• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.563-572
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• 1997

E-mail has been eidely used for unformation delivery as an Inernet serive. As multimedia etchnologies are developed rapidly, most of the recent Unternet infornation servies support multimedia data. E-mail system also needs to suport multimedia nesage. But Internet mail servise using simple maiol transfer protocol(SMTP) speci-fied in RFC 821/822 handles only ASCII text messages repressented with 7-bit code. Each line the message has the length limitation as well. Those are why it cannot satisfy the diverse user'w demands. Multipuepose Unternet mail extensions(MIMZE), which is a modification and supplement of RFC 822,was proposed for supporting transportation of multimedia data.It can solve the limitations of sizes and types in contents of a message. In this study the E-mail system has been designed and implemented according to the MIME standard in order to solve the limitations of transpotation of messages regardless of the message content type. Hypertext markup language(HTML)syntax is applied to the mail system, and so it is possible to display a message consisting of differnt media as an intergrated from for the purpose of better understanding a message. No application program is needed for displaying a message including image data,and convenience for user is considered in the system. The futuer work is to improve the E-mail system so that it may support motion pictures and sound information,Thereby tge perfor multimeda E-mail system providing inergrated user's wiew will be developed.

• Science of Emotion and Sensibility
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• v.20 no.4
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• pp.89-100
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• 2017

Since smart devices are able to efficiently provide information without barriers of time and location, they are widely utilized with advent of the hyper-connected society. Especially, the smart devices have been developed in the form of wearable devices for mutual interaction between human and objects. Smart clothing, which embeds smart devices within clothes, measures and obtains a variety of bio-signals as it is in close contact with the human bodies. Conventional smart clothing generated wearers' discomfort because they were developed by simple attachment of electronic devices to clothes. Therefore, it is highly recommended to develop novel smart clothing based on smart textiles which integrate electronic devices as parts of textiles. As smart watches are currently the most available wearable devices in the market, smart watch users were selected in this study, for the purpose of investigating core needs of wearable smart device users based on the user experience and user's sensibility. Qualitative research was performed through semi-structured interview in order to obtain detailed answers about user sensibility based on smart watch user experience. After the in-depth interview, the user's sensibility was categorized into four aspects; functional, aesthetic, social, and empirical. Sensibility adjectives and key words were assigned to each aspect and their frequency was analyzed. It was the functional aspect of sensibility that the wearable device users require the most. The results of this study will be utilized as a fundamental data to develop the smart textiles required for the next generation of smart clothing which is attracting as a future wearable device.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.485-496
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• 2018

So far, the deep learning, a field of artificial intelligence, has achieved remarkable results in solving problems from unstructured data. However, it is difficult to comprehensively judge situations like humans, and did not reach the level of intelligence that deduced their relations and predicted the next situation. Recently, deep neural networks show that artificial intelligence can possess powerful relational reasoning that is core intellectual ability of human being. In this paper, to analyze and observe the performance of Relation Networks (RN) among the neural networks for relational reasoning, two types of RN-based deep neural network models were constructed and compared with the baseline model. One is a visual question answering RN model using Sort-of-CLEVR and the other is a text-based question answering RN model using bAbI task. In order to maximize the performance of the RN-based model, various performance improvement experiments such as hyper parameters tuning have been proposed and performed. The effectiveness of the proposed performance improvement methods has been verified by applying to the visual QA RN model and the text-based QA RN model, and the new domain model using the dialogue-based LL dataset. As a result of the various experiments, it is found that the initial learning rate is a key factor in determining the performance of the model in both types of RN models. We have observed that the optimal initial learning rate setting found by the proposed random search method can improve the performance of the model up to 99.8%.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.2
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• pp.221-234
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• 2019

Cancer is genetically, metabolically and infectiously induced life threatening disorder showing aggressive growing pattern with invasive tendency. In order to prevent this global menace from jeopardizing human life, enormous studies on carcinogenesis and treatment for chemotherapy resistance have been intensively researched. Hinokitiol (${\beta}$-thujaplicin) extracted from heart wood of cupressaceous is a well-known bioactive compound demonstrating anti-inflammation, anti-bacteria and anti-cancer effects on several cancer types via apoptosis and autophagy. This study proposed that hinokitiol activates transcription factor EB (TFEB) nuclear translocation for autophagy and lysosomal biogenesis regardless of nutrient condition in cancer cells. Mitophagy and ${\beta}$-catenin translocation into the nucleus under treatment of hinokitiol on non-small cell lung cancer (NSCLC) cells and HeLa cells were investigated. Hinokitiol exerted cytotoxicity on HeLa and HCC827 cells; moreover, artificially induced autophagy by overexpression of TFEB granted imperfect sustainability onto HeLa cells. Taken together, hinokitiol is the prominent autophagy inducer and activator of TFEB nuclear translocation. Alternative cancer therapy via autophagy is pros and cons since the autophagy in cancer cells is related to prevention and survival mechanism depending on nutrition. To avoid paradox of autophagy in cancer therapy, fine-tuned regulation and application of hinokitiol in due course for successful suppressing cancer cells are recommended.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.26-34
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• 2021

In the era of the Fourth Industrial Revolution, the future battlefield will carry out multi-area operations with hyper-connected, high-speed and mobile systems. In order to prepare for changes in the future, the Korean military intends to develop various weapons systems and form a multi-layer tactical network to support On The Move communication. However, current tactical networks are limited in support of On The Move communications. In other words, the operation of multi-beam antennas is necessary to efficiently construct a multi-layer tactical network in future warfare. Therefore, in this paper, we look at the need for multi-beam antennas through the operational scenario of a multi-layer tactical network. In addition, based on development consideration factors, features of rotary-wing and fixed-wing aircraft, we present the location and operation of airborne relay drone installations of multi-beam antennas.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.180-193
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• 2021

The complex resistivity method is an exploration technique that can obtain various characteristic information of underground media by measuring resistivity and phase in the frequency domain, and its utilization has recently increased. In this paper, a three-dimensional inversion algorithm for the CR data was developed to increase the utilization of this method. The Poisson equation, which can be applied when the electromagnetic coupling effect is ignored, was applied to the modeling, and the inversion algorithm was developed by modifying the existing algorithm by adopting comlex variables. In order to increase the stability of the inversion, a technique was introduced to automatically adjust the Lagrangian multiplier according to the ratio of the error vector and the model update vector. Furthermore, to compensate for the loss of data due to noisy phase data, a two-step inversion method that conducts inversion iterations using only resistivity data in the beginning and both of resistivity and phase data in the second half was developed. As a result of the experiment for the synthetic data, stable inversion results were obtained, and the validity to real data was also confirmed by applying the developed 3D inversion algorithm to the analysis of field data acquired near a hydrothermal mine.

• Korean Journal of Social Welfare
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• v.69 no.4
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• pp.41-65
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• 2017

This study aimed to examine the social transition, which is often metaphorized as the Fourth Industrial Revolution, within the context of social work practice and to explore measures to improve social work practice in such transition. Four social welfare researchers held seven discussions to predict the social changes in the near future centered on the Fourth Industrial Revolution and find the corresponding development strategies in social work practice; collective autobiography method was used to analyze the discussion. The analysis ascertained hyper connectivity, the advent and expansion of new communities, diversification and individualization, and the emergence of new criteria for the assessment of one's quality of life as the distinctive qualities of the near future. It was analyzed that humans and organic materials will be interconnected through spatial and temporal transcendence and that humans liberated from labor will seek for diverse communities while the number of atomized individual will increase simultaneously. Furthermore, the rise of new order of life accompanied by both the expansion of diversification and individualization and the ecological worldview brought forth by post materialistic trend was predicted. Meanwhile, the disengagement from macroscopic context, a biased inclination towards technique orientated professionalism, and individualistic social work practices without integrity were identified as the limitations of the current social work practice. This study presented three goals for social work practice to help it overcome its current shortcomings and correspond to the social changes: first, the rearrangement of practice knowledge, technique, and value so that it is based on humans and society, which are the essence of social practice work; second, the practice, such as sharing economy, that expands the individuals' boundaries of life to the community; three, the restoration of the desirability of professional social works by examining its special nature.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.383-391
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• 2017

In image processing, image segmentation has been studied by various methods in a long time. Image segmentation is the process of partitioning a digital image into multiple objects and face detection is a typical image segmentation field being used in a variety of applications that identifies human faces in digital images. In this paper, we propose a method for extracting the contours of faces included in images. Using the Viola-Jones algorithm, to do this, we detect the approximate locations of faces from images. But, the Viola-Jones algorithm could detected the approximate location of face not the correct position. In order to extract a more accurate face region from image, we use skin color in this paper. In details, face region would be extracted using the analysis of horizontal and vertical histograms on the skin area. Finally, the face contour is extracted using snake algorithm for the extracted face area. In this paperr, a modified snake energy function is proposed for face contour extraction based snake algorithm proposed by Williams et al.[7]

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.1-18
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• 2023

With the recent developments in big data and deep learning, a variety of traffic information is collected widely and used for traffic operations. In particular, long short-term memory (LSTM) is used in the field of traffic information prediction with time series characteristics. Since trends, seasons, and cycles differ due to the nature of time series data input for an LSTM, a trial-and-error method based on characteristics of the data is essential for prediction models based on time series data in order to find hyperparameters. If a methodology is established to find suitable hyperparameters, it is possible to reduce the time spent in constructing high-accuracy models. Therefore, in this study, a traffic information prediction model is developed based on highway vehicle detection system (VDS) data and LSTM, and an impact assessment is conducted through changes in the LSTM evaluation indicators for each hyperparameter. In addition, a methodology for finding hyperparameters suitable for predicting highway traffic information in the transportation field is presented.