• The KIPS Transactions:PartD
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• v.17D no.6
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• pp.453-462
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• 2010

The switch from analog to digital broadcast television is extended rapidly. The DTV can offer multiple programming choices, interactive capabilities and so on. Moreover, with the spread of Internet, the information exchange between the communities is increasing, too. These facts lead to the new TV service environment which can offer customized TV programs to personal/community users. This paper proposes a 'Customized Digital TV System for Individuals/Communities based on Data Stream Mining' which can analyze user's pattern of TV watching behavior. Due to the characteristics of TV program data stream and EPG(electronic program guide), the data stream mining methods are employed in the proposed system. When a user is watching DTV, the proposed system can control the surrounding circumstances as using the user behavior profiles. Furthermore, the channel recommendation system on the smart phone environment is proposed to utilize the profiles widely.

• Geomechanics and Engineering
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• v.23 no.1
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• pp.61-69
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• 2020

The uniaxial compressive strength (UCS) of rock is a basic parameter in underground engineering design. The disadvantages of this commonly employed laboratory testing method are untimely testing, difficulty in performing core testing of broken rock mass and long and complicated onsite testing processes. Therefore, the development of a fast and simple in situ rock UCS testing method for field use is urgent. In this study, a multi-function digital rock drilling and testing system and a digital core bit dedicated to the system are independently developed and employed in digital drilling tests on rock specimens with different strengths. The energy analysis is performed during rock cutting to estimate the energy consumed by the drill bit to remove a unit volume of rock. Two quantitative relationship models of energy analysis-based core drilling parameters (ECD) and rock UCS (ECD-UCS models) are established in this manuscript by the methods of regression analysis and support vector machine (SVM). The predictive abilities of the two models are comparatively analysed. The results show that the mean value of relative difference between the predicted rock UCS values and the UCS values measured by the laboratory uniaxial compression test in the prediction set are 3.76 MPa and 4.30 MPa, respectively, and the standard deviations are 2.08 MPa and 4.14 MPa, respectively. The regression analysis-based ECD-UCS model has a more stable predictive ability. The energy analysis-based rock drilling method for the prediction of UCS is proposed. This method realized the quick and convenient in situ test of rock UCS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.1-10
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• 2018

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

• The KIPS Transactions:PartD
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• v.18D no.4
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• pp.237-244
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• 2011

Opinion Mining summarizes with classifying sensitive opinions of customers in huge online customer reviews for the attributes of products or services by positive and negative opinions. Because the customers represent their interests through subjective opinions as well as objective facts, the existing opinion mining techniques, which can analyze just the sensitive opinions, need to be expanded.. In this paper, We propose the novel entity association network model which expands the existing opinion mining techniques. The entity association model can not only represent positive and negative degree of the sensitive opinions, but also can represent the degree of the associations and relative importances between entities. We designed and implemented the customer reviews analysis system based on the entity association network model. We recognized that the system can represent more abundant information than the existing opinion mining techniques.

• Geomechanics and Engineering
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• v.25 no.3
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• pp.179-194
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• 2021

In this paper, to study the stability of surrounding rock during roadway excavation in different rock mass structures, the physical model test for roadway excavation process in three types of intact rock mass, layered rock mass and massive rock mass were carried out by using the self-developed two-dimensional simulation testing system of complex underground engineering. Firstly, based on the engineering background of a deep mine in eastern China, the similar materials of the most appropriate ratio in line with the similarity theory were tested, compared and determined. Then, the physical models of four different schemes with 1000 mm (height) × 1000 mm (length) × 250 mm (width) were constructed. Finally, the roadway excavation was carried out after applying boundary conditions to the physical model by the simulation testing system. The results indicate that the supporting effect of rockbolts has a great influence on the shallow surrounding rock, and the rock mass structure can affect the overall stability of the surrounding rock. Furthermore, the failure mechanism and bearing capacity of surrounding rock were further discussed from the comparison of stress evolution characteristics, distribution of stress arch, and failure modes in different schemes.

• Geomechanics and Engineering
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• v.32 no.4
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• pp.375-385
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• 2023

There are many joint fissures distributed in the engineering rock mass. In the process of geological history, the underground rock mass undergoes strong geological processes, and undergoes complex geological processes such as fracture breeding, expansion, recementation, and re-expansion. In this paper, the damage-stick-slip process (DSSP), an analysis model used for rock mass failure slip, was established to examine the master control and time-dependent mechanical properties of the new and primary fractures of a multi-fractured rock mass under the action of stress loading. The experimental system for the recemented multi-fractured rock mass was developed to validate the above theory. First, a rock mass failure test was conducted. Then, the failure stress state was kept constant, and the fractured rock mass was grouted and cemented. A secondary loading was applied until the grouted mass reached the intended strength to investigate the bearing capacity of the recemented multi-fractured rock mass, and an acoustic emission (AE) system was used to monitor AE events and the update of damage energy. The results show that the initial fracture angle and direction had a significant effect on the re-failure process of the cement rock mass; Compared with the monitoring results of the acoustic emission (AE) measurements, the master control surface, key blocks and other control factors in the multi-fractured rock mass were obtained; The triangular shaped block in rock mass plays an important role in the stress and displacement change of multi-fracture rock mass and the long fissure and the fractures with close fracture tip are easier to activate, and the position where the longer fractures intersect with the smaller fractures is easier to generate new fractures. The results are of great significance to a multi-block structure, which affects the safety of underground coal mining.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Journal of Broadcast Engineering
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• v.14 no.5
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• pp.625-637
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• 2009

With advent of TV environment and increasing of variety of program contents, users are able to experience more various and complex environment for watching TV contents. According to the change of content watching environment, users have to make more efforts to choose his/her interested TV program contents or TV channels than before. Also, the users usually watch the TV program contents with their own regular way. So, in this paper, we suggests personalized TV program schedule recommendation system based on the analyzing users' TV watching history data. And we extract the users' watched program patterns using the sequential pattern mining method. Also, we proposed a new sequential pattern mining which is suitable for TV watching environment and verify our proposed method have better performance than existing sequential pattern mining method in our application area. In the future, we will consider a VoD characteristic for extending to IPTV program schedule recommendation system.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.57-67
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• 2005

Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilized Holland career search test results, TOEIC score, course work list and GPA score as the input for data mining, and we were able to generate knowledge and rules with regard to the college road map advisory service. Factor analysis and AHP(Analytic Hierarchy Process) were the primary techniques deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained from the human student advice experts.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.51-66
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• 2023

In this paper, we propose a new type of process discovery framework, which is named as control-path-driven process group discovery framework, to be used for process mining and process reengineering in supporting life-cycle management of business process models. In addition, we develop a process mining system based on the proposed framework and perform experimental verification through it. The process execution event logs applied to the experimental effectiveness and verification are specially defined as Process BIG-Logs, and we use it as the input datasets for the proposed discovery framework. As an eventual goal of this paper, we design and implement a control path-driven process group discovery algorithm and framework that is improved from the ρ-algorithm, and we try to verify the functional correctness of the proposed algorithm and framework by using the implemented system with a BIG-Log dataset. Note that all the process mining algorithm, framework, and system developed in this paper are based on the structural information control net process modeling methodology.