• Korean Journal of Construction Engineering and Management
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• v.14 no.3
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• pp.78-87
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• 2013

The modern society has been drastically changed from the industrial economic society to the knowledge based society, to catch up with the knowledge and the change of technology required for the modern people, the people can not live in the modern society without the continued study or education. In case of architectural design firm, it is concentrating on the productivity of enterprise by cultivating the working level through the self education focused on the improvement of inner capacity. In connection with this, the efficiency of enterprises are analyzed by carrying out the Data Envelopment Analysis(DEA) utilizing the financial ratio index in the various field of industries recently, the analysis study for the efficiency utilizing DEA is increased in the construction industries as well. However, in case of construction industries, the study focused on the efficiency of administration only has been progressed, it is the real situation that the approach for the analysis of education efficiency of each enterprise is very insufficient. Therefore, this study analyzed the education efficiency of architectural design firm after the selection of input and output variables by utilizing the DEA model and utilizing the AHP analysis technique by deducting the variables through the preceding study in relation to the education efficiency and the interview with the specialists.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.595-604
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• 2004

Automatic analysis of concepts or semantic relations from text documents enables not only an efficient acquisition of relevant information, but also a comparison of documents in the concept level. We present a multiple cause model-based approach to text analysis, where latent topics are automatically extracted from document sets and similarity between documents is measured by semantic kernels constructed from the extracted topics. In our approach, a document is assumed to be generated by various combinations of underlying topics. A topic is defined by a set of words that are related to the same topic or cooccur frequently within a document. In a network representing a multiple-cause model, each topic is identified by a group of words having high connection weights from a latent node. In order to facilitate teaming and inferences in multiple-cause models, some approximation methods are required and we utilize an approximation by Helmholtz machines. In an experiment on TDT-2 data set, we extract sets of meaningful words where each set contains some theme-specific terms. Using semantic kernels constructed from latent topics extracted by multiple cause models, we also achieve significant improvements over the basic vector space model in terms of retrieval effectiveness.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.1
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• pp.76-86
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• 2008

This paper estimates the TFP in IT manufacturing (total factor productivity) by employment size of establishment and analyses the determinants of it. And the panel data is consisted of time series and cross section data of 4 employment size of establishment over $1990{\sim}2004$. During the period from 1991 to 1997 TFP increased positively irrespective of the employment size of establishment, but from 1998 to 2004 TFP increase rate turned negative except large size(more than 300) of establishment. TFP assume macro variables and policy variables as the determinants of IT manufacturing TFP. The analysis of whole size of establishment shows that sales growth rate is significantly positive, which makes us conclude that there is a teaming by doing effect and economy of scale. But some variables(i.e. IT capital stock, policy financing, and openness etc.) are significant in only a few models. So there may be different effect by employment size of establishment. In TFP determinants analysis by employment size of establishment, we find that coefficients of policy financing and openness variables are significantly positive. The larger employment size of establishment is, the larger scale economy is. And for large size(more than 300) establishment, IT capital stock helps propel the increase of the productivity.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.87-104
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• 2015

Currently the IoT (Internet of Things) environments and related technologies are being developed rapidly through the networks for connecting many intelligent objects. The IoT is providing artificial intelligent services combined with context recognition based knowledge and communication methods between human and objects and objects to objects. With the help of IoT technology, many research works are being developed using the C-ITS (Cooperative Intelligent Transport System) which uses road infrastructure and traveling vehicles as traffic control infrastructures and resources for improving and increasing driver's convenience and safety through two way communication such as bus-only lane and license plate recognition and road accidents, works ahead reports, which are eventually for advancing traffic effectiveness. In this paper, a system for deciding whether the traveling vehicle is possible or not to drive on bus-only lane in highway is researched using the lane and number plate recognition on the road in C-ITS traffic infrastructure environments. The number plates of vehicles on the straight ahead and sides are identified after the location of bus-only lane is discovered through the lane recognition method. Research results and experimental outcomes are presented which are supposed to be used by traffic management infrastructure and controlling system in future.

• Journal of the Korea Society for Simulation
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• v.27 no.1
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• pp.61-73
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• 2018

In the defense modeling and simulation, for the detailed analysis of the communication system, many studies have carried out the analysis under the C3 SoS(system of systems) which consists of C2(command and control) and C(communication). However, it requires time and space constraints of the C2 system. To solve this problem, this paper proposes a communication analysis method in the standalone system environment which is combined with the C system after abstracting the C2 system. In the abstraction process, we hypothesize the traffic model and mobility model for C system analysis and learn the parameters in the model based on machine learning. Through the proposed method, it is possible to construct traffic and mobility model with different output according to the battlefield. This case study shows how the process can be applied to the C3 SoS and the enhanced accuracy than the existing method. We expect that it is possible to carry out the efficient communication analysis against many experimental scenarios with various communication parameters.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.750-762
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• 2019

The importance of the call center as a contact point for the enterprise is growing. However, call centers have difficulty with their operating agents due to the agents' lack of knowledge and owing to frequent agent turnover due to downturns in the business, which causes deterioration in the quality of customer service. Therefore, through an N-bank call center case study, we developed a system to reduce the burden of keeping up business knowledge and to improve customer service quality. It is a "real-time agent advisor" system that provides agents with answers to customer questions in real time by combining AI technology for speech recognition, natural language processing, and questions & answers for existing call center information systems, such as a private branch exchange (PBX) and computer telephony integration (CTI). As a result of the case study, we confirmed that the speech recognition system for real-time call analysis and the corpus construction method improves the natural speech processing performance of the query response system. Especially with name entity recognition (NER), the accuracy of the corpus learning improved by 31%. Also, after applying the agent advisor system, the positive feedback rate of agents about the answers from the agent advisor was 93.1%, which proved the system is helpful to the agents.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.435-442
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• 2018

Outlier detection means to detect data samples that deviate significantly from the distribution of normal data. Most outlier detection methods calculate an outlier score that indicates the extent to which a data sample is out of normal state and determine it to be an outlier when its outlier score is above a given threshold. However, since the range of an outlier score is different for each data and the outliers exist at a smaller ratio than the normal data, it is very difficult to determine the threshold value for an outlier score. Further, in an actual situation, it is not easy to acquire data including a sufficient amount of outliers available for learning. In this paper, we propose a clustering-based outlier detection method by constructing a model representing a normal data region using only normal data and performing binary classification of outliers and normal data for new data samples. Then, by dividing the given normal data into chunks, and constructing a clustering model for each chunk, we expand it to the ensemble method combining the decision by the models and apply it to the streaming data with dynamic changes. Experimental results using real data and artificial data show high performance of the proposed method.

• Korean Journal of Heritage: History & Science
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• v.51 no.2
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• pp.154-169
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• 2018

In modern society, cultural heritage has played a role in constituting national identity. The Cultural Heritage Education Project started in the 2000s by the Cultural Heritage Administration was also aware of the issue of sustainable development and cultural diversity as major cultural issues at the time. However, the main purpose of previous cultural heritage education was to foster national identity. The Cultural Heritage Administration has executed cultural heritage education programs since 2006. The education program of the cultural heritage teacher visiting the school, the project to designate a cultural heritage school, and an education program to experience cultural heritage at an archaeological site were carried out. In the 2010s, the theme of cultural rights and enjoyment of cultural heritage in life was raised as an important issue. Cultural heritage education had to accept the 'new meaning of cultural heritage', 'cultural rights', and 'learnercentered education'. In this context, the local cultural heritage education project started. The region is a space where various identities are reconstructed. However, local cultural heritage education itself cannot realize cultural heritage enjoyment in life. Therefore, it is necessary to seek cultural heritage in life through the various efforts of local cultural heritage education.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.105-112
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• 2021

In the era of big data, interest in data is exploding. In particular, the development of the Internet and social media has led to the creation of new data, enabling the realization of the era of big data and artificial intelligence and opening a new chapter in convergence technology. Also, in the past, there are many demands for analysis of data that could not be handled by programs. In this paper, an analysis model was designed and verified for classification of unstructured data, which is often required in the era of big data. Data crawled DBPia's thesis summary, main words, and sub-keyword, and created a database using KoNLP's data dictionary, and tokenized words through morpheme analysis. In addition, nouns were extracted using KAIST's 9 part-of-speech classification system, TF-IDF values were generated, and an analysis dataset was created by combining training data and Y values. Finally, The adequacy of classification was measured by applying three analysis algorithms(random forest, SVM, decision tree) to the generated analysis dataset. The classification model technique proposed in this paper can be usefully used in various fields such as civil complaint classification analysis and text-related analysis in addition to thesis classification.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.33-39
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• 2021

Speech recognition technology is being combined with deep learning and is developing at a rapid pace. In particular, voice recognition services are connected to various devices such as artificial intelligence speakers, vehicle voice recognition, and smartphones, and voice recognition technology is being used in various places, not in specific areas of the industry. In this situation, research to meet high expectations for the technology is also being actively conducted. Among them, in the field of natural language processing (NLP), there is a need for research in the field of removing ambient noise or unnecessary voice signals that have a great influence on the speech recognition recognition rate. Many domestic and foreign companies are already using the latest AI technology for such research. Among them, research using a convolutional neural network algorithm (CNN) is being actively conducted. The purpose of this study is to determine the non-voice section from the user's speech section through the convolutional neural network. It collects the voice files (wav) of 5 speakers to generate learning data, and utilizes the convolutional neural network to determine the speech section and the non-voice section. A classification model for discriminating speech sections was created. Afterwards, an experiment was conducted to detect the non-speech section through the generated model, and as a result, an accuracy of 94% was obtained.