• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.449-460
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• 2021

The development process of cyber literature theory in the 1990s clearly shows the duality of the pursuit of symbolic power and desire through the formation and conflict of literary fields, collective intelligence. All desires are bound to be power-oriented, and cyber literature is meaningful in that network-space critics developed while advocating the humanities of coexistence. The failure of cyber literature theory is due to the conflicting desire of critical power between real and virtual spaces. Cyber literature theory in the 1990s was the first literary response to the birth of artificial nature, although the contradiction of desires revealed in symbolic power and the limitations of barking are clear. Literature discourse has always explored the relationship between the social conditions of the time (including technological progress) and art texts. Producing a new critical discourse encompassing the whole within the literary field of artificial nature is an important task in literature in the era of technology compilation, and humanities and technology must coexist. Through this paper, we examined the impact of the birth of artificial nature on humanities. This study is an important achievement of humanities engineering that understands, interprets, and leads technology.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.198-210
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• 2022

The southern strait of Jeju is a divergence point of the Tsushima Warm Current (TWC), and it is the starting point of the thermohaline circulation in the waters of the Korean Peninsula, affecting the size and frequency of marine disasters such as typhoons and tsunamis, and has a very important oceanographic impact, such as becoming a source of harmful organisms and radioactively contaminated water. Therefore, for an immediate response to these maritime disasters, real-time ocean observation is required. However, compared to other straits, in the case of southern Jeju, such wide area marine observations are insufficient. Therefore, in this study, surface current field of the southern strait of Jeju was calculated using High-Frequency radar (HF radar). the large surface current field is calculated, and post-processing and data improvement are carried out through APM (Antenna Pattern Measurement) and FOL (First Order Line), and comparative analysis is conducted using actual data. As a result, the correlation shows improvement of 0.4~0.7 and RMSE of about 1~19 cm/s. These high-frequency radar observation results will help solve domestic issues such as response to typhoons, verification of numerical models, utilization of wide area wave data, and ocean search and rescue in the future through the establishment of an open data network.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.

• Journal of the Korean Geotechnical Society
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• v.39 no.9
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• pp.13-24
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• 2023

To investigate the stability of temporary retaining walls during excavation, it is essential to develop reverse analysis technologies capable of precisely evaluating the properties of the ground and a learning model that can assess stability by analyzing real-time data. In this study, we targeted excavation sites where the C.I.P method was applied. We developed a Deep Neural Network (DNN) model capable of evaluating the stability of the retaining wall, and estimated the physical properties of the ground being excavated using a Differential Evolution Algorithm. We performed reverse analysis on a model composed of a two-layer ground for the applicability analysis of the Differential Evolution Algorithm. The results from this analysis allowed us to predict the properties of the ground, such as the elastic modulus, cohesion, and internal friction angle, with an accuracy of 97%. We analyzed 30,000 cases to construct the training data for the DNN model. We proposed stability evaluation grades for each assessment factor, including anchor axial force, uneven subsidence, wall displacement, and structural stability of the wall, and trained the data based on these factors. The application analysis of the trained DNN model showed that the model could predict the stability of the retaining wall with an average accuracy of over 94%, considering factors such as the axial force of the anchor, uneven subsidence, displacement of the wall, and structural stability of the wall.

• Journal of Korea Entertainment Industry Association
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• v.13 no.3
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• pp.13-25
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• 2019

Since reaching the top in the Billboard Main Album Chart 'Billboard 200' with Love Yourself: Tear in May of 2018, BTS once again took first place after just three months in the 'Billboard 200'(September 3, 2018) with the repackaged album Love Yourself: Answer. It opened the doors to the 'Hallyu 4.0' by conquering the main Billboard Chart with a song sung in Korean. BTS rose to the top on the 'Billboard 200' twice, thus being recognized globally for their musical talent(song, dance, promotion, etc.), and took their place in the mainstream music market of the world. BTS moved away from intuitive interaction such as mysticism, abnormality, irregularity, etc. but instead created their own world(BTS Universe) with fans around the world through two-directional communication such as consensus, sharing and co-existence. They are recognized as artists that went beyond being an idol group that simply released a few hit songs that had now elevated popular music to a new form of art. In result, they retained a highly loyal global fan base(A.R.M.Y.) and they are continuously creating good influence with them. This study analyzed the success factors of BTS using the S-M-C-R-E model as follows. ① Sender: BTS'7-person 7-colors fantasy and 'All-in-one storytelling' strategy of producer Bang Shi-hyuk ② Message: Create global consensus of 'you' rather than 'me' ③ Channel: Created real-time common grounds with global fans through social network platforms such as Youtube, Facebook and Instagram ④ Receiver: Formed highly loyal global fandom(A.R.M.Y.) that extends outside of Korea and Asia ⑤ Effect: Created additional economic value and spread good influence

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1283-1293
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• 2022

The purpose of this study is to predict residual chlorine in order to maintain stable residual chlorine concentration in sedimentation basin by using artificial intelligence algorithms in water treatment process employing pre-chlorination. Available water quantity and quality data are collected and analyzed statistically to apply into mathematical multiple regression and artificial intelligence models including multi-layer perceptron neural network, random forest, long short term memory (LSTM) algorithms. Water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage data are used as the input parameters to develop prediction models. As results, it is presented that the random forest algorithm shows the most moderate prediction result among four cases, which are long short term memory, multi-layer perceptron, multiple regression including random forest. Especially, it is result that the multiple regression model can not represent the residual chlorine with the input parameters which varies independently with seasonal change, numerical scale and dimension difference between quantity and quality. For this reason, random forest model is more appropriate for predict water qualities than other algorithms, which is classified into decision tree type algorithm. Also, it is expected that real time prediction by artificial intelligence models can play role of the stable operation of residual chlorine in water treatment plant including pre-chlorination process.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.189-198
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• 2023

As crimes frequently occur on the street, the spread of CCTV is increasing. However, due to the shortcomings of passively operated CCTV, the need for intelligent CCTV is attracting attention. Due to the heavy system of such intelligent CCTV, high-performance devices are required, which has a problem in that it is expensive to replace the general CCTV. To solve this problem, an intelligent CCTV system that recognizes low-quality images and operates even on devices with low performance is required. Therefore, this paper proposes a Saying CCTV system that can detect threats in real time by using the AWS cloud platform to lighten the system and convert images into text. Based on the data extracted using YOLO v4 and OpenPose, it is implemented to determine the risk object, threat behavior, and threat situation, and calculate the risk using machine learning. Through this, the system can be operated anytime and anywhere as long as the network is connected, and the system can be used even with devices with minimal performance for video shooting and image upload. Furthermore, it is possible to quickly prevent crime by automating meaningful statistics on crime by analyzing the video and using the data stored as text.

• Nutrition Research and Practice
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• v.17 no.4
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• pp.682-697
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• 2023

BACKGROUND/OBJECTIVES: Tibetan tea is a kind of dark tea, due to the inherent complexity of natural products, the chemical composition and beneficial effects of Tibetan tea are not fully understood. The objective of this study was to unravel the composition of Tibetan tea using knowledge-guided multilayer network (KGMN) techniques and explore its potential antioxidant and hypolipidemic mechanisms in mice. MATERIALS/METHODS: The C57BL/6J mice were continuously gavaged with Tibetan tea extract (T group), green tea extract (G group) and ddH2O (H group) for 15 days. The activity of total antioxidant capacity (T-AOC) and superoxide dismutase (SOD) in mice was detected. Transcriptome sequencing technology was used to investigate the molecular mechanisms underlying the antioxidant and lipid-lowering effects of Tibetan tea in mice. Furthermore, the expression levels of liver antioxidant and lipid metabolism related genes in various groups were detected by the real-time quantitative polymerase chain reaction (qPCR) method. RESULTS: The results showed that a total of 42 flavonoids are provisionally annotated in Tibetan tea using KGMN strategies. Tibetan tea significantly reduced body weight gain and increased T-AOC and SOD activities in mice compared with the H group. Based on the results of transcriptome and qPCR, it was confirmed that Tibetan tea could play a key role in antioxidant and lipid lowering by regulating oxidative stress and lipid metabolism related pathways such as insulin resistance, P53 signaling pathway, insulin signaling pathway, fatty acid elongation and fatty acid metabolism. CONCLUSIONS: This study was the first to use computational tools to deeply explore the composition of Tibetan tea and revealed its potential antioxidant and hypolipidemic mechanisms, and it provides new insights into the composition and bioactivity of Tibetan tea.

• Journal of Service Research and Studies
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• v.11 no.1
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• pp.59-79
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• 2021

Service-related research in accordance with the transition to the service economy era has been conducted in a wide variety of ways, but the development of a service-related scale suitable for the present time is still insignificant. The purpose of this study is to define the nature of services and to develop measurement items for them. First, four categories of service nature were adopted in the previous study. The four categories are 'relationship', 'interactivity', 'horizontality', and 'harmony'. In this study, sub-factors and specific items of each of these four service essences were extracted and developed as measurable items. As a qualitative study, the four categories of sub-factors were extracted, and a mixed study was adopted to prove the reliability and validity of the extracted factors through quantitative studies. The scale items were identified through literature study, free response method, and Delphi technique, and the measurement items were refined through a second questionnaire of 30 Delphi panels composed of experts. As a result of the study, 15 out of 52 questions for relationship, 11 out of 45 questions for bilateral direction, 9 out of 33 questions for horizontality, and 17 out of 61 questions for harmonization were derived after secondary refining. Through this study, it was possible to uncover new essential items suitable for the service economy era. SNS, network, synergy, platform, system, real name, and breakthrough are concepts that have not been obtained in previous studies, and can be seen as contributions of this study. However, due to various limitations, this study did not cover all aspects of the service, but mainly dealt with people-centered services, which are part of the service. In the future, it is necessary to study the development of service essence measurement items for the overall aspect of services developed according to the evolution of the service economy era.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.77-84
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• 2024

In the fourth industrial-revolution era, the construction industry is transitioning from traditional methods to digital processes. This shift has been challenging owing to the industry's employment of diverse processes and extensive human resources, leading to a gradual adoption of digital technologies through trial and error. One critical area of focus is the safety management at construction sites, which is undergoing significant research and efforts towards digitization and automation. Despite these initiatives, recent statistics indicate a persistent occurrence of accidents and fatalities in construction sites. To address this issue, this study utilizes large-scale language-model artificial intelligence to analyze big data from a construction safety-management information network. The findings are integrated into on-site models, which incorporate real-time updates from detailed design models and are enriched with location information and spatial characteristics, for enhanced safety management. This research aims to develop a big-data-driven safety-management platform to bolster facility and worker safety by digitizing construction-site safety data. This platform can help prevent construction accidents and provide effective education for safety practices.