• ETRI Journal
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• v.30 no.6
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• pp.874-876
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• 2008

Ethernet ring protection is a new technology introduced in ITU-T Recommendation G.8032, which utilizes the generic Ethernet MAC functions. We introduce an alternative enhanced protection switching scheme to suppress penalty in the switching transient, in which the Ethernet MAC filtering database (FDB) is actively and directly modified by information disseminated from the nodes adjacent to failure. The modified FDB at all nodes are guaranteed to be consistent to form a complete new ring network topology immediately. This scheme can reduce the capacity requirement of the G.8032 by several times. This proposed scheme can be also applied in IP protection rings.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.123-128
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• 2007

It has been argued that Enterprise systems (ES) implementations are overshadowed by a high failure rate despite their promised benefits. One of the commonly cited reasons for ES implementation failures in the context of mandatory use is end-user's unwillingness or sabotage to adopt or use systems. Considering that the appropriate management of expectations may play an important role in making positive behavior toward newly implemented systems, this study examines the effect of outcome expectations on the system use activity in the mandatory use context of information systems from the Social Cognitive Theory perspective. Structural equation model analysis using LISREL 8.7 provides significant support for the proposed relationships. The empirical results suggest that outcome expectations and user satisfaction have positive effects on system use activity conceptualized by immersion, reinvention, and learning. Theoretical and practical implications of the study shed some light on how to improve system use activity in the mandatory use context of information systems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1356-1362
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• 2015

Modular snake-like robots are robust for failure and have flexible locomotions for obstacle environment than of walking robot. This requires an adaptation capability which is obtained from a learning approach, but has not been analysed as well. In order to investigate the property of adaptation of locomotion for different terrains, NEAT controllers are trained for a flat terrain and tested for obstacle terrains. The input and output characteristics of the adaptation for the neural network controller are analyzed for different terrains in frequency domain.

• Nuclear Engineering and Technology
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• v.45 no.1
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• pp.13-20
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• 2013

This paper looks at the Fukushima disaster from the perspective of resilience engineering, which replaces a search for causes with an understanding of how the system failed in its performance. Referring to the four resilience abilities of responding, monitoring, learning, and anticipating, the paper focuses on how inadequate engineering anticipation or risk assessment during the design, in combination with inadequate response capabilities, precipitated the disaster. One lesson is that systems such as nuclear power plants are complicated, not only in how they function during everyday or exceptional conditions, but also during their whole life cycle. System functions are intrinsically coupled synchronically and diachronically in ways that may affect the ability to respond to extreme conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.345-346
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• 2018

Internet of Things (IoT) data is collected in real time and is treated as highly reliable data because of its high precision. However, IoT data is not always highly reliable data. Because, data be often incomplete values for reasons such as sensor aging and failure, poor operating environment, and communication problems. So, we propose the methodology for solve this problem. Our methodology implements multiple LSTM networks to individually process the data collected from the sensors and a single LSTM network that batches the input data into an array. And, we propose an efficient method for constructing LSTM in IoT environment.

• The Journal of Asian Finance, Economics and Business
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• v.2 no.2
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• pp.27-34
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• 2015

The 21st century belongs to biotechnology as it made profound impact in the field of health, food, agriculture and environmental protection. India's biotechnology industry is poised to record substantial growth, perhaps even overtake the robust IT industry. The objectives of the study are to determine the existing HR practices in Biotech Industry and to understand the need for the up gradation in existing HR Policies. Conclusive and descriptive research design has been used. Data is collected from 122 employees in 23 companies of Biotech Industry. It was found that Biotechnology companies require managers with unique qualities. The lack of solid managerial training and the associated risk of failure often have long-term consequences for the careers of research professionals. The efforts to achieve excellence through a focus on learning, quality, teamwork, and reengineering are driven by the way organizations get things done and how they treat people. Biotech industry is trying to establish itself in India for last one decade but is not showing any phenomenal growth because they still do not valuing their human resource as much they should be.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.211-220
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• 2021

The process of inspecting SMDs on the PCB boards improves the product quality, performance and reduces frequent issues in this field. However, undesirable scenarios such as assembly failure and device breakdown can occur sometime during the assembly process and result in costly losses and time-consuming. The detection of these components with a model based on deep learning may be effective to reduce some errors during the inspection in the manufacturing process. In this paper, YOLO models were used due to their high speed and good accuracy in classification and target detection. A SMD detection and classification method using YOLO networks based on robust data preprocessing and augmentation techniques to deal with various types of variation such as illumination and geometric changes is proposed. For 9 different components of data provided from a PCB manufacturer company, the experiment results show that YOLOv4 is better with fast detection and classification than YOLOv3.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.407-419
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• 2022

The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.

• School Mathematics
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• v.10 no.1
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• pp.123-138
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• 2008

The objective of this paper is to reveal the cause of failure of New Math in the field of the SMSG area education from the didactical point of view. At first, we analyzed Euclid's (Elements), De Morgan's (Elements of arithmetic), and Legendre's (Elements of geometry and trigonometry) in order to identify characteristics of the area conception in the SMSG. And by analyzing the controversy between Wittenberg(1963) and Moise(1963), we found that the elementariness and the mental object of the area concept are the key of the success of SMSG's approach. As a result, we conclude that SMSG's approach became separated from the mathematical contents of the similarity concept, the idea of same-area, incommensurability and so on. In this account, we disclosed that New Math gave rise to the lack of elementariness and geometrical mental object, which was the fundamental cause of failure of New Math.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.