• Journal of The Korean Astronomical Society
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• v.53 no.6
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• pp.161-168
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• 2020

We report the discovery of a giant exoplanet in the microlensing event OGLE-2017-BLG-1049, with a planet-host star mass ratio of q = 9.53 ± 0.39 × 10-3 and a caustic crossing feature in Korea Microlensing Telescope Network (KMTNet) observations. The caustic crossing feature yields an angular Einstein radius of θE = 0.52 ± 0.11 mas. However, the microlens parallax is not measured because the time scale of the event, tE ≃ 29 days, is too short. Thus, we perform a Bayesian analysis to estimate physical quantities of the lens system. We find that the lens system has a star with mass Mh = 0.55+0.36-0.29 M⊙ hosting a giant planet with Mp = 5.53+3.62-2.87 MJup, at a distance of DL = 5.67+1.11-1.52 kpc. The projected star-planet separation is a⊥ = 3.92+1.10-1.32 au. This means that the planet is located beyond the snow line of the host. The relative lens-source proper motion is μrel ~ 7 mas yr-1, thus the lens and source will be separated from each other within 10 years. After this, it will be possible to measure the flux of the host star with 30 meter class telescopes and to determine its mass.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.79-79
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• 2021

기상학적 측면에서 강수 부족으로 인한 수생태환경(하천), 호소환경(저수지) 및 유역환경(중권역)으로 미치는 환경학적 가뭄의 영향을 평가하기 위한 시도는 매우 중요하다. 만약 동일한 규모의 강수부족 현상이 발생할지라도, 환경적 측면에서의 수질 및 수생태에 미치는 영향이 매우 큰 유역이 있고, 반면 어느 정도의 복원력을 유지할 수 있는 유역이 있을 것이다. 즉, 서로 다른 유역환경에 따라 가뭄으로 인한 환경적 영향은 달라질 가능성이 크며, 이처럼 환경적 가뭄에 취약한 지역을 위해서는 지속적인 환경가뭄 모니터링이 중요하다. 환경적 측면에서 가뭄의 영향을 평가하기 위해서는 다양한 수질 관련 항목을 연계한 환경가뭄 감시가 중요하며, 이와 더불어 가뭄과 관련한 다양한 이해관계자 간의 효율적인 의사결정 도구가 필요하다. 따라서 본 연구에서는 다양한 시나리오 정보를 제공할 수 있는 베이지안 네트워크 모형을 적용하여 환경가뭄 민감도 평가 방안을 제시하고자 한다. 본 모형에서는 수질 문제가 가장 심하게 대두되고 있는 낙동강 유역을 대상으로, 기상학적 가뭄에 의한 수생태 및 환경 관련 변수들(BOD, T-P, TOC)의 복잡한 상호의존성을 파악할 수 있는 베이지안 네트워크 모형을 활용하였다. 또한, 기상학적 가뭄에 의한 상류와 하류 간의 환경적 영향을 연계하여 해석하기 위한 모형을 구축하였다. 그 결과, 기상학적 가뭄으로 인한 환경적 민감도가 크게 나타나는 중권역(예: 임하댐유역)과 이와 반대인 중권역(예: 병성천유역)의 구분이 가능하였다. 또한, 상류에서 발생한 심한 기상학적 가뭄이 하류 지역 내 환경적인 영향을 지속할 가능성이 있음을 확인되었다. 따라서 본 연구에서 제안한 방법은 환경적 가뭄의 취약지역을 우선 선정하고, 나아가 상-하류 간의 환경적 가뭄을 감시하는 데 있어 활용도가 있을 것으로 기대된다.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• Journal of Life Science
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• v.21 no.3
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• pp.385-392
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• 2011

The otter (Lutra lutra) in Korea is classified as a first grade endangered species and is managed under state control. We performed a phylogenetic analysis of the otter that inhabits the Changnyeong, Jinju, and Geoje areas in Gyeongsangnamdo, Korea using mtDNA and microsatellite (MS) markers. As a result of the analysis using the 676-bp D-loop sequence of mtDNA, six haplotypes were estimated from five single nucleotide polymorphisms. The genetic distance between the Jinju and Geoje areas was greater than distances within the areas, and the distance between Jinju and Geoje was especially clear. From the phylogenetic tree estimated using the Bayesian Markov chain Monte Carlo analysis by the MrBays program, two subgroups, one containing samples from Jinju and the other containing samples from the Changnyeong and Geoje areas were clearly identified. The result of a parsimonious median-joining network analysis also showed two clear subgroups, supporting the result of the phylogenetic analysis. On the other hand, in the consensus tree estimated using the genetic distances estimated from the genotypes of 13 MS markers, there were clear two subgroups, one containing samples from the Jinju, Geoje and Changnyeong areas and the other containing samples from only the Jinju area. The samples were not identically classified into each subgroup defined by mtDNA and MS markers. It could be inferred that the differential classification of samples by the two different marker systems was because of the different characteristics of the marker systems used, that is, the mtDNA was for detecting maternal lineage and the MS markers were for estimating autosomal genetic distances. Nonetheless, the results from the two marker systems showed that there has been a progressive genetic fixation according to the habitats of the otters. Further analyses using not only newly developed MS markers that will possess more analytical power but also the whole mtDNA are needed. Expansion of the phylogenetic analysis using otter samples collected from the major habitats in Korea should be helpful in scientifically and efficiently maintaining and preserving them.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.137-146
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• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.83-97
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• 2000

The traffic management schemes through traffic signal control and information provision could be effective when the link-level data and trip-level data were used simultaneously in analysis Procedures. But, because the trip-level data. such as origin, destination and departure time, can not be obtained through the existing surveillance systems directly. It is needed to estimate it using the link-level data which can be obtained easily. Therefore the objective of this study is to develop the model to estimate O-D demand using only the link flows in highway network as a real time. The methodological approaches in this study are kalman filer, least-square method and normalized least-square method. The kalman filter is developed in the basis of the bayesian update. The normalized least-square method is developed in the basis of the least-square method and the natural constraint equation. These three models were experimented using two kinds of simulated data. The one has two abrupt changing Patterns in traffic flow rates The other is a 24 hours data that has three Peak times in a day Among these models, kalman filer has Produced more accurate and adaptive results than others. Therefore it is seemed that this model could be used in traffic demand management. control, travel time forecasting and dynamic assignment, and so forth.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Information Systems Review
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• v.10 no.2
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• pp.137-158
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• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.70-77
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• 2009

In an RFID network consisting of a single reader and many tags, a framed and slotted ALOHA, which provides a number of slots for the tags to respond, was introduced for arbitrating a collision among tags' responses. In a framed and slotted ALOHA, the number of slots in each frame should be optimized to attain the maximal efficiency in tag cognizance. While such an optimization necessitates the knowledge about the number of tags, the reader hardly knows it. In this paper, we propose a tag cognizance scheme based on framed and slotted ALOHA, which is characterized by directly taking a Bayes action on the number of slots without estimating the number of tags separately. Specifically, a Bayes action is yielded by solving a decision problem which incorporates the prior distribution the number of tags, the observation on the number of slots in which no tag responds and the loss function reflecting the cognizance rate. Also, a Bayes action in each frame is supported by an evolution of prior distribution for the number of tags. From the simulation results, we observe that the pair of evolving prior distribution and Bayes action forms a robust scheme which attains a certain level of cognizance rate in spite of a high discrepancy between the Due and initially believed numbers of tags. Also, the proposed scheme is confirmed to be able to achieve higher cognizance completion probability than a scheme using classical estimate of the number of tags separately.