• Journal of the Microelectronics and Packaging Society
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• v.17 no.4
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• pp.73-76
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• 2010

A two-facet approach was used to investigate the parametric performance of functional high-speed DDR3 (Double Data Rate) DRAM (Dynamic Random Access Memory) die placed in different types of BGA (Ball Grid Array) packages: wire-bonded BGA (FBGA, Fine Ball Grid Array), flip-chip (FCBGA) and lead-bonded $microBGA^{(R)}$. In the first section, packaged live DDR3 die were tested using automatic test equipment using high-resolution shmoo plots. It was found that the best timing and voltage margin was obtained using the lead-bonded microBGA, followed by the wire-bonded FBGA with the FCBGA exhibiting the worst performance of the three types tested. In particular the flip-chip packaged devices exhibited reduced operating voltage margin. In the second part of this work a test system was designed and constructed to mimic the electrical environment of the data bus in a PC's CPU-Memory subsystem that used a single DIMM (Dual In Line Memory Module) socket in point-to-point and point-to-two-point configurations. The emulation system was used to examine signal integrity for system-level operation at speeds in excess of 6 Gb/pin/sec in order to assess the frequency extensibility of the signal-carrying path of the microBGA considered for future high-speed DRAM packaging. The analyzed signal path was driven from either end of the data bus by a GaAs laser driver capable of operation beyond 10 GHz. Eye diagrams were measured using a high speed sampling oscilloscope with a pulse generator providing a pseudo-random bit sequence stimulus for the laser drivers. The memory controller was emulated using a circuit implemented on a BGA interposer employing the laser driver while the active DRAM was modeled using the same type of laser driver mounted to the DIMM module. A custom silicon loading die was designed and fabricated and placed into the microBGA packages that were attached to an instrumented DIMM module. It was found that 6.6 Gb/sec/pin operation appears feasible in both point to point and point to two point configurations when the input capacitance is limited to 2pF.

• Journal of Ecology and Environment
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• v.34 no.2
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• pp.149-156
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• 2011

Phenological variables derived from remote sensing are useful in determining the seasonal cycles of ecosystems in a changing climate. Satellite remote sensing imagery is useful for the spatial continuous monitoring of vegetation phenology across broad regions; however, its applications are substantially constrained by atmospheric disturbances such as clouds, dusts, and aerosols. By way of contrast, a tower-based ground remote sensing approach at the canopy level can provide continuous information on canopy phenology at finer spatial and temporal scales, regardless of atmospheric conditions. In this study, a tower-based ground remote sensing system, called the "Phenological Eyes Network (PEN)", which was installed at the Gwangneung Deciduous KoFlux (GDK) flux tower site in Korea was introduced, and daily phenological progressions at the canopy level were assessed using ratios of red, green, and blue (RGB) spectral reflectances obtained by the PEN system. The PEN system at the GDK site consists of an automatic-capturing digital fisheye camera and a hemi-spherical spectroradiometer, and monitors stand canopy phenology on an hourly basis. RGB data analyses conducted between late March and early December in 2009 revealed that the 2G_RB (i.e., 2G - R - B) index was lower than the G/R (i.e., G divided by R) index during the off-growing season, owing to the effects of surface reflectance, including soil and snow effects. The results of comparisons between the daily PEN-obtained RGB ratios and daily moderate-resolution imaging spectroradiometer (MODIS)-driven vegetation indices demonstrate that ground remote sensing data, including the PEN data, can help to improve cloud-contaminated satellite remote sensing imagery.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• Health Policy and Management
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• v.28 no.4
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• pp.402-410
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• 2018

Background: Monitoring appropriate medication categories can provide early warning of certain disease outbreaks. This study aimed to present a methodology for selecting and monitoring medications relevant to the surveillance of acute respiratory tract infections, such as influenza. Methods: To estimate correlations between acute febrile respiratory tract infection and some medication categories, the cross-correlation coefficient (CCC) was used and established. Two databases were used: real-time prescription trend of antivirals, anti-inflammatory drugs, antibiotics using Drug Utilization Review Program between 2012 and 2015 and physicians' number of encounters with acute febrile respiratory tract infections such as influenza outbreaks using the national level health insurance claims data. The seasonality was also evaluated using the CCC. Results: After selecting six candidate diseases that require extensive monitoring, influenza with highly specific medical treatment according to the health insurance claims data and its medications were chosen as final candidates based on a data-driven approach. Antiviral medications and influenza were significantly correlated. Conclusion: An annual correlation was observed between influenza and antiviral medications, anti-inflammatory drugs. Suitable models should be established for syndromic surveillance of influenza.

• Journal of the Korea Computer Graphics Society
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• v.29 no.4
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• pp.7-16
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• 2023

For decades, creating a desired locomotive motion in a goal-oriented manner has been a challenge in character animation. Data-driven methods using generative models have demonstrated efficient ways of predicting long sequences of motions without the need for explicit conditioning. While these methods produce high-quality long-term motions, they can be limited when it comes to synthesizing motion for challenging novel scenarios, such as punching a random target. A state-of-the-art solution to overcome this limitation is by using a GAN Discriminator to imitate motion data clips and incorporating reinforcement learning to compose goal-oriented motions. In this paper, our research aims to create characters performing combat sports such as boxing, using a novel reward design in conjunction with existing GAN-based approaches. We experimentally demonstrate that both the Adversarial Motion Prior [3] and Adversarial Skill Embeddings [4] methods are capable of generating viable motions for a character punching a random target, even in the absence of mocap data that specifically captures the transition between punching and locomotion. Also, with a single learned policy, multiple task controllers can be constructed through the TimeChamber framework.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.6
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• pp.347-361
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• 2023

Water utilities are making various efforts to reduce water losses from water networks, and an essential part of them is to recognize the moment when a pipe burst occurs during operation quickly. Several physics-based methods and data-driven analysis are applied using real-time flow and pressure data measured through a SCADA system or smart meters, and methodologies based on machining learning are currently widely studied. Water utilities should apply various approaches together to increase pipe burst detection. The most intuitive and explainable water balance method and its procedure were presented in this study, and the applicability and detection performance were evaluated by applying this approach to water supply pipelines. Based on these results, water utilities can establish a mass balance-based pipe burst detection system, give a guideline for installing new flow meters, and set the detection parameters with expected performance. The performance of the water balance analysis method is affected by the water network operation conditions, the characteristics of the installed flow meter, and event data, so there is a limit to the general use of the results in all sites. Therefore, water utilities should accumulate experience by applying the water balance method in more fields.

• Spatial Information Research
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• v.1 no.1
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• pp.39-53
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• 1993

A geographical information systems(GIS) was a useful aid in the assessment of urban nonpoint source pollution and the development of a pollution control strategy. The GIS was used for data integration and display, and to provide data for a nonpoint source model. An empirical nonpoint source loading model driven by land use was used to estimate pollutant loadings of priority pollutant. Pollutant loadings were estimated at fine spatial resolution and aggregated to storm sewer drainage basins(sewershedsl. Eleven sewersheds were generated from digital versions of sewer maps. The pollutant loadings of individual land use polygons, derived as the unit of analysis from street blocks, were aggregated to get total pollutant loading within each sewershed. Based on the model output, a critical sewershed was located. Pollutant loadings at major sewer junctions within the critical sewershed were estimated to develop a mi t igat ion strategy. Two approaches based on the installat ion of wet ponds were investigated -- a regional approach using one large wet pond at the major sewer outfall and a multi-site approach using a number of smaller sites for each major sewer junction. Cost analysis showed that the regional approach would be more cost effective, though it would provide less pollution control.

• Journal of the Korean Geographical Society
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• v.43 no.1
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• pp.134-153
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• 2008

The main purpose of the research is to illustrate the value of the spatial statistical approach to residential differentiation by providing a framework for exploratory spatial data analysis (ESDA) using a local spatial separation measure. ESDA aims, by utilizing a variety of statistical and cartographic visualization techniques, at seeking to detect patterns, to formulate hypotheses, and to assess statistical models for spatial data. The research is driven by a realization that ESDA based on local statistics has a great potential for substantive research. The main results are as follows. First, a local spatial separation measure is correspondingly derived from its global counterpart. Second, a set of significance testing methods based on both total and conditional randomization assumptions is provided for the local measure. Third, two mapping techniques, a 'spatial separation scatterplot map' and a 'spatial separation anomaly map', are devised for ESDA utilizing the local measure and the related significance tests. Fourth, a case study of residential differentiation between the highly educated and the least educated in major Korean metropolitan cities shows that the proposed ESDA techniques are beneficial in identifying bivariate spatial clusters and spatial outliers.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.91-105
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• 1999

Spatial data integration using multiple geo-based data sets has been regarded as one of the primary GIS application issues. As for this issue, several integration schemes have been developed as the perspectives of mathematical geology or geo-mathematics. However, research-based approaches for statistical/quantitative assessments between integrated layer and input layers are not fully considered yet. Related to this niche point, in this study, spatial data integration using multiple geoscientific data sets by known integration algorithms was primarily performed. For spatial integration by using raster-based GIS functionality, geological, geochemical, geophysical data sets, DEM-driven data sets and remotely sensed imagery data sets from the Ogdong area were utilized for geological thematic mapping related by mineral potential mapping. In addition, statistical/quantitative information extraction with respective to relationships among used data sets and/or between each data set and integrated layer was carried out, with the scope of multiple data fusion and schematic statistical assessment methodology. As for the spatial integration scheme, certainty factor (CF) estimation and principal component analysis (PCA) were applied. However, this study was not aimed at direct comparison of both methodologies; whereas, for the statistical/quantitative assessment between integrated layer and input layers, some statistical methodologies based on contingency table were focused. Especially, for the bias reduction, jackknife technique was also applied in PCA-based spatial integration. Through the statistic analyses with respect to the integration information in this case study, new information for relationships of integrated layer and input layers was extracted. In addition, influence effects of input data sets with respect to integrated layer were assessed. This kind of approach provides a decision-making information in the viewpoint of GIS and is also exploratory data analysis in conjunction with GIS and geoscientific application, especially handing spatial integration or data fusion with complex variable data sets.