• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.89-101
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• 2014

In this paper, we proposed a new local micro pattern, Signed Local Directional Pattern(SLDP). SLDP uses information of edges to represent the face's texture. This can produce a more discriminating and efficient code than other state-of-the-art methods. Each micro pattern of SLDP is encoded by sign and its major directions in which maximum edge responses exist-which allows it to distinguish among similar edge patterns that have different intensity transitions. In this paper, we divide the face image into several regions, each of which is used to calculate the distributions of the SLDP codes. Each distribution represents features of the region and these features are concatenated into a feature vector. We carried out facial expression recognition with feature vectors and SVM(Support Vector Machine) on Cohn-Kanade and JAFFE databases. SLDP shows better classification accuracy than other existing methods.

• Journal of East-West Nursing Research
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• v.26 no.2
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• pp.109-117
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• 2020

Purpose: We aimed to develop domains for the resilience improvement of unmarried mothers to prevent child abuse based on a nursing model of resilience. Methods: We conducted a literature review and in-depth interviews with unmarried mothers. Results: Based on Polk's nursing model of resilience, we derived 4 patterns, 10 domains, and 24 sub-domains for improving the resilience of unmarried mothers. Philosophical pattern includes the domain of parenthood preparation and dispositional pattern includes the domains of emotional support, control of emotions, and child abuse awareness correction. Situational pattern includes the domains of maternal health promotion, understanding of child development and improvement of parenting skills, and assessment of the domestic environment and modification of risk factors. Relational pattern includes the domains of enhancement of mother-infant attachment, family support, and social support. Conclusion: We identified domains for enhancing resilience based on the situational and personal characteristics of unmarried mothers. The results of this study may contribute to child abuse precention by promoting the resilience of unmarried mothers.

• Journal of the Korean GEO-environmental Society
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• v.12 no.7
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• pp.5-12
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• 2011

When choosing the support pattern of tunnel, the characteristics of rock are identified from the result of the surface geologic survey, boring, and geophysical prospecting and laboratory test. And a rock mass rating is classified and excavation method and standard support pattern are designed considering rock classification, domestic and international construction practices, numerical analysis. According to the revised design standard for tunnel, it was recommended to classify the rock mass rating for the design of tunnel into a rating based on RMR. If necessary, it proposed a flexible standard allowed applying more atomized the rock mass rating and Q-System. Also, the resonable verification of the support pattern must be accompanied because the factors affecting the structure and behavior of ground during the construction of tunnel are the main factors of uncertainty factors such as the nature of ground, ground water and the characteristics of structural materials. These days, such verification method is getting more specialized and diversified. In this study, the empirical method, numerical analysis and comparative analysis of in situ measurements were used to prove the reasonableness in the support pattern by RMR and Q-value on the Imha Dam emergency spillway.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.799-803
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• 2007

In this paper, we propose a new fuzzy membership function for FSVM(Fuzzy Support Vector Machines). We apply a fuzzy membership to each input point of SVM and reformulate SVM into fuzzy SVM (FSVM) such that different input points can make different contributions to the learning of decision surface. The proposed method enhances the SVM in reducing the effect of outliers and noises in data points. This paper compares classification and estimated performance of SVM, FSVM(1), and FSVM(2) model that are getting into the spotlight in time series prediction.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.31-33
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• 2007

Devising an efficient one-pass frequent pattern mining algorithm has been an issue in data mining research in recent past. Pattern growth algorithms like FP-Growth which are found more efficient than candidate generation and test algorithms still require two database scans. Moreover, FP-growth approach requires rebuilding the base-tree while mining with different support counts. In this paper we propose an item-based tree, called I-Tree that not only efficiently mines frequent patterns with single database scan but also provides multiple mining scopes with multiple support thresholds. The 'build-once-mine-many' property of I-Tree allows it to construct the tree only once and perform mining operation several times with the variation of support count values.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.983-986
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• 2002

Support Vector Machine (SVM) is one of the methods of pattern recognition that separate input data using hyperplane. This method has high capability of pattern recognition by using the technique, which says kernel trick, and the Radial basis function (RBF) kernel is usually used as a kernel function in kernel trick. In this paper we propose using the q-normal distribution to the kernel function, instead of conventional RBF, and compare two types of the kernel function.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.39-52
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• 2006

With the recent the use of spatio-temporal data mining which can extract various knowledge such as movement patterns of moving objects in history data of moving object gets increasing. However, the existing movement pattern extraction methods create lots of candidate movement patterns when the minimum support is low. Therefore, in this paper, we suggest the STMPE(Spatio-Temporal Movement Pattern Extraction) algorithm in order to efficiently extract movement patterns of moving objects from the large capacity of spatio-temporal data. The STMPE algorithm generalizes spatio-temporal and minimizes the use of memory. Because it produces and keeps short-term movement patterns, the frequency of database scan can be minimized. The STMPE algorithm shows more excellent performance than other movement pattern extraction algorithms with time information when the minimum support decreases, the number of moving objects increases, and the number of time division increases.

• Design Convergence Study
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• v.14 no.2
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• pp.51-65
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• 2015

The purpose of this study is to derive the design characteristics of the pattern, which can effectively support the building facade design and to analyze the correlation of their characteristics. This paper deals with five different design patterns suggested by Ben Pell. They are the applied pattern, perforated pattern, layered pattern, casting pattern, and tiled pattern. This paper analyzes the design characteristics of the applied pattern, which shows the highest point in the previous survey. The 'continuous' has a high emotional correlation. The high correlation with the emotional word 'continuous' and 'regular' was derived. The final emotional design characteristic is 'continuous-discontinuous', and 'irregular-regular'. and these emotional words can be summarized as the relatively highest correlation. In this paper, it is worth analyzing design characteristics of building facade by a support of digital technology and discussing utilization of design characteristics derived.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5023-5038
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• 2017

Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1071-1079
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• 2018

In this study, the design methodology as well as network architecture of Support Vector Machine based Polynomial Neural Network, which is a kind of the dynamically generated neural networks, is introduced. The Support Vector Machine based polynomial neural networks is given as a novel network architecture redesigned with the aid of polynomial neural networks and Support Vector Machine. The generic polynomial neural networks, whose nodes are made of polynomials, are dynamically generated in each layer-wise. The individual nodes of the support vector machine based polynomial neural networks is constructed as a support vector machine, and the nodes as well as layers of the support vector machine based polynomial neural networks are dynamically generated as like the generation process of the generic polynomial neural networks. Support vector machine is well known as a sort of robust pattern classifiers. In addition, in order to enhance the structural flexibility as well as the classification performance of the proposed classifier, multi-objective particle swarm optimization is used. In other words, the optimization algorithm leads to sequentially successive generation of each layer of support vector based polynomial neural networks. The bench mark data sets are used to demonstrate the pattern classification performance of the proposed classifiers through the comparison of the generalization ability of the proposed classifier with some already studied classifiers.