• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.6
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• pp.751-759
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• 2007

We have developed several methods for the optimization problem having large-scale and highly nonlinear system. First, step by step method in optimization process was employed to improve the convergence. In addition, techniques of furnishing good initial guesses for analysis using sensitivity information acquired from optimization iteration, and of manipulating analysis/optimization convergency criterion motivated from simultaneous technique were used. We applied them to flow control problem and verified their efficiency and robustness. However, they are based on quasi-Newton method that approximate the Hessian matrix using exact first derivatives. However solution of the Navier-Stokes equations are very cost, so we want to improve the efficiency of the optimization algorithm as much as possible. Thus we develop a true Newton method that uses exact Hessian matrix. And we apply that to the three-dimensional problem of flow around a sphere. This problem is certainly intractable with existing methods for optimal flow control. However, we can attack such problems with the methods that we developed previously and true Newton method.

• Toxicological Research
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• v.6 no.2
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• pp.205-213
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• 1990

The regulation of neurotoxicants has usually been based upon setting reference doses by dividing a no observed adverse effect level (NOAEL) by uncertainty factors that theoretically account for interspecies and intraspecies extraploation of experimental results in animals to humans. Recently, we have proposed a four-step alternative procedure which provides quantitative estimates of risk as a function of dose. The first step is to establish a mathematical relationship between a biological effect or biomarker and the dose of chemical administered. The second step is to determine the distribution (variability) of individual measurements of biological effects or their biomarkers about the dose response curve. The third step is to define an adverse or abnormal level of a biological effect or biomarker in an untreated population. The fourth and final step is to combine the information from the first three steps to estimate the risk (proportion of individuals exceeding on adverse or abnormal level of a biological effect or biomarker) as a function of dose. The primary purpose of this report is to enhance the certainty of the first step of this procedure by improving our understanding of the relationship between a biomarker and dose of administered chemical. Several factors which need to be considered include: 1) the pharmacokinetics of the parent chemical, 2) the target tissue concentrations of the parent chemical or its bioactivated proximate toxicant, 3) the uptake kinetics of the parent chemical or metabolite into the target cell(s) and/or membrane interactions, and 4) the interaction of the chemical or metabolite with presumed receptor site(s). Because these theoretical factors each contain a saturable step due to definitive amounts of required enzyme, reuptake or receptor site(s), a nonlinear, saturable dose-response curve would be predicted. In order to exemplify this process, effects of the neurotoxicant, methlenedioxymethamphetamine (MDMA), were reviewed and analyzed. Our results and those of others indicate that: 1) peak concentrations of MDMA and metabolites are ochieved in rat brain by 30 min and are negligible by 24 hr, 2) a metabolite of MDMA is probably responsible for its neurotoxic effects, and 3) pretreatment with monoamine uptake blockers prevents MDMA neurotoxicity. When data generated from rats administerde MDMA were plotted as bilolgical effect (decreases in hippocampal serotonin concentrations) versus dose, a saturation curve best described the observed relationship. These results support the hypothesis that at least one saturable step is involved in MDMA neurotoxicity. We conclude that the mathematical relationship between biological effect and dose of MDMA, the first step of our quantitative neurotoxicity risk assessment procedure, should reflect this biological model information generated from the whole of the dose-response curve.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.109-115
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• 2023

The extraction and adsorption characteristics for three phenolic ketones with high physicochemical similarity among phenolic compounds, which are alcohol fermentation inhibitors in lignocellulosic biomass hydrolysates, were investigated. The most suitable basic extractant for selectively separating acetosyringone from three phenol ketones by reactive extraction was found to be trioctylphosphine oxide. In addition, it was found that adsorption using XAD16, a polymer neutral resin adsorbent, or physical extraction using hexane, was a suitable separation method for separation of 4'-hydroxyacetophenone (HAP) and acetovanillone (AVO). A five-step fractionation process including extraction and adsorption mentioned above has been first proposed to separate and concentrate the three phenol ketones present at equal mass percentages. When physical extraction with n-hexane and re-extraction with an aqueous NaOH solution were used as the steps 4 and 5 in the fractionation process respectively, it was possible to obtain almost 70% or more of the purity of three phenolic ketones.

• Korean Journal of Heritage: History & Science
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• v.48 no.2
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• pp.142-157
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• 2015

This study examines how traditional knowledge functions in the specific techniques to make pottery in terms of the traditional knowledge on the pottery techniques of Onggi potters. It focuses on how traditional pottery manufacturing skills are categorized and what aspects are observed with regard to the techniques. The pottery manufacturing process is divided into the preparation step of raw material, the molding step of pottery, and the final plasticity step. Each step involves unique traditional knowledge. The preparation step mainly comprises the knowledge on different kinds of mud. The knowledge is about the colors and properties of mud, the information on the regional distribution of quality mud, and the techniques to optimize mud for pottery manufacturing. The molding step mainly involves the structure and shape of spinning wheels, the techniques to accumulate mud, ways to use different kinds of tools, the techniques to dry processed pottery. The plasticity step involves the knowledge on kilns and the scheme to build kilns, the skills to stack pottery inside of the kilns, the knowledge on firewood and efficient ways of wood burning, the discrimination of different kinds of fire and the techniques to stoke the kilns. These different kinds of knowledge may be roughly divided into three categories : the preparation of raw material, molding, and plasticity. They are closely connected with one another, which is because it becomes difficult to manufacture quality pottery even with only one incorrect factor. The contents of knowledge involved in the manufacturing process of pottery focused are mainly about raw material, color, shape, distribution aspect, fusion point, durability, physical property, etc, which are all about science. They are rather obtained through the experimental learning process of apprenticeship, not through the official education. It is not easy to categorize the knowledge involved. Most of the knowledge can be understood in the category of ethnoscience. In terms of the UNESCO world heritage of intangible cultural assets, the knowledge is mainly about 'the knowledge on nature and universe'. Unique knowledge and skills are, however, identified in the molding step. They can be referred to 'body techniques', which unify the physical stance of potters, tools they employ, and the conceived pottery. Potters themselves find it difficult to articulate the knowledge. In case stated, it cannot be easily understood without the experience and knowledge on the field. From the preparation of raw material to the complete products, the techniques and traditional knowledge involved in the process of manufacturing pottery are closely connected, employing numerous categories and levels. Such an aspect can be referred to as a 'techniques chain'. Here the techniques mean not only the scientific techniques but also, in addition to the skills, the knowledge of various techniques and levels including habitual, unconscious behaviors of potters.

• Restorative Dentistry and Endodontics
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• v.38 no.3
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• pp.146-153
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• 2013

Objectives: Aluminum step wedge (ASW) equivalent radiodensity (eRD) has been used to quantify restorative material's radiodensity. The aim of this study was to evaluate the effects of image acquisition control (IAC) of a digital X-ray system on the radiodensity quantification under different exposure time settings. Materials and Methods: Three 1-mm thick restorative material samples with various opacities were prepared. Samples were radiographed alongside an ASW using one of three digital radiographic modes (linear mapping (L), nonlinear mapping (N), and nonlinear mapping and automatic exposure control activated (E)) under 3 exposure time settings (underexposure, normal-exposure, and overexposure). The ASW eRD of restorative materials, attenuation coefficients and contrasts of ASW, and the correlation coefficient of linear relationship between logarithms of gray-scale value and thicknesses of ASW were compared under 9 conditions. Results: The ASW eRD measurements of restorative materials by three digital radiographic modes were statistically different (p = 0.049) but clinically similar. The relationship between logarithms of background corrected grey scale value and thickness of ASW was highly linear but attenuation coefficients and contrasts varied significantly among 3 radiographic modes. Varying exposure times did not affect ASW eRD significantly. Conclusions: Even though different digital radiographic modes induced large variation on attenuation of coefficient and contrast of ASW, E mode improved diagnostic quality of the image significantly under the underexposure condition by improving contrasts, while maintaining ASW eRDs of restorative materials similar. Under the condition of this study, underexposure time may be acceptable clinically with digital X-ray system using automatic gain control that reduces radiation exposure for patient.

• Journal of Technology Innovation
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• v.20 no.2
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• pp.109-134
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• 2012

In this era of globalization and fierce competition, R&D is an essential part of corporate development strategy. Accordingly, much effort has been devoted to identify and verify best practices for successful R&D and to improve R&D processes. Nevertheless, there is still lack of a comprehensive framework for assessing the quality of R&D process, which can be used as a step-by-step guide for process improvement. Therefore, this study purposes to develop a framework for assessing R&D process based on the concept of CMMI, which is a process improvement approach that defines the essential elements of effective processes. In particular, we suggest three types of R&D process-new product development, new service creation and new technology creation-and then develop a customized framework for them. For this purpose, we firstly investigated the characteristics of R&D activities for each type of process and identify its base practices from an extensive literature review. Then, the CMMI approach was adopted and modified to suit the R&D process. Finally, an illustrative example was provided to demonstrate the assessment process and a prototype web-based assessment system was suggested. Research findings will help understand the characteristics of different types of R&D process and provide a customized guidelines for R&D process assessment and improvement.

• 보존과학연구
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• s.7
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• pp.278-291
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• 1986

In this report the practical case of Viking ship's conservation in Denmarke specially among the Eurpoean nations is introduced. The contents of it are summarized as follows :From 1957 to 1962 the Danish National Museum Salvaged five Viking ships from the bottom of Roskilde Fjord, Which were composed of the pieces of timber whose surface was soft because they had lain on the sea bed for about a thousand years. Excavation had been carried out in the same way as in the field by driving down a sheet piling around the wrecks and pumping the water out. These pieces of the wreck ships were packed in airtight plastic bags one by one to be transported for Brede and then immidiately had to go through the treatment for conservation. The conservation treatment process for the pieces includes three steps ; the preliminary process prior to the hardening treatment, the hardening and the assemble of the ships. In the first step ; the preliminary process, all remains of mud and shells from the fjord bed are washed off, and measuring followed ; every single piece of wreckage was drawn so that the form and size of the piece, nail holes, and breaks were registered before conservation. In the second ; the hardening treatment step, the pieces of the woreckage were filled with P.E.G. This Polyethylene Glycol method was the best to handle in the subsequent mounting of the ships in the museum. In the final, the Glycol-treated pieces were pieced together to spips with support of a system of reinforcements. They were to fit in place after corrections of the form were made several times.

• Journal of Powder Materials
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• v.27 no.2
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• pp.111-118
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• 2020

The convergence of artificial intelligence with smart factories or smart mechanical systems has been actively studied to maximize the efficiency and safety. Despite the high improvement of artificial neural networks, their application in the manufacturing industry has been difficult due to limitations in obtaining meaningful data from factories or mechanical systems. Accordingly, there have been active studies on manufacturing components with sensor integration allowing them to generate important data from themselves. Additive manufacturing enables the fabrication of a net shaped product with various materials including plastic, metal, or ceramic parts. With the principle of layer-by-layer adhesion of material, there has been active research to utilize this multi-step manufacturing process, such as changing the material at a certain step of adhesion or adding sensor components in the middle of the additive manufacturing process. Particularly for smart parts manufacturing, researchers have attempted to embed sensors or integrated circuit boards within a three-dimensional component during the additive manufacturing process. While most of the sensor embedding additive manufacturing was based on polymer material, there have also been studies on sensor integration within metal or ceramic materials. This study reviews the additive manufacturing technology for sensor integration into plastic, ceramic, and metal materials.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1660-1665
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• 2022

Recently, many industries are using three dimensional scanning technology. As the performance of the 3D scanner gradually improves, a sampling step to reduce a point data or a remove step to remove a part determined to be noise are generally performed in post processing. However, total point data by long time scanning cannot be processed at once in spite of performing such those additional processes. In general, a method using a multi threaded environment is widely used, but as the scanning process work time increases, the processing performance gradually decreases due to various environmental conditions and accumulated operations. This paper proposes a method to initially remove point data judged to be unnecessary by calculating accumulated fast point feature histogram values from coming point data of the 3D scanner in real time. The entire 3D scanning process can be reduced using this approach.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.17-26
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). The recognition rate of our algorithm was 97.34% better than that of the Actical device(91.74%) by 5.6%.