• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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• pp.57-63
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• 1996

Monitoring of grinding troble occurring during the process is classified into the quantitative data which depends upon a sensor and the qualitative knowledge which relies upon an empirical knowledge. Since grinding operation is highly related with a large amount of functional parameters, it is actually deficulty in copying wiht the grinding troubles through the process. To cope with grinding trouble, it is an effective monitoring systems when occurring the grinding process. The use of neural networks is an effective method of detection and/or monitroing on the grinding trouble. In this paper, four parameters which are derived from the AE(Acoustic Emission) signatures are identified, and grinding monitoring system utilized a back propagation learning algorithm of PDP neural networks is presented.

• Journal of the Korean Geographical Society
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• v.40 no.1 s.106
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• pp.78-95
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• 2005

The purpose of this paper is to analyze industrial and innovation networks of long-live area of Honam Region and to suggest a policy direction for regional development of rural areas where have been neglected in the knowledge-based information society. Four counties (Sunchang, Damyang, Gokseong, and Gurye) in the Southwestern region of Korea are regarded as long-live belt of Korea. Production and innovation networks :Ire analyzed based on intensive surveys of firms in the belt. Major findings from the surveys are as follows. First, there are considerably strong local networks of production firms in terms of supply of input materials and labor. There are strong backward industrial linkages of the production firms with agricultural activities and considerable forward linkages with tourism industry. In addition, Internet is becoming a useful tool for sales of the new products. Second, the analysis of the innovation networks in the long-live area suggests the development of 'virtual innovation cluster' in the era of knowledge-based information society. The results imply that this innovation networks can be developed as a virtual innovation cluster in the rural areas, which can be the basis for the development of rural innovation systems.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.890-900
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• 2010

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

• Knowledge Management Research
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• v.9 no.2
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• pp.109-127
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• 2008

This cross-level study of 77 samples from Korean 1st-tier suppliers participating in knowledge-sharing networks examined the impact of partnering(joint establishment of goal and joint problem solving system) on supplier relationship outcomes(competativeness improvement and innovative idea suggestion). The findings showed that joint establishment of goal and joint problem solving system were positively related to both supplier's competativeness improvement and its innovative idea suggestion. Whereas, joint problem solving system did account for a significant variance only in innovative idea suggestion. The findings, further, demonstrated that mutual trust moderated the relationship between joint problem solving system and supplier relationship outcomes. Implications for theory and practice are suggested.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.317-340
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• 2015

This study clarified the knowledge structure of international collaboration in psychiatry based on analyzing networks in order to construct cooperation networks for international collaboration in psychiatry in South Korea. The result of analysis of knowledge structure at a state-level is as follows. First, this study found that the rate of collaboration for five years is high as 89.97%. Moreover, this study investigated the change of rate of collaboration and international collaboration according to the passage of time, and ascertained that while the rate of international collaboration has increased, Second, this study examined the trend of research on collaboration between Asian countries, and found that collaboration between Asian countries is on a low level. Third, the country (or group) that the number of papers of international collaboration and the value of centrality are the highest is EU-28. The result of analysis of knowledge structure at a research output-level is as follows. this study analyzed the correlation of centrality with research output, and found that positive correlation exists in the three indicators of centrality, and a country with high centrality has good research output.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1598-1605
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• 2021

Recently, convolutional neural networks (CNNs) have been widely used with excellent performance in various computer vision fields, including super-resolution (SR). However, CNN is computationally intensive and requires a lot of memory, making it difficult to apply to limited hardware resources such as mobile or Internet of Things devices. To solve these limitations, network lightening studies have been actively conducted to reduce the depth or size of pre-trained deep CNN models while maintaining their performance as much as possible. This paper aims to lighten the SR CNN model, SRGAN, using the knowledge distillation among network lightening technologies; thus, it proposes four techniques with different methods of transferring the knowledge of the teacher network to the student network and presents experiments to compare and analyze the performance of each technique. In our experimental results, it was confirmed through quantitative and qualitative evaluation indicators that student networks with knowledge transfer performed better than those without knowledge transfer, and among the four knowledge transfer techniques, the technique of conducting adversarial learning after transferring knowledge from the teacher generator to the student generator showed the best performance.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1019-1029
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• 2020

In this paper, we propose a novel knowledge distillation algorithm to create an compressed deep ensemble network coupled with the combined use of local and global features of face images. In order to transfer the capability of high-level recognition performances of the ensemble deep networks to a single deep network, the probability for class prediction, which is the softmax output of the ensemble network, is used as soft target for training a single deep network. By applying the knowledge distillation algorithm, the local feature informations obtained by training the deep ensemble network using facial subregions of the face image as input are transmitted to a single deep network to create a so-called compressed ensemble DCNN. The experimental results demonstrate that our proposed compressed ensemble deep network can maintain the recognition performance of the complex ensemble deep networks and is superior to the recognition performance of a single deep network. In addition, our proposed method can significantly reduce the storage(memory) space and execution time, compared to the conventional ensemble deep networks developed for face recognition.

• Journal of Korea Technology Innovation Society
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• v.20 no.2
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• pp.487-517
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• 2017

In the context of an increased demand in universities' expansion of networks between other innovation actors, this research attempts to make a comparison on university-centered SCIE knowledge networks between regions. Using regional comparison, we have looked into these networks in regards to their characteristics, the importance of regional boundaries, and the effect of the regional industrial policy. As a result of this comparative analysis, we discovered that the point universities and research universities hold high centrality in regional knowledge networks, and that the characteristics of regions are reflected into this network. For instance, the Gyeonggi province had a preeminent level of industry-academy relationship, while for Daejeon it was public research institutions and academy, and Gangwon province it was between academy between academy. As a network analysis based on journals above SCIE levels, regional boundaries were not very clear in the network structures. However, within these boundaries, the impact of regional industrial policies were proven to be stronger in the Gang-won province where the academy-academy network was most prominent. The implication of this research outcome is that for regional innovation, government should more actively implement policies that can link academic institutes' knowledge to industry by expanding knowledge networks. In addition, we emphasize on the necessity of a regionally-appropriate policy, rather than a generalized industrial policy. And fundamentally, in regards to innovation, establishing a sound industrial infrastructure for regional development and efforts to link relevant actors are required.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1308-1313
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• 2008

Neighbor knowledge in wireless ad hoc networks provides important functionality for a number of protocols. The neighbor knowledge is acquired via Hello packets. Hello packets are periodically broadcasted by the nodes which want to advertise their existence. Usage of periodic Hello packet, however, is a big burden on the wireless ad hoc networks. This paper deals with an approach where each node acquires neighbor knowledge by observing not only Hello packets but also broadcasting packets. Analysis and computer simulation results show that the method using broadcast packets offers significant improvement over the method using Hello packet only. When the required hello packet transmission interval and the average broadcasting interval are equal, the overhead reduction is about 42%.

• Journal of the Korea Institute of Building Construction
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• v.20 no.6
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• pp.577-584
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• 2020

Construction automation and robotics have been widely adopted in the construction industry as a promising solution to such issues like a shortage of skilled labor and the difficulties workers face in harsh working environments. The analysis of the knowledge structure and its evolution from the existing articles helps identify essential knowledge elements and possible future research directions. This study attempts to (1) construct keyword networks from the papers published in the International Symposium on Automation and Robotics in Construction (ISARC), (2) investigate how keywords and keyword communities are associated with each other, and (3) examine the changes in the crucial keywords over time. Through cluster analysis, 79 keywords were categorized into four groups (BIM, Building construction, Sensing, and GPS as representative keywords) with similar structural positions. Research trends show that research themes related to Infrastructure, Construction equipment, and 3D have consistently received a large amount of attention, regardless of geographical region. Research on as-built status model utilization through BIM and Laser scanning and improving Energy performance is taking place more frequently. In contrast, research studies related to problem-solving based on Neural networks are not as common as previously. This study provides useful insights into the construction automation field, at both the macro and micro levels.