• Journal of Environmental Health Sciences
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• v.43 no.4
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• pp.298-306
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• 2017

Objectives: Since 1998, the Korea National Health and Nutrition Examination Survey (KNHANES) has been conducted in order to investigate the health and nutritional status of Koreans. The food intake data of individuals in the KNHANES has also been utilized as source dataset for risk assessment of chemicals via food. To improve the reliability of intake estimation and prevent missing data for less-responded foods, the structure of integrated long-standing datasets is significant. However, it is difficult to merge multi-year survey datasets due to ineffective cleaning processes for handling extensive numbers of codes for each food item along with changes in dietary habits over time. Therefore, this study aims at 1) cleaning the process of abnormal data 2) generation of integrated long-standing raw data, and 3) contributing to the production of consistent dietary exposure factors. Methods: Codebooks, the guideline book, and raw intake data from KNHANES V and VI were used for analysis. The violation of the primary key constraint and the $1^{st}-3rd$ normal form in relational database theory were tested for the codebook and the structure of the raw data, respectively. Afterwards, the cleaning process was executed for the raw data by using these integrated codes. Results: Duplication of key records and abnormality in table structures were observed. However, after adjusting according to the suggested method above, the codes were corrected and integrated codes were newly created. Finally, we were able to clean the raw data provided by respondents to the KNHANES survey. Conclusion: The results of this study will contribute to the integration of the multi-year datasets and help improve the data production system by clarifying, testing, and verifying the primary key, integrity of the code, and primitive data structure according to the database normalization theory in the national health data.

• Composites Research
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• v.36 no.6
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• pp.395-401
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• 2023

In this study, finite element modeling of unidirectional composite materials of the computed tomography (CT) was conducted using a supervised learning-based segmentation technique. Firstly, Micro-CT scan was performed to obtain the raw volume of unidirectional composite materials, providing microstructure information. From the CT volume images, actual microstructure of the cross-section of unidirectional composite materials was extracted by the labeling process. Then, a U-net deep learning model was trained with a small number of raw images as inputs and their labeled images as outputs to generate a segmentation model. Subsequently, most of remaining images were input to the trained U-net deep learning model to segment all raw volume for identifying complex microstructure, which was used for the generation of finite element model. Finally, the fiber volume fraction of the finite element model was compared with that of experimentally measured volume to validate the appropriateness of the proposed method.

• Phonetics and Speech Sciences
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• v.16 no.1
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• pp.67-76
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• 2024

Recent advances in text-to-speech (TTS) technology have significantly improved the quality of synthesized speech, reaching a level where it can closely imitate natural human speech. Especially, TTS models offering various voice characteristics and personalized speech, are widely utilized in fields such as artificial intelligence (AI) tutors, advertising, and video dubbing. Accordingly, in this paper, we propose a one-shot multi-speaker TTS system that can ensure acoustic diversity and synthesize personalized voice by generating speech using unseen target speakers' utterances. The proposed model integrates a speaker encoder into a TTS model consisting of the FastSpeech2 acoustic model and the HiFi-GAN vocoder. The speaker encoder, based on the pre-trained RawNet3, extracts speaker-specific voice features. Furthermore, the proposed approach not only includes an English one-shot multi-speaker TTS but also introduces a Korean one-shot multi-speaker TTS. We evaluate naturalness and speaker similarity of the generated speech using objective and subjective metrics. In the subjective evaluation, the proposed Korean one-shot multi-speaker TTS obtained naturalness mean opinion score (NMOS) of 3.36 and similarity MOS (SMOS) of 3.16. The objective evaluation of the proposed English and Korean one-shot multi-speaker TTS showed a prediction MOS (P-MOS) of 2.54 and 3.74, respectively. These results indicate that the performance of our proposed model is improved over the baseline models in terms of both naturalness and speaker similarity.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.46 no.3
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• pp.282-286
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• 2013

This study was conducted to compare extruded pellets (EP) and soft extruded pellets (SEP) with a raw fish-based moist pellet (MP) diet on the growth and flesh quality of Korean rockfish Sebastes schlegeli. Three groups of 20,000 fish (initial mean weight 133 g) per net-cage ($6{\times}12{\times}7m$) were fed commercial EP, SEP or MP for 16 months. The survival of fish fed SEP was higher than those of fish fed EP or MP. The highest growth performances were observed in the mean weight gain, total weight gain, and feed efficiency of fish fed MP, followed by those fed EP and SEP. Among the fish fed on extruded pellets, the total weight gain of fish fed SEP was higher than that of those fed EP, while fish fed EP grew faster than those fed SEP. No notable differences in body composition, sensory scores or textural properties of the dorsal muscle were observed in fish fed on EP, SEP or MP. Thus, it is suggested that extruded pellets, rather than raw fish-based moist pellets, could be fed to Korean rockfish without compromising flesh quality.

• Journal of Sericultural and Entomological Science
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• v.13 no.1
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• pp.73-78
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• 1971

The purpose of this study is to find out the method of conditioned weight test by which the dealing weight of raw silk can be calculated from true fiber in order to do the fair trading. The results of this study were as follows. 1. It is more reasonable than the current test that conditioned weight as a dealing weigh can be calculated by boil-off and moisture regain which is a percentage of boil-on and moisture regain to net weight. Because the boil-off and moisture regain can show directly the amount of true fiber and reproductibility in raw silk. In this study the boil-off and moisture regain is to take dry weight after boiling-off from net weight. 2. To calculate the conditioned weight from boil-of and moisture regain it would be proper that the standard additional ratio is 44 per cent of dry weight after boiling-off. 3. Boil-of percent of the sizing sample skein used in the size test did not show a statistical significance comparing with the boil-off percent of sample skeins (24 skeins) which may represent that of a lot. To observe this result boil-off percent of the sizing sample skein may represent that of a lot. 4. In Korea if conditioned weight test substitute for test of boil-off and moisture regain, we make a profit of two billion won in a year at the current market-price.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.303-314
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• 2023

Voice conversion, a technology that allows an individual's speech data to be regenerated with the acoustic properties(tone, cadence, gender) of another, has countless applications in education, communication, and entertainment. This paper proposes an approach based on the StarGAN-VC model that generates realistic-sounding speech without requiring parallel utterances. To overcome the constraints of the existing StarGAN-VC model that utilizes one-hot vectors of original and target speaker information, this paper extracts feature vectors of target speakers using a pre-trained version of Rawnet3. This results in a latent space where voice conversion can be performed without direct speaker-to-speaker mappings, enabling an any-to-any structure. In addition to the loss terms used in the original StarGAN-VC model, Wasserstein distance is used as a loss term to ensure that generated voice segments match the acoustic properties of the target voice. Two Time-Scale Update Rule (TTUR) is also used to facilitate stable training. Experimental results show that the proposed method outperforms previous methods, including the StarGAN-VC network on which it was based.

• Geomechanics and Engineering
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• v.8 no.3
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• pp.415-440
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• 2015

In the present case study, High Strain Dynamic Testing of piles is conducted at 3 different locations of Kolkata city of India. The raw field data acquired is analyzed using Pile Driving Analyzer (PDA) and CAPWAP (Case Pile Wave Analysis Programme) computer software and load settlement curves along with variation of force and velocity with time is obtained. A finite difference based numerical software FLAC3D has been used for simulating the field conditions by simulating similar soil-pile models for each case. The net pile displacement and ultimate pile capacity determined from the field tests and estimated by using numerical analyses are compared. It is seen that the ultimate capacity of the pile computed using FLAC3D differs from the field test results by around 9%, thereby indicating the efficiency of FLAC3D as reliable numerical software for analyzing pile foundations subjected to impact loading. Moreover, various parameters like top layers of cohesive soil varying from soft to stiff consistency, pile length, pile diameter, pile impedance and critical height of fall of the hammer have been found to influence both pile displacement and net pile capacity substantially. It may, therefore, be suggested to include the test in relevant IS code of practice.

• Nutritional Sciences
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• v.3 no.2
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• pp.66-70
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• 2000

The study was conducted to determine the nutiritional value of Masoor (Lens esculenta) in raw and cooked forms. Supplement value of various types of meat i.e. poultry, mutton and beef at 10, 15, and 20 percent levels for dite containing cooked Masoor was also assessed. Nutritional value of Masoor was determined by chemical analysis as well as through rat assay. Masoor contained an average of 23.18 percent protein and less than two percent fibre. Conventional method of cooking resulted in about 2 per cent increase in Masoor protein. Masoor had 0.83 percent of lysine and cooking destroyed 18 percent of it. Other amino acids in Masoor also showed losses on cooking. Protein efficiency ratio (PER) of diets containing raw Masoor was 1.49 and was reduced to 1.44 by cooking. Cooking of Masoor did not alter true digestibility (TD) percentage. However, net protein utilization (NPU) was improved from 44.60 in raw to 47.77 in cooked. Diets containing cooked Masoor and supplemented with different types of meat significantly improved PER (1.45 to 1.65), TD 76.03 to 87.84 percent and NPU 42.84 to 50.72 percent over non supplemented diets. 20 percent level of supplemented meat showed comparatively better results than other levels in case of improvement in PER, TD and NPU.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.3
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• pp.475-479
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• 2011

Although the storage period of raw baechu cabbage could be 2 months at $0{\sim}2^{\circ}C$, 1 month was appropriate considering the quality of the baechu cabbage, waste ratio, and storage cost. The polyethylene container was the most efficient storage container among polypropylene box, polypropylene net and polyethylene container. pH of a brined baechu cabbage using raw baechu cabbage was 4.0~4.3 after 8 weeks and its total bacteria and lactic acid bacteria counts were $10^7$ cfu/g, and textural property (springiness) lower than 50% was at 8th week of storage at $0{\sim}2^{\circ}C$ and thus its storage period was limited to 8 weeks. When brined baechu cabbage was prepared by raw baechu cabbage stored for 1 month at $0{\sim}2^{\circ}C$, its pH, microorganism counts and springiness showed similar trends to the brined cabbage using raw baechu stored for 0 month. However, its rates of change were faster than the brined baechu cabbage using the raw baechu, and the storage period was limited to 6 weeks. Brined baechu cabbage using the raw cabbage stored for 2 months and its storage period was limited by about 4 weeks judging by its indicated quality characteristics.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.787-800
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• 2021

Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.