• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.139-147
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• 2022

The goal of this study is to develop a mobile application so that a person who is new to chemistry can easily acquire the knowledge necessary for chemical structure learning using image tracking technology. The point of this study is to provide a new chemical structure learning experience by recognizing a two-dimensional picture, augmenting the chemical structure into a three-dimensional object, showing it on the user's screen, and using a service that simultaneously provides related information in multiple fields. characteristic. Login API and real-time database technology were used for safe and real-time data management, and an application was developed using image tracking technology for image recognition and 3D object augmentation service. In the future, we plan to use the chemical structure data library to efficiently load and output data.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.5
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• pp.9-15
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• 2022

Discarded bio-wastes, such as seeds and rinds, cause environmental problems. Multiple studies have recycled bio-wastes as eco-friendly energy sources to solve these problems. This study uses bio-waste to fabricate a mandarin peel powder based triboelectric nanogenerator (MPP-TENG). The MPP-TENG is based on the contact separation mode. It generates an open-circuit voltage and short-circuit current of 156V and 2µA, respectively. In addition, MPP-TENG shows stable operation over continuous 3000s without any deviation in output. Also, the device exhibits maximum power density of 5.3㎼/cm² when connected to a resistance of 100MΩ. In an energy storage capacity test for 1000s, the MPP-TENG stores an energy of 171.6µJ in a 4.7µF capacitor. The MPP-TENG can power 9 blue LEDs and 54 green lettering LEDs. These results confirm that the MPP-TENG can provide a new avenue for eco-friendly energy harvesting device fabrication.

• Health Policy and Management
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• v.4 no.1
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• pp.107-122
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• 1994

Many alternatives have been discussed to reduce the medical expenditure and to use the medical resources effectively. Many studies about the economies of scale have been done for the last several decades. This study has analyzed the relationship between the number of beds and the mean expense per hospitalization day in Korea. A Cost Function Model was identified and we wanted to see the minimum optimal size with the cheapest mean expense per hospitalization day. The result is as follows; 1. In the Cost Function Mode, (the number of beds)$^{2}$, the number of personnel, productivity and training institutions are the factors that statisticaly influence the mean expenses. 2. By the univariate analysis the mean expense proved to be the smallest as the level of 150-200bed, The breaked down of the components of expenses shows that the mean labor cost is much different from the mean value of material and administration costs, and that hospital with 150-200 beds also have the minimal expense. The mean expense goes up dramatically in hospitals of 450 beds or more. 3. When the other conditions are constant, according to the multiple regression analysis of the mean expense per adjusted hospitalization day the minimum optimal size with the cheapest expense is a hospital with 191 beds and the hospital with 230 beds takes the lowest mean labor cost. The material or administration costs are not influenced by hospital size. This research has limitation in measuring the variables that influence hospital xpenses, in estimating hospital output by the number of beds in considering outpatient cost and in securing representativeness of hospitals because many hospitals made no responses to the research questionnare. But it is valuable and helpful for development of health policy to figure out the number of beds with the cheapest expense per hospitalization day.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.425-432
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• 2009

The construction industry has been a main pillar and serves as a regulator of the Hong Kong economy. Subsequently, the fluctuations in the level of construction output can induce significant rippling effects to the economy. The Asian Financial Crisis started in 1997 and the SARS outbreak in 2003 both introduced major challenges and impacts to the Hong Kong economy and consequently the construction sector. Such decline in the importance of construction has suggested a possible structural change in the sector. It is worth investigating the driving forces behind the construction demand and see if they have changed after the heavy impacts in the past decade. The above considerations have, therefore, been the motivation of the present study to model the Hong Kong residential construction demand through multiple regression technique which can identify the significant influencing factors to the residential demand. The residential construction is studied as it constitutes a significant portion of the total construction volume. The residential sector has great influence to the general economy of Hong Kong. It is found that the underlying market structure and the driving factors for Hong Kong residential demand before and after the Asian Economic Crisis and SARS outbreak are different, suggesting that the residential construction sector or even the larger construction industry may have undergone a major structural change as Hong Kong's economy approaches maturity. It is also observed that the past literatures on construction demand are mostly focusing on predicting demand under a stable economic environment. Hence, it is worth examining if it is possible to model during economic hardship when the residential sector fluctuate dramatically under different external impacts, such as the recent global financial tsunami.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.34-36
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• 2022

In the existing text sentiment analysis models, the entire text is usually directly modeled as a whole, and the hierarchical relationship between text contents is less considered. However, in the practice of sentiment analysis, many texts are mixed with multiple emotions. If the semantic modeling of the whole is directly performed, it may increase the difficulty of the sentiment analysis model to judge the sentiment, making the model difficult to apply to the classification of mixed-sentiment sentences. Therefore, this paper proposes a sentiment analysis model BHGCN that considers the text hierarchy. In this model, the output of hidden states of each layer of BERT is used as a node, and a directed connection is made between the upper and lower layers to construct a graph network with a semantic hierarchy. The model not only pays attention to layer-by-layer semantics, but also pays attention to hierarchical relationships. Suitable for handling mixed sentiment classification tasks. The comparative experimental results show that the BHGCN model exhibits obvious competitive advantages.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.175-182
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• 2023

Differential cryptanalysis is one of the analysis techniques for block ciphers, and uses the property that the output difference with respect to the input difference exists with a high probability. If random data and differential data can be distinguished, data complexity for differential cryptanalysis can be reduced. For this, many studies on deep learning-based neural distinguisher have been conducted. In this paper, a deep learning-based neural distinguisher for PIPO 64/128 is proposed. As a result of experiments with various input differences, the 3-round neural distinguisher for the differential characteristics for 0, 1, 3, and 5-rounds achieved accuracies of 0.71, 0.64, 0.62, and 0.64, respectively. This work allows distinguishing attacks for up to 8 rounds when used with the classical distinguisher. Therefore, scalability was achieved by finding a distinguisher that could handle the differential of each round. To improve performance, we plan to apply various neural network structures to construct an optimal neural network, and implement a neural distinguisher that can use related key differential or process multiple input differences simultaneously.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1759-1768
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• 2022

This paper proposes a model to detect depression-related emotions in a user's speech using wellness dialogue scripts provided by AI Hub, topic-specific daily conversation datasets, and chatbot datasets published on Github. There are 18 emotions, including depression and lethargy, in depression-related emotions, and emotion classification tasks are performed using KoBERT and KOELECTRA models that show high performance in language models. For model-specific performance comparisons, we build diverse datasets and compare classification results while adjusting batch sizes and learning rates for models that perform well. Furthermore, a person performs a multi-classification task by selecting all labels whose output values are higher than a specific threshold as the correct answer, in order to reflect feeling multiple emotions at the same time. The model with the best performance derived through this process is called the Depression model, and the model is then used to classify depression-related emotions for user utterances.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.27-39
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• 2023

Detecting greenhouses from the remote sensing datasets is useful in identifying the illegal agricultural facilities and predicting the agricultural output of the greenhouses. This research proposed a methodology for automatically detecting greenhouses from a given Planetscope satellite imagery acquired in the areas of Gimje City using the deep learning technique through a series of steps. First, multiple training images with a fixed size that contain the greenhouse features were generated from the five training Planetscope satellite imagery. Next, the YOLO(You Only Look Once) model was trained using the generated training images. Finally, the greenhouse features were detected from the input Planetscope satellite image. Statistical results showed that the 76.4% of the greenhouse features were detected from the input Planetscope satellite imagery by using the trained YOLO model. In future research, the high-resolution satellite imagery with a spatial resolution less than 1m should be used to detect more greenhouse features.

• Information Systems Review
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• v.22 no.3
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• pp.81-97
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• 2020

Recent trends in sentiment analysis have been focused on applying single label classification approaches. However, when considering the fact that a review comment by one person is usually composed of several topics or aspects, it would be better to classify sentiments for those aspects respectively. This paper has two purposes. First, based on the fact that there are various aspects in one sentence, aspect mining is performed to classify the emotions by each aspect. Second, we apply the multiple label classification method to analyze two or more dependent variables (output values) at once. To prove our proposed approach's validity, online review comments about musical performances were garnered from domestic online platform, and the multi-label classification approach was applied to the dataset. Results were promising, and potentials of our proposed approach were discussed.

• Archives of Plastic Surgery
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• v.51 no.1
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• pp.30-35
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• 2024

Background Breast aesthetics evaluation often relies on subjective assessments, leading to the need for objective, automated tools. We developed the Seoul Breast Esthetic Scoring Tool (S-BEST), a photometric analysis software that utilizes a DenseNet-264 deep learning model to automatically evaluate breast landmarks and asymmetry indices. Methods S-BEST was trained on a dataset of frontal breast photographs annotated with 30 specific landmarks, divided into an 80-20 training-validation split. The software requires the distances of sternal notch to nipple or nipple-to-nipple as input and performs image preprocessing steps, including ratio correction and 8-bit normalization. Breast asymmetry indices and centimeter-based measurements are provided as the output. The accuracy of S-BEST was validated using a paired t-test and Bland-Altman plots, comparing its measurements to those obtained from physical examinations of 100 females diagnosed with breast cancer. Results S-BEST demonstrated high accuracy in automatic landmark localization, with most distances showing no statistically significant difference compared with physical measurements. However, the nipple to inframammary fold distance showed a significant bias, with a coefficient of determination ranging from 0.3787 to 0.4234 for the left and right sides, respectively. Conclusion S-BEST provides a fast, reliable, and automated approach for breast aesthetic evaluation based on 2D frontal photographs. While limited by its inability to capture volumetric attributes or multiple viewpoints, it serves as an accessible tool for both clinical and research applications.