• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.399-406
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• 2021

With the steady growth of the content industry, the need for research that automatically recommending content suitable for individual tastes is increasing. In order to improve the accuracy of automatic content recommendation, it is needed to fuse existing recommendation techniques using users' preference history for contents along with recommendation techniques using content metadata or features extracted from the content itself. In this work, we propose a new graph-based music recommendation method which learns an LSTM-based classification model to automatically extract appropriate tagging words from sound data and apply the extracted tagging words together with the users' preferred music lists and music metadata to graph-based music recommendation. Experimental results show that the proposed method outperforms existing recommendation methods in terms of the recommendation accuracy.

• Journal of KIBIM
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• v.10 no.2
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• pp.1-11
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• 2020

BIM(Building Information Modeling) is a salient technology for influential innovation in the construction industry. The patent network analysis is useful for suggesting the direction of technology development and exploring the research and development field. Therefore, the purpose of this study is to analyze the BIM technology structure and core technologies according to the convergence of BIM technology and market expansion. In this study, social network analysis was conducted by establishing a co-classification IPC network for the United States BIM patent. In particular, the characteristics of the major technical areas in the BIM technology network were identified through centrality analysis. G06F017/00, digital computing or data processing method, is a core technology field in the BIM network. Arrangements, apparatus or systems for transmission of digital information, H04L029/00 is an influential technology across the network. B25J009/00 for program controlled manipulators is an intermediary technology field and G06T019/00, manipulating 3D models or images for computer graphics, is an important field for technological development competitiveness.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.1-7
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• 2021

With the advent of industry 4.0, big data and machine learning techniques are being widely adopted in the maintenance domain. Inverters are widely used in many engineering applications. However, overloading and complex operation conditions may lead to various failures in inverters. In this study, failure mode effect analysis was performed on inverters and voltages collected to investigate the over-voltage effect on capacitors. Several features were extracted from the collected sensor data, which indicated the health state of the inverter. Based on this correlation, the best features were selected for classification. Moreover, random forest classifiers were used to classify the healthy and faulty states of inverters. Different performance metrics were computed, and the classifiers' performance was evaluated in terms of various health features.

• Annual Conference of KIPS
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• 2008.11a
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• pp.325-327
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• 2008

In electrical industry, classification methodology has been an important issue for analyzing power consumption patterns. It has many applications including decisions on energy purchasing, load switching as well as helping in infrastructure development. Our aim in this work is to classify the electrical section and find potentially non-safe electrical sections. For this purpose, we use Emerging Patterns based classification. The classification method uses the aggregate score of emerging patterns to build classifier. The proposed methodology was applied to a set of electrical section data of the Korea power. The test data and relational electricity information and knowledge are supported by Korea Electric Power Research Institute (KEPRI).

• Journal of Agricultural Extension & Community Development
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• v.21 no.4
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• pp.1093-1124
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• 2014

Studies on the role of agricultural science colleges are mostly divided into agricultural production, which is the primary function of agriculture, and other functions, which have recently begun to be emphasized as a result of social needs. With the green revolution and the aging of the farming population, there is a strong view that the role of agricultural science colleges should remain as it is. However, agriculture is expanding in terms of concept and content by converging with other industries not traditionally associated with agricultural production. Thus, the fields that now need to form part of agricultural science knowledge are becoming more detailed and expansive. The government's perception remains at the level of merely fostering farmers. This was evident in a survey on the employment rate, a factor used to evaluate colleges, in which the role of agricultural science colleges was limited to fostering farmers. Agro- industry fields, other than agriculturalists, include general industries in which the academic fields of agricultural science are combined with other academic fields. Thus, even when someone is employed in an industry that requires background knowledge of agricultural science, there is often a perception that he or she is employed in a field that is irrelevant to the major. This study examines the role of agricultural science colleges in agriculture and farm villages by focusing on the employment of graduates of these colleges within agro-industry. We categorize academic research on agricultural science into 16 fields, based on the medium level of the National Standard Science and Technology Classification Codes. Then, we categorize the employment fields into 168 fields, based on the small classification level of the inter-industry relations classification. Thus, we investigate 220 departments of 37 colleges, nationwide. Our findings show that the average employment rate of graduates of agricultural science colleges is 69.0%. Furthermore, 33.0% of all employees work in agro-industry fields that require background knowledge in agricultural science, which is one out of three job seekers. Then, 3.6% of employees work in business startups in agro-industry. The aforementioned government survey showed that only 0.1% of all college graduates in Korea were employed as agriculturalists in 2013. However, our results showed that 13.3% of graduates were working as agriculturalists, which is significantly different to the results of the government survey. These results confirm that agricultural science colleges contribute greatly to the employment of graduates, including farmers, agro-industry, and business startups in agro-industry fields.

• Journal of Agricultural Extension & Community Development
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• v.23 no.3
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• pp.243-255
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• 2016

This study aims to identify the characteristics of businesses influencing the choice of their type in the 6th industry and analyze how they work. This study analyzed data of 752 businesses certified as belonging to the 6th industry in 2015 through the classification and regression tree (CART) algorithm in decision tree analysis. The results of analysis showed that the type of agricultural product processing, region, the type of service, and the production percentage in a province affected a choice of the type. The most important variable that impacted how businesses in the 6th industry chose their type was the type of agricultural product processing, and if a business produced simple agricultural products, it was likely to specialize into $1st^*2nd$ or $1st^*3rd$. Access to large consumption areas was a critical factor in the growth of 2nd and 3rd industrial functions. These findings would contribute to establishing a model to develop the 6th industry and empirically demonstrate the importance of access to large consumption areas for agricultural businesses and rural tourism.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.256-266
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• 2020

Spatial distribution analysis of industrial locations plays a very important role in the establishment of relevant spatial policies and plans. The first thing to consider in this analysis is what analysis indicators and spatial units are used, because the interpretation of the analysis results may vary depending on the analysis indicators and the spatial units. Therefore, this study first examines various industrial integration indicators considering spatial autocorrelation and suggests the classification of regional types of industrial aggregation through the combination of related indicators. And then, this paper aims to empirically analyze the root industry by presenting a methodology for analyzing industrial integration by various spatial units such as individual locations, grids, and administrative districts. The results of the empirical analysis show that the grid in the spatial unit can be analyzed in more detail than the administrative unit. In addition, it is expected to overcome the limitations such as differences in interpretation that may occur due to the setting of spatial units. In the classification of regional types, the south-eastern region of Ulsan, Busan, and Changwon, and the western region of the SMA of Incheon, Hwaseong, and Ansan were analyzed as the industrial cluster type.

• Safety and Health at Work
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• v.8 no.3
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• pp.267-275
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• 2017

Background: The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent. Methods: The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc. Results: The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed. Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1161-1168
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• 2014

As rapidly growing industry, ICT industry is emphasized for its importance and the demand to figure out for the performances of investment of R&D for ICT have been increasing. The performance for investment of R&D analyze mainly as commercialization, sales, hiring employee and intellectual property and so on. In this paper, we propose an analytical method for performance as focusing an application time of patent that are acquired as a result after perform the national project of ICT. We classify 4 subdivision technologies and 17 detailed classification of Korean industrial standard for 35,551 item of ICT area among national R&D project. This paper proposes computational method for required time about patent's performance. As a analyzed result we verify that the activity of technology's commercialization is most active in communication as 1.2 year of ICT among national project.

• The Journal of Society for e-Business Studies
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• v.19 no.1
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• pp.145-157
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• 2014

The advent of ubiquitous connectivity from smart devices and network is changing the lifestyles of workers and work patterns. However, the smart work for the knowledge workers and mobile workers is not yet popular due to not enough services in supporting smart work and difficulties in employing smart work. For these reasons, it is necessary to study the current smart work industry to provide the bases for incubating smart work industry and increasing the smart work adoption. In this study, first, we reviewed and analyzed the smart work services. And then, we classified the smart work industry based on their services. Second, we conducted a survey study to identify the factors that affect on the adoption of smart work. Finally, we provided the current adoption rate of smart work services and discussed the Willing-To-Use, cost, and barriers from the smart work adoption.