• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.431-438
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• 2020

In this paper, the method to extract lip contour by multiple threshold is proposed. Spyridonos et. el. proposed a method to extract lip contour. First step is get Q image from transform of RGB into YIQ. Second step is to find lip corner points by change point detection and split Q image into upper and lower part by corner points. The candidate lip contour can be obtained by apply threshold to Q image. From the candidate contour, feature variance is calculated and the contour with maximum variance is adopted as final contour. The feature variance 'D' is based on the absolute difference near the contour points. The conventional method has 3 problems. The first one is related to lip corner point. Calculation of variance depends on much skin pixels and therefore the accuracy decreases and have effect on the split for Q image. Second, there is no analysis for color systems except YIQ. YIQ is a good however, other color systems such as HVS, CIELUV, YCrCb would be considered. Final problem is related to selection of optimal contour. In selection process, they used maximum of average feature variance for the pixels near the contour points. The maximum of variance causes reduction of extracted contour compared to ground contours. To solve the first problem, the proposed method excludes some of skin pixels and got 30% performance increase. For the second problem, HSV, CIELUV, YCrCb coordinate systems are tested and found there is no relation between the conventional method and dependency to color systems. For the final problem, maximum of total sum for the feature variance is adopted rather than the maximum of average feature variance and got 46% performance increase. By combine all the solutions, the proposed method gives 2 times in accuracy and stability than conventional method.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.230.2-230.2
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• 2015

Nanoparticles of magnesium ferrite are used as a heterogeneous catalyst, humidity sensor, oxygen sensor and cure of local hyperthermia. These applications usually utilize the magnetic behavior of these nanoparticles. Moreover, magnetic properties of nanoferrites exhibit rather complex behavior compared to bulk ferrite. The magnetic properties of ferrites are complicated by spins at vortices, surface spins. Reports till date indicate strong dependency on the structural parameters, oxidation state of metal ions and their presence in octahedral and tetrahedral environment. Thus we have carried out investigation on magnesium ferrite nanoparticles in order to study coordination, oxidation state and structural distortion. For present work, magnesium ferrite nanoparticles were synthesized using nitrates of metal ions and citric acid. Fe L-edge spectra measured for these nanoparticles shows attributes of $Fe^{3+}$ in high spin state. Moreover O K-edge spectra for these nanoparticles exhibit spectral features that arises due to unoccupied states of O 2p character hybridized with metal ions. Mg K-edge spectra shows spectral features at 1304, 1307, 1311 and 1324 eV for nanoparticles obtained after annealing at 400, 500, 600, 800, 1000, and $1200^{\circ}C$. Apart from this, spectra for precursor and nanoparticles obtained at $300^{\circ}C$ exhibit a broad peak centered around 1305 eV. A shoulde rlike structure is present at 1301 eV in spectra for precursor. This feature does not appear after annealing. After annealing a small kink appear at ~1297 eV in Mg K-edge spectra for all nanoparticles. This indicates changes in local electronic structure during annealing of precursor. Observed behavior of change in local electronic structure will be discussed on the basis of existing theories.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1564-1572
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• 2011

In this paper, we propose a new histogram based hand recognition algorithm for augmented reality. Hand recognition system makes it possible a useful interaction between an user and computer. However, there is difficulty in vision-based hand gesture recognition with viewing angle dependency due to the complexity of human hand shape. A new hand recognition system proposed in this paper is based on the features from hand geometry. The proposed recognition system consists of two steps. In the first step, hand region is extracted from the image captured by a camera and then hand gestures are recognized in the second step. At first, we extract hand region by deleting background and using skin color information. Then we recognize hand shape by determining hand feature point using histogram of the obtained hand region. Finally, we design a augmented reality system by controlling a 3D object with the recognized hand gesture. Experimental results show that the proposed algorithm gives more than 91% accuracy for the hand recognition with less computational power.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.681-687
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• 2001

In order to raise a class discrimination power by combining multiple classifiers under the Bayesian decision theory, the upper bound of a Bayes error rate bounded by the conditional entropy of a class variable and decision variables obtained from training data samples should be minimized. Wang and Wong proposed a tree dependence first-order approximation scheme of a high order probability distribution composed of the class and multiple feature pattern variables for minimizing the upper bound of the Bayes error rate. This paper presents an extended high order product approximation scheme dealing with higher order dependency more than the first-order tree dependence, based on the minimization of the upper bound of the Bayes error rate. Multiple recognizers for unconstrained handwritten numerals from CENPARMI were combined by the proposed approximation scheme using the Bayesian formalism, and the high recognition rates were obtained by them.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.8
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• pp.394-406
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• 2008

Even though context is the most important feature in context-aware systems, the existing requirements engineering cannot support methodology for elicitation and specification of contexts. In this paper, we propose a requirements elicitation method and a requirements specification method for context-aware systems. Our requirements elicitation method is a 6-stepped, incremental, and iterative process. At the beginning steps in the process, we identify the requirements for business logic. Afterwards, we gather the requirements for context logic, model contexts, and identify subsystems. For requirements specification, we suggest a context-aware use case diagram, a context diagram for context modeling, and a context-type-use-case-dependency diagram for the traceability of use cases on the change of context types. We also introduce a case study that we apply our approaches to a real system, and a qualitative evaluation of our approaches. Our study will help stakeholders to efficiently elicit requirements for context-aware systems and to specify them clearly.

• Korean Journal of Cognitive Science
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• v.19 no.1
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• pp.89-105
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• 2008

In this paper, we suggest a rule-based system for the prediction of natural prosodic phrase breaks from Korean texts. For the implementation of the rule-based system, (1) sentence constituents are sub-categorized according to their syntactic functions, (2) syntactic phrases are recognized using the dependency relations among sub-categorized constituents, (3) rules for predicting prosodic phrase breaks are created. In addition, (4) the length of syntactic phrases and sentences, the position of syntactic phrases in a sentence, sense information of contextual words have been considered as to determine the variable prosodic phrase breaks. Based on these rules and features, we obtained the accuracy over 90% in predicting the position of major break and no break which have high correlation with the syntactic structure of the sentence. As for the overall accuracy in predicting the whole prosodic phrase breaks, the suggested system shows Break_Correct of 87.18% and Juncture Correct of 89.27% which is higher than that of other models.

• JSTS:Journal of Semiconductor Technology and Science
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• v.6 no.1
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• pp.22-29
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• 2006

We demonstrate highly manufacturable Multi-channel Field Effect Transistor (McFET) on bulk Si wafer. McFET shows excellent transistor characteristics, such as $5{\sim}6 times higher drive current than planar MOSFET, ideal subthreshold swing, low drain induced barrier lowering (DIBL) without pocket implantation and negligible body bias dependency, maintaining the same source/drain resistance as that of a planar transistor due to the unique feature of McFET. And suitable threshold voltage ($V_T$) for SRAM operation and high static noise margin (SNM) are achieved by using TiN metal gate electrode.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.133-140
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• 2020

The IISS(Integrated Interface Storage System) software uses communication methods such as DSS(Data Sharing Service), UDP to perform the function of sending all messages from the Combat Management System to the analytical computer. Because IISS software handles all message used in the Combat Management System, the source code is large and has a highly dependent feature on message changes. Modification of software is a task that requires a lot of labor, such as series of software reliability test. so research has been conducted to reduce software development costs, including minimizing software modifications. In this paper, We study the method of messages receiving and architectural structure improvement to minimize reliance on message changes in the Combat Management System and improve the modifiability. Reduced message dependency by changing the way DSS and UDP protocols are communicated to Packet Sniffing. In addition, Factory Method Pattern were applied to improve the software design. Test comparing existing software and development elements have confirmed that the software has improved its modifiability and reuse.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.95-105
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• 2009

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series including bad or missing observation that result from mechanical problems or sensing environmental condition. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. An adaptive feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. In this study, the Normalized Difference Vegetation Index (NDVI) image was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula, and the adaptive reconstruction of harmonic model was then applied to the NDVI time series from 1996 to 2000 for tracking changes on the ground vegetation. The results show that the adaptive approach is potentially very effective for continuously monitoring changes on near-real time.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.