• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.403-411
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• 2016

This research has the purpose to develop a method to evaluate whether station’s area of influence has been formed, and verify formation of the area of influence through empirical analysis of all subway stations in Seoul. First, we created buffers of 100m intervals from 100m to 1000m, based on subway station exits, calculated the average land price of each buffer, and divided station areas of influence into 10 clusters using K-means clustering with the average land prices as values of observation. Subsequently, we have assumed a decreasing price curve from increasing distance from a nearby subway station, estimated a price curve and evaluated whether the area of influence actually exists using regression analysis of each cluster. The 10 area of influence clusters were largely divided into strong, weak, and no area of influence of subway station. The stations where the strong areas of influence are formed are mainly located in center, sub-centers, and local centers; stations where weak and no areas of influence are formed are mostly located in the adjacent areas of center or sub-centers or suburbs.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.40-45
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• 2015

Online social networks(OSN) are very popular nowadays. As OSNs grows, the commercial markets are expanding their social commerce by applying Influence Maximization. However, in reality, there exist more than two players(e.g., commercial companies or service providers) in this same market sector. To address the Influence Maximization problem between adversaries, we first introduced Influence Maximization against the social adversaries' problem. Then, we proposed an algorithm that could efficiently solve the problem efficiently by utilizing social network properties such as Betweenness Centrality, Clustering Coefficient, Local Bridge and Ties and Triadic Closure. Moreover, our algorithm performed orders of magnitudes better than the existing Greedy hill climbing algorithm.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.637-644
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• 2006

Biomedical image is often complex. An applied image analysis system should deal with the images which are of quite low quality and are challenging to segment. This paper presents a framework for color cell image segmentation by learning and classification online. It is a robust two-stage scheme using kernel method and watershed transform. In first stage, a two-class SVM is employed to discriminate the pixels of object from background; where the SVM is trained on the data which has been analyzed using the mean shift procedure. A real-time training strategy is also developed for SVM. In second stage, as the post-processing, local watershed transform is used to separate clustering cells. Comparison with the SSF (Scale space filter) and classical watershed-based algorithm (those are often employed for cell image segmentation) is given. Experimental results demonstrate that the new method is more accurate and robust than compared methods.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.3
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• pp.331-343
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• 2002

This paper proposes a fuzzy rule-based model as a means to build usability models between emotional satisfaction and design variables of consumer products. Based on a subtractive clustering algorithm, this model obtains partially overlapping rules from existing data and builds multiple local models each of which has a form of a linear regression equation. The best subset procedure and cross validation technique are used to select appropriate input variables. The proposed technique was applied to the modeling of luxuriousness, balance, and attractiveness of office chairs. For comparison, regression models were built on the same data in two different ways; one using only potentially important variables selected by the design experts, and the other using all the design variables available. The results showed that the fuzzy rule-based model had a great benefit in terms of the number of variables included in the model. They also turned out to be adequate for predicting the usability of a new product. Better yet, the information on the product classes and their satisfaction levels can be obtained by interpreting the rules. The models, when combined with the information from the regression models, are expected to help the designers gain valuable insights in designing a new product.

• Journal of the Economic Geographical Society of Korea
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• v.13 no.1
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• pp.147-158
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• 2010

Knowledge spillover among firms is a crucial ingredient in realizing the benefits of agglomeration. This paper provides an overview and critical assessment of the Shihwa Industrial Complex (SIC) in Korea. In order to identify the relationships and interplay among different agents in the area, a survey was conducted on business networking of industry-academia-government collaboration in research and development. Unlike the closely knit input-output relationship, the findings suggest that the technological linkages in the SIC still have room for improvement. As the role of small and medium enterprises as catalysts of regional economic development in Korea is expected to grow in importance, more effort should be made to nurture clustering and R&D networking among them.

• Mycobiology
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• v.46 no.4
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• pp.361-369
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• 2018

The rice blast fungus, Magnaporthe oryzae, is an important pathogen of rice plants. It is well known that genes encoded in the genome have different evolutionary histories that are related to their functions. Phylostratigraphy is a method that correlates the evolutionary origin of genes with evolutionary transitions. Here we applied phylostratigraphy to partition total gene content of M. oryzae into distinct classes (phylostrata), which we designated PS1 to PS7, based on estimation of their emergence time. Genes in individual phylostrata did not show significant biases in their global distribution among seven chromosomes, but at the local level, clustering of genes belonging to the same phylostratum was observed. Our phylostrata-wide analysis of genes revealed that genes in the same phylostratum tend to be similar in many physical and functional characteristics such as gene length and structure, GC contents, codon adaptation index, and level of transcription, which correlates with biological functions in evolutionary context. We also found that a significant proportion of genes in the genome are orphans, for which no orthologs can be detected in the database. Among them, we narrowed down to seven orphan genes having transcriptional and translational evidences, and showed that one of them is implicated in asexual reproduction and virulence, suggesting ongoing evolution in this fungus through lineage-specific genes. Our results provide genomic basis for linking functions of pathogenicity factors and gene emergence time.

• Journal of Acupuncture Research
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• v.38 no.4
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• pp.300-311
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• 2021

Background: So far there is no confidence in the basics of acupoint/meridian phenomena, specifically in spatial and temporal electrical manifestations in the skin. Methods: Using the skin electrodynamic introscopy, the skin areas of 32 × 64 mm² were monitored for spectral electrical impedance landscape with spatial resolution of 1 mm, at 2 kHz and 1 MHz frequencies. The detailed baseline and 2D test-induced 2 kHz-impedance phase dynamics and the 4-parameter time plots of dozens of individual points in the St32-34 regions were examined in a healthy participant and a patient with mild gastritis. Non-thermal stimuli were used: (1) (for the sick subject), microwaves and ultraviolet radiation applied alternately from opposite directions of the meridian; and (2) (for the healthy one) microwaves to St17, and cathodic/anodic stimulation of the outermost St45, alternately. Results: In both cases, the following phenomena have been observed: emergence of in-phase and/or antiphase coherent structures, exceeding the acupoint conditional size of 1 cm; collective movement along the meridian; reversible with a reversed stimulus; counter-directional dynamics of both whole structures and adjacent points; local abnormalities in sensitivity and dynamics of the 1 MHz and 2 kHz parameters indicating existence of different waveguide paths. Conclusion: It is assumed that these findings necessitate reconsideration of some basic methodological issues regarding neurogenic/acupuncture points as spatial and temporal phenomena; this requires development of an appropriate approach for identifying the acuzones patterns. These findings may be used for developing new approaches to personalized/controlled therapy/treatment.

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.1-20
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• 2020

Purpose - This paper shows an unexplored area related to involuntary delisting. Specifically, this research investigates the effect of target firm information asymmetry on the likelihood that the acquirer or newly merged firm will be forcibly delisted post-merger. Design/methodology/approach - The research uses a sample gathered on local US mergers and acquisitions from the Thomson Reuters Securities Data Company (SDC) Platinum Mergers and Acquisitions database. It applies the logistic regression with industry and year effects and corrects the error term using clustering at the industry level. The research also matches the forced delisted firms to control firms based on industry, acquisition completion year, and firm size and then employs a matched sample analysis. Findings - Findings show that M&As between firms where the target firm is opaque and burdened with high information asymmetry issues are likely to be paid for using majority stock and that M&As involving such opaque targets also have a higher likelihood of getting delisted post-merger. Research implications or Originality - Our results are relevant given the very nature of M&As which involve two players: the acquirer and target who both may have different incentives. Acquirers especially have the tendency to suffer losses and even get delisted if they over-pay for or get merged to a poor target which conceals its poor performance evidenced by higher accruals quality.

• Journal of Korea Planning Association
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• v.54 no.3
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• pp.75-90
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• 2019

As the 24-hour society arrives, human activities in daytime and nighttime urban spaces are changing drastically, and the need for new urban management policies is steadily increasing. This study analyzes the types and characteristics of Seoul's human dynamics using location-based big data and the results are summarized as follows. First, the pattern of human dynamics in Seoul repeats itself every 7 days. Second, the types of human dynamics in Seoul can be classified into five types, and each of type has its own unique time-series and local characteristics. Third, the degree of match between human dynamics and zoning system in urban planning legislation was highest in 'Type 1' residence pattern and low in other types. The following implications can be drawn from these results. First, This paper examined the methodology of analyzing the regional characteristics of Seoul through the human dynamics and obtained meaningful results. Second, This paper can derive reliable and objective pattern analysis results using Big data that reflect the overall population characteristics. Third, the scale of night-time activity in the urban space of Seoul was understood, and its distribution, patterns and characteristics identified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1223-1237
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• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.