• Journal of Construction Engineering and Project Management
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• v.2 no.2
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• pp.1-17
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• 2012

The automated construction machines have been recently developed to help solve the construction industry problems that significantly affect labour, productivity, quality, and profit. Despite the importance of performance analysis to commercialize the automated construction machines, previous studies have mainly concentrated on developing hardware and software of automated construction machines. This research now focuses on two objectives: (1) to propose an analysis model which can measure productivity, quality, and safety improvement rate of automated construction machines based on research and development (R&D); and (2) to develop a performance analysis system which will aid the evaluator in analysing the performance of automated construction machines. Finally, it is anticipated that the effective use of the performance analysis model and computerized system will ably develop the high-performance, automated construction machines and establish the marketing strategy to increase not only the commercial value but also the upkeep and development of construction machines.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.7
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• pp.377-392
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• 2005

A lot of studies to measure and analyze the system performance have been done in areas such as system modeling, performance measurement, monitoring, and performance prediction since the advent of a computer system. Studies on a framework to unify the performance related areas have rarely been performed although many studies in the various areas have been done, however. In the case of TMO(Time-Triggered Message-Triggered Object), a real-time programming model, it hardly provides tools and frameworks on the performance except a simple run-time monitor. So it is difficult to analyze the performance of the real-time system and the process based on TMO. Thus, in this paper, we propose a framework for the dynamic analysis of the real-time system based on TMO, TDAF(TMO based Dynamic Analysis Framework). TDAF treats all the processes for the performance measurement and analysis, and Provides developers with more reliable information systematically combining a load model, a performance model, and a reporting model. To support this framework, we propose a load model which is extended by applying TMO model to the conventional one, and we provide the load calculation algorithm to compute the load of TMO objects. Additionally, based on TMO model, we propose performance algorithms which implement the conceptual performance metrics, and we present the reporting model and algorithms which can derive the period and deadline for the real-time processes based on the load and performance value. In last, we perform some experiments to validate the reliability of the load calculation algorithm, and provide the experimental result.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.407-414
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• 2009

This paper describes a conceptual model for the performance analysis simulator of a weapon launching system. The system performance analysis simulator is envisioned to provide an integrated analysis environment in which the system performance and operational effectiveness can be analyzed in more rapid and efficient way. The conceptual model for the simulator describes a referent independently of specific technology and implementation, and it can be used to transform the simulator requirements into the simulator system specifications.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.19-42
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• 2011

This paper proposes a new framework to predict decision performance by investigating the cognitive fit of decision makers. We assume that every decision maker has two kinds of schema: emotional and rational. Cognitive fit is believed to have a close relationship with the two schemata and decision performance. In the literature on decision performance there is few studies investigating the relationship between the two schemata and cognitive fit. Therefore, our research purposes are twofold: (1) to provide a theoretical basis for the proposed framework describing the causal relationships among the two schemata, cognitive fit. and decision performance, and (2) to empirically prove its validity in the application to an Internet shopping environment. Based on the questionnaires from 104 respondents, we used a second order, confirmatory factor analysis (CFA) model to extract valid constructs, and a structural equation model (SEM) to calculate path coefficients and prove the statistical validity of our proposed research model. The experimental results supported our research model.

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.87-94
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• 2021

The purpose of this paper is to improve the inappropriate analysis results when the end of the brace on braced frame is applied as pinned connection in practice. The stiffness of the gusset plate connection on the braced frame has the amount of between pinned and rigid connection, and the analysis model that applies the stiffness of the connection must be used for accurate performance evaluation. In this study, the stiffness of the gusset plate designed by the balanced design procedure are quantified, and applied to the analysis model to simulate the gusset plate connection. The proposed model was verified through nonlinear static analysis (pushover analysis) of SAP2000. The effect of the connection on the seismic performance of the braced frame was analyzed by comparing the proposed model and pinned model. As a result, it was confirmed that the performance of the braced frame was evaluated conservatively in practice, and the ductility was overestimated. Therefore, it is important to consider the connection for accurate and economical performance evaluation.

• Journal of the Korea Society for Simulation
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• v.23 no.3
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• pp.33-40
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• 2014

Drifter is equipment which is hard to localize. Performance of prototype hasn't performed well compared to product of leading companies even though advanced foreign firm's product were dead copied. This study shows cases of approaching the factor which produces performance gap through drifter hydraulic analysis model which is core component of rock drill. Progression of procedure is following. 1) Securing reliability of the analysis model by comparing impact test result with analysis result. 2) Drawing a graph which indicates performance gap between prototype and drifter of advanced foreign firm by using analysis model. 3) Approaching the factor which produces performance gap with analysing variable of the analysis model. Software used for this analysis is SimulationX.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.369-376
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• 2022

Accurately evaluating and predicting the performance of facilities is a key task in establishing a maintenance strategy for facilities. The importance of performance evaluation is becoming more pronounced, especially when the aging of facilities requires a huge budget. In this study, performance assessment models were developed for road and railway tunnels. Delphi analysis was performed to identify sub-elements necessary to evaluate the performance of a tunnel. The relative importance of the evaluation factors was derived from the AHP analysis. The correlation analysis was performed between each assessment factor and the final result to verify the significance of the model. For the correlation analysis, the survey data measured through precision safety diagnosis in tunnels in use was applied. The cost effectiveness analysis was also conducted according to the scenarios with different composition of performance factors in order to improve the practical applicability of the evaluation model developed in this study.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.275-278
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• 2000

This paper focuses on a performance analysis model for optimizing the freight terminal design and layout using AutoMod simulator and numerical analysis. We developed a model to analyze the freiht terminal performance per unit time and the material handling cost based on both throughput and waiting due to conjestion. We developed computer program for this model and some sample results by both numerical method and AutoMod simulator are compared.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.71-79
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• 2016

This study proposes a performance model of a shared bus multi-processor system and analyzes the effect of input/output types on system performance and overload of shared resources. This system performance model reflects the memory reference time in relation to the effect of input/output types on shared resources and the input/output processing time in relation to the input/output processor, disk buffer, and device standby places. In addition, it demonstrates the contribution of input/output types to system performance for comprehensive analysis of system performance. As the concept of workload in the probability theory and the presented model are utilized, the result of operating and analyzing the model in various conditions of processor capability, cache miss ratio, page fault ratio, disk buffer hit ratio (input/output processor and controller), memory access time, and input/output block size. A simulation is conducted to verify the analysis result.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.