• Korean Journal of Applied Biomechanics
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• v.28 no.2
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• pp.127-134
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• 2018

Objective: In a statistical linear model estimating the center of rotation of a human hip joint, which is the parameter related to the mean of response vectors, assumptions of homoscedasticity and independence of position vectors measured repeatedly over time in the model result in an inefficient parameter. We, therefore, should take into account the variance-covariance structure of longitudinal responses. The purpose of this study was to estimate the efficient center of rotation vector of the hip joint by using covariance pattern models. Method: The covariance pattern models are used to model various kinds of covariance matrices of error vectors to take into account longitudinal data. The data acquired from functional motions to estimate hip joint center were applied to the models. Results: The results showed that the data were better fitted using various covariance pattern models than the general linear model assuming homoscedasticity and independence. Conclusion: The estimated joint centers of the covariance pattern models showed slight differences from those of the general linear model. The estimated standard errors of the joint center for covariance pattern models showed a large difference with those of the general linear model.

• Transactions of Materials Processing
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• v.7 no.6
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• pp.545-553
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• 1998

The purpose of this paper is to develop a CAD/CAM system for generation of designing and manufacturing information such as total drawing sub-assembly drawing, part drawing detail drawing part list and NC data for machining by CNC lathe were CUT machining center. Through this study the CAD/CAM system for deep drawing transfer die in mechanical press process has been developed The developed CAD system can generate the drawings of transfer die in mechanical press. Using these results from CAD system. it can generate NC data to machine die's elements on the CAD system. This system can reduce design man-hours and human errors. In order to construct the system it is used to automate the design process using knowledge base system. The developed system is based on the knowledge base system which is involved a lot of expert's empirical knowhow in the practice field. Using AutoLISp language under the Auto CAD system. CTK customer language of SmartCAM is used as the overall CAD/CAM environment. Results of this system will be provide effective aids to the designer and manufacturer in this field

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1998.06a
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• pp.57-64
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• 1998

The purpose of this research is to develop a CAD/CAM system for generation all kind of information such as, total drawing, sub assembly drawing, part drawing, detail drawing, part list, and NC data for machining by CNC lathe, Wire CUT, machining center. Through this study the CAD/CAM System for deep drawing transfer die in mechanical press process has been developed. The developed CAD system can generate the drawing of transfer die in mechanical press. Using these results from CAD system, it can generate the NC data to machine die's elements on the CAD system. This system can reduce design man-hours and human errors. In order to construct the system, it is used to automate the design process using knowledge base system. The developed system is based on the knowledge base system which is involved a lot of expert's technology in the practice field. Using AutoLISP language under the AutoCAD system, CTK customer language of SmartCAM is used as the overall CAD/CAM environment. Results of this system will be provide effective aids to the designer and manufacturer in this field.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.11
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• pp.209-218
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• 2004

The purpose of this paper is to suggest a practical and simple method for the identification of the joint mechanical properties and to apply it to human knee joints. The passive moment at a joint was modeled by three mechanical parts, that is, a gravity term, a linear damper term and a nonlinear spring term. Passive pendulum tests were performed in 5 fat and 5 thin men. The data of pendulum test were used to identify the mechanical properties of joints through sequential quadratic programming (SQP) with random initial values. The identification was successful where the normalized root-mean-squared (RMS) errors between the simulated and experimental joint angle trajectories were less than 10％. The parameter values of mechanical properties obtained in this study agreed with literature. The inertia, gravity and the damping constant were greater at fat men, which indicates more resistance to body movement and more energy consumption fer fat men. The suggested method is noninvasive and requires simple setup and short measurement time. It is expected to be useful in the evaluation of joint pathologies.

• Asia pacific journal of information systems
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• v.25 no.1
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• pp.83-97
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• 2015

Recently, the exhibition industry has developed rapidly with the development of information technologies. Most exhibitors in an exhibition plan and deploy many events that may provide advantages to visitors as a method of effective promotion. The growth and propagation of wireless technologies is a powerful marketing tool for exhibitors. However, exhibitors still rely on domain experts who are costly and time consuming because of the manual knowledge input procedure. Moreover, it is prone to biases and errors and not suitable for managing fast-growing and tremendous amounts of data that far exceed a human's ability to comprehend. To overcome these problems, data mining technology may be a great alternative, but it needs to be fit to each exhibition. This study uses data mining technology with the Predictive Model Markup Language (PMML) to suggest a system that supports intelligent services and that improves stakeholder satisfaction. This system provides advantages to the exhibitor, show organizer, and system designer, and is first enhanced by integrating data mining technologies through the knowledge of exhibition experts. Second, using the PMML, the system can automate the process of applying data mining models to solve real-time processing problems in the exhibition environment.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.411-420
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• 2021

Based on the Financial Management Information Systems (FMIS) variables and supporting arenas, the current study aims to highlight the importance of FMIS in supporting organizations to achieve organizational excellence (Managing Liabilities, Support Decision Making, Cost Efficiency, Financial Quality, and Security). A quantitative approach was utilized by adopting a questionnaire as a tool. A convenient sample of (249) individuals from different Kuwaiti SMEs answered the questionnaire. SPSS v. 26 was used to analyze gathered data. The study's findings revealed that FMIS has a significant impact on organizational trials to achieve organizational excellence. This impact was most noticeable on the level of cost-efficiency, with an R-value of 0.583, followed by a positive impact on security, with an R-value of 0.453. Based on the results, it can be widely generalized that FMIS can help the organization reach organizational excellence through managing its financial affairs. As a result, the study recommends paying more attention to the quality of data presented to FMIS, keeping in mind that human errors in data entry might result in incorrect and malfunctioned data, even if it is processed by FMIS. FMIS also improves the ability of an organization to schedule financial information, such as obligations, receivables, debts, payments, and expenses.

• Tribology and Lubricants
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• v.37 no.2
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• pp.41-47
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• 2021

An experimental device was developed for analysis of hydraulic oil characteristics with dielectric constant sensors. Online analysis is the most effective method of the three methods used for analyzing lubricant oils. This is because it can monitor the machine condition effectively using oil sensors in real time without requiring excellent analysis skill and eliminates human errors. Determining the oil quality usually requires complex laboratory equipment for measuring factors such as density, viscosity, base number, acid number, water content, additive, and wear debris. However, the electric constant is another indicator of oil quality that can be measured on-site. The electric constant is the ratio of the capacitance of a capacitor using that material as a dielectric, compared with a similar capacitor that has a vacuum as its dielectric. The electric constant affects the factors such as the base oil, additive, temperature, electric field frequency, water content, and contaminants. In this study, the tendency of the electric constant is investigated with a variation of temperature, water content, and dust weight. The experimental device can control working temperature and mix the contaminants with oil. A machine condition monitoring program developed to analyze hydraulic oil is described. This program provides graph and digital values with variation of time. Moreover, it includes an alarm system for when the oil condition is bad.

• Journal of Climate Change Research
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• v.8 no.1
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• pp.1-10
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• 2017

Over the past centuries, industrialization in developed and developing countries has had a negative impact on global warming, releasing $CO_2$ emissions into the Earth's atmosphere. In recent years, the transportation sector, which emits one-third of total $CO_2$ emissions in the United States, has adapted by implementing a climate change action plan to reduce $CO_2$ emissions. Having an environmental policy might be an essential factor in mitigating the man-made global warming threats to protect public health and the coexistent needs of current and future generations; however, to my best knowledge, no research has been conducted in such a context with appropriate statistical validation process to evaluate the effects of climate change policy on $CO_2$ emission reduction in recent years in the U.S. transportation. The empirical findings using an entity fixed-effects model with valid statistical tests show the positive effects of climate change policy on $CO_2$ emission reduction in a state. With all the 49 states joining the climate change action plans, the U.S. transportation sector is expected to reduce its $CO_2$ emissions by 20.2 MMT per year, and for the next 10 years, the cumulated $CO_2$ emission reduction is projected to reach 202.3 MMT, which is almost equivalent to the $CO_2$ emissions from the transportation sector produced in 2012 by California, the largest $CO_2$ emission state in the nation.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.444-452
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• 2019

To assess the availability of a nuclear power plant's dynamic systems, it is necessary to consider the impact of dynamic interactions, such as components, software, and operating processes. However, there is currently no simple, easy-to-use tool for assessing the availability of these dynamic systems. The existing method, such as Markov chains, derives an accurate solution but has difficulty in modeling the system. When using conventional fault trees, the reliability of a system with dynamic characteristics cannot be evaluated accurately because the fault trees consider reliability of a specific operating configuration of the system. The dynamic reliability graph with general gates (DRGGG) allows an intuitive modeling similar to the actual system configuration, which can reduce the human errors that can occur during modeling of the target system. However, because the current DRGGG is able to evaluate the dynamic system in terms of only reliability without repair, a new evaluation method that can calculate the availability of the dynamic system with repair is proposed through this study. The proposed method extends the DRGGG by adding the repair condition to the dynamic gates. As a result of comparing the proposed method with Markov chains regarding a simple verification model, it is confirmed that the quantified value converges to the solution.

• ETRI Journal
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• v.42 no.6
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• pp.886-898
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• 2020

Unusual data patterns or outliers can be generated because of human errors, incorrect measurements, or malicious activities. Detecting outliers is a difficult task that requires complex ensembles. An ideal outlier detection ensemble should consider the strengths of individual base detectors while carefully combining their outputs to create a strong overall ensemble and achieve unbiased accuracy with minimal variance. Selecting and combining the outputs of dissimilar base learners is a challenging task. This paper proposes a model that utilizes heterogeneous base learners. It adaptively boosts the outcomes of preceding learners in the first phase by assigning weights and identifying high-performing learners based on their local domains, and then carefully fuses their outcomes in the second phase to improve overall accuracy. Experimental results from 10 benchmark datasets are used to train and test the proposed model. To investigate its accuracy in terms of separating outliers from inliers, the proposed model is tested and evaluated using accuracy metrics. The analyzed data are presented as crosstabs and percentages, followed by a descriptive method for synthesis and interpretation.