• Journal of Ocean Engineering and Technology
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• v.34 no.2
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• pp.147-154
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• 2020

Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical background of several related machine learning techniques is introduced in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.1 no.2
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• pp.169-181
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• 1997

Some association rules can have very high confidence in a sub-interval or a subrange of the domain, though not quite high confidence in the whole domain. In this paper, we define a ranged association rule, an association with high confidence worthy of special attention in a sub-domain, and further propose an efficient algorithm which finds out ranged association rules. The proposed algorithm is data-driven method in a sense that hypothetical subranges are built based on data distribution itself. In addition, to avoid redundant database scanning, we devise an effective in-memory data structure, that is collected through single database scanning. The simulation shows that the suggested algorithm has reliable performance at the acceptable time cost in actual application areas.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.226-228
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• 2005

Predicting the cellular location of an unknown protein gives valuable information for inferring the possible function of the protein. For more accurate Prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting . The overall prediction accuracy evaluated by the 5-fold cross-validation reached $88.53\%$ for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful forpredicting subcellular localization of proteins.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.31-44
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• 2019

The main objective of this research is to develop a hydroclimate drought index (HCDI) using the gridded climate data inputs in a Variable Infiltration Capacity (VIC) modeling platform. Typical drought indices, including, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Self-calibrated Palmer Drought Severity Index (SC-PDSI) in South Korea are also used and compared. Inverse Distance Weighting (IDW) method is applied to create the gridded climate data from 56 ground weather stations using topographic information between weather stations and the respective grid cell ($12km{\times}12km$). R statistical software packages are used to visualize HCDI in Google Earth. Skill score (SS) are computed to evaluate the drought predictability based on water information derived from the observed reservoir storage and the ground weather stations. The study indicates that the proposed HCDI with the gridded climate data input is promising in the sense that it can help us to predict potential drought extents and to mitigate its impacts in a changing climate. The longer term drought prediction (e.g., 9 and 12 month) capability, in particular, shows higher SS so that it can be used for climate-driven future droughts.

• Journal of Distribution Science
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• v.21 no.2
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• pp.35-43
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• 2023

Purpose: As Thailand seeks to become a regional startup hub, Thai startups have been acquiring growth and scalability in the last ten years. Hence, this paper examines influential factors in Thailand's growth of logistics tech startups. The conceptual framework incorporates sensing user needs, sensing technological options, conceptualizing, scaling, and stretching, co-producing, and orchestrating, business strategy, strategic flexibility, and startup growth. Research design, data, and methodology: The quantitative method was applied to distribute the questionnaire to 500 managers and above in logistics tech startups in Thailand. The sampling techniques involve judgmental, convenience, and snowball samplings. Before the data collection, The Item Objective Congruence (IOC) Index and pilot test (n=45) were employed for content validity and reliability. The data were mainly analyzed by Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The findings revealed that sensing technological options, scaling, and stretching, co-producing, and orchestrating, and business strategy significantly influence the growth of startups in Thailand. Nevertheless, sensing user needs, conceptualizing, and strategic flexibility have no significant relationship with startup growth. Conclusions: For Thailand to accelerate its digital economy driven by tech startups, firms must emphasize influential factors to accelerate growth by providing the right tech solutions for people's lives.

• Journal of the Korean Geotechnical Society
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• v.26 no.7
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• pp.161-170
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• 2010

For the development of load and resistance factor design, reliability analysis is required to calibrate resistance factors in the framework of reliability theory. The distribution of measured-to-predicted pile resistance ratio was obrained based on only the results of load tests conducted to failure for the assessment of uncertainty regarding pile resistance and used in the conventional reliability analysis. In other words, successful pile load test (piles resisted twice their design loads without failure) results were discarded, and therefore, were not reflected in the reliability analysis. In this paper, a new systematic method based on Bayesian theory is used to update reliability indices of driven steel pipe piles by adding more proof pile load test results, even not conducted to failure, to the prior distribution of pile resistance ratio. Fifty seven static pile load tests performed to failure in Korea were compiled for the construction of prior distribution of pile resistance ratio. The empirical method proposed by Meyerhof is used to calculate the predicted pile resistance. Reliability analyses were performed using the updated distribution of pile resistance ratio. The challenge of this study is that the distribution updates of pile resistance ratio are possible using the load test results even not conducted to failure, and that Bayesian updates are most effective when limited data are available for reliability analysis.

• Convergence Security Journal
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• v.23 no.5
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• pp.45-55
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• 2023

With the widespread use of digital devices, anonymous communication technologies such as the dark web and deep web are becoming increasingly popular for criminal activity. Because these technologies leave little local data on the device, they are difficult to track using conventional crime investigation techniques. The United States and the United Kingdom have enacted laws and developed systems to address this issue, but South Korea has not yet taken any significant steps. This paper proposes a new blockchain-based crime investigation method that uses physical memory data analysis to track the behavior of anonymous network users. The proposed method minimizes infringement of basic rights by only collecting physical memory data from the device of the suspected user and storing the tracking information on a blockchain, which is tamper-proof and transparent. The paper evaluates the effectiveness of the proposed method using a simulation environment and finds that it can track the behavior of dark website users with a residual rate of 77.2%.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.1-9
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• 2010

Authoring tools for sketching the motion of characters to be animated have been studied. However the natural interface for sound editing has not been sufficiently studied. In this paper, I present a novel method that sound sample is selected by speaking sound-imitation words(onomatopoeia). Experiment with the method based on statistical models, which is generally used for pattern recognition, showed up to 97% in the accuracy of recognition. In addition, to address the difficulty of data collection for newly enrolled sound samples, the GLR Test based on only one sample of each sound-imitation word showed almost the same accuracy as the previous method.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.38-50
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• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• Journal of the Korean Solar Energy Society
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• v.38 no.6
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• pp.65-72
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• 2018

Since data-driven building technologies have been widely applied to building energy systems, the accuracy of building sensors has more impacts on the building performance and system performance analysis. Various building sensors, however, can have typical errors including a random error (noise) and a systematic error (bias). The systematic error is indicated by the difference between the mean of measurements and their true value. It may occur due to the sensor's physical condition, measured phenomena, working environments inside the systems. Unfortunately, a conventional calibration method has limitations in calibrating the systematic errors because of the difference between working environments and calibration conditions. In such situations, a novel sensor calibration method is needed to handle various sensor errors, especially for systematic errors, in building energy systems having various thermodynamic environments. This study proposes a building sensor calibration method named Virtual In-situ Calibration (VIC) and shows how it is applied into a real building system and how it solves the sensor errors.