• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.6
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• pp.544-550
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• 2015

The opinion mining technique which analogize significant information from SNS message is increasingly important because opinions communicated through SNS are increasing. This paper propose SEIAS(SNS Emotional Information Analysis Strategy) based on opinion mining that analogize emotional information from SNS setting a different weight according to position of antonym and adverb. Firstly, the proposed SEIAS constructs a emotion dictionary for opinion mining analysis, Secondly, it collects SNS data on real time, compare it with emotion dictionary and calculates opinion value of SNS data. Specially, it increases the precision of opinion analysis result compared to the existing SO-PMI because it sets up the different value according to the position of antonym and adverb when it calculates the opinion value of data.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.15-37
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• 2004

With the growth in the size of datasets, data mining has recently become an important research topic. Especially, interests about spatio-temporal data mining has been increased which is a method for analyzing massive spatio-temporal data collected from a wide variety of applications like GPS data, trajectory data of surveillance system and earth geographic data. In the former approaches, conventional clustering algorithms are applied as spatio-temporal data mining techniques without any modification. In this paper, we focused to SOM that is the most common clustering algorithm applied to clustering analysis in data mining wet and develop the spatio-temporal data mining module based on it. In addition, we analyzed the clustering results of developed SOM module and compare them with those of K-means and Agglomerative Hierarchical algorithm in the aspects of homogeneity, separation, separation, silhouette width and accuracy. We also developed specialized visualization module fur more accurate interpretation of mining result.

• Journal of Microbiology and Biotechnology
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• v.24 no.3
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• pp.313-323
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• 2014

Significant alteration in the microbial community can occur across reclamation areas suffering subsidence from mining. A reclamation site undergoing fertilization practices and an adjacent coal-excavated subsidence site (sites A and B, respectively) were examined to characterize the bacterial diversity using 454 high-throughput 16S rDNA sequencing. The dominant taxonomic groups in both the sites were Proteobacteria, Acidobacteria, Bacteroidetes, Betaproteobacteria, Actinobacteria, Gammaproteobacteria, Alphaproteobacteria, Deltaproteobacteria, Chloroflexi, and Firmicutes. However, the bacterial communities' abundance, diversity, and composition differed significantly between the sites. Site A presented higher bacterial diversity and more complex community structures than site B. The majority of sequences related to Proteobacteria, Gemmatimonadetes, Chloroflexi, Nitrospirae, Firmicutes, Betaproteobacteria, Deltaproteobacteria, and Anaerolineae were from site A; whereas those related to Actinobacteria, Planctomycetes, Bacteroidetes, Verrucomicrobia, Gammaproteobacteria, Nitriliruptoria, Alphaproteobacteria, and Phycisphaerae originated from site B. The distribution of some bacterial groups and subgroups in the two sites correlated with soil properties and vegetation due to reclamation practice. Site A exhibited enriched bacterial community, soil organic matter (SOM), and total nitrogen (TN), suggesting the presence of relatively diverse microorganisms. SOM and TN were important factors shaping the underlying microbial communities. Furthermore, the specific plant functional group (legumes) was also an important factor influencing soil microbial community composition. Thus, the effectiveness of 454 pyrosequencing in analyzing soil bacterial diversity was validated and an association between land ecological system restoration, mostly mediated by microbial communities, and an improvement in soil properties in coal-mining reclamation areas was suggested.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.133-140
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• 2011

For both customers and companies, it is very important to analyze online customer reviews, which consist of small documents that include opinions or experiences about products or services, because the customers can get good informations and the companies can establish good marketing strategies. In this paper, we propose the association model for the opinion mining which can analyze customer opinions posted on web. The association model is to modify the association rules mining model in data mining in order to apply efficiently and effectively the association mining techniques to the opinion mining. We designed and implemented the opinion mining systems based on the modified association model and the grouping idea which would enable it to generate significant rules more.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.457-462
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• Safety and Health at Work
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• v.14 no.2
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• pp.193-200
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• 2023

Background: International evidence shows that mining workers are at greater risk of suicide than other workers; however, it is not known whether this applies to the Australian mining sector. Methods: Using data from the National Coronial Information System, rates of suicide among male mining workers were compared to those of three comparators: construction workers, mining and construction workers combined, and all other workers. Age-standardized suicide rates were calculated for 2001-2019 and across three intervals '2001-2006', '2007-2011', and '2012-2019'. Incidence rate ratios for suicide were calculated to compare incidence rates for mining workers, to those of the three comparative groups. Results: The suicide rate for male mining workers in Australia was estimated to be between 11 and 25 per 100,000 (likely closer to 25 per 100,000) over the period of 2001-2019. There was also evidence that the suicide rate among mining workers is increasing, and the suicide rate among mining workers for the period 2012-2019 was significantly higher than the other worker group. Conclusions: Based on available data, we tentatively deduce that suicide mortality among male mining workers is of concern. More information is needed on both industry and occupation of suicide decedents in order to better assess whether, and the extent to which, mining workers (and other industries and occupations) are at increased risk of suicide.

• Journal of Internet Computing and Services
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• v.14 no.1
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• pp.63-69
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• 2013

Approximate Frequent pattern mining is to find approximate patterns, not exact frequent patterns with tolerable variations for more efficiency. As the size of database increases, much faster mining techniques are needed to deal with huge databases. Moreover, it is more difficult to discover exact results of mining patterns due to inherent noise or data diversity. In these cases, by mining approximate frequent patterns, more efficient mining can be performed in terms of runtime, memory usage and scalability. In this paper, we study the characteristics of an approximate mining algorithm based on probabilistic technique and run performance evaluation of the efficient approximate frequent pattern mining algorithm. Finally, we analyze the test results for more improvement.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.3
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• pp.35-46
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• 2016

Social network services (SNS) that help to build relationship network and share a particular interest or activity freely according to their interests by posting comments, photos, videos,${\ldots}$ on online communities such as blogs have adopted and developed widely as a social phenomenon. Several researches have been done to explore the pattern and valuable information in social networks data via text mining such as opinion mining and semantic analysis. For improving the efficiency of text mining, keyword-based approach have been applied but most of researchers argued the limitations of the rules of Korean orthography. This research aims to construct a database of non-standard Korean words which are difficulty in data mining such abbreviations, slangs, strange expressions, emoticons in order to improve the limitations in keyword-based text mining techniques. Based on the study of subjective opinions about specific topics on blogs, this research extracted non-standard words that were found useful in text mining process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3131-3149
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• 2016

In-band Full-duplex (IBFD) wireless communication allows improved throughput for wireless networks. The current Half-duplex (HD) medium access mechanism Request to Send/Clear to Send (RTS/CTS) has been directly applied to IBFD wireless networks. However, this is only able to support a symmetric dual link, and does not provide the full advantages of IBFD. To increase network throughput in a superior way to the HD mechanism, a novel three-way handshaking access mechanism RTS/SRTS (Second Request to Send)/CTS is proposed for point to multipoint (PMP) IBFD wireless networks, which can support both symmetric dual link and asymmetric dual link communication. In this approach, IBFD wireless communication only requires one channel access for two-way simultaneous packet transmissions. We first describe the RTS/SRTS/CTS mechanism and the symmetric/asymmetric dual link transmission procedure and then provide a theoretical analysis of network throughput and delay using a Markov model. Using simulations, we demonstrate that the RTS/SRTS/CTS access mechanism shows improved performance relative to that of the RTS/CTS HD access mechanism.

• Journal of Power Electronics
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• v.13 no.6
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• pp.1080-1089
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• 2013

In this paper, the superposition principle of a heat sink temperature rise is verified based on the mathematical model of a plate-fin heat sink with two mounted heat sources. According to this, the distributed coupling thermal impedance matrix for a heat sink with multiple devices is present, and the equations for calculating the device transient junction temperatures are given. Then methods to extract the heat sink thermal impedance matrix and to measure the Epoxy Molding Compound (EMC) surface temperature of the power Metal Oxide Semiconductor Field Effect Transistor (MOSFET) instead of the junction temperature or device case temperature are proposed. The new thermal impedance model for the power converters in Switched Reluctance Motor (SRM) drivers is implemented in MATLAB/Simulink. The obtained simulation results are validated with experimental results. Compared with the Finite Element Method (FEM) thermal model and the traditional thermal impedance model, the proposed thermal model can provide a high simulation speed with a high accuracy. Finally, the temperature rise distributions of a power converter with two control strategies, the maximum junction temperature rise, the transient temperature rise characteristics, and the thermal coupling effect are discussed.