes are given. These research contents and achievements build a foundation for stepping into the situation intelligent cognition from combat situation cognition.

Preliminary Study Of Modeling And Simulation Technology Oriented To Neo-Type Artificial Intelligent Systems, Libo Hu, Xudong Chai, Zhang Lin, Li Tan, Duzheng Qing, Tingyu Lin, Liu Yang

Journal of System Simulation

Abstract: A brief interpretation of the rapidly developing “New Internet+ Big Data+ Artificial Intelligence+” era is given in the paperand the essence and the architectureof neo-type artificial intelligence systems are explained. The meaning of neo-type artificial intelligence system oriented modelling and simulation technology is proposed and the new challenges they are facing are discussed. The research contents and preliminaryresults on neo-type artificial intelligence system oriented modelling and simulation technology are given, which includeneo-type artificial intelligence system oriented modelling/secondary modelling, intelligent simulation computer, smart cloud simulation and intelligent simulation hardware/software supporting system technology, and intelligent simulation system application engineering technology. Several …

Assessment Of Adaptability Of A Supply Chain Trading Agent’S Strategy: Evolutionary Game Theory Approach, Yoon Sang Lee, Riyaz T. Sikora

Journal of International Technology and Information Management

With the increase in the complexity of supply chain management, the use of intelligent agents for automated trading has gained popularity (Collins, Arunachalam, B, et al. 2006). The performance of supply-chain agents depends on not just the market environment (supply and demand patterns) but also on what types of other agents they are competing with. For designers of such agents it is important to ascertain that their agents are robust and can adapt to changing market and competitive environments. However, to date there has not been any work done that assesses the adaptability of a trading agent’s strategy in the …

Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

The increasing CPU power and memory capacity of computers, and now computing appliances, in the 21st century has allowed accelerated integration of artificial intelligence (AI) into organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational processes including medical diagnosis, automated stock trading, integrated robotic production systems, telecommunications routing systems, and automobile fuzzy logic controllers. Self-driving automobiles are just the latest extension of AI. This thrust of AI into organizations and everyday life rests on the AI community’s unstated assumption that “…every aspect of human learning and intelligence could be so precisely described …

Evaluating Forecasting Methods By Considering Different Accuracy Measures, Nijat Mehdiyev, David Lee Enke, Peter Fettke, Peter Loos

Engineering Management and Systems Engineering Faculty Research & Creative Works

Choosing the appropriate forecasting technique to employ is a challenging issue and requires a comprehensive analysis of empirical results. Recent research findings reveal that the performance evaluation of forecasting models depends on the accuracy measures adopted. Some methods indicate superior performance when error based metrics are used, while others perform better when precision values are adopted as accuracy measures. As scholars tend to use a smaller subset of accuracy metrics to assess the performance of forecasting models, there is a need for a concept of multiple accuracy dimensions to assure the robustness of evaluation. Therefore, the main purpose of this …

Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar

Research Collection School Of Computing and Information Systems

We present a new perspective on the classical shortest path routing (SPR) problem in graphs. We show that the SPR problem can be recast to that of probabilistic inference in a mixture of simple Bayesian networks. Maximizing the likelihood in this mixture becomes equivalent to solving the SPR problem. We develop the well known Expectation-Maximization (EM) algorithm for the SPR problem that maximizes the likelihood, and show that it does not get stuck in a locally optimal solution. Using the same probabilistic framework, we then address an NP-Hard network design problem where the goal is to repair a network of …

Determination Of Rule Patterns In Complex Event Processing Using Machine Learning Techniques, Nijat Mehdiyev, Julian Krumeich, David Lee Enke, Dirk Werth, Peter Loos

Engineering Management and Systems Engineering Faculty Research & Creative Works

Complex Event Processing (CEP) is a novel and promising methodology that enables the real-time analysis of stream event data. The main purpose of CEP is detection of the complex event patterns from the atomic and semantically low-level events such as sensor, log, or RFID data. Determination of the rule patterns for matching these simple events based on the temporal, semantic, or spatial correlations is the central task of CEP systems. In the current design of the CEP systems, experts provide event rule patterns. Having reached maturity, the Big Data Systems and Internet of Things (IoT) technology require the implementation of …

Probabilistic Inference Based Message-Passing For Resource Constrained Dcops, Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Distributed constraint optimization (DCOP) is an important framework for coordinated multiagent decision making. We address a practically useful variant of DCOP, called resource-constrained DCOP (RC-DCOP), which takes into account agents’ consumption of shared limited resources. We present a promising new class of algorithm for RC-DCOPs by translating the underlying co- ordination problem to probabilistic inference. Using inference techniques such as expectation- maximization and convex optimization machinery, we develop a novel convergent message-passing algorithm for RC-DCOPs. Experiments on standard benchmarks show that our approach provides better quality than previous best DCOP algorithms and has much lower failure rate. Comparisons against an …

A Computational Intelligence Approach To System-Of-Systems Architecting Incorporating Multi-Objective Optimization, David M. Curry, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions …

Designing A Portfolio Of Parameter Configurations For Online Algorithm Selection, Aldy Gunawan, Hoong Chuin Lau, Mustafa Misir

Research Collection School Of Computing and Information Systems

Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We …

Towards Operations Excellence: Optimising Staff Scheduling For New Emergency Department, Amr Arisha, Waleed Abo Hamad

Conference papers

This paper presents a case study of an Emergency Department of a public hospital in Dublin, and uses an integrated approach to determine optimal staffing levels to meet the challenges of its dynamic patient demand levels. A comprehensive stochastic model is developed to incorporate patients care pathways and the resources required along their treatment journeys. Analytical Hierarchical Process is utilised to enable decision makers to set their preferences for the facility’s strategic objectives. Evolutionary algorithms are applied to optimise staff schedules. The resulted optimized schedules maintains continuity of care delivery for patients while ensuring a balanced equilibrium among available staff.

Using Quality Attributes And Computational Intelligence To Generate And Evaluate System Architecture Alternatives, Atmika Singh, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper proposes an evolutionary algorithm based approach for evolving architecture alternatives using quality attributes as design drivers. A novel fuzzy architecture assessment approach is presented to quantitatively evaluate the set of possible solutions based on linguistic assessments of architecture quality attributes elicited from the stakeholders. The proposed approach makes a valuable contribution to the systems architecting knowledge base by presenting a measurable and quantifiable approach to architecture design and evaluation.

Reinforcement Learning-Based Output Feedback Control Of Nonlinear Systems With Input Constraints, Pingan He, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A novel neural network (NN) -based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multi-input-multi-output (MIMO) discrete-time strict feedback nonlinear systems. Reinforcement learning in discrete time is proposed for the output feedback controller, which uses three NN: 1) a NN observer to estimate the system states with the input-output data; 2) a critic NN to approximate certain strategic utility function; and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. The magnitude constraints are manifested as saturation nonlinearities in the output feedback …

Automated Manpower Rostering: Techniques And Experience, C. M. Khoong, Hoong Chuin Lau, L. W. Chew

Research Collection School Of Computing and Information Systems

We present ROMAN, a comprehensive, generic manpower rostering toolkit that successfully handles a wide spectrum of work policies found in service organizations. We review the use of various techniques and methodologies in the toolkit that contribute to its robustness and efficiency, and relate experience gained in addressing manpower rostering problems in industry.

Development Of A Knowledge-Based System For Human Error Analysis, Bang-Chiang Sung

Theses

Human Factors is one of several essential issues in a discussion of system productivity. Its consideration must be included in the design, development, operation, and maintenance of any human-machine system. In order to maximize the system productivity, humans must perform with their maximum possible efficiency. To achieve this, all human-related activities must be evaluated to determine their possible causes of errors and the consequences. Then, if unable to completely remove, the causes of human errors must be minimized.

This thesis integrates a knowledge of expert system into an analysis of human errors. Its objective is to develop a knowledge-based system …

The Application Of Expert Systems To Automated Storage And Retrieval, Robert E. Mcdonald

Retrospective Theses and Dissertations

This report discusses the major functions, decisions, and strategies of automated storage and retrieval systems (AR/RS). The report surveys the essential features of expert systems and discusses how they can be applied in automated warehousing environments. A blackboard expert system architecture was examined and found to be a flexible and responsive control system for automated warehousing applications. A simple AR/RS expert system was constructed using an expert system software package. A warehousing simulation was performed which compared the expert system’s performance to a typical AR/RS control system. The expert system produced increased efficiency of operation because of the intelligent rules …