• International journal of advanced smart convergence
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• v.9 no.4
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• pp.184-191
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• 2020

With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.135-143
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• 2022

In order to convert Korean folk paintings called Minhwa, a part of traditional Korean heritage, into DBs, it is necessary to design a classification system suitable for the characteristics of folk paintings. A classification system and the generating of unique codes are required to classify and save them. To realize this, a basic classification system was created by listing objects depicted in folk paintings, and keywords were extracted by reclassifying them for each object. In order to assign a unique code to each piece, we organize the English names of each Minhwa since the English names of the folk painting contain the names of objects. The code name is extracted by applying the order of nouns and consonant priority rules in English names and attaching five Arabic numerals. These codes are later assigned to each image file stored in the database and are input together with the keyword. The Minhwa DB constructed in this way enables storage and search centered on objects and keywords and the intuitive inferring of the type of object from the code name.

• Journal of Platform Technology
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• v.10 no.3
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• pp.69-78
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• 2022

Job offer and job search data related to employment are in the form of highly-unstructured texts that occur in real-time, NCS duty, learning modules, and job dictionaries. Job announcements and training information have a high data value amid changes in industrial technology, such as the Fourth Industrial Evolution. This study developed a job data dictionary by defining relevant data to intuitively understand and harness information on job offers and job searches. This study also designed, constructed, and evaluated a data map based on ontology to enable linking and inferring data about public announcement-job-training. Through this, it was found that the inference function centered on work ability enables QoS support that can satisfy users by minimizing mismatch between consumers and optimizing the data dictionary.

• Journal of the Korean Institute of Rural Architecture
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• v.24 no.1
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• pp.1-8
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• 2022

Among Korea's national folk cultural heritage, eight villages have been designated, and 37 years have passed since Hahoe Village was first designated in 1984. All eight villages have been constantly inhabited by residents from the past to the present, so the cultural value of inferring the lives of our ancestors in the past is very high. However, due to the inconvenience of the settlement environment in existing houses due to changes in the living environment, buildings such as various residential facilities and warehouses have been installed and expanded without permission, losing the original cultural property landscape and building. In addition, complaints and conflicts from residents are accumulating due to the poor living environment in the village. Therefore, this study intends to present guidelines for conservation and maintenance that can embrace changes in resident's housing and living environment, based on the 'maintenance of original form', the grand principle of the Cultural Properties Protection Law about the Jeju Seongeup Village. In particular, the maintenance plan is largely subdivided into legalization, demolition, expansion and reconstruction, and detailed standards for each item and examples applied are proposed. Through this, it aims to become basic data on the starting point of realistic improvement measures for cultural properties and residents to coexist.

• Animal Systematics, Evolution and Diversity
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• v.39 no.3
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• pp.99-113
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• 2023

The nudibranch is one of the most colorful gastropod species found in oceans worldwide. Unlike many other gastropod groups, the nudibranch loses an external shell in the adult stage, but instead develops various chemical defense systems. More than 2,500 nudibranch species have been reported worldwide, and 73 species are currently recorded in Korean waters. In this study, we present morphological descriptions, DNA barcode information of mtDNA cox1 sequence, and taxonomic review for five nudibranch species: Apata pricei (MacFarland, 1966), Doto rosacea Baba, 1949, Janolus toyamensis Baba and Abe, 1970, Polycera abei (Baba, 1960), and Trinchesia sibogae (Bergh, 1905). Of these, we also provide in-depth discussion of taxonomic issue of A. pricei that was previously subdivided into two subspecies, A. pricei pricei and A. pricei komandorica. Our morphological examination and molecular analyses of the mtDNA cox1 sequences indicate that these two subspecies are not taxonomically warranted. The phylogenetic information for the other nudibranch species from mtDNA cox1 sequence analysis is also included, providing a molecular basis for species identification and inferring their local phylogenies within each of the species groups discussed herein.

• Journal of Wellbeing Management and Applied Psychology
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• v.7 no.2
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• pp.23-29
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• 2024

Purpose: This study aims to investigate consumer attitudes towards healthy foods, focusing on low-calorie options, and their impact on purchasing behavior. Methods: The study utilizes structural equation modeling, which incorporates statistical tools such as SPSS and AMOS for thorough analysis. This involves collecting data over a period of time and then inferring patterns and relationships through correlation and trend analysis. Results: The study found that attributes such as quality, price, functional value, and emotional value have a significant impact on customer satisfaction and repurchase intentions, but not on nutrition and brand. The study provides a comprehensive understanding of the factors that influence consumer attitudes and purchase behavior toward healthy indulgences in the context of low-calorie foods, and has important implications for both academic research and practical marketing strategies. Conclusions and Implications: This study provides new insights into consumer behavior theory by validating the impact of the perceived value of low-calorie products on attitudes and purchase behavior, which is of great academic value. It is also expected to provide useful information for the formulation of effective marketing strategies for low-calorie products and the development of products that meet consumer needs.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.167-176
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• 2024

Lip-reading is the task of inferring the speaker's utterance from silent video based on learning of lip movements. It is very challenging due to the inherent ambiguities present in the lip movement such as different characters that produce the same lip appearances. Recent advances in deep learning models such as Transformer and Temporal Convolutional Network have led to improve the performance of lip-reading. However, most previous works deal with English lip-reading which has limitations in directly applying to Korean lip-reading, and moreover, there is no a large scale Korean lip-reading dataset. In this paper, we introduce the first large-scale Korean lip-reading dataset with more than 120 k utterances collected from TV broadcasts containing news, documentary and drama. We also present a preprocessing method which uniformly extracts a facial region of interest and propose a transformer-based model based on grapheme unit for sentence-level Korean lip-reading. We demonstrate that our dataset and model are appropriate for Korean lip-reading through statistics of the dataset and experimental results.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.1-6
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• 2024

Recently, artificial neural networks have been playing a crucial role and advancing across various fields. Artificial neural networks are typically categorized into feedforward neural networks and recurrent neural networks. However, feedforward neural networks are primarily used for processing static spatial patterns such as image recognition and object detection. They are not suitable for handling temporal signals. Recurrent neural networks, on the other hand, face the challenges of complex training procedures and requiring significant computational power. In this paper, we propose memristors suitable for an advanced form of recurrent neural networks called reservoir computing systems, utilizing a mask processor. Using the characteristic equations of Ti/TiOx/TaOy/Pt, Pt/TiOx/Pt, and Ag/ZnO-NW/Pt memristors, we generated current-voltage curves to verify their memristive behavior through the confirmation of hysteresis. Subsequently, we trained and inferred reservoir computing systems using these memristors with the NIST TI-46 database. Among these systems, the accuracy of the reservoir computing system based on Ti/TiOx/TaOy/Pt memristors reached 99%, confirming the Ti/TiOx/TaOy/Pt memristor structure's suitability for inferring speech recognition tasks.

• Korean Journal of Agricultural Science
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• v.50 no.2
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• pp.235-248
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• 2023

Orchidaceae species account for one-tenth of all angiosperms including more than 30,000 species having significant ecological, evolutionary, and economic importance. Despite Orchidaceae being one of the largest families among flowering plants, crucial cytogenetic information for studying species diversification, inferring phylogenetic relationships, and designing efficient breeding strategies is lacking, except for 10% or less of orchid species cases involving mostly chromosome number or karyotype analysis. Also, only approximately 1.5% of the identified orchid species from less than a hundred genera have genome size data that provide crucial information for breeders and molecular geneticists. Various molecular cytogenetic techniques, such as fluorescence in situ hybridization (FISH) and genomic in situ hybridization (GISH), have been developed for determining ploidy levels, analyzing karyotypes, and evaluating hybridity, in several ornamental crops including orchids. The estimation of genome size and the determination of nuclear DNA content using flow cytometry have also been employed in some Orchidaceae subfamilies. These different techniques have played an important role in supplementing beneficial knowledge for effective plant breeding programs and other related plant research. This review focused on orchid breeding summarizes the status of current cytogenetic tools in terms of background, advancements, different techniques, significant findings, and research challenges. Principal roles and applications of cytogenetics in orchid breeding as well as different ploidy level determination methods crucial for breeding are also discussed.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.347-356
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• 2008

High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.