• Mycobiology
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• v.50 no.1
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• pp.1-11
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• 2022

The ascomycete fungus Cordyceps militaris infects lepidopteran larvae and pupae and forms characteristic fruiting bodies. Owing to its immune-enhancing effects, the fungus has been used as a medicine. For industrial application, this fungus can be grown on geminated soybeans as an alternative protein source. In our study, we performed a comprehensive transcriptomic analysis to identify core gene sets during C. militaris cultivation on germinated soybeans. RNA-Seq technology was applied to the fungal cultures at seven-time points (2, 4, and 7-day and 2, 3, 5, 7-week old cultures) to investigate the global transcriptomic change. We conducted a time-series analysis using a two-step regression strategy and chose 1460 significant genes and assigned them into five clusters. Characterization of each cluster based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases revealed that transcription profiles changed after two weeks of incubation. Gene mapping of cordycepin biosynthesis and isoflavone modification pathways also confirmed that gene expression in the early stage of GSC cultivation is important for these metabolic pathways. Our transcriptomic analysis and selected genes provided a comprehensive molecular basis for the cultivation of C. militaris on germinated soybeans.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.270-278
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• 2023

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.

• Journal of Industrial Convergence
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• v.18 no.4
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• pp.9-14
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• 2020

The purpose of this study is to analyze the relationship between high school teachers' grit and stress coping strategies and to understand differences in stress coping strategies by cluster types of grit. 226 in-service teachers participated in the survey who took the professional development program in the national educational training institute. Results are as follows. First, perseverance of effort in grit was the positively independent variable toward task-oriented and emotion-oriented strategies. The avoidance-oriented strategy was negatively influenced by consistency of interest in grit. Also, the clusters of high grit and high perseverance of effort in grit showed higher means in task-oriented and emotion-oriented strategies than the cluster of low grit. Finally, researchers discussed the importance of strengthening high school teachers' grit for them to cope with job stress effectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.92-101
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• 2011

This study analyzed the urban structure of Jinju city where urban doughnut phenomena, development of new town at suburban zone and establishment of innovation city appear. The sphere of this study was set limit to Jinju's dong area due to taking the limitation of data. Multivariate analysis was done by using 24 variables to classify into seven clusters(CBD, Industrial Area, Residential Area etc). We studied regional condition and problems at the relation between analyzed regional features of this study and development principles at the upper planning. Jinju city needs urban redevelopment, reconstruction works and redevelopment promotion project for urban outworn zone in view of the regional conditions to innovate outdated city image and restore western Gyeongnam as a central city and also they should promote innovative city that is progressing now and construction of new town that is linked with Sangpyeong industrial complex removal as well as the whole Chojang-dong zone. In conclusion, this study will help to understand regional phenomenon like regional development project and urban management.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• Journal of the Korean Academic Society of Industrial Cluster
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• v.1 no.1
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• pp.16-28
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• 2007

This paper analyzes the effects of networking and competition on individual companies' R&D investment, focusing on pharmaceuticals, PCB (Printed Circuit Board), and auto parts sectors. Data were obtained through a survey on firms operating in Seoul, Incheon, and Gyung-gi metropolitan area. The estimation results suggest the networking with other actors in the clusters tends to increase R&D investments of individual firm's. But competition in a cluster tend to reduce individual firm's R&D investment. These results suggest the public policy promoting networking in a cluster could induce private firms' R&D investments and, therefore, should be maintained.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1157-1164
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• 2011

In this study, association of small business is divided into four groups: Franchise, Joint Brand, Industry Association and Registered Retailer. Cluster analysis is taken to find what kind of strategic considerations associated small businesses choose when they set up new strategies. The results show that there are some differences in the perception of association, effects of association and final performance of management by gender, academic background, and age. The data also find three clusters: price competitive, marketing competitive and neither group. Implications of this study is that government should focus more on not only improving infrastructures of self-businesses but also associating small businesses, modernizing managerial systems in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3963-3970
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• 2009

This study proposes a method to evaluate the level of 'soft-hard' of color quantitatively by evaluating the shape with edge sharpness automatically and by evaluating color in the color image scale in a drawing in art therapy using a computer. The dependent variable is the rank for the color experts to rate the level of 'soft-hard'. The mean and standard deviation of Value(V), and Chroma(C), colors, main color, clusters, length of edge, and sharp line rate of edge are considered as the independent variable. The appropriate independent variables to explain the dependent variable are selected through the step wise regression analysis. The inter-rater reliability of two raters is checked and the validity of developed system is verified by the rank correlations coefficient between the ranks of rater's and system's. This system can be used to evaluate of the shape or color in a drawing objectively and quantitatively for art therapy assessment, and to give the useful information to the fashion, textile, interior industry as well as color psychology and art therapy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3675-3681
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• 2009

One of the most important factors in the lifetime of WSN(Wireless Sensor Network) is the limited resources and static control problem of the sensor nodes. In order to achieve energy efficiency and network utilities, sensor nodes can be well organized into one cluster and selected head node and normal node by dynamic conditions. Various clustering algorithms have been proposed as an efficient way to organize method based on LEACH algorithm. In this paper, we propose an efficient clustering algorithm using information entropy theory based on LEACH algorithm, which is able to recognize environmental differences according to changes from data of sensor nodes. To measure and analyze the changes of clusters, we simply compute the entropy of sensor data and applied it to probability based clustering algorithm. In experiments, we simulate the proposed method and LEACH algorithm. We have shown that our data balanced and energy efficient scheme, has high energy efficiency and network lifetime in two conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.459-466
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• 2011

In this paper, we proposed a data mining framework for the management of alerts in order to improve the performance of the intrusion detection systems. The proposed alert data mining framework performs alert correlation analysis by using mining tasks such as axis-based association rule, axis-based frequent episodes and order-based clustering. It also provides the capability of classify false alarms in order to reduce false alarms. We also analyzed the characteristics of the proposed system through the implementation and evaluation of the proposed system. The proposed alert data mining framework performs not only the alert correlation analysis but also the false alarm classification. The alert data mining framework can find out the unknown patterns of the alerts. It also can be applied to predict attacks in progress and to understand logical steps and strategies behind series of attacks using sequences of clusters and to classify false alerts from intrusion detection system. The final rules that were generated by alert data mining framework can be used to the real time response of the intrusion detection system.