Although ready meals have recently increased their market share in the Korean food industry, a literature review found that the use of ready meals triggers feelings of guilt in homemakers. Such guilt arises as a result of several factors apparently related to consumers' health. Consequently, levels of guilt might be expected to vary depending on consumers' perceived health locus. The present study aims to examine (a) how health locus affects guilty feelings about ready-meal consumption, (b) how the effect varies in relation to the consumption of different types of ready meal, and (c) the relationship between consumers' guilty feelings and willingness to buy ready meals. Three dimensions of health locus of control (HLC) -internal HLC (IHLC), powerful-others HLC (PHLC), and chance HLC (CHLC)- were presumed to influence consumers' feelings of guilt in association with ready meals. Data were collected via an online survey, and participants were randomly assigned to either of two groups: one group was instructed to heat meals in a microwave (ready-to-heat [RTH] group, n=104) and the other cooked using a pan with additional ingredients (ready-to-cook [RTC] group, n=101). The study found that guilty feelings about consuming RTH meals increased in line with increased external HLCs, namely, PHLC and CHLC. For the RTC group, guilt increased in line with increased PHLC. IHLC had no significant effect on guilty feelings in either group. Willingness to buy ready meals decreased for both groups as consumers' feelings of guilt increased. Even RTC meals, which require more time and energy in food preparation, did not reduce guilty feelings among consumers with higher PHLC. RTC meals are preferable for consumers with higher CHLC, since their sense of greater involvement in the cooking process alleviates their feelings of guilt. Cooking with already prepared and uncooked ingredients brought fun and joy, both for the participants and their significant others. This interpretation may be developed into a strategic plan by ready-meal producers to strengthen their marketing strategy.
KIPS Transactions on Software and Data Engineering
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v.11
no.8
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pp.331-338
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2022
Successor representation (SR) is a model of human reinforcement learning (RL) mimicking the underlying mechanism of hippocampal cells constructing cognitive maps. SR utilizes these learned features to adaptively respond to the frequent reward changes. In this paper, we evaluated the performance of SR under the context where changes in latent variables of environments trigger the reward structure changes. For a benchmark test, we adopted SR-Dyna, an integration of SR into goal-driven Dyna RL algorithm in the 2-stage Markov Decision Task (MDT) in which we can intentionally manipulate the latent variables - state transition uncertainty and goal-condition. To precisely investigate the characteristics of SR, we conducted the experiments while controlling each latent variable that affects the changes in reward structure. Evaluation results showed that SR-Dyna could learn to respond to the reward changes in relation to the changes in latent variables, but could not learn rapidly in that situation. This brings about the necessity to build more robust RL models that can rapidly learn to respond to the frequent changes in the environment in which latent variables and reward structure change at the same time.
The objective of this study is to survey Social Participation of the Elderly's quality of life according to their activity levels and examine the differences by their gender and age. The subjects of this study were elderly aged 60 or older who are living in the metropolitan area and those reportedly participating in various social activities at the time of the survey. A total of 586 cases were used for the analysis. Data were analyzed through multi group analysis using a structural equation model, and AMOS 7.0 was used in statistical processing. The results of this study showed that, first, the quality of life was significantly and positively affected by flow experiences in economic participation, social fellowship participation, and self development participation. Second, the results of multi group analysis on the relations between the social participation level and the quality of life according to gender demonstrated that the there were gender differences on the full path model, and that there were significant differences in relationships between the volume of social fellowship participation and the quality of life between men and women. Third, a similar finding was found for the age group that the paths between flow experiences in economic participation and the quality of life significantly differed by age groups. Based on these findings, implications for theory and practice were discussed.
Bereket Roba Gamo;Yoon-Ji Choi;Jung-Shin Choi;Joo-Lee Son
Journal of Agricultural Extension & Community Development
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v.29
no.4
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pp.265-280
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2022
Rural life improvement has remained a key policy concern for the governments of most developing countries. However, developing countries mostly focused on agricultural productivity and technology development while implementing rural development policies. This paper was aimed at constructing the trends and identify the major tasks implemented through the rural life improvement programs in Korea and describing rural development efforts in Ethiopia after the Second World War. The data was generated through an intensive review of literature and focus group interview in Korea. The two countries in general and their rural areas in particular, were poor and almost similar initially. While the condition of rural Korea rapidly transformed since 1960s, rural Ethiopia has not yet experienced major improvement. Although different rural development efforts have been made in Ethiopia, erratic policies implemented by the different political regimes across time emerge to be one of the main factors behind the poor performance of the of the rural sector. Further, while the Korean government's rural development policy gave equal emphasis to improvement of agricultural production base as well as rural life improvement right from its inception, the Ethiopian rural development policy has rather neglected the rural life improvement aspect. Diversification of rural economy was also another priority area in Korea's rural development policy through agro-processing, rural tourism, and non-farm employment opportunities whereas this has not been the case in Ethiopia's rural development policy. We suggest some lessons that Ethiopia might adapt for its rural life improvement endeavors.
Lee Chung-Sub;Lim Dong-Wook;Noh Si-Hyeong;Kim Tae-Hoon;Ko Yousun;Kim Kyung Won;Jeong Chang-Won
KIPS Transactions on Computer and Communication Systems
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v.12
no.3
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pp.119-126
/
2023
Sarcopenia is not well known enough to be classified as a disease in 2021 in Korea, but it is recognized as a social problem in developed countries that have entered an aging society. The diagnosis of sarcopenia follows the international standard guidelines presented by the European Working Group for Sarcopenia in Older People (EWGSOP) and the d Asian Working Group for Sarcopenia (AWGS). Recently, it is recommended to evaluate muscle function by using physical performance evaluation, walking speed measurement, and standing test in addition to absolute muscle mass as a diagnostic method. As a representative method for measuring muscle mass, the body composition analysis method using DEXA has been formally implemented in clinical practice. In addition, various studies for measuring muscle mass using abdominal images of MRI or CT are being actively conducted. In this paper, we develop an AI image segmentation model based on abdominal images of CT with a relatively short imaging time for the diagnosis of sarcopenia and describe the multicenter validation. We developed an artificial intelligence model using U-Net that can automatically segment muscle, subcutaneous fat, and visceral fat by selecting the L3 region from the CT image. Also, to evaluate the performance of the model, internal verification was performed by calculating the intersection over union (IOU) of the partitioned area, and the results of external verification using data from other hospitals are shown. Based on the verification results, we tried to review and supplement the problems and solutions.
KIPS Transactions on Software and Data Engineering
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v.12
no.3
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pp.141-148
/
2023
Accurate overlay metrology is essential to achieve high yields of semiconductor products. Overlay metrology performance is greatly affected by overlay target design and measurement method. Therefore, in order to improve the performance of the overlay target, measurement methods applicable to various targets are required. In this study, we propose a new algorithm that can measure image-based overlay. The proposed measurement algorithm can estimate the sub-pixel position by using a motion vector. The motion vector may estimate the position of the sub-pixel unit by applying a quadratic equation model through polynomial expansion using pixels in the selected region. The measurement method using the motion vector can calculate the stacking error in all directions at once, unlike the existing correlation coefficient-based measurement method that calculates the stacking error on the X-axis and the Y-axis, respectively. Therefore, more accurate overlay measurement is possible by reflecting the relationship between the X-axis and the Y-axis. However, since the amount of computation is increased compared to the existing correlation coefficient-based algorithm, more computation time may be required. The purpose of this study is not to present an algorithm improved over the existing method, but to suggest a direction for a new measurement method. Through the experimental results, it was confirmed that measurement results similar to those of the existing method could be obtained.
In this study, we described the production of an antibody to living modified organisms (LMOs) containing the gene encoding for 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) from Agrobacterium tumefaciens strain CP4 EPSPS provides resistance to the herbicide glyphosate (N- (phosphonomethyl) glycine). These LMOs were approved and have recently been used in the feed, food production, and processing industries in South Korea. Highly efficient monoclonal antibody (mAb) production is crucial for developing assays that enable the proper detection and quantification of the CP4 EPSPS protein in LMOs. This study describes the purification and characterization of recombinant CP4 EPSPS protein in E. coli BL21 (DE3) based on sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and matrixassisted laser desorption/ionization time-of-flight mass spectrometry. The production of mAbs was undertaken based on the standard operating procedure of Abclon, Inc.(South Korea), and the purity of the mAbs was assessed using SDS-PAGE. The following five mAb clones were produced: 2F2, 4B9, 6C11, 10A9, and 10G9. To verify the efficiency and specificity of the five developed mAbs, we performed Western blotting analysis using the LM (living modified) cotton crude extracts. All mAbs could detect the CP4 EPSPS protein in the LM cotton traits MON1445 and MON88913 with high specificity, but not in any other LM cottons or non-LM cottons. These data indicate that these five mAbs to CP4 EPSPS could be successfully used for the further development of antibody-based detection methods to target CP4 EPSPS protein in LMOs.
KIPS Transactions on Software and Data Engineering
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v.11
no.12
/
pp.499-508
/
2022
A lot of research has been carried out on the Hangeul generation model using deep learning, and recently, research is being carried out how to minimize the number of characters input to generate one set of Hangul (Few-Shot Learning). In this paper, we propose a CKFont2 model using only 14 letters by analyzing and improving the CKFont (hereafter CKFont1) model using 28 letters. The CKFont2 model improves the performance of the CKFont1 model as a model that generates all Hangul using only 14 characters including 24 components (14 consonants and 10 vowels), where the CKFont1 model generates all Hangul by extracting 51 Hangul components from 28 characters. It uses the minimum number of characters for currently known models. From the basic consonants/vowels of Hangul, 27 components such as 5 double consonants, 11/11 compound consonants/vowels respectively are learned by deep learning and generated, and the generated 27 components are combined with 24 basic consonants/vowels. All Hangul characters are automatically generated from the combined 51 components. The superiority of the performance was verified by comparative analysis with results of the zi2zi, CKFont1, and MX-Font model. It is an efficient and effective model that has a simple structure and saves time and resources, and can be extended to Chinese, Thai, and Japanese.
Yonghan Jung ;Eontaek Lim ;Jaewook Suk;Seul Koo;Seongsam Kim
Korean Journal of Remote Sensing
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v.39
no.5_2
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pp.849-858
/
2023
Investigating disaster sites such as earthquakes and landslides involves significant risks due to potential secondary disasters like facility collapse. In situations where direct access is challenging, there is a need to develop methods for safely acquiring high-precision 3D disaster information using light detection and ranging (LiDAR) equipped drone survey systems. In this study, the feasibility of using drone LiDAR in disaster scenarios was examined, focusing on the collapse accident at Jeongja Bridge in Bundang-gu, Seongnam City, in April 2023. High-density point clouds for the accident bridge were collected, and the bridge's 3D terrain information was reconstructed and compared to the measurement performance of 10 ground control points. The results showed horizontal and vertical root mean square error values of 0.032 m and 0.055 m, respectively. Additionally, when compared to a point cloud generated using ground LiDAR for the same target area, a vertical difference of approximately 0.08 m was observed, but overall shapes showed minimal discrepancies. Moreover, in terms of overall data acquisition and processing time, drone LiDAR was found to be more efficient than ground LiDAR. Therefore, the use of drone LiDAR in disaster sites with significant risks allows for safe and rapid onsite investigations.
KIPS Transactions on Software and Data Engineering
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v.12
no.10
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pp.461-470
/
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.
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