• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.435-442
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• 2018

Outlier detection means to detect data samples that deviate significantly from the distribution of normal data. Most outlier detection methods calculate an outlier score that indicates the extent to which a data sample is out of normal state and determine it to be an outlier when its outlier score is above a given threshold. However, since the range of an outlier score is different for each data and the outliers exist at a smaller ratio than the normal data, it is very difficult to determine the threshold value for an outlier score. Further, in an actual situation, it is not easy to acquire data including a sufficient amount of outliers available for learning. In this paper, we propose a clustering-based outlier detection method by constructing a model representing a normal data region using only normal data and performing binary classification of outliers and normal data for new data samples. Then, by dividing the given normal data into chunks, and constructing a clustering model for each chunk, we expand it to the ensemble method combining the decision by the models and apply it to the streaming data with dynamic changes. Experimental results using real data and artificial data show high performance of the proposed method.

• Korean Journal of Ecology and Environment
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• v.53 no.4
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• pp.380-389
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• 2020

Research on diagnosing and predicting the response of ecosystems caused by environmental changes such as artificial disturbance and climate change is emerging as the most important issue of biodiversity and ecosystem researches. This study aims to clean, standardize, and provide the results of National Ecosystem Survey which should be considered fundamentally in diagnosing and predicting ecosystem changes in the form of dataset. To refine and clean the dataset we developed a simple verification program based on the fifth National Ecosystem Survey Guideline and applied that program to the data from the second (1997~2005), third (2006~2013) and fourth (2014~2018) National Ecosystem Survey. Data quality control processes were implemented including (1) standardization of terminology, (2) similar data table integration, (3) unnecessary attribute and error elimination, (4) unification of different input items, (5) data arrangement in codes, and (6) code mapping for input items. These approaches and methods are the first attempt propose an option for ecological data standardization in Korea. The standardized dataset of National Ecosystem Survey in Korea will be easily accessible, reusable for both researchers and public. In addition, we expect it will contribute to the establishment of diverse environmental policies concerning environmental assessments, habitat conservation, prediction of endangered species distribution and ecological risks due to climate change. The dataset through this study is open freely online via EcoBank (nie-ecobank.kr) which is the first ecological information portal system in Korea developed by National Institute of Ecology.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.323-332
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• 2022

As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, and efficient AI service production will be possible.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.85-95
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• 2021

Currently, we stand between computers as creative tools and computers as creators. A new genre of movies, which can be called a post-cinema situation, is emerging. This paper aims to diagnose the possibility of the emergence of AI cinema. To confirm the possibility of AI cinema, it was examined through a case study whether the creation of a story, narrative, image, and sound, which are necessary conditions for film creation, is possible by artificial intelligence. First, we checked the visual creation of AI painting algorithms Obvious, GAN, and CAN. Second, AI music has already entered the distribution stage in the market in cooperation with humans. Third, AI can already complete drama scripts, and automatic scenario creation programs using big data are also gaining popularity. That said, we confirmed that the filmmaking requirements could be met with AI algorithms. From the perspective of Manovich's 'AI Genre Convention', web documentaries and desktop documentaries, typical trends post-cinema, can be said to be representative genres that can be expected as AI cinemas. The conditions for AI, web documentaries and desktop documentaries to exist are the same. This article suggests a new path for the media of the 4th Industrial Revolution era through research on AI as a creator of post-cinema.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.3
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• pp.11-20
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• 2022

The main purpose of this study is to investigate the applicability of the shear wave tomography technology as a non-destructive testing method to evaluate the debonding between the track concrete layer (TCL) and the hydraulically stabilized based course (HSB) of concrete slab tracks for the Korea high-speed railway system. A commercially available multi-channel shear wave measurement device (MIRA) is used to evaluate debonding defects in full-scaled mock-up test specimen that was designed and constructed according to the Rheda 200 system. A part of the mock-up specimen includes two artificial debonding defects with a length and a width of 400mm and thicknesses of 5mm and 10mm, respectively. The tomography images obtained by a MIRA on the surface of the concrete specimens are effective for visualizing the debonding defects in concrete. In this study, a simple image processing method is proposed to suppress the noisy signals reflected from the embedded items (reinforcing steel, precast sleeper, insert, etc.) in TCL, which significantly improves the readability of debonding defects in shear wave tomography images. Results show that debonding maps constructed in this study are effective for visualizing the spatial distribution and the depths of the debondiing defects in the railway concrete slab specimen.

• Korean Journal of Ichthyology
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• v.33 no.4
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• pp.287-296
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• 2021

This study was conducted to investigate the species composition collected by fyke net and characteristics of the Erythroculter erythropterus population in the Asan Lake and Chungju Lake from June to September, 2021. The collected fish in Asan lake were identified as 4,977 individuals of 13 species from a total of six families and in Chungju lake were identified as 2,436 individuals of 18 species from a total of eight families. The dominant species in Asan lake, both the individuals and biomass were E. erythropterus with 4,470 (89.8%) and 498,424g (84.5%). The dominant species in Chungju lake, both the individuals and biomass were E. erythropterus with 1,327 (54.5%) and 301,818 g (77.5%). The results of the community analysis showed that a dominant index value of Asan lake was 0.93, higher than 0.71 of Chungju lake, and a diversity, evenness, and richness index value of Chungju lake were higher than of Asan lake. The frequency distribution of the total length analysis of the E. erythropterus population showed the appearance rate of 1~2 year olds was high in Asan lake, and the appearance rate of more than 2 years old were high in Chungju lake. The length-weight analysis of E. erythropterus in Asan Lake and Chungju Lake showed a regression coefficient b of 3.06 and 3.04, a condition factor (K) of 0.000128 and 0.000051 with a positive slope. This study could be served as baseline data for assessing habitat characteristics based on the species composition of fishes, and identifying health conditions of E. erythropterus in Asan Lake and Chungju Lake, artificial lakes.

• Korean Journal of Environment and Ecology
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• v.36 no.1
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• pp.72-86
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• 2022

This study was conducted to identify structural characteristics of the evergreen broad-leaved forests distributed in Hak-dong, Geojae island. For a survey, 52 sites were set up in areas with changes in the vegetation community or location environment where Cinnamomum yabunikkei, Neolitsea sericea, and Machilus thunbergii dominated or appeared in the canopy, sub-canopy, or shrub layer. The community classification with TWINSPAN identified the following communities: N. sericea-C. yabunikkei, C. yabunikkei-Camellia japonica, Ca. japonica, Quercus variabilis-Ca. japonica, Pinus thunbergii-Ca. japonica, Castanopsis sieboldii, P. thunbergii, and Platycarya strobilacea-Mallotus japonicus. Considering the result of the study that succession series of warm-temperate forest reflecting the latent natural vegetation is the transition of conifers and deciduous broad-leaved forest to evergreen broad-leaved forest, the communities predominated by the communities predominated by the communities predominated by P. thunbergii, Q. variabilis, and Pl. strobilacea are likely to transform into the evergreen forest predominated by N. sericea and C. yabunikkei. The sites where C. yabunikkei, N. sericea, and Castanopsis sieboldii are dominant in the canopy and sub-canopy layers are likely to maintain the status quo if there is no artificial disturbance. The relationship between the impact of the environmental factors and the vegetation distribution showed silt among the physical properties of the soil directly or indirectly affected it, which was judged to be due to the fact that it was located on a steep slope. The soil acidity (pH) was 5-5.84, electrical conductivity 0.047-0.139 dS/m, and organic matter content was 3.32-12.06%. Although there were differences by the colony, they were generally low.

• Journal of Animal Science and Technology
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• v.65 no.2
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• pp.324-335
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• 2023

Korea, located in East Asia in the northern hemisphere, is experiencing severe climate changes. Specifically, the heat stress caused by global warming is negatively affecting the dairy sector, including milk production and reproductive performance, as the major dairy cattle Holstein-Friesian is particularly susceptible to heat stress. Here, we collected artificial insemination and pregnancy data of the Holstein and the Jersey cows from a dairy farm from 2014 to 2021 and analyzed the association between the conception rate and the temperature-humidity index, calculated using the data from the closest official weather station. As the temperature-humidity index threshold increased, the conception rate gradually decreased. However, this decrease was steeper in the Holstein breed than in the Jersey one at a temperature-humidity index threshold of 75. To evaluate the effects of heat stress on the oocyte quality, we examined the nuclear and cytoplasmic maturation of Holstein (n = 158, obtained from six animals) and Jersey oocytes (n = 123, obtained from six animals), obtained by ovum pick-up. There were no differences in the nuclear maturation between the different conditions (heat stress: 40.5℃, non- heat stress: 37.5℃) or breeds, although the Holstein oocytes seemed to have a lower metaphase II development (p = 0.0521) after in vitro maturation under heat stress conditions. However, we found that the Holstein metaphase II oocytes exposed to heat stress presented more reactive oxygen species and a peripheral distribution of the mitochondria, compared to those of the Jersey cattle. Here, we show that weather information from local meteorological stations can be used to calculate the temperature-humidity index threshold at which heat stress influences the conception rate, and that the Jersey cows are more tolerant to heat stress in terms of their conception rate at a temperature-humidity index over 75. The lower fertility of the Holstein cows is likely attributed to impaired cytoplasmic maturation induced by heat stress. Thus, the Jersey cows can be a good breed for the sustainability of dairy farms for addressing climate changes in South Korea, as they are more resistant to hyperthermia.

• Convergence Security Journal
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• v.22 no.2
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• pp.131-141
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• 2022

Future battlefield domains are expanding to ground, sea, air, space, and cyber, so future military operations are expected to be carried out simultaneously and complexly in various battlefield domains. In addition, the application of convergence technologies that create innovations in all fields of economy, society, and defense, such as artificial intelligence, IoT, and big data, is being promoted. However, since the current Korean military C4I system manages warfighting function DBs in one DB server, the efficiency of combat performance is reduced utilization and distribution speed of data and operation response time. To solve this problem, research is needed on how to apply the 4th industrial revolution technologies such as AI, IoT, 5G, big data, and cloud to the Korean military C4I system, but research on this is insufficient. Therefore, this paper analyzes the problems of the current Korean military C4I system and proposes to apply the 4th industrial revolution technology in terms of operational mission, network and data link, computing environment, cyber operation, interoperability and interlocking capabilities.