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Prof. Nam-Wook Cho
Seoul National University of Science and Technology

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0 Business Process Management
0 Patent Analysis
0 Process Mining
0 Social Network Analysis
0 outlier detection

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Journal article
Published: 31 October 2020 in Sustainability
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This study analyzes the technology fusion phenomena and its characteristics, focusing on the solar photovoltaic (PV) industry in South Korea. Co-occurrence networks of international patent classification (IPC) codes have been analyzed based on the photovoltaic patents in South Korea during a 15-year period (2002–2016). The results reveal that, while the strength of technology fusion has greatly increased during the period, the structural pattern of fusion has been diversified or decentralized. In the early stage, widespread emergence of new technologies has been observed but, in the later stage, the focus of fusion shifted to the utilization of existing technologies. The characteristics of key technologies also changed as the technology fusion progressed. In the early stage, product technologies such as materials and components played a central role, while operation technologies such as monitor, structure, and arrangement were the drivers of fusion during the later stage.

ACS Style

Sungho Son; Nam-Wook Cho. Technology Fusion Characteristics in the Solar Photovoltaic Industry of South Korea: A Patent Network Analysis Using IPC Co-Occurrence. Sustainability 2020, 12, 9084 .

AMA Style

Sungho Son, Nam-Wook Cho. Technology Fusion Characteristics in the Solar Photovoltaic Industry of South Korea: A Patent Network Analysis Using IPC Co-Occurrence. Sustainability. 2020; 12 (21):9084.

Chicago/Turabian Style

Sungho Son; Nam-Wook Cho. 2020. "Technology Fusion Characteristics in the Solar Photovoltaic Industry of South Korea: A Patent Network Analysis Using IPC Co-Occurrence." Sustainability 12, no. 21: 9084.

Journal article
Published: 31 December 2019 in Journal of Society of Korea Industrial and Systems Engineering
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ACS Style

Kyung-Soo Kim; Nam-Wook Cho. A Case Study on Qualitative Efficiency of National R&D Projects on Technical Performances : Focused on Livestock Quarantine. Journal of Society of Korea Industrial and Systems Engineering 2019, 42, 8 -15.

AMA Style

Kyung-Soo Kim, Nam-Wook Cho. A Case Study on Qualitative Efficiency of National R&D Projects on Technical Performances : Focused on Livestock Quarantine. Journal of Society of Korea Industrial and Systems Engineering. 2019; 42 (4):8-15.

Chicago/Turabian Style

Kyung-Soo Kim; Nam-Wook Cho. 2019. "A Case Study on Qualitative Efficiency of National R&D Projects on Technical Performances : Focused on Livestock Quarantine." Journal of Society of Korea Industrial and Systems Engineering 42, no. 4: 8-15.

Journal article
Published: 29 November 2018 in Automation in Construction
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The Industry Foundation Classes (IFC), an open and neutral ISO standard, plays a key role in enabling interoperability, allowing entity and relationship data to be exchanged seamlessly between Building Information Modeling (BIM) applications. However, due to the lack of formal rigidity, data exchanges can often be arbitrary and susceptible to errors, omissions and misrepresentations. This research applied support vector machines (SVM), a technique of machine learning, to check the semantic integrity of mappings between BIM elements and IFC classes. The SVM was trained to distinguish model elements from a dataset of 4187 unique elements collected from six architectural BIM models, based on their geometric and relational features. Using a two staged approach, the SVM was first tested to classify the elements with respect to eight IFC classes. Secondly, the SVM was further tested to distinguish between the element subtypes within individual IFC classes. Results of high accuracy (ACC) and F1 scores in both stages attested to the power and generality of the algorithm. The developed approach provides a way to verify BIM models for data consistency, as well as add semantics required for domain-specific analysis. Practically, the approach is envisioned to be of value for automating the quality checks of BIM deliverables, which is still largely a manual process.

ACS Style

Bonsang Koo; Sunmin La; Nam-Wook Cho; Youngsu Yu. Using support vector machines to classify building elements for checking the semantic integrity of building information models. Automation in Construction 2018, 98, 183 -194.

AMA Style

Bonsang Koo, Sunmin La, Nam-Wook Cho, Youngsu Yu. Using support vector machines to classify building elements for checking the semantic integrity of building information models. Automation in Construction. 2018; 98 ():183-194.

Chicago/Turabian Style

Bonsang Koo; Sunmin La; Nam-Wook Cho; Youngsu Yu. 2018. "Using support vector machines to classify building elements for checking the semantic integrity of building information models." Automation in Construction 98, no. : 183-194.

Journal article
Published: 26 October 2018 in Journal of Risk and Financial Management
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Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however, either depend on subjective judgements or require significant efforts. To overcome the limitations, this paper proposes a novel approach for supplier segmentation. The objective of this paper is to develop an automated and effective way to identify core suppliers, whose profit impact on a buyer is significant. To achieve this objective, the application of a supervised machine learning technique, Random Forests (RF), to e-invoice data is proposed. To validate the effectiveness, the proposed method has been applied to real e-invoice data obtained from an automobile parts manufacturer. Results of high accuracy and the area under the curve (AUC) attest to the applicability of our approach. Our method is envisioned to be of value for automating the identification of core suppliers. The main benefits of the proposed approach include the enhanced efficiency of supplier segmentation procedures. Besides, by utilizing a machine learning method to e-invoice data, our method results in more reliable segmentation in terms of selecting and weighting variables.

ACS Style

Jung-Sik Hong; Hyeongyu Yeo; Nam-Wook Cho; Taeuk Ahn. Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning. Journal of Risk and Financial Management 2018, 11, 70 .

AMA Style

Jung-Sik Hong, Hyeongyu Yeo, Nam-Wook Cho, Taeuk Ahn. Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning. Journal of Risk and Financial Management. 2018; 11 (4):70.

Chicago/Turabian Style

Jung-Sik Hong; Hyeongyu Yeo; Nam-Wook Cho; Taeuk Ahn. 2018. "Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning." Journal of Risk and Financial Management 11, no. 4: 70.

Research article
Published: 10 November 2016 in PLOS ONE
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As one of data mining techniques, outlier detection aims to discover outlying observations that deviate substantially from the reminder of the data. Recently, the Local Outlier Factor (LOF) algorithm has been successfully applied to outlier detection. However, due to the computational complexity of the LOF algorithm, its application to large data with high dimension has been limited. The aim of this paper is to propose grid-based algorithm that reduces the computation time required by the LOF algorithm to determine the k-nearest neighbors. The algorithm divides the data spaces in to a smaller number of regions, called as a “grid”, and calculates the LOF value of each grid. To examine the effectiveness of the proposed method, several experiments incorporating different parameters were conducted. The proposed method demonstrated a significant computation time reduction with predictable and acceptable trade-off errors. Then, the proposed methodology was successfully applied to real database transaction logs of Korea Atomic Energy Research Institute. As a result, we show that for a very large dataset, the grid-LOF can be considered as an acceptable approximation for the original LOF. Moreover, it can also be effectively used for real-time outlier detection.

ACS Style

Jihwan Lee; Nam-Wook Cho. Fast Outlier Detection Using a Grid-Based Algorithm. PLOS ONE 2016, 11, e0165972 .

AMA Style

Jihwan Lee, Nam-Wook Cho. Fast Outlier Detection Using a Grid-Based Algorithm. PLOS ONE. 2016; 11 (11):e0165972.

Chicago/Turabian Style

Jihwan Lee; Nam-Wook Cho. 2016. "Fast Outlier Detection Using a Grid-Based Algorithm." PLOS ONE 11, no. 11: e0165972.

Journal article
Published: 15 June 2016 in Journal of the Korean Institute of Industrial Engineers
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ACS Style

Sungmin Song; Nam-Wook Cho; Taegu Kim. Success Factors of Game Products by Using a Diffusion Model and Cluster Analysis. Journal of the Korean Institute of Industrial Engineers 2016, 42, 222 -230.

AMA Style

Sungmin Song, Nam-Wook Cho, Taegu Kim. Success Factors of Game Products by Using a Diffusion Model and Cluster Analysis. Journal of the Korean Institute of Industrial Engineers. 2016; 42 (3):222-230.

Chicago/Turabian Style

Sungmin Song; Nam-Wook Cho; Taegu Kim. 2016. "Success Factors of Game Products by Using a Diffusion Model and Cluster Analysis." Journal of the Korean Institute of Industrial Engineers 42, no. 3: 222-230.

Journal article
Published: 01 September 2015 in International Journal of Distributed Sensor Networks
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Voice over Internet Protocol (VoIP) is an emerging communication service that has advanced in ubiquitous computing environments. Although VoIP is inexpensive and offers additional services, there has been little provision for attacks at the weak points. With the advances of Wireless Sensor Network (WSN) technologies, the risk is increasing. Due to the resource constraints of WSN, attacks have become easier, making protection of the network more difficult. In this work, we attempt to distinguish fraud call attacks as outliers from normal calls on the basis of call detail records. We adopted and applied a Local Outlier Factor (LOF) method on real call data, which include actual fraud call attacks. Our results show the outlier detection method can be effective in detecting fraud calls. Moreover, introducing two additional attributes related to fraud call characteristics enhanced the detection performance.

ACS Style

Kyung-Il Kim; Taegu Kim; Nam-Wook Cho; Minsoo Kim. Toll Fraud Detection of VoIP Service Networks in Ubiquitous Computing Environments. International Journal of Distributed Sensor Networks 2015, 11, 1 .

AMA Style

Kyung-Il Kim, Taegu Kim, Nam-Wook Cho, Minsoo Kim. Toll Fraud Detection of VoIP Service Networks in Ubiquitous Computing Environments. International Journal of Distributed Sensor Networks. 2015; 11 (9):1.

Chicago/Turabian Style

Kyung-Il Kim; Taegu Kim; Nam-Wook Cho; Minsoo Kim. 2015. "Toll Fraud Detection of VoIP Service Networks in Ubiquitous Computing Environments." International Journal of Distributed Sensor Networks 11, no. 9: 1.

Journal article
Published: 28 June 2014 in The Journal of the Korea Contents Association
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ACS Style

Tae-Gu Kim; Nam-Wook Cho; Jung-Sik Hong. Characteristics of Korean Film Market by Using Social Network Analysis. The Journal of the Korea Contents Association 2014, 14, 93 -107.

AMA Style

Tae-Gu Kim, Nam-Wook Cho, Jung-Sik Hong. Characteristics of Korean Film Market by Using Social Network Analysis. The Journal of the Korea Contents Association. 2014; 14 (6):93-107.

Chicago/Turabian Style

Tae-Gu Kim; Nam-Wook Cho; Jung-Sik Hong. 2014. "Characteristics of Korean Film Market by Using Social Network Analysis." The Journal of the Korea Contents Association 14, no. 6: 93-107.