• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.5
• /
• pp.343-353
• /
• 2018

Ocean tides in Antarctica are not well constrained mostly due to the lack of tidal observations. Especially, tides underneath and around ice shelves are uncertain. InSAR (Interferometric Synthetic Aperture Radar) data has been used to observe ice shelf movements primarily caused by ocean tides. Here, we demonstrate that it is possible to estimate tidal constituents underneath the Sulzberger ice shelf, West Antarctica, solely using ERS-1/2 tandem mission DInSAR (differential InSAR) observations. In addition, the tidal constituents can be estimated in a high-resolution (~200 m) grid which is beyond any tidal model resolution. We assume that InSAR observed ocean tidal heights can be derived after correcting the InSAR data for the effect of atmospheric loading using the inverse barometric effect, solid earth tides, and ocean tide loading. The ERS (European Remote Sensing) tandem orbit configuration of a 1-day separation between SAR data takes diminishes the sensitivity to major tidal constituents including $K_1$ and $S_2$. Here, the dominant tidal constituent $O_1$ is estimated using 8 differential interferograms underneath the Sulzberger ice shelf. The resulting tidal constituent is compared with a contemporary regional tide model (CATS2008a) and a global tide model (TPXO7.1). The InSAR estimated tidal amplitude agrees well with both models with RMS (root-mean-square) differences of < 2.2 cm and the phase estimate corroborating both tide models to within $8^{\circ}$. We conclude that fine spatial scale (~200 m) Antarctic ice shelf ocean tide determination is feasible for dominant constituents using C-band ERS-1/2 tandem mission InSAR.

• ALGAE
• /
• v.33 no.1
• /
• pp.55-68
• /
• 2018

Foliose species of the Bangiaceae (Porphyra s. l.) are very important in Korean fisheries, and their taxonomy and ecophysiology have received much attention because of the potential for developing or improving aquaculture techniques. Although 20 species of foliose Bangiales have been listed from the Korean coast, some of them remain uncertain and need further comparative morphological studies with molecular comparison. In this study, we confirm the distribution of four Pyropia species from the east coast of Korea, Pyropia kinositae, P. moriensis, P. onoi, and P. retorta sp. nov., based on morphology and rbcL sequence data. Although P. onoi was listed in North Korea in old floral works, its occurrence on the east coast of South Korea is first revealed in this study based on molecular data. P. kinositae and P. moriensis, which were originally described from Hokkaido, Japan, are first reported on the east coast of Korea in this study. Pyropia retorta sp. nov. and P. yezonesis share a similar thallus color and narrow spermatangial patches in the upper portion of the frond, and they have a sympatric distribution. However, P. retorta can be distinguished by the curled or twisted thalli and by molecular data. The biogeographic pattern of the two native species, P. kinositae and P. retorta, suggests that the east coast of Korea may have been a place of refugia during the Last Glacial Maximum (LGM), and then recolonized to the northern part of Japan through the restored East Korean Warm Current after the LGM.

• Journal of the Korean Society for Advanced Composite Structures
• /
• v.1 no.2
• /
• pp.51-58
• /
• 2010

This work examines the identification of stiffness reduction in damaged reinforced concrete bridges under moving loads, and carries out the performance assessment after repairing using advanced composite materials. In particular, the change of stiffness in each element before and after repairing, based on the Microgenetic algorithm as an advanced inverse analysis, is described and discussed by using a modified bivariate Gaussian distribution function. The proposed method in the study is more feasible than the conventional element-based method from computation efficiency point of view. The validity of the technique is numerically verified using a set of dynamic data obtained from a simulation of the actual bridge modeled with a three-dimensional solid element. The numerical examples show that the proposed technique is a feasible and practical method which can inspect the complex distribution of deteriorated stiffness although there is a difference between actual bridge and numerical model as well as uncertain noise occurred in the measured data.

• The Journal of Industrial Distribution & Business
• /
• v.9 no.2
• /
• pp.47-55
• /
• 2018

Purpose - To meet the needs of various customers in an uncertain market environment, many companies use product modularization strategies. Modularization of a product means that one product consists of several components and that the type of product can be changed according to the combination of components. The greatest feature of modularity is that changes in one component do not significantly affect the physical changes in the other component to which they are connected. Modularization of products is recognized as a very important strategy to reflect increasingly complicated customer requirements to products and respond to the needs of various markets. Many studies have been made in connection with the concept of mass customer satisfaction. There are many prior studies that modularization of such products positively affects the operational performance (manufacturing cost, fast delivery, etc.) and innovation of the product. However, excessive modularization has been found to have a negative effect on this performance. However, there are very few studies on the nonlinear relationship between product modularization and customer satisfaction. Supplementing these academically insufficient parts is very necessary when considering the current market environment. Research design, data, and methodology - In order to make up for the shortcomings of academic research in Korea, this study collects data through questionnaires in electronic, auto, and defense industry. This is because these industries are using modularity of products. based on lots of previous studies and information overload theory, we made two hypothesis and verify with empirical analysis. All 108 data were used. We used the R program and SPSS program for statistical verification. Results - As a result of the study, modularization of products showed positive relationship with customer satisfaction to a certain level. However, it has been found that when the modularization is over and beyond a certain level, there is a negative relationship with customer satisfaction. Conclusions - Excessive modularization of products can have a negative impact on customer satisfaction. This result can be understood as a result of human limited rationality due to information overload. Therefore, it is important for companies to apply appropriate modularity to product design.

• Journal of the Korea Computer Industry Society
• /
• v.4 no.12
• /
• pp.986-998
• /
• 2003

A moving object management system manages spatiotemporal data o( moving objects which change their location continuously over time such as people, animals, cars, cellular phones, and so on. This system can be applied to location based services such as vehicle tracking systems, digital battlefields, and animal habitat management. The existing systems neither suggest location estimation of the moving objects nor handle the loss data of the moving objects in real-time environment. Thus the existing systems have problems that they give the uncertain results of the query processing to the user query. In this paper, we design a new moving object management system. The proposed system processes the past and future location information of the moving objects by the location change function. Also we propose a location triggering method, which supplements loss of the location data of the mobile objects in real-time environment. Finally, we implement and apply the proposed system to a vehicle tracking system based on PDA. Thus we ascertain that the proposed system can be applied to the location based system.

• KSCI Review
• /
• v.14 no.2
• /
• pp.159-164
• /
• 2006

MES(Manufacturing Execution System) is a control system which supports basic activities(scheduling, working process and qualify management, etc) to execute working on the shop floor. As especially MES is a system to decrease the gap between production planning and operating, it executes functions that make decision between management and labor using real-time data. MES for real-time information processing requires certain conditions such as data modeling of RFID, which has recently attracted attentions, and monitoring of each product unit from manufacture to sales. However, in the middle of processing the unit with a RFID tag, transponders(readers) can't often read the tag due to reader's malfunctions, intentional damages, loss and the circumstantial effects; for that reason, users are unable to confirm the location of the product unit. In this case, users cannot avoid tracing the path of units with uncertain clues. In this paper, we suggest that the unique MES based on RFID and Bayesian Network can immediately track the product unit, and show how to evaluate it.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 2004.11a
• /
• pp.317-322
• /
• 2004

As a construction project in urban area tends to be high-rise and huge, the importance of the project's underground work, in terms of the cost and the schedule, is gradually increasing. It's extremely significant to choose a proper filing method, at the stage of underground work. However, in piling work many change orders have been occurred since a piling method is experientially selected based on uncertain information and many earth factors to consider. It has effects on the cost and the schedule of the project. In this study, we have suggested a decision model for piling method that can be used to determine and verify the suitable piling method in design and pre-construction phase of a project. Based on historical data, a neural network model has already proven to be efficient. The tests of the model for selecting a suitable piling method have progressed exactly with the data of 150 piling works which were done room 2000 to 2004 in Korea. The optimization or the developed neural network model has progressed with the data for teaming. The validity of the neural network model has been verified.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.3
• /
• pp.227-234
• /
• 2017

In this paper, we propose a system whereby one can automatically classifies categories based on image data of the products for a shopping mall platform. Many products sold within internet shopping malls are classified their category defined by the same use of product names and products. However, it is difficult to search by category classification when the classification of the product is uncertain and the product classified by the shopping mall seller judgment is different from the purchasing user judgment. We proposes classification and retrieval method by Deep Learning technique solely using product image. The system can categorize products by using their images and its speed and accuracy are quantified using test data. The performance is evaluated with the test data. In addition, its usability is tested with the participants.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.37 no.9
• /
• pp.1133-1140
• /
• 2013

A computer model is a useful tool that provides solution via physical modeling instead of expensive testing. In reality, however, it often does not agree with the experimental data owing to simplifying assumption and unknown or uncertain input parameters. In this study, a Bayesian approach is proposed to calibrate the computer model in a probabilistic manner using the measured data. The elasto-plastic analysis of a pyrotechnically actuated device (PAD) is employed to demonstrate this approach, which is a component that delivers high power in remote environments by the combustion of a self-contained energy source. A simple mathematical model that quickly evaluates the performance is developed. Unknown input parameters are calibrated conditional on the experimental data using the Markov Chain Monte Carlo algorithm, which is a modern computational statistics method. Finally, the results are applied to determine the reliability of the PAD.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.3
• /
• pp.306-311
• /
• 2005

To analyze the consumer's purchase pattern, we must consider a factor which is a cultural, social, individual, psychological and so on. If we consider the internal state by the consumer's purchase, Both the consumer's purchase action and the purchase factor can be predicted, so the corporation can use effectively in suitable goods development in a consumer's preference. These factors need a technology that treat uncertain information, because it is difficult to analyze by directly information processing. Therefore, bayesian network manages elements those the observation of inner state such as consumer's purchase is difficult. In addition, it is interpretable about data that the observation is impossible. In this paper, we examine the seller's know-how and the way of consumer's purchase to analyze consumer's purchase action pattern through goods purchase. Also, we compose the bayesian network based on the examined data, and propose the method that predicts purchase patterns. Finally, we remove the data including unnecessary attribute using the bayesian network, and analyze the consumer's Purchase pattern using Kohonen's SOM method.