• Journal of the Society of Naval Architects of Korea
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• v.33 no.1
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• pp.40-53
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• 1996

In this paper, the wave drift damping is studied. An approximate method is adopted to calculate the wave drift damping for the sake of practical applications. By assuming the ship's forward speed to be low, the Green function and the velocity potential are expanded asymptotically with respect to the Brard number(${\tau}$) and terms up to the first order of ${\tau}$ are retained. Mean wave drift forces are computed straightforwardly. The wave drift damping is estimated as the change rate of the mean wave drift force with respect to the ship's speed. In order to validate the present method, Series 60(Cb=0.7) ship is exemplified for forward speed of Fn=0, 0.02 and 0.04. To predict the wave drift damping experimentally, three geosym models of the Esso-Osaka tanker are used. Also the effect of drift angle on the wave drift damping is also considered. Comparisons between numerical and experimental results show reasonable agreements.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.63-70
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• 2022

Although 2D Object detection has been largely improved in the past years with the advance of deep learning methods and the use of large labeled image datasets, 3D object detection from 2D imagery is a challenging problem in a variety of applications such as robotics, due to the lack of data and diversity of appearances and shapes of objects within a category. Google has just announced the launch of Objectron that has a novel data pipeline using mobile augmented reality session data. However, it also is corresponding to 2D-driven 3D object detection technique. This study explores more mature 2D object detection method, and applies its 2D projection to Objectron 3D lifting system. Most object detection methods use bounding boxes to encode and represent the object shape and location. In this work, we explore a stochastic representation of object regions using Gaussian distributions. We also present a similarity measure for the Gaussian distributions based on the Hellinger Distance, which can be viewed as a stochastic Intersection-over-Union. Our experimental results show that the proposed Gaussian representations are closer to annotated segmentation masks in available datasets. Thus, less accuracy problem that is one of several limitations of Objectron can be relaxed.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.473-483
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• 2020

This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.461-470
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• 2023

While speech animation generation employing deep learning has been actively researched for English, there has been no prior work for Korean. Given the fact, this paper for the very first time employs supervised deep learning to generate Korean speech animation. By doing so, we find out the significant effect of deep learning being able to make speech animation research come down to speech recognition research which is the predominating technique. Also, we study the way to make best use of the effect for Korean speech animation generation. The effect can contribute to efficiently and efficaciously revitalizing the recently inactive Korean speech animation research, by clarifying the top priority research target. This paper performs this process: (i) it chooses blendshape animation technique, (ii) implements the deep-learning model in the master-servant pipeline of the automatic speech recognition (ASR) module and the facial action coding (FAC) module, (iii) makes Korean speech facial motion capture dataset, (iv) prepares two comparison deep learning models (one model adopts the English ASR module, the other model adopts the Korean ASR module, however both models adopt the same basic structure for their FAC modules), and (v) train the FAC modules of both models dependently on their ASR modules. The user study demonstrates that the model which adopts the Korean ASR module and dependently trains its FAC module (getting 4.2/5.0 points) generates decisively much more natural Korean speech animations than the model which adopts the English ASR module and dependently trains its FAC module (getting 2.7/5.0 points). The result confirms the aforementioned effect showing that the quality of the Korean speech animation comes down to the accuracy of Korean ASR.

• Journal of the Korean Institute of Gas
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• v.16 no.3
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• pp.54-64
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• 2012

DWTT (Drop Weigh Tearing Test) is one critical method that can exhibit the fracture properties of line pipe steel, since it estimates the properties with real pipe steel. In this study, the ductile portion, inverse fracture ratio and absorbed energy of API X65 and X70 line pipe steels were estimated with temperature variation. Both steels showed that the ratio of ductile area and absorbed energy were decreased with respect to decreasing the test temperature. However, while the ductile fracture behavior exhibited until $-40^{\circ}C$ for the X70 steel, but it showed until $-30^{\circ}C$ for the X65 steel. The fracture properties were discussed with respect to test temperatures.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.68-74
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• 2017

This paper presents a joint demosaicking and arbitrary-ratio down sampling algorithm for color filter array (CFA) images. Color demosaiking is a necessary part of image signal processing pipeline for many types of digital image recording system using single sensor. Also, such as smart phone, obtained high resolution image from image sensor has to be down-sampled to be displayed on the screen. The conventional solution is "Demosaicking first and down sampling later". However, this scheme requires a significant amount of memory and computational cost. Also, artifacts can be introduced or details get damaged during demosaicking and down sampling process. In this paper, we propose a method in which demosaicking and down sampling are working simultaneously. We use inverse mapping of Bayer CFA and then joint demosaicking and down sampling with arbitrary-ratio scheme based on signal decomposition of high and low frequency component in input data. Experimental results show that our proposed algorithm has better image quality performance and much less computational cost than those of conventional solution.

• Journal of the Korean Society of Radiology
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• v.14 no.2
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• pp.157-168
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• 2020

Advancements in segmentation methodology has made automatic segmentation of brain structures using structural images accurate and consistent. One method of automatic segmentation, which involves registering atlas information from template space to subject space, requires a high quality atlas with accurate boundaries for consistent segmentation. The Allen Mouse Brain Atlas, which has been widely accepted as a high quality reference of the mouse brain, has been used in various segmentations and can provide accurate coordinates and boundaries of mouse brain structures for tractography. Through probabilistic tractography, diffusion tensor images can be used to map comprehensive neuronal network of white matter pathways of the brain. Comparisons between neural networks of mouse and human brains showed that various clinical tests on mouse models were able to simulate disease pathology of human brains, increasing the importance of clinical mouse brain studies. However, differences between brain size of human and mouse brain has made it difficult to achieve the necessary image quality for analysis and the conditions for sufficient image quality such as a long scan time makes using live samples unrealistic. In order to secure a mouse brain image with a sufficient scan time, an Ex-vivo experiment of a mouse brain was conducted for this study. Using FSL, a tool for analyzing tensor images, we proposed a semi-automated segmentation and tractography analysis pipeline of the mouse brain and applied it to various mouse models. Also, in order to determine the useful signal-to-noise ratio of the diffusion tensor image acquired for the tractography analysis, images with various excitation numbers were compared.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.1-7
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• 2022

Recently, as the use of applications such as big data programs and machine learning programs that are driven while generating large amounts of data in the program itself becomes common, the existing main memory alone lacks memory, making it difficult to execute the program quickly. In particular, the need to derive results more quickly has emerged in a situation where it is necessary to analyze whether the entire sequence is genetically altered due to the outbreak of the coronavirus. As a result of measuring performance by applying large-capacity data to a computing system equipped with a self-developed memory pool MOCA host adapter instead of processing large-capacity data from an existing SSD, performance improved by 16% compared to the existing SSD system. In addition, in various other benchmark tests, IO performance was 92.8%, 80.6%, and 32.8% faster than SSD in computing systems equipped with memory pool MOCA host adapters such as SortSampleBam, ApplyBQSR, and GatherBamFiles by task of workflow. When analyzing large amounts of data, such as electrical dielectric pipeline analysis, it is judged that the measurement delay occurring at runtime can be reduced in the computing system equipped with the memory pool MOCA host adapter developed in this research.

• Journal of the Korea Institute of Building Construction
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• v.23 no.3
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• pp.305-313
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• 2023

With an increasing interest in the commercialization of research results in the present societal climate, especially in the construction industry, preliminary product analysis plays a critical role when introducing a new product to the market. It significantly influences the product's success or failure. In this context, this study aims to investigate the utility of Importance-Performance Analysis (IPA) as a management strategy tool for preliminary analysis in the commercialization of new architectural technologies. The study specifically assesses a smart ball product engineered for pipeline inspection. The evaluation is carried out based on product quality, convenience, and usability categories. Seventeen factors are recognized as sub-items, and a survey is conducted among relevant experts and consumer groups. From the survey, four key items are chosen: "Keep up the good work," "Concentrate here," "Low priority," and "Possible overkill." Suitable strategic measures are derived for each item. By conducting a correlation analysis between product importance and performance, this study offers a method to establish priority directions for future development. This analysis assists in identifying areas that necessitate improvement or additional focus to increase the product's commercial potential. On the whole, this study contributes to understanding and applying Importance-Performance Analysis as a valuable tool in the preliminary analysis and commercialization of novel technologies in the field of architecture.

• Journal of Animal Science and Technology
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• v.48 no.3
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• pp.479-486
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• 2006

The current study was conducted to examine the effect of feeding and management practices on milk quality and dairy farm productivity in Korea. Fifty dairy farms in Gyunggi (11), Gangwon (22), Chungnam (17) provinces were surveyed to collect data on the herd size, housing style, feeding management, waste disposal, milking practices and milk yield. Milk tank samples from all farms under study were also collected to enumerate its composition and quality parameters. Large dairy herds are equiped with better housing, milking and waste control facilities than medium and small dairy herds. Higher concentrate feeding to lactating cows was noticed in small dairy herds (47.51 %) than in medium (32.59 %) and large dairy herds (31.82 %). The decrease in concentrate feeding to lactating cows with increase in number of cows per farm resulted in a simultaneous increase in the use of imported forages. Bacterial count in milk was affected by housing and milking facilities at dairy farms. Higher bacterial counts (Coliform and E. coli) in milk were observed in cows housed in stanchion than those under free stall with saw dust bedding. The bacterial counts were higher with bucket milking system than with pipe-line and parlour systems. The increase in the number of dairy cows per farm and thus better management and milking facilities resulted in a reduction in somatic cell score. Milk yield (per cow) was higher in herds with less somatic cell score. Average milk protein concentration was between 2.89 to 2.98 % and milk urea nitrogen was between 21.81 to 23.31mg/ml on surveyed dairy farms. This study concluded that large herd size with better dairy cow management facilities is crucial to produce quality milk with better dairy farm income.