• Korean Institute of Interior Design Journal
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• v.24 no.3
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• pp.104-112
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• 2015

This study purposes drawing changing tendency of apartment kitchen areas and character of that by figuring out type, form and per period cause of change of brand apartment kitchen areas. I adopted subject of study as the six leading construction companies considering the apartment rankings of brand and subcontract from 2001 to 2015 when brand apartment was activated(Samsung C&T corporation Raemian, Daelim Industrial Co., Ltd E-pyeonhansesang, Daewoo E&C Prugio, GS E&C XI, Posco E&C The sharp, Hyundai E&C Hillstate) and analyzed kitchen plane of $84m^2{\sim}102m^2$ housing units which were in great demand from apartment housing market. Also, I drew changing trends of kitchen areas by conducting a model house field survey. Through this study result, I will expect the utility of baseline data which is necessary for space development by figuring out kitchen culture, flows and cause of change.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5546-5559
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• 2019

Facial action units (AUs) have recently drawn increased attention because they can be used to recognize facial expressions. A variety of methods have been designed for frontal-view AU detection, but few have been able to handle multi-view face images. In this paper we propose a method for multi-view facial AU detection using a fused multilayer, multi-task, and multi-label deep learning network. The network can complete two tasks: AU detection and facial view detection. AU detection is a multi-label problem and facial view detection is a single-label problem. A residual network and multilayer fusion are applied to obtain more representative features. Our method is effective and performs well. The F1 score on FERA 2017 is 13.1% higher than the baseline. The facial view recognition accuracy is 0.991. This shows that our multi-task, multi-label model could achieve good performance on the two tasks.

• Smart Structures and Systems
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• v.8 no.1
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• pp.39-52
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• 2011

We are developing a biomimetic robot based on the Sea Lamprey. The robot consists of a cylindrical electronics bay propelled by an undulatory body axis. Shape memory alloy (SMA) actuators generate propagating flexion waves in five undulatory segments of a polyurethane strip. The behavior of the robot is controlled by an electronic nervous system (ENS) composed of networks of discrete-time map-based neurons and synapses that execute on a digital signal processing chip. Motor neuron action potentials gate power transistors that apply current to the SMA actuators. The ENS consists of a set of segmental central pattern generators (CPGs), modulated by layered command and coordinating neuron networks, that integrate input from exteroceptive sensors including a compass, accelerometers, inclinometers and a short baseline sonar array (SBA). The CPGs instantiate the 3-element hemi-segmental network model established from physiological studies. Anterior and posterior propagating pathways between CPGs mediate intersegmental coordination to generate flexion waves for forward and backward swimming. The command network mediates layered exteroceptive reflexes for homing, primary orientation, and impediment compensation. The SBA allows homing on a sonar beacon by indicating deviations in azimuth and inclination. Inclinometers actuate a bending segment between the hull and undulator to allow climb and dive. Accelerometers can distinguish collisions from impediment to allow compensatory reflexes. Modulatory commands mediate speed control and turning. A SBA communications interface is being developed to allow supervised reactive autonomy.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.5
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• pp.1-9
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• 2010

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration ($ET_0$) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, Korea. Four climate factors, daily maximum temperature ($T_{max}$), daily minimum temperature ($T_{min}$), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 (Hadley Centre Coupled Model, ver.3) A1B scenario. The evapotranspirations were 549.7 mm/yr in baseline period (1973-2008), 558.1 mm/yr in 2011-2030, 593.0 mm/yr in 2046-2065, and 641.1 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

• Journal of Astronomy and Space Sciences
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• v.33 no.1
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• pp.45-54
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• 2016

This study developed an approach for improving Carrier-phase Differential Global Positioning System (CDGPS) based realtime satellite relative navigation by applying laser baseline measurement data. The robustness against the space operational environment was considered, and a Synthetic Wavelength Interferometer (SWI) algorithm based on a femtosecond laser measurement model was developed. The phase differences between two laser wavelengths were combined to measure precise distance. Generated laser data were used to improve estimation accuracy for the float ambiguity of CDGPS data. Relative navigation simulations in real-time were performed using the extended Kalman filter algorithm. The GPS and laser-combined relative navigation accuracy was compared with GPS-only relative navigation solutions to determine the impact of laser data on relative navigation. In numerical simulations, the success rate of integer ambiguity resolution increased when laser data was added to GPS data. The relative navigational errors also improved five-fold and two-fold, relative to the GPS-only error, for 250 m and 5 km initial relative distances, respectively. The methodology developed in this study is suitable for application to future satellite formation-flying missions.

• Smart Structures and Systems
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• v.7 no.2
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• pp.133-152
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• 2011

System identification is a fundamental step towards the application of structural health monitoring and damage detection techniques. On this respect, the development of evolved identification strategies is a priority for obtaining reliable and repeatable baseline modal parameters of an undamaged structure to be adopted as references for future structural health assessments. The paper presents the identification of the modal parameters of the Guangzhou New Television Tower, China, using a data-driven stochastic subspace identification (SSI-data) approach complemented with an appropriate automatic mode selection strategy which proved to be successful in previous literature studies. This well-known approach is based on a clustering technique which is adopted to discriminate structural modes from spurious noise ones. The method is applied to the acceleration measurements made available within the task I of the ANCRiSST benchmark problem, which cover 24 hours of continuous monitoring of the structural response under ambient excitation. These records are then subdivided into a convenient number of data sets and the variability of modal parameter estimates with ambient temperature and mean wind velocity are pointed out. Both 10 minutes and 1 hour long records are considered for this purpose. A comparison with finite element model predictions is finally carried out, using the structural matrices provided within the benchmark, in order to check that all the structural modes contained in the considered frequency interval are effectively identified via SSI-data.

• Proceedings of the KSRS Conference
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• v.1
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• pp.200-203
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• 2006

Cloud detection algorithm is being developed as major one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-1R and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithm and preliminary test result of both algorithms.

• Safety and Health at Work
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• v.7 no.3
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• pp.185-193
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• 2016

Background: Management and workers in small and medium-sized enterprises (SMEs) often find it hard to comprehend the requirements related to controlling risks due to exposure to substances. An intervention study was set up in order to support 45 SMEs in improving the management of the risks of occupational exposure to chemicals, and in using the control banding tool and exposure model Stoffenmanager in this process. Methods: A 2-year intervention study was carried out, in which a mix of individual and collective training and support was offered, and baseline and effect measurements were carried out by means of structured interviews, in order to measure progress made. A seven-phase implementation evolutionary ladder was used for this purpose. Success and failure factors were identified by means of company visits and structured interviews. Results: Most companies clearly moved upwards on the implementation evolutionary ladder; 76% of the companies by at least one phase, and 62% by at least two phases. Success and failure factors were described. Conclusion: Active training and coaching helped the participating companies to improve their chemical risk management, and to avoid making mistakes when using and applying Stoffenmanager. The use of validated tools embedded in a community platform appears to support companies to organize and structure their chemical risk management in a business-wise manner, but much depends upon motivated occupational health and safety (OHS) professionals, management support, and willingness to invest time and means.

• Journal of Environmental Health Sciences
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• v.37 no.5
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• pp.342-347
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• 2011

Healthy worker effect (HWE) refers to the consistent tendency for actively employed individuals to have a more favorable mortality experience than the population at large. Although HWE has been well known since the 1970s, only a few studies in occupational epidemiology have attempted to fully define and evaluate HWE. HWE can be separated into effects on the initial hiring into the workforce (healthy worker hire effect) and those on continuing employment (healthy worker survival effect). In this review, we summarize the methods for minimizingor adjusting for the healthy worker effect available in occupational epidemiology. It is noteworthy that healthy worker survival effect appears complicated, considering that employment status plays simultaneous roles as a counfounding variable and intermediate variable, whereas healthy worker hire effect may be adjusted by incorporating health status at baseline into the statistical model. In addition, two retrospective cohort studies for workers in the semiconductor industry and Vietnam veterans in Korea, respectively, were introduced, and their results were explained in terms of healthy worker effect.

• KSBB Journal
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• v.26 no.1
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• pp.33-40
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• 2011

Anti-diabetic effect of the exopolysaccharides (EPS) produced from submerged mycelial culture of Cordyceps sinensis (Cs) was studiedin a type II diabetic animal model (C57BL/6J ob/ob). This study was designed to determine whether Cs-EPS improves clinical symptoms of type II diabetes in ob/ob mice. After Cs-EPS treatment at doses of 200 mg/kg body weight, the fasting blood glucose levels decreased by 47% after 7 weeks compared with those of the control mice. According to the oral glucose tolerance test, the glucose levels recovered its baseline after 120 min in Cs-EPS-treated mice, although the blood glucose levels increased significantly after 30 min. On the other hand, the control group (not-treated) did not recovered its initial level of glucose after 120 min. Furthermore, food intake, body weight, total plasma cholesterol and triglyceride concentrations in ob/ob mice treated with Cs-EPS were significantly decreased, compared with those in control ob/ob mice. Cs-EPS treatment increased significantly the plasma insulin level and the expression of leptin mRNA in adipose tissue of Cs-EPS-treated ob/ob mice. From these results, it is demonstrated that Cs-EPS could be effective for regulating normal blood glucose levels by increasing the amounts of plasma insulin and leptin expression in ob/ob mice, indicating that this compound could be a candidate material as a dietary supplement to control hyperglycemia in patients suffering from type II diabetes.