• Journal of Wetlands Research
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• v.10 no.3
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• pp.27-35
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• 2008

Sediment cores were obtained from Upo and Mokpo in Upo Wetland and core samples were divided by depth into 20 ~ 21 subsamples. The heavy metal concentrations of Fe, Mn, Zn, AS, Cu, Cd, Ni, Pb, and Cr in the sediments of each depth were determined by ICP-MS. The texture of sediemnts from Upo Wetland appeared to be clayey silt with average grain size of $7.52{\sim}11.15{\mu}m$ for physical properties. It was found to have a clear tendency of depth profile with respect to TOC and ignition loss. Organics were stabilized in the range of 0.5 ~ 0.7 % TOC and 8 ~ 9 % ignition loss in 30 years, whereas, the surficial sediments have the highest concentrations of about 3.0 % of TOC and 13 ~ 15 % ignition loss. Those are much higher than the values of the main stream, the Nakdong River, which reflects the deposit of biodegradable organics from plants and other lifes. The vertical distribution of heavy metals in two sediment cores was investigated to elucidate historical trends of heavy metals deposited into Upo wetland. The depth profile concentrations of each heavy metal were compared and discussed with the Concensus-Based Sediment Quality Guidelines for freshwater ecosystems. All the Cd data for the vertical distribution in the sediments were detected above PEC value for Cd, which predict harmful effects on sediment-dwelling organisms expected to occur frequently. The concentrations of Zn, Cu, and Cr in all sediment samples for depth profile were detected below the TEC values, which provided a basis predicting the absence of toxicity by Zn, Cu, and Cr.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.591-592
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• 2015

X1822-371 is a low mass X-ray binary with an accretion disk corona exhibiting partial eclipses and pulsations in the X-ray band. We update its orbital ephemeris by combining new RXTE observations and historical records, with a total time span of 34 years. There were 11 RXTE observations in 2011 but the eclipsing profile can be seen in only 4 of them. The eclipsing center times were obtained by fitting the profile with the same model as previous studies. Combined with the eclipsing center times reported by Iaria et al. (2011), the O-C analysis was processed. A quadratic model was applied to fit the O-C results and produced a mean orbital period derivative of $\dot{P}_{orb}=1.339(25){\times}10^{-10}s/s$, which is slightly smaller than previous records. In addition to the orbital modulation from the orbital profile, we also present our preliminary results for measuring the orbital parameters using the orbital Doppler effect from the pulsation of the neutron star in X1822-371. The updated orbital parameters from eclipsing profiles will be further compared with the ones from pulsar timing.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.167-176
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• 2020

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient's feedback with their information regarding their treatment. Patient's preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient's feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor's in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients' health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.

• Journal of Acupuncture Research
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• v.21 no.5
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• pp.149-162
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• 2004

Quality assurance with high safety profile is one of the most critical issues to medical device manufacturing. In general, this issue was not paid proper attention with respect to acupuncture needle and its related devices. Acupuncture needles are manufactured through various standards ranging from purely hand-made, partially hand-made and partially machine-made, to fully machine-made mainly in China, Korea, and Japan. There is a large difference in quality between needles from different manufacturers. In order to provide a standard manufacturing guideline, it seems necessary to make a Korean Industrial standard (KS). The authors review this topic from an historical view point, investigate the current situation of the standardization of acupuncture needles in other developed countries, and inspect the general procedure to establish a KS in Korea in order to suggest a KS for acupuncture needles.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.4
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• pp.27-36
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• 2019

An accurate trajectory prediction is a key to the safe and efficient operations of aircraft. One way to improve trajectory prediction accuracy is to develop a model for aircraft ground speed prediction. This paper proposes a generative model for posterior aircraft ground speed prediction. The proposed method fits the Gaussian Mixture Model(GMM) to historical data of aircraft speed, and then the model is used to generates probabilistic speed profile of the aircraft. The performances of the proposed method are demonstrated with real traffic data in Incheon Flight Information Region(FIR).

• 한국지구물리탐사학회:학술대회논문집
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• 2004.08a
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• pp.49-69
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• 2004

One of the important roles of geophysical exploration in archeological survey may be to provide the subsurface information for effective and systematic excavations of historical remains. Ground Penetrating Radar (GPA) can give us images of shallow subsurface structure with high resolution and is regarded as a useful and important technology in archeological exploration. Since the buried cultural relics are the three-dimensional (3-D) objects in nature, the 3-D or areal survey is more desirable in archeological exploration. 3-D GPR survey based on the very dense data in principle, however, might need much higher cost and longer time of exploration than the other geophysical methods, thus it could have not been applied to the wide area exploration as one of routine procedures. Therefore, it is important to develop an effective way of 3-D GPR survey. In this study, we applied 3-D GPR method to investigate the possible historical remains of Baekje Kingdom at Gatap-Ri, Buyeo city, prior to the excavation. The principal purpose of the investigation was to provide the subsurface images of high resolution for the excavation of the surveyed area. Besides this, another purpose was to investigate the applicability and effectiveness of the continuous data acquisition system which was newly devised for the archeological investigation. The system consists of two sets of GPR antennas and the precise measurement device tracking the path of GPR antenna movement automatically and continuously Besides this hardware system, we adopted a concept of data acquisition that the data were acquired arbitrary not along the pre-established profile lines, because establishing the many profile lines itself would make the field work much longer, which results in the higher cost of field work. Owing to the newly devised system, we could acquire 3-D GPR data of an wide area over about $17,000 m^2$ as a result of the just two-days field work. Although the 3-D GPR data were gathered randomly not along the pre-established profile lines, we could have the 3-D images with high resolution showing many distinctive anomalies which could be interpreted as old agricultural lands, waterways, and artificial structures or remains. This case history led us to the conclusion that 3-D GPR method can be used easily not only to examine a small anomalous area but also to investigate the wider region of archeological interests. We expect that the 3-D GPR method will be applied as a one of standard exploration procedures to the exploration of historical remains in Korea in the near future.

• Journal of Korean Society of Transportation
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• v.25 no.5
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• pp.33-42
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• 2007

The current left-turn split model adopted in COSMOS has an inherent limitation when a loop detector in the left-turn lanes was disconnected for a period of time. In this instance, the current model always allocated minimum green time to the left-turn phase, thus optimal split and efficient signal operation for the intersection was not guaranteed. In this paper, four mathmatical models using detector information of the intersection and four empirical models using historical profiles were developed and investigated for different traffic conditions to improve the operational efficiency of the intersection. From the model evaluation test, the empirical model using a four-week historical profile produced the least error among the eight models investigated. NETSIM simulation test results also showed that the proposed model could give significantly reduced delay time as compared to the current model. From these results, the operational efficency of the signalized intersections under the real-time control can be greatly improved by using the model proposed in case of the left-turn detector failure.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2007.11a
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• pp.109-113
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• 2007

This paper introduces a system, Simulation based Stochastic Markup Estimation System (S2ME), for estimating optimum markup for a project. The system was designed and implemented to better represent the real world system involved in construction bidding. The findings obtained from the analysis of existing assumptions used in the previous quantitative markup estimation methods were incorporated to improve the accuracy and predictability of the S2ME. The existing methods has four categories of assumption as follows; (1) The number of competitors and who is the competitors are known, (2) A typical competitor, who is fictitious, is assumed for easy computation, (3) the ratio of bid price against cost estimate (B/C) is assumed to follow normal distribution, (4) The deterministic output obtained from the probabilistic equation of existing models is assumed to be acceptable. However, these assumptions compromise the accuracy of prediction. In practice, the bidding patterns of the bidders are randomized in competitive bidding. To complement the lack of accuracy contributed by these assumptions, bidding project was randomly selected from the pool of bidding database in the simulation experiment. The probability to win the bid in the competitive bidding was computed using the profile of the competitors appeared in the selected bidding project record. The expected profit and probability to win the bid was calculated by selecting a bidding record randomly in an iteration of the simulation experiment under the assumption that the bidding pattern retained in historical bidding DB manifest revival. The existing computation, which is handled by means of deterministic procedure, were converted into stochastic model using simulation modeling and analysis technique as follows; (1) estimating the probability distribution functions of competitors' B/C which were obtained from historical bidding DB, (2) analyzing the sensitivity against the increment of markup using normal distribution and actual probability distribution estimated by distribution fitting, (3) estimating the maximum expected profit and optimum markup range. In the case study, the best fitted probability distribution function was estimated using the historical bidding DB retaining the competitors' bidding behavior so that the reliability was improved by estimating the output obtained from simulation experiment.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Journal of Ecology and Environment
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• v.32 no.3
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• pp.167-176
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• 2009

Land cover changes associated with urbanization have driven climate change and pollution, which alter properties of ecosystems at local, regional, and continental scales. Thus, the relationships among urban ecological variables such as community composition, structure, health, soil and functioning need to be better understood to restore and improve urban ecosystems. In this study, we discuss urban ecosystem management and research from a futuristic perspective based on analyses of vegetation structure, composition, and successional trends, as well as the chemical properties of soils and the distribution of heat along an urban-rural gradient. Urban thermo-profile analysis using satellite images showed an obvious mitigating effect of vegetation on the Seoul heat island. Community attributes of Quercus mongolica stands reflected the effects of urbanization, such as pronounced increases in disturbance-related and pollution-tolerant species, such as Styrax japonica and Sorbus alnifolia. Retrogressive successional trends were detected in urban sites relative to those in rural sites. Changes in the urban climate and biotic environment have the potential to significantly influence the practice and outcomes of ecological management, restoration and forecasting because of the associated changes in future bio-physical settings. Thus, for management (i.e., creation and restoration) of urban green spaces, forward-thinking perspectives supported by historical information are necessary.