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Full-Text Articles in Engineering
Quantitative Characterization Of Complex Systems—An Information Theoretic Approach, Aditya Akundi, Eric Smith
Quantitative Characterization Of Complex Systems—An Information Theoretic Approach, Aditya Akundi, Eric Smith
Manufacturing & Industrial Engineering Faculty Publications and Presentations
A significant increase in System-of-Systems (SoS) is currently observed in the social and technical domains. As a result of the increasing number of constituent system components, Systems of Systems are becoming larger and more complex. Recent research efforts have highlighted the importance of identifying innovative statistical and theoretical approaches for analyzing complex systems to better understand how they work. This paper portrays the use of an agnostic twostage examination structure for complex systems aimed towards developing an information theorybased approach to analyze complex technical and socio-technical systems. Towards the goal of characterizing system complexity with information entropy, work was carried …
Research On Generation Technology Of Computer Generated Force In Lvc Training System, Gao Ang, Zhiming Dong, Guohui Zhang, Liang Tao, Qisheng Guo
Research On Generation Technology Of Computer Generated Force In Lvc Training System, Gao Ang, Zhiming Dong, Guohui Zhang, Liang Tao, Qisheng Guo
Journal of System Simulation
Abstract: LVC training system of the combat equipment under the condition of confrontation is an effective means of training, aiming at the problem that in LVC training system, computer generated forces are difficult to meet the demand of training problems. The concept of LVC training and LVC training system is clarified, according to the relationship between model and system structure, the corresponding modeling technology requirements of three different hierarchical models, namely logical range entity configuration, command entity and combat entity, are expounded. According to the specific requirements, four computer-generated force generation methods are proposed, namely, logical target range virtual and …
Modeling And Analyzing Of Battlefield Information Sharing Effectiveness Based On Complex Networks, Zhang Qiang, Jianhua Li, Shen Di
Modeling And Analyzing Of Battlefield Information Sharing Effectiveness Based On Complex Networks, Zhang Qiang, Jianhua Li, Shen Di
Journal of System Simulation
Abstract: Battlefield information sharing is the basis of acquiring information superiority to get decision and action superiority in information war。Aiming at the influence of information sharing on operational effectiveness, internal essence of the information sharing activity in information warfare was qualitatively descripted. By using operational network model which clearly reflects node's function and edge's type, the operational network with different information sharing degree was constructed, and the characteristic which depicts information sharing effectiveness was defined, and the influence law of information sharing degree on sharing effectiveness measurement indexes of operational network with simulation was analyzed. The simulation result validates that …
Pinning Control For Synchronization Of Hr Biological Neural Networks:Linear Active Disturbance Rejection Approach, Wei Wei, Wen Jiao
Pinning Control For Synchronization Of Hr Biological Neural Networks:Linear Active Disturbance Rejection Approach, Wei Wei, Wen Jiao
Journal of System Simulation
Abstract: Synchronization of a complex network whose nodes are Hindmarsh-Rose biological neurons was considered. Coupling strengths of the whole network were always taken as the tuning variables. However, coupling strengths may not be changeable. In addition, synchronization was affected by different disturbances. In view of above factors, pinning control was utilized and linear active disturbance rejection control (LADRC) was designed. By this approach, synchronization was achieved in according to synchronization errors of the controlled nodes and the couplings among nodes. In the simulations, coupling strength was fixed at a relative small value, and no external disturbance case …
Modeling And Simulation On Influence Of Complex Network Nodes Based On Data Field In, Chenxi Shao, Xiaoqi Chen, Xingfu Wang, Fuyou Miao
Modeling And Simulation On Influence Of Complex Network Nodes Based On Data Field In, Chenxi Shao, Xiaoqi Chen, Xingfu Wang, Fuyou Miao
Journal of System Simulation
Abstract: Research on the influence of complex network nodes is an important part of data mining. Mining the influential nodes in complex networks not only has important academic significance, but also helps to suppress the outbreak of epidemics, control the spread of rumors, and promote e-commercial products and so on. By selecting the Mixed Degree Decomposition (MDD) value of each node as its mass, the complex network is abstracted into a data field, the influential nodes are identified by combining the data field model, and some well-known centralities are compares with. The classical Susceptible-Infected-Recovered (SIR) epidemic model is …
When Optimal Isn’T Optimal, Robin Burk
When Optimal Isn’T Optimal, Robin Burk
Operations Management Presentations
Whether you’re an operations manager, a financial executive, an engineer, or an analyst, chances are you’ve been taught to optimize the outcomes of your decisions. And taken in a vacuum, that makes a good deal of sense. But as the new discipline of complex adaptive networks shows, in our heavily interconnected, interdependent world optimizing can introduce hidden vulnerabilities to surprisingly expensive failures. In this webinar we’ll discuss examples of such failures and identify conditions for which a satisfactory but not optimal solution is the best approach overall.
Modeling And Simulation On Entities’ Belief In Cyberspace, Jiuyang Tao, Wu Lin, Xiaoyuan He, Rong Ming
Modeling And Simulation On Entities’ Belief In Cyberspace, Jiuyang Tao, Wu Lin, Xiaoyuan He, Rong Ming
Journal of System Simulation
Abstract: Of all cyber-attacks, 'fabrication' which is aimed at impacting one’s awareness is becoming the common means, so it is of great importance to explore ways so as to defend such attacks. An attack can success or not always rely on the entities’ belief of certain events. In this paper, two different ways of 'fabrication' attack are put forward, and three essential conditions for 'fabrication' attack are analyzed. Second, a cyberspace belief model in terms of the Dempster-Shafer framework based on situation awareness theory is built to study the evolution of the cyber entities’ belief under the 'fabrication' attack. In …
Complex Network Modeling And Visualization Analysis For Ocean Observation Data, Sun Xin, Zhenhua Li, Junyu Dong, Xinyan Luo, Yuting Yang
Complex Network Modeling And Visualization Analysis For Ocean Observation Data, Sun Xin, Zhenhua Li, Junyu Dong, Xinyan Luo, Yuting Yang
Journal of System Simulation
Abstract: Ocean data analysis is one of the important foundations in marine science research. Analysis on the sea surface temperature based on complex network theory helps explore the marine dynamics in a new perspective. The ocean is divided into grids, and the annual average of the sea surface temperature is calculated to reflect the properties of the corresponding grid area. The mutual information and the Pearson correlation coefficient are used to measure the similarity between different areas. The nonlinear and linear complex network models which reflect the station of the global marine climate can be built. Finally some popular measures …
Group Consensus Of Multi-Agent Networks With Multiple Time Delays, Lianghao Ji, Xinyue Zhao
Group Consensus Of Multi-Agent Networks With Multiple Time Delays, Lianghao Ji, Xinyue Zhao
Journal of System Simulation
Abstract: The group consensus problems for first-order and second-order multi-agent networks with multiple time delays are investigated respectively. Based on the theory of frequency-domain, some algebraic criteria are analytically proposed, which can guarantee the achievement of group consensus. The results show that the input time delays, the coupling weights and the coupling strengths between the agents play key roles in reaching group consensus, whereas communication time delays can only affect the convergence rate of the systems. The validity of the results is verified by several simulated examples.
Simulation Of Two Stages-Variant Growth Evolution Model Of High-Speed Railway And Civil Aviation Compound Network, Xu Feng, Jinfu Zhu, Jianjun Miao, Rongrong Ding
Simulation Of Two Stages-Variant Growth Evolution Model Of High-Speed Railway And Civil Aviation Compound Network, Xu Feng, Jinfu Zhu, Jianjun Miao, Rongrong Ding
Journal of System Simulation
Abstract: Combining the special evolving characteristics of the high-speed railway and civil aviation compound network, an evolution model which has two stages with variant growth mechanism and variable parameters is constructed, and the rationality of the model is verified through mathematical simulation. The model divides the evolutionary process into two stages: the stage of single-point growth pattern, and the stage of variant and alternant growth pattern. The probability of two sub-networks’ random and commutative growth could be controlled by changing the parameters’ values, and then the evolving rhythm and scale of the compound network could be adjusted. The simulation results …
Applications Of Complex Network Analysis In Electric Power Systems, Mahmoud Saleh, Yusef Esa, Ahmed Mohamed
Applications Of Complex Network Analysis In Electric Power Systems, Mahmoud Saleh, Yusef Esa, Ahmed Mohamed
Publications and Research
This paper provides a review of the research conducted on complex network analysis (CNA) in electric power systems. Moreover, a new approach is presented to find optimal locations for microgrids (MGs) in electric distribution systems (EDS) utilizing complex network analysis. The optimal placement in this paper points to the location that will result in enhanced grid resilience, reduced power losses and line loading, better voltage stability, and a supply to critical loads during a blackout. The criteria used to point out the optimal placement of the MGs were predicated on the centrality analysis selected from the complex network theory, the …
Time Series Classification Using Deep Learning For Process Planning: A Case From The Process Industry, Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Lee Enke, Peter Fettke, Peter Loos
Time Series Classification Using Deep Learning For Process Planning: A Case From The Process Industry, Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Lee Enke, Peter Fettke, Peter Loos
Engineering Management and Systems Engineering Faculty Research & Creative Works
Multivariate time series classification has been broadly applied in diverse domains over the past few decades. However, before applying the classification algorithms, the vast majority of current studies extract hand-engineered features that are assumed to detect local patterns in the time series. Therefore, the efficiency and precision of these classification approaches are heavily dependent on the quality of variables defined by domain experts. Recent improvements in the deep learning domain offer opportunities to avoid such an intensive hand-crafted feature engineering which is particularly important for managing the processes based on time-series data obtained from various sensor networks. In our paper, …
Application Of An Artificial Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns, Cihan H. Dagli
Application Of An Artificial Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
This paper presents a neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A multi-layer feedforward network with backpropagation learning is used as the model framework. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. Nine input variables consist of categorical and numeric data elements including: high school rank, high school quality, standardized test scores, high school faculty assessments, extra-curricular activity score, parent's education status, and time since high school graduation. These inputs and the multi-layer neural network model are used …
Entity Resolution Using Convolutional Neural Network, Ram Deepak Gottapu, Cihan H. Dagli, Bharami Ali
Entity Resolution Using Convolutional Neural Network, Ram Deepak Gottapu, Cihan H. Dagli, Bharami Ali
Engineering Management and Systems Engineering Faculty Research & Creative Works
Entity resolution is an important application in field of data cleaning. Standard approaches like deterministic methods and probabilistic methods are generally used for this purpose. Many new approaches using single layer perceptron, crowdsourcing etc. are developed to improve the efficiency and also to reduce the time of entity resolution. The approaches used for this purpose also depend on the type of dataset, labeled or unlabeled. This paper presents a new method for labeled data which uses single layered convolutional neural network to perform entity resolution. It also describes how crowdsourcing can be used with the output of the convolutional neural …
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 …
Using Neural Networks To Forecast Volatility For An Asset Allocation Strategy Based On The Target Volatility, Youngmin Kim, David Lee Enke
Using Neural Networks To Forecast Volatility For An Asset Allocation Strategy Based On The Target Volatility, Youngmin Kim, David Lee Enke
Engineering Management and Systems Engineering Faculty Research & Creative Works
The objective of this study is to use artificial neural networks for volatility forecasting to enhance the ability of an asset allocation strategy based on the target volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. However, a challenge to data-driven approaches is the limited availability of data since periods of high volatility, such as during financial crises, are relatively rare. To resolve this issue, we apply a stability-oriented approach to compare data for the current period to a past set of data for a period of low volatility, providing …
Analyzing Responses From Likert Surveys And Risk-Adjusted Ranking: A Data Analytics Perspective, Abhijit Gosavi
Analyzing Responses From Likert Surveys And Risk-Adjusted Ranking: A Data Analytics Perspective, Abhijit Gosavi
Engineering Management and Systems Engineering Faculty Research & Creative Works
We broadly consider the topic of ranking entities from surveys/opinions. Often, numerous ranks from different respondents are available for the same entity, e.g., a candidate from a pool, and yet an averaging of those ranks may not serve the purpose of identifying a consensus candidate. We first consider a risk-adjusted paradigm for ranking, where the rank is defined as the average (mean) rank plus a scalar times the risk in the rank; we use standard deviation as a risk metric. In case of a candidate being ranked either on the basis of opinions of a selection committee's members or on …
Noise Canceling In Volatility Forecasting Using An Adaptive Neural Network Filter, Soheil Almasi Monfared, David Lee Enke
Noise Canceling In Volatility Forecasting Using An Adaptive Neural Network Filter, Soheil Almasi Monfared, David Lee Enke
Engineering Management and Systems Engineering Faculty Research & Creative Works
Volatility forecasting models are becoming more accurate, but noise looks to be an inseparable part of these forecasts. Nonetheless, using adaptive filters to cancel the noise should help improve the performance of the forecasting models. Adaptive filters have the advantage of changing based on the environment. This feature is vital when they are used along with a model for volatility forecasting and error cancellation in the financial markets. Nonlinear Autoregressive (NAR) neural networks have simple structures, but they are efficient tools in error cancelation systems when working with non-stationary and random walk noise processes. For this research, an adaptive threshold …