• Analytical Science and Technology
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• v.19 no.3
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• pp.244-254
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• 2006

Flame retardants are added to prevent catching fire and to slow down the burning process. PBDEs are known to affect thyroid hormones and hormone disruption. The aim of this study was to propose a manual for determination of PBDEs, and investigate the accumulation of PBDEs(BDE-28, 47, 99, 100, 153, 154 and 183) in fish&shellfish and human milk samples. Pre-treatment for PBDEs determination, alkali digestion and L-L(Liquid-Liquid) extraction method could be applied to fish and shellfish. When Multi-layer column was used for cleaning up the sample, 50 mL of hexane and 100 mL of hexane:dichloromethane(9:1) solutions were used for pre- and post-elution, respectively. Activated-carbon column was optimized by a 100 mL of hexane:dichloromethane(3:1). The result of fish, highest concentration was detected in flatfish, 890 pg/g(wet weight). The other side, lowest concentration was detected in pollack, 40 pg/g(wet weight). The result of breast milk, PBDEs was detected 2,580 and 3,600 pg/g(lipid weight) from breast milk of Seoul and Juju, respectively. BDE-153 and 183 were not detected in all samples. There was no difference in PBDEs level was not difference between first and second delivery. In this study, we could find that PBDEs level in Korea is lower than other countries.

• Korean Journal of Poultry Science
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• v.50 no.2
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• pp.81-90
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• 2023

Animal welfare was introduced relatively late to Korea in comparison with Western countries. Nonetheless, the Korean government has continuously improved animal welfare-friendly regulations as policy instruments. Given the current situation, it is predicted that spontaneous settlement of the animal welfare policies will be difficult and may cause conflict in the farm animal industry. To identify and categorize conflicts caused by animal-welfare-related policies in the last five years, we investigated the awareness of animal welfare among Korean hen farms and the level of compliance with the animal welfare regulations. We collected a sample of 53 egg-laying chicken farm operators (e.g., owners or head managers) was collected through the on-site survey (90% confidence level (Z-score: 1.65) and 10.18% tolerance, based on a number of 797 egg-laying farms in 2020). Ethical conflicts on the farms were categorized into three different types according to the hen farm's ethical awareness level: passive, moderate, and active. Additionally, we investigated the correlation between compliance with regulations and ethical consideration. This study confirmed that compliance with animal welfare-related regulations significantly correlated to the level of ethical consideration of farm operators. Interestingly, we also observed that farm operators did not comply with the regulation despite their high level of awareness of animal welfare. This conflict implies contradiction and unresolved ethical dilemmas. Therefore, this study argues that the policies cause conflict in the field despite the certain level of effectiveness on animal welfare regulations.

• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.41-48
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• 2023

The presence of abnormalities in the subgrade of roads poses safety risks to users and results in significant maintenance costs. In this study, we aimed to experimentally evaluate the temperature distributions in abnormal areas of subgrade materials using infrared cameras and analyze the data with machine learning techniques. The experimental site was configured as a cubic shape measuring 50 cm in width, length, and depth, with abnormal areas designated for water and air. Concrete blocks covered the upper part of the site to simulate the pavement layer. Temperature distribution was monitored over 23 h, from 4 PM to 3 PM the following day, resulting in image data and numerical temperature values extracted from the middle of the abnormal area. The temperature difference between the maximum and minimum values measured 34.8℃ for water, 34.2℃ for air, and 28.6℃ for the original subgrade. To classify conditions in the measured images, we employed the image analysis method of a convolutional neural network (CNN), utilizing ResNet-101 and SqueezeNet networks. The classification accuracies of ResNet-101 for water, air, and the original subgrade were 70%, 50%, and 80%, respectively. SqueezeNet achieved classification accuracies of 60% for water, 30% for air, and 70% for the original subgrade. This study highlights the effectiveness of CNN algorithms in analyzing subgrade properties and predicting subsurface conditions.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.473-487
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• 2023

Korea's multi-purpose dams, which were constructed in the 1970s and 1980s, have a single outlet located near the bottom for hydropower generation. Problems such as freezing damage to crops due to cold water discharge and an increase the foggy days have been raised downstream of some dams. In this study, we analyzed the effect of water intake depth on the reservoir's water temperature stratification structure and outflow temperature targeting Hapcheon Reservoir, where hypolimnetic withdrawal is drawn via a fixed depth outlet. Using AEM3D, a three-dimensional hydrodynamic water quality model, the vertical water temperature distribution of Hapcheon Reservoir was reproduced and the seasonal water temperature stratification structure was analyzed. Simulation periods were wet and dry year to compare and analyze changes in water temperature stratification according to hydrological conditions. In addition, by applying the intake depth change scenario, the effect of water intake depth on the thermal structure was analyzed. As a result of the simulation, it was analyzed that if the hypolimnetic withdrawal is changed to epilimnetic withdrawal, the formation location of the thermocline will decrease by 6.5 m in the wet year and 6.8 m in the dry year, resulting in a shallower water depth. Additionally, the water stability indices, Schmidt Stability Index (SSI) and Buoyancy frequency (N²), were found to increase, resulting in an increase in thermal stratification strength. Changing higher withdrawal elevations, the annual average discharge water temperature increases by 3.5℃ in the wet year and by 5.0℃ in the dry year, which reduces the influence of the downstream river. However, the volume of the low-water temperature layer and the strength of the water temperature stratification within the lake increase, so the water intake depth is a major factor in dam operation for future water quality management.

• Journal of The Korean Association For Science Education
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• v.26 no.2
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• pp.200-211
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• 2006

The purpose of this study was to analyze Korean middle school student achievement in environmental science based on the TIMSS 2003 (Trends in International Mathematics and Science Study), a student comparison of 46 participating nations. Korea ranked the fourth with a mean score of 554 in environmental science. However, all 3 environment science topics assessed in TIMSS are not included in the Korean science curriculum through 8th grade, even though they are included in most other participating nations' curricula. The average percent correct of items was analyzed according to the main topic, the item type and the cognitive domain. Items that showed differences between the average percent correct of Korea and the international average as well as differences between the average percent correct of boys and girls were further analyzed. Results revealed that Korean students performed better than the international average, especially in 'use and conservation of natural resources', multiple-choice items, and items requiring 'factual knowledge'. Also, male students demonstrated significantly higher achievement than female students. On the other hand, Korean students showed relatively lower achievement in constructed-response items, items that contained content they had not learned in science lessons and items requiring descriptions of the uses and effect of science and technology. Moreover, Korean student lacked understanding about acid rain, global warming, and ozone layer destruction. Korean female students showed relatively lower environmental conceptions and lower performance on items requiring data analysis than Korean male students. On the basis of these results, this study suggested that topics of environmental science be included in the science curriculum and taught in the science classroom to help middle school students more fully comprehend environmental issues.

• Sim-seong Yeon-gu
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• v.29 no.2
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• pp.127-156
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• 2014

This thesis is an psychological study investigating the meanings of skin from the perspective of analytical psychology. Skin, as the outermost layer of our body, protects the body and carries out essential physiologic functions. It is an organ of the body and also psychological contents can be expressed on it in various forms. We can find sociocultural connotations of skin, some of which are demonstrated in our language. Skin may become a carrier of persona which defines a person's role in the society. And it can be a place where ego is expressed. Eros is the principle of relationship by Jung's definition and skin is the space where eros is realized intensely. Skin may carry meanings as a symbol of transformation. Skin disease can be interpreted as a message from Self in certain cases. The theme of casting off skin in myths and dreams can be an analogy of an individual's sacrifice for individuation, and putting on a skin may imply taking special properties in psychological level.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.740-754
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• 2009

Sound in the ocean is scattered by inhomogeneities of many different kinds, such as the sea surface, the sea bottom, or the randomly distributed bubble layer and school of fish. The total sum of the scattered signals from these scatterers is called reverberation. In order to simulate the reverberation signal precisely, combination of a propagation model with proper scattering models, corresponding to each scattering mechanism, is required. In this article, we develop a reverberation model based on the ray theory easily combined with the existing scattering models. Developed reverberation model uses (1) Chapman-Harris empirical formula and APL-UW model/SSA model for the sea surface scattering. For the sea bottom scattering, it uses (2) Lambert's law and APL-UW model/SSA model. To verify our developed reverberation model, we compare our results with those in Ellis' article and 2006 reverberation workshop. This verified reverberation model SNURM is used to simulate reverberation signal for the neighboring seas of South Korea at mid frequency and the results from model are compared with experimental data in time domain. Through comparison between experiment data and model results, the features of reverberation signal dependent on environment of each sea is investigated and this analysis leads us to select an appropriate scattering function for each area of interest.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.493-498
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• 2024

With the rapid advancement of generative artificial intelligence technology, there is growing interest in how to utilize it in practical applications. Additionally, the importance of prompt engineering to generate results that meet user demands is being newly highlighted. Exploring the new possibilities of generative AI can hold significant value. This study aims to utilize ChatGPT 4.0, a leading generative AI, to propose an effective method for evaluating user experience through the analysis of online customer review data. The user experience evaluation method was based on the six-layer elements of user experience: 'functionality', 'reliability', 'usability', 'convenience', 'emotion', and 'significance'. For this study, a literature review was conducted to enhance the understanding of prompt engineering and to grasp the clear concept of the user experience hierarchy. Based on this, prompts were crafted, and experiments for the user experience evaluation method were carried out using the analysis of collected online customer review data. In this study, we reveal that when provided with accurate definitions and descriptions of the classification processes for user experience factors, ChatGPT demonstrated excellent performance in evaluating user experience. However, it was also found that due to time constraints, there were limitations in analyzing large volumes of data. By introducing and proposing a method to utilize ChatGPT 4.0 for user experience evaluation, we expect to contribute to the advancement of the UX field.