• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.94-101
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• 2012

This paper proposes an improved image steganalysis scheme to raise the detection rate of stego images out of cover images. To improve the detection rate of stego image in the steganalysis, tiny variation caused by data hiding should be amplified. For this, we extract feature vectors of cover image and stego image by two steps. First, we separate image into upper 4 bit subimage and lower 4 bit subimage. As a result, stego noise is expanded more than two times. We decompose separated subimages into twelve subbands by applying 3-level Haar wavelet transform and calculate co-occurrence probabilities of two different subbands in the same scale. Since co-occurrence probability of the two wavelet subbands is affected by data hiding, it can be used as a feature to differentiate cover images and stego images. The extracted feature vectors are used as the input to the multilayer perceptron(MLP) classifier to distinguish between cover and stego images. We test the performance of the proposed scheme over various embedding rates by the LSB, S-tool, COX's SS, and F5 embedding method. The proposed scheme outperforms the previous schemes in detection rate to existence of hidden message as well as exactness of discrimination.

• Journal of the Korean Recycled Construction Resources Institute
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• v.4 no.4
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• pp.431-438
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• 2016

Recently, Both rapid economic growth and high-quality native finishing materials demand in buildings such as local infrastructure facilities and cultural facilities have increased along with local quarries. So, increasing local quarries and environmental pollution occurred in quarries get the eyes to damaged area of the surroundings. As an example, carcinogen such as solid formed to fixing asbestos and dust have damaged to local resident. Especially, Radon gas released from asbestos can exist everywhere on earth, released soil and rock as radioactive substances, can be caused lung cancer followed by a smoking. When pollution source to indoor air quality that lacking ventilation rate of the residential building moved in a cycle, human responses such as headache, dizziness, etc. get appear, so on it threatened resident's physical condition. Thus, we need to urgent attention to reduction harmful substance. In the case of radon gas of the pollution source to indoor air quality in housing, it has characteristic that keep on going through half-life released from source, we need to control radon gas source than source removal. We set on vermiculite addition ratio to 10% which has harmful substances adsorption performance, proceed experiment to basalt waste rock addition ratio 50, 60, 70, 80(%). The result of an experiment, based on 'KS F 4035, precast terrazzo', we can be obtainable in the best terrazzo at basalt waste rock addition ratio 70%.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.643-652
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• 2006

Decision trees are mainly used for the classification and prediction in data mining. The distribution of spatial data and relationships with their neighborhoods are very important when conducting classification for spatial data mining in the real world. Spatial decision trees in previous works have been designed for reflecting spatial data characteristic by rating Euclidean distance. But it only explains the distance of objects in spatial dimension so that it is hard to represent the distribution of spatial data and their relationships. This paper proposes a decision tree based on spatial entropy that represents the distribution of spatial data with the dispersion and dissimilarity. The dispersion presents the distribution of spatial objects within the belonged class. And dissimilarity indicates the distribution and its relationship with other classes. The rate of dispersion by dissimilarity presents that how related spatial distribution and classified data with non-spatial attributes we. Our experiment evaluates accuracy and building time of a decision tree as compared to previous methods. We achieve an improvement in performance by about 18%, 11%, respectively.

• Korean Chemical Engineering Research
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• v.49 no.6
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• pp.857-864
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• 2011

Oxy-fuel combustion with many advantages such as high combustion efficiency, low flue gas flow rate and low NOx emission has emerged as a promising CCS technology for coal combustion facilities. In this study, the effects of the direct sulfation reaction on $SO_2$ removal efficiency were evaluated in a drop tube furnace under typical oxy-fuel combustion conditions represented by high concentrations of $CO_2$ and $SO_2$ formed by gas recirculation to control furnace combustion temperature. The effects of the operating parameters including the reaction temperature, $CO_2$ concentration, $SO_2$ concentration, Ca/S ratio and humidity on $SO_2$ removal efficiency were investigated experimentally. $SO_2$ removal efficiency increased with reaction temperature up to 1,200 due to promoted calcination of limestone reagent particles. And $SO_2$ removal efficiency increased with $SO_2$ concentrations and the humidity of the bulk gas. The increase of $SO_2$ removal efficiency with $CO_2$ concentrations showed that $SO_2$ removal by limestone was mainly done by the direct sulfation reaction under oxy-fuel combustion conditions. From the impact assessment of operation parameters, it was shown that these parameters have an effects on the desulfurization reaction by the order of the Ca/S ratio > residence time > $O_2$ concentration > reaction temperature > $SO_2$ concentration > $CO_2$ concentration > water vapor. The semi-empirical model equation for to evaluate the effect of the operating parameters on the performance of in-furnace desulfurization for oxy-fuel combustion was established.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.675-686
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• 2005

In this paper, we propose an efficient face region detection technique for the content-based video summarization. To segment video, shot changes are detected from a video sequence and key frames are selected from the shots. We select one frame that has the least difference between neighboring frames in each shot. The proposed face detection algorithm detects face region from selected key frames. And then, we provide user with summarized frames included face region that has an important meaning in dramas or movies. Using Bayes classification rule and statistical characteristic of the skin pixels, face regions are detected in the frames. After skin detection, we adopt the projection method to segment an image(frame) into face region and non-face region. The segmented regions are candidates of the face object and they include many false detected regions. So, we design a classifier to minimize false lesion using CART. From SGLD matrices, we extract the textual feature values such as Inertial, Inverse Difference, and Correlation. As a result of our experiment, proposed face detection algorithm shows a good performance for the key frames with a complex and variant background. And our system provides key frames included the face region for user as video summarized information.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.741-754
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• 2008

Due to the characteristics of a data stream, it is very important to confine the memory usage of a data mining process regardless of the amount of information generated in the data stream. For this purpose, this paper proposes the Prime pattern tree(PPT) for finding frequent itemsets over data streams with using the confined memory space. Unlike a prefix tree, a node of a PPT can maintain the information necessary to estimate the current supports of several itemsets together. The length of items in a prime pattern can be reduced the total number of nodes and controlled by split_delta $S_{\delta}$. The size and the accuracy of the PPT is determined by $S_{\delta}$. The accuracy is better as the value of $S_{\delta}$ is smaller since the value of $S_{\delta}$ is large, many itemsets are estimated their frequencies. So it is important to consider trade-off between the size of a PPT and the accuracy of the mining result. Based on this characteristic, the size and the accuracy of the PPT can be flexibly controlled by merging or splitting nodes in a mining process. For finding all frequent itemsets over the data stream, this paper proposes a PPT to replace the role of a prefix tree in the estDec method which was proposed as a previous work. It is efficient to optimize the memory usage for finding frequent itemsets over a data stream in confined memory space. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.19-30
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• 2014

For the expansion of the Semantic Web, studies in converting the data stored in the relational database into the ontology are actively in process. Such studies mainly use an RDB to RDF mapping model, the model to map relational database components to RDF components. However, pre-proposed mapping models have got different expression modes and these damage the accessibility and reusability of the users. As a consequence, the necessity of the standardized mapping language was raised and the W3C suggested the R2RML as the standard mapping language for the RDB to RDF model. The R2RML has a characteristic that converts only the relational database schema data to RDF. For the same reasons above, the ontology about the relation data between table name and column name of the relational database cannot be added. In this paper, we propose an RDB to RDF mapping system considering semantic relations of RDB components in order to solve the above issue. The proposed system generates the mapping data by adding the RDFS attribute data into the schema data defined by the R2RML in the relational database. This mapping data converts the data stored in the relational database into RDF which includes the RDFS attribute data. In this paper, we implement the proposed system as a Java-based prototype, perform the experiment which converts the data stored in the relational database into RDF for the comparison evaluation purpose and compare the results against D2RQ, RDBToOnto and Morph. The proposed system expresses semantic relations which has richer converted ontology than any other studies and shows the best performance in data conversion time.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.13-21
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• 2016

Insolvency of construction companies that play the role of main contractors can lead to clients' losses due to non-fulfillment of construction contracts, and it can have negative effects on the financial soundness of construction companies and suppliers. The construction industry has the cash flow financial characteristic of receiving a project and getting payment based on the progress of the construction. As such, insolvency during project progress can lead to financial losses, which is why the prediction of construction companies is so important. The prediction of insolvency of Korean construction companies are often made through the KMV model from the KMV (Kealhofer McQuown and Vasicek) Company developed in the U.S. during the early 90s, but this model is insufficient in predicting construction companies because it was developed based on credit risk assessment of general companies and banks. In addition, the predictive performance of KMV value's insolvency probability is continuously being questioned due to lack of number of analyzed companies and data. Therefore, in order to resolve such issues, the Bayesian Probabilistic Approach is to be combined with the existing insolvency predictive probability model. This is because if the Prior Probability of Bayesian statistics can be appropriately predicted, reliable Posterior Probability can be predicted through ensured conditionality on the evidence despite the lack of data. Thus, this study is to measure the Expected Default Frequency (EDF) by utilizing the Bayesian Probabilistic Approach with the existing insolvency predictive probability model and predict the accuracy by comparing the result with the EDF of the existing model.

• Korean Journal of Construction Engineering and Management
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• v.18 no.3
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• pp.33-41
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• 2017

A modular specification is a contract document that specifies the requirements of production, transformation and installation in a modular construction. A problem of the currently used specification is that it does not guarantee the level of performance required by the modular construction and fails to accommodate unique characteristics of each construction because it widely uses the standard specification as it is. This study proposes a specification that reflects the unique feature of modular construction in order to improve the quality of modular construction. Research focuses on the leakage in waterproof and mechanical equipment because it is the area with the highest rate of defects. This study suggests ways to improve the modular specification as follows: 1)Descriptive specifying (following MasterFormat) 2)Propose leakage verification method and test clause 3) Reflect characteristic of modular construction. Proposed modifications to modular specification are reviewed by experts for validation and their opinion verifies suggested improvements as a way to secure the quality of modular construction and to promise characteristics of the modular specification. This improvement will be used for reference when writing construction project specification, which can be an important contract document, so that it could lower occurrence rate of the modular defects.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.254-260
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• 2017

The purpose of this study is to evaluate the sound absorption characteristics of a human body according to the amount of clothing worn by using reverberation method measurement method for developing standard test dummy, which can be used for testing absorption of occupied audience chair. Test method was based on the previous study (Conti et al., 2004), each experiment is conducted in the reverberation room and a subject wearing clothes is standing in that chamber. In this experiment, the sound absorption area of each frequency band according to the wearing of various material clothing was measured. As a result of measurements, the average sound absorption area of the whole frequency band was $0.25m^2-0.48m^2$ in case of a subject not wearing outer clothes, and $0.38m^2-0.98m^2$ in case of wearing of outerwear. Polyester tops by showing the maximum value, the highest characteristics in the 800 Hz to 1 kHz band among the rest of fiber materials. The outer jacket made of the wool and cotton materials show a higher absorption area as the frequency increases to the higher frequency band. The change of the sound absorption area according to the clothing amount was divided by the thermal resistance (clo) of the worn clothes and the weight per body surface area.