• Journal of Korea Water Resources Association
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• v.50 no.4
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• pp.241-252
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• 2017

This study aims to evaluate applicability of both online and offline parameter calibration techniques on rainfall-runoff modeling using a conceptual lumped hydrologic model. To achieve the goal, the storage function model was selected and then two different automatic calibration techniques: SCE-UA (offline method) and particle filter (online method) were applied to calibrate the optimal parameter sets for 9 rainfall events in the Cheoncheon catchment, upper area of the Yongdam multi-purpose dam. In order to assess reproducibility of hydrographs from the parameter sets of both techniques, the observed discharge of each event was divided into low flow (below average flow) and high flow (over average flow). The results show that the particle filter method, updating the parameters in real-time, provides more stable reproducibility than the SCE-UA method regardless of low and high flow. The optimal parameters estimated by SCE-UA are very sensitive to the selected objective functions used in this study: RMSE and HMLE. In particular, the parameter sets from RMSE and HMLE demonstrate superior goodness-of-fit values for high flow and low flow periods, respectively.

• Journal of the Ergonomics Society of Korea
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• v.24 no.2
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• pp.57-63
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• 2005

Ergonomic intervention techniques were adopted to assess and control potential risk factors of work-related musculoskeletal disorders(WMSDs) in the automated light assembly manufacturing system. Ten different kinds of assembly workstations implemented with a conveyor system and twelve female workers were observed and evaluated with careful video film analysis. Several close examinations using sets of checklists established by qualified safety and health organizations, such as NIOSH, OSHA, and ANSI, were conducted and every workers and staffs in the site participated in the self-report questionnaires and off-line interviews. Typical risk factors and symptoms of the upper extremity musculoskeletal disorders were found and categorized into specific parts of body. To reduce risk factors of WMSDs and improve system productivity new revised workstation standards, physical dimensions, were suggested accordant with anthropometric characteristics of workers and a heuristic decision strategy of rotating shift work schedules according to work contents has proposed to mitigate cumulative physical stress. Finally, ergonomic programs of entire company to prevent WMSDs were structured.

• ETRI Journal
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• v.42 no.4
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• pp.562-574
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• 2020

Quadrature spatial modulation (QSM) utilizes the in-phase and quadrature spatial dimensions to transmit the real and imaginary parts of a single signal symbol, respectively. The improved QSM (IQSM) transmits two signal symbols per channel use through a combination of two antennas for each of the real and imaginary parts. The main contributions of this study can be summarized as follows. First, we derive an upper bound for the error performance of the IQSM. We then design constellation sets that minimize the error performance of the IQSM for several system configurations. Second, we propose a double QSM (DQSM) that transmits the real and imaginary parts of two signal symbols through any available transmit antennas. Finally, we propose a parallel IQSM (PIQSM) that splits the antenna set into equal subsets and performs IQSM within each subset using the same two signal symbols. Simulation results demonstrate that the proposed constellations significantly outperform conventional constellations. Additionally, DQSM and PIQSM provide a performance similar to that of IQSM while requiring a smaller number of transmit antennas and outperform IQSM with the same number of transmit antennas.

• Atmosphere
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• v.20 no.4
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• pp.505-518
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• 2010

We investigated the impacts of the diabatic heating location, vertical profile and basic state on the Rossby wave propagation. To examine the dynamical process of individual responses on the regional heat source, a dry version of the linear baroclinic model was used with climatological summertime (JJA) mean basic state and vertical structure of the diabatic heating for 1979-2008. Two sets of diabatic heating were constructed of those positioned in the mid-latitudes (Tibetan Plateau, eastern Mediterranean Sea, and the west-central Asia) and the tropics (the southern India, Bay of Bengal, and western Pacific). It was found that using the principal component analysis, atmospheric response to diabatic heating reaches to the steady state in 19th days in time. The prescribed mid-latitude forcing forms equivalent barotropic Rossby wave propagation along the westerly Asia jets, whereas the tropical forcing generates the Rossby wave train extending from the tropics to mid-latitudes. In relation to the maximum vertical profile, the mid-level forcing reveals a stronger response than the lower-level forcing, which may be caused by more effective Rossby wave response by the upper-level divergent flow. Under the different sub-seasonal mean state, both of the tropical and mid-latitude forcing induce the different sub-seasonal response intensity, due to the different basic-state wind.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.92-102
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• 2017

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system's data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.

• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.75-78
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• 1997

A dual birdcage RF coil is proposed for MRI and MRA of the human leg. The proposed coil c can be used to cover the whole human leg by imaging upper and lower parts of the leg separately. In the conventional leg imaging scheme using a single RF coil, the leg has to be r relocated when changing the position of the RF coil thus causing problems in matching the i images from two sets of coils. When using the proposed dual RF coil, however, only the bed h has to be moved to select the imaging region while only one part of the dual coil is used each t time by using current switching and PIN diodes. The utility of proposed coil has been verified b by volunteer MRI and MRA and the imaging results show that the coil is useful for the clinical MRI and MRA of the leg.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.17-23
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• 2009

In many cases, clustering algorithms have been used for extracting and discovering useful information from non-linear data. They have made a great effect on performances of the systems dealing with non-linear data. Thus, this paper presents a new approach called hierarchically structured clustering algorithm, and it is applied to the prediction system for non-linear time series data. The proposed hierarchically structured clustering algorithm (called HCKA: Hierarchical Cross-correlation and K-means clustering Algorithms) in which the cross-correlation and k-means clustering algorithm are combined can accept the correlationship of non-linear time series as well as statistical characteristics. First, the optimal differences of data are generated, which can suitably reveal the characteristics of non-linear time series. Second, the generated differences are classified into the upper clusters for their predictors by the cross-correlation clustering algorithm, and then each classified differences are classified again into the lower fuzzy sets by the k-means clustering algorithm. As a result, the proposed method can give an efficient classification and improve the performance. Finally, we demonstrates the effectiveness of the proposed HCKA via typical time series examples.

• Economic and Environmental Geology
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• v.14 no.1
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• pp.35-49
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• 1981

The studied area is largely occupied by thick piles of the late Cretaceous volcanic rocks of the Yucheon group, which is northeastern border part of the vast volcanic region in the Yucheon basin. The Yucheon group overlies the Geoncheonri Formation and is intruded by granitic and dioritic stocks and dykes. The group can be devided into two parts; the lower is Jusasan andesitic rocks which was called as Jusasan Porphyrite Formation by Tadeiwa in 1929, and the upper is Unmunsa rhyolitic rocks. The volcanic pile consists mainly of various tuffs such as tuff breccia, lapilli tuff, coarse to fine tuff and tuffaceous sediments, and interlayered flows, which range from basaltic andesite to rhyolite in their lithology. The results of petrochemical and volcanostratigraphic studies on the Jusasan andesitic socks suggest that the volcanic rocks were derived from two cyclic evolutions of magmatic fractionation. Systematic study of 5226 joints from the area reveals two sets of steep joints striking $N20^{\circ}-40^{\circ}E$ and $N40^{\circ}-70^{\circ}W$, are dominant and coincide with the fault pattern developed in the area. Three defferent maximum principal stress axes were recognized from conjugate shear joints, which are trending east-west, north-northwest, and north-northeast.

• Convergence Security Journal
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• v.6 no.2
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• pp.81-89
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• 2006

System level diagnosis algorithms use the properties of t-diagnosable system where the maximum number of the faults does not exceed t. The existing diagnosis algorithms have limit when dealing with large fault sets in large multiprocessor systems. Somani and Peleg proposed t/k-diagnosable system to diagnose more faults than t (dimension) by allowing upper bounded few number of units to be diagnosed incorrectly. In this paper, we propose hypercube diagnosis algorithm using t/k-diagnosable system. When the number of faults exceeds t, we allow k faults to be diagnosed incorrectly. Simulation shows that the performance of the proposed algorithm is better than Feng's HADA algorithm. The proposed algorithm also gives similar performance compared to HYP-DIAG algorithm.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.2
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• pp.323-338
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• 1994

This paper outlines a framework for performing intelligent sensor validation for a diagnostic expert system while reasoning under uncertainty. The emphasis is on the algorithmic preprocess technique. A companion paper focusses on heuristic post-processing. Sensor validation plays a vital role in the ability of the overall system to correctly detemine the state of a plant monitored by imperfect sensors. Especially, several theoretical developments were made in understanding uncertain sensory data in statistical aspect. Uncertain information in sensory values is represented through probability assignments on three discrete states, "high", "normal", and "low", and additional sensor confidence measures in Algorithmic Sv.Upper and lower warning limits are generated from the historical learning sets, which represents the borderlines for heat rate degradation generated in the Algorithmic SV initiates a historic data base for better reference in future use. All the information generated in the Algorithmic SV initiate a session to differentiate the sensor fault from the process fault and to make an inference on the system performance. This framework for a diagnostic expert system with sensor validation and reasonig under uncertainty applies in HEATXPRT$^{TM}$, a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants.