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Articles 1 - 30 of 32
Full-Text Articles in Entire DC Network
Research And Development Of Simulation Training Platform For Multi-Agent Collaborative Decision-Making, Cheng Cheng, Zhijie Chen, Ziming Guo, Ni Li
Research And Development Of Simulation Training Platform For Multi-Agent Collaborative Decision-Making, Cheng Cheng, Zhijie Chen, Ziming Guo, Ni Li
Journal of System Simulation
Abstract: Reinforcement learning simulation platform can be an interactive and training environment for reinforcement learning. In order to make the simulation platform compatible with the multi-agent reinforcement learning algorithms and meet the needs of simulation in military field, the similar processes in multi-agent reinforcement learning algorithms are refined and a unified interface is designed to embed and verify different types of deep reinforcement learning algorithms on the simulation platform and to optimize the back-end service of the simulation platform to accelerate the training process of the algorithm model. The experimental results show that, by unifing the interface, the simulation platform …
Prediction Using Fire Data Based On Machine Learning Algorithms, Oybek Zokirovich Koraboshev
Prediction Using Fire Data Based On Machine Learning Algorithms, Oybek Zokirovich Koraboshev
Chemical Technology, Control and Management
In this article, the process of real-time prediction of the dynamics of fire development in non-stationary and gas-fired areas is carried out by developing a real-time prediction method based on artificial intelligence and deep machine learning (Deep Machine Learning) algorithms. The most commonly used KNN algorithm, CVM algorithm, Random Forest algorithm and Naive Bayes algorithm were used for prediction fire situations.
Development Opportunities And Application Prospects Of Aero-Engine Simulation Technology Under Digital Transformation, Jianguo Cao
Development Opportunities And Application Prospects Of Aero-Engine Simulation Technology Under Digital Transformation, Jianguo Cao
Journal of System Simulation
Abstract: The development of China's social economy and the improvement of its national defense capability in the new era put forward higher requirements for the development of aero-engines. It is urgent to promote the digital transformation of aero-engines in order to achieve coordinated, agile and efficient aero-engine development. Based on the current research and development of aero-engine in China, this paper clarifies the new connotation of "speediness and efficiency, accurate mapping, comprehensive coverage, and dynamic prediction" given by the development of emerging cutting-edge technologies to aero-engine simulation technology, as well as the new technical features of "spatio-temporal ubiquity, data driven, …
Hard-Real-Time Computing Performance In A Cloud Environment, Alvin Cornelius Murphy
Hard-Real-Time Computing Performance In A Cloud Environment, Alvin Cornelius Murphy
Engineering Management & Systems Engineering Theses & Dissertations
The United States Department of Defense (DoD) is rapidly working with DoD Services to move from multi-year (e.g., 7-10) traditional acquisition programs to a commercial industrybased approach for software development. While commercial technologies and approaches provide an opportunity for rapid fielding of mission capabilities to pace threats, the suitability of commercial technologies to meet hard-real-time requirements within a surface combat system is unclear. This research establishes technical data to validate the effectiveness and suitability of current commercial technologies to meet the hard-real-time demands of a DoD combat management system. (Moreland Jr., 2013) conducted similar research; however, microservices, containers, and container …
Team Air Combat Using Model-Based Reinforcement Learning, David A. Mottice
Team Air Combat Using Model-Based Reinforcement Learning, David A. Mottice
Theses and Dissertations
We formulate the first generalized air combat maneuvering problem (ACMP), called the MvN ACMP, wherein M friendly AUCAVs engage against N enemy AUCAVs, developing a Markov decision process (MDP) model to control the team of M Blue AUCAVs. The MDP model leverages a 5-degree-of-freedom aircraft state transition model and formulates a directed energy weapon capability. Instead, a model-based reinforcement learning approach is adopted wherein an approximate policy iteration algorithmic strategy is implemented to attain high-quality approximate policies relative to a high performing benchmark policy. The ADP algorithm utilizes a multi-layer neural network for the value function approximation regression mechanism. One-versus-one …
Human-Machine Collaboration In Healthcare Innovation, Breeze Fenton
Human-Machine Collaboration In Healthcare Innovation, Breeze Fenton
Electronic Theses and Dissertations
Almost every individual has visited a healthcare institute, whether for an annual checkup, surgery, or a nursing home. Ensuring healthcare institutes are using human-machine collaboration systems correctly can improve daily operations. A maturity assessment and an implementation plan have been developed to help healthcare institutes monitor the human-machine collaboration systems. A maturity model, the Smart Maturity Model for Health Care (SMMHC), is a tool designed for maturity assessment. A four-step implementation plan was also created in this research. The implementation plan views the maturity of the institute and develops a strategy on how to improve it. The research utilized Integrated …
Enterprise Engineering And Intellectual Technologies For Lifecycle Management Of Industrial Production, Nodirbek Rustambekovich Yusupbekov, Valery Borisovich Tarasov, Shukhrat Manapovich Gulyamov, Fahritdin Raupovich Abdurasulov
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
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 …
Administrative Law In The Automated State, Cary Coglianese
Administrative Law In The Automated State, Cary Coglianese
All Faculty Scholarship
In the future, administrative agencies will rely increasingly on digital automation powered by machine learning algorithms. Can U.S. administrative law accommodate such a future? Not only might a highly automated state readily meet longstanding administrative law principles, but the responsible use of machine learning algorithms might perform even better than the status quo in terms of fulfilling administrative law’s core values of expert decision-making and democratic accountability. Algorithmic governance clearly promises more accurate, data-driven decisions. Moreover, due to their mathematical properties, algorithms might well prove to be more faithful agents of democratic institutions. Yet even if an automated state were …
Application Of Analogical Reasoning For Use In Visual Knowledge Extraction, Kara Lian Combs
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
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
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
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
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
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
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
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
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 …
A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi
A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi
FIU Electronic Theses and Dissertations
The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex optimization algorithms and improve them into nonconvex objective? For application, …
Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter
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
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
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
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
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
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
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
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.
Improving Coalition Planning By Making Plans Alive, Jitu Patel, Michael C. Dorneich, David Mott, Ali Bahrami, Cheryl Giammanco
Improving Coalition Planning By Making Plans Alive, Jitu Patel, Michael C. Dorneich, David Mott, Ali Bahrami, Cheryl Giammanco
Michael C. Dorneich
The Collaborative Planning Model (CPM) supports human planners in managing planning information and facilitating automated reasoning. It aims to make plans 'alive' by digitizing planning concepts to facilitate their dynamic use, modification, dissemination, and reuse.
Using Quality Attributes And Computational Intelligence To Generate And Evaluate System Architecture Alternatives, Atmika Singh, Cihan H. Dagli
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
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 …