• Smart Structures and Systems
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• v.28 no.6
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• pp.839-853
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• 2021

In this paper, a novel multimode liquid metal-based pressure sensor is developed. The main body of the sensor is composed of polydimethylsiloxane (PDMS) elastomer. The structure of the sensor looks like a sandwich, in which the upper structure contains a cylindrical cavity, and the bottom structure contains a spiral microchannel, and the middle partition layer separates the upper and the bottom structures. Then, the liquid metal is injected into the top cavity and the bottom microchannel. Based on linear elastic fracture mechanics, the deformation of the microchannel cross-section is theoretically analyzed. The changes of resistance, capacitance, and inductance of the microchannel under pressure are deduced, and the corresponding theoretical models are established. The theoretical values of the pressure sensor are in good agreement with experimental data, implying that the developed theoretical model can explain the performance of the sensor well.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.22-30
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• 2023

The portable mobile devices with high performance and high speed 5G network activate and explode the demands for ubiquitous information services that remove the limitations of time for the communication and places to request for the information. NN (Nearest Neighbor) search is one of the most important types of queries to be processed efficiently in the information services. Various indexes have been proposed to support efficient NN search in the wireless broadcast system. The indexes adopting Hilbert curve, grid partition or Voronoi diagram enable the clients to search for NN quickly in the wireless broadcast channel. It is necessary that an efficient means to evaluate the performances of various indexes. In this paper, we propose an open framework that can adopt a variety of indexing schemes and evaluate and compare the performances of them. The proposed framework is organized with open and flexible structure that can adopt hybrid indexing schemes extensible to Voronoi diagram as well as simple indexing schemes. With the implemented framework, we demonstrate the efficiency and scalability and flexibility of the proposed framework by evaluating various indexing schemes for NN query.

• Annual Conference of KIPS
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• 2021.11a
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• pp.334-336
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• 2021

허가형 블록체인 기반 데이터 공유 시스템은 분산 환경에서 신뢰 수준을 구축하고 일관된 메시지를 기록 및 공유함으로써 서비스의 상호 운용성을 가능하게 한다. 그러나 허가형 블록체인은 종종 탈중앙화, 보안 및 상호 운용성과 충돌한다. 이는 중앙 집중식 시스템으로 돌아가거나 데이터의 독점 및 남용 및 오용으로 이어질 수 있다. 따라서 CAP (Consistency, Availability, Partition tolerance)에 이론 검증에 따라 메시지 공유, 비잔틴 내결함성 및 메시지 일관성을 고려하고 적용해야 한다. 기존의 PBFT(Practical Byzantine Fault Tolerance) 합의 알고리즘는 노드의 증가시, 장애내성을 갖기위해 계산되어야 할 합의 처리시간이 증가하며, DPOS(Delegated Proof of Stake) 알고리즘은 보상, 리더 선출의 공정성 문제 등에 따라 허가형 블록체인에서의 적합한 방식이 연구되고 있다. 본 논문에서는 서비스의 상호 운용성과 과제에 대해 논의하고 허가된 블록체인의 합의 개선을 통한 데이터 공유 시스템을 제안한다.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.39-57
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• 2001

Categorical data has sometimes misclassification errors. If this data will be analyzed, then estimated cell probabilities could be biased and the standard Pearson X2 tests may have inflated true type I error rates. On the other hand, if we regard wellclassified data with misclassified one, then we might spend lots of cost and time on adjustment of misclassification. It is a necessary and important step to ask whether categorical data is misclassified before analyzing data. In this paper, when data is misclassified at one of two variables for two-dimensional contingency table and marginal sums of a well-classified variable are fixed. We explore to partition marginal sums into each cells via the concepts of Bound and Collapse of Sebastiani and Ramoni (1997). The double sampling scheme (Tenenbein 1970) is used to obtain informations of misclassification. We propose test statistics in order to solve misclassification problems and examine behaviors of the statistics by simulation studies.

• Journal of KIISE
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• v.41 no.12
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• pp.1126-1135
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• 2014

In recent years, RDF partitioning schemes have been studied for the effective distributed storage and management of large-scale RDF data. In this paper, we propose an RDF dynamic partitioning scheme to support load balancing in dynamic environments where the RDF data is continuously inserted and updated. The proposed scheme creates clusters and sub-clusters according to the frequency of the RDF data used by queries to set graph partitioning criteria. We partition the created clusters and sub-clusters by considering the workloads and data sizes for the servers. Therefore, we resolve the data concentration of a specific server, resulting from the continuous insertion and update of the RDF data, in such a way that the load is distributed among servers in dynamic environments. It is shown through performance evaluation that the proposed scheme significantly improves the query processing time over the existing scheme.

• Journal of KIISE:Databases
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• v.31 no.2
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• pp.122-131
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• 2004

Traditional works on indexing have been suggested for low dimensional data under dynamic environments. But recent database applications require efficient processing of huge sire of high dimensional data under static environments. Thus many indexing strategies suggested especially in partitioning ones do not adapt to these new environments. In our study, we point out these facts and propose a new partitioning strategy, which complies with new applications' requirements and is derived from analysis. As a preliminary step to propose our method, we apply a packing technique on the one hand and exploit observations on the Minkowski-sum cost model on the other, under uniform data distribution. Observations predict that unbalanced partitioning strategy may be more query-efficient than balanced partitioning strategy for high dimensional data. Thus we propose our method, called CSP (Cyclic Spliced Partitioning method). Analysis on this method explicitly suggests metrics on how to partition high dimensional data. By the cost model, simulations, and experiments, we show excellent performance of our method over balanced strategy. By experimental studies on other indices and packing methods, we also show the superiority of our method.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.172-178
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• 2003

In the recent researches, a variety of ways for determining the locations of RBF centers have been proposed assuming that the number of RBF centers is known. But they have also many numerical drawbacks. We propose a new method to overcome such drawbacks. The strength of our method is to determine the locations and the number of RBF centers at the same time without any assumption about the number of RBF centers. The proposed method consists of two phases. The first phase is to determine the number and the locations of RBF centers using bi-section search method and enhanced k-medoids clustering which overcomes drawbacks of clustering algorithm. In the second phase, network weights are computed and the design of RBF network is completed. This new method is applied to several benchmark data sets. Benchmark results show that the proposed method is competitive with the previously reported approaches for center selection.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.27 no.4
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• pp.314-339
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• 1982

Fluctuating climate is still most important environmental constrain, although improved modem agricultural technology has succeeded to increase crop production in the world. To stabilize the food production under fluctuating weather conditions, it is very needed to obain the quantitative information of interactions between crops and climate. The main purpose of this paper is three hold. Using the JIBP-data, the dry matter accumulation of rice crops is studied in relation to weather indexes (\SigmaTa and \SigmaSt). Temperature dependence of the yield index of rice is analyzed as to air temperature and water temperature. \SigmaT$_{10}$ -fluctuations are studied using meteorological data at various stations. The possible shift of \SigmaT$_{10}$ -isopleths due to climate fluctuation is evaluated. The second interest is in the plant climate of rice crops. Using results of canopy photosynthesis, it is pointed that the canopy structure has most important implication in plant climate. Leaf-air, stomatal, and mesophyll resistances of rice crops are described in relation to weather conditions. The change in light condition and aerodynamical property of rice crops with the growth is illustrated. The energy partition is also studied at different growing stages. Third point is to show in more detail effective countermeasures against cold irrigation water and cool summer. Heat balance of warming pond and polyethylene tube as a heat exchanger is studied to make nomo-grams for evaluating the necessary area and necessary length. Effects of windbreak net on rice crops are illustrated by using experimental and simulation results.lts.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.31-39
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• 2015

This paper deals with a method for estimating variance components on the basis of projections under the assumption of random effects model. It discusses how to use projections for getting sums of squares to estimate variance components. The use of projections makes the vector subspace generated by the model matrix to be decomposed into subspaces that are orthogonal each other. To partition the vector space by the model matrix stepwise procedure is used. It is shown that the suggested method is useful for obtaining Type I sum of squares requisite for the ANOVA method.