• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.538-546
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• 2008

It has limits that system administrator deals with many problems occurred in systems because computing environments are increasingly complex. It is issued that systems have an ability to recognize system's situations and adapt them by itself in order to resolve these limits. But it requires much experiences and knowledge to build the Self-Adaptive System. The difficulty that builds the Self-Adaptive System has been problems. This paper proposes a technique that generates automatically the codes of the Self-Adaptive System in order to make the system to be built more easily. This Self-Adaptive System resolves partially the problems about ineffectiveness of the exceeded usage of the system resource that was previous research's problem and incorrect operation that is occurred by external factors such as virus. In this paper, we applied the proposed approach to the file transfer module that is in the video conferencing system in order to evaluate it. We compared the length of the codes, the number of Classes that are created by the developers, and development time. We have confirmed this approach to have the effectiveness.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.120-128
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• 2010

A hypernetwork is a generalized hypo-graph and a probabilistic graphical model based on evolutionary learning. Hypernetwork models have been applied to various domains including pattern recognition and bioinformatics. Nevertheless, conventional hypernetwork models have the limitation that they can manage data with categorical or discrete attibutes only since the learning method of hypernetworks is based on equality comparison of hyperedges with learned data. Therefore, real-valued data need to be discretized by preprocessing before learning with hypernetworks. However, discretization causes inevitable information loss and possible decrease of accuracy in pattern classification. To overcome this weakness, we propose a novel feature-wise L1-distance based method for real-valued attributes in learning hypernetwork models in this study. We show that the proposed model improves the classification accuracy compared with conventional hypernetworks and it shows competitive performance over other machine learning methods.

• Steel and Composite Structures
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• v.23 no.3
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• pp.273-283
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• 2017

In this paper, it is aimed to present a detail investigation related to structural behavior of laterally unrestrained steel cantilever roof of tribune with slender cross section. The structure is located in Tutak town in $A{\breve{g}}r{{\i}}$ and collapsed on October 25, 2015 at eastern part of Turkey is considered as a case study. This mild sloped roof structure was built from a variable I beam, and supported on steel columns of 5.5 m height covering totally $240m^2$ closed area in plan. The roof of tribune collapsed completely without any indication during first snowfall after construction at midnight a winter day, fortunately before the opening hours. The meteorological records and observations of local persons are combined together to estimate the intensity of snow load in the region and it is compared with the code specified values. Also, the wide/thickness and height/thickness ratios for flange and web are evaluated according to the design codes. Three dimensional finite element model of the existing steel tribune roof is generated considering project drawings and site investigations using commercially available software ANSYS. The displacements, principal stresses and strains along to the cantilever length and column height are given as contour diagrams and graph format. In addition to site investigation, the numerical and analytical works conducted in this study indicate that the unequivocal reasons of the collapse are overloading action of snow load intensity, some mistakes made in the design of steel cantilever beams, insufficient strength and rigidity of the main structural elements, and construction workmanship errors.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.795-807
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• 2005

The classical approaches for computing Live Variable Analysis(LVA) use iterative algorithms across the entire programs based on the Data Flow Analysis framework. In case of Zephyr compiler, average execution time of LVA takes $7\%$ of the compilation time for the benchmark programs. The classical LVA algorithm has many aspects for improvement. The iterative algorithm for LVA scans useless basic blocks and calculates large sets of variables repeatedly. We propose the improvement of Iterative algorithm for LVA based on used variables' upward movement. Our algorithm produces the same result as the previous iterative algorithm. It is based on use-def chain. Reordering of applying the flow equation in DFA reduces the number of visiting basic blocks and redundant flow equation executions, which improves overall processing time. Experimental results say that our algorithm ran reduce $36.4\%\;of\;LVA\;execution\;time\;and\;2.6\%$ of overall computation time in Zephyr compiler with benchmark programs.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.775-784
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• 2002

Microarray data, obtained from DNA chip technologies, is the measurement of the expression level of thousands of genes in cells or tissues. It is used for gene function prediction or cancer diagnosis based on gene expression patterns. Among diverse methods for data analysis, the Bayesian network represents the relationships among data attributes in the form of a graph structure. This property enables us to discover various relations among genes and the characteristics of the tissue (e.g., the cancer type) through microarray data analysis. However, most of the present microarray data sets are so sparse that it is difficult to apply general analysis methods, including Bayesian networks, directly. In this paper, we harness an efficient structural learning algorithm and data dimensionality reduction in order to analyze microarray data using Bayesian networks. The proposed method was applied to the analysis of real microarray data, i.e., the NC160 data set. And its usefulness was evaluated based on the accuracy of the teamed Bayesian networks on representing the known biological facts.

• The Transactions of the Korea Information Processing Society
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• v.5 no.1
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• pp.86-96
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• 1998

The class hierarchy structure in an object-oriented design model is effective to the software reusabilily and lhe design of complex syslem. This paper suggests lhe objecl-orienled class hierarchy structure design melhod using lhe rapid prololyping lechniques. In this method, relationship recognition and similarity are estimated by the new class classification in object modeling level. Then lhe estimation of aUribute and method in class is needed. Each design module such as class hierarchy struclure which is generaled wilh inleractive and repealed work consisls of reference relationship, inheritance relationship and composite relationship. These information are slored in lhe table to maintenance lhe program and implementation, the class relationship is represented with graph and the node class is iconized. This method is effective in reslructuring of class hierarchy are reusing of design information, because of addition of new class and deletion with ease. The efficiency of syslem analysis, design and implementation is enhanced by converting into prololype system and real system.

• Journal of Korean Society of Rural Planning
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• v.21 no.4
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• pp.177-186
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• 2015

The main objective of this study was to assess the applicability of IoT (Internet of Things)-based flood management under climate change by developing intelligent water level monitoring platform based on IoT. In this study, Arduino Uno was selected as the development board, which is an open-source electronic platform. Arduino Uno was designed to connect the ultrasonic sensor, temperature sensor, and data logger shield for implementing IoT. Arduino IDE (Integrated Development Environment) was selected as the Arduino software and used to develop the intelligent algorithm to measure and calibrate the real-time water level automatically. The intelligent water level monitoring platform consists of water level measurement, temperature calibration, data calibration, stage-discharge relationship, and data logger algorithms. Water level measurement and temperature calibration algorithm corrected the bias inherent in the ultrasonic sensor. Data calibration algorithm analyzed and corrected the outliers during the measurement process. The verification of the intelligent water level measurement algorithm was performed by comparing water levels using the tape and ultrasonic sensor, which was generated by measuring water levels at regular intervals up to the maximum level. The statistics of the slope of the regression line and $R^2$ were 1.00 and 0.99, respectively which were considered acceptable. The error was 0.0575 cm. The verification of data calibration algorithm was performed by analyzing water levels containing all error codes in a time series graph. The intelligent platform developed in this study may contribute to the public IoT service, which is applicable to intelligent flood management under climate change.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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• pp.4863-4867
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• 2015

Background: Triple negative breast cancer (TNBC), characterized as estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 Her2 negative and accounting for 10-17% of all breast carcinomas, is only partially responsive to chemotherapy and suffers from a lack of clinically established targeted therapies. The aim of the current study was to evaluate the patterns of treatment and clinicopathology figures in Kurdish patients with triple-negative breast cancer, and to compare these to other reports. Materials and Methods: Between 2001 and 2014, 950 breast cancer patients were referred to our clinic. There were 74 female patients with TNBC, including 70 patients was invasive ductal carcinoma entered into our study. ER and PR positivity was defined as positive immunohistochemical staining in more than 10% of tumor cells. Immunohistochemistry assay with anti-HER2 antibodies was used to identify HER negative (0 and 1+) or positive (2+ and 3+). HER2 gene amplification was determined by fluorescent in situ hybridization (FISH). Overall survival (OS) was plotted with GraphPad Prism 5 Software using Kaplan-Meier and log-rank tests for comparison of results. Results: The mean age in the first diagnosis for 70 patients with triple TNBC and invasive ductal carcinoma was 49.6 years that range of age was 27-82 years. All of the patients were female. Of 70 patients, 23 patients had metastasis. Thirty-two patients (45.7%) were treated with tamoxifen and 39 (55.7%) with radiotherapy. Three-year, 5-year and 10-year OS rates for all patients were 82%, 72% and 64%, respectively. Conclusions: The OS in our West Iran TNBC patients is less than reported elsewhere. However, treatment with combination of tamoxifen plus radiation increases the OS and reduces the mortality rate.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.7
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• pp.3477-3482
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• 2016

Background: Thyroid cancer is the most common endocrine malignancy, and exact causes remain unknown. The role of CYP450 1A1 (CYP1A1) in cancer initiation and progression has been investigated. The aim of this work was to analyze, for the first time, CYP1A1 gene expression and its relationship with several clinicopathological factors in Mexican patients diagnosed with thyroid cancer. Materials and Methods: Real-time PCR analysis was conducted on 32 sets of thyroid tumors and benign pathologies. Expression levels were tested for correlations with clinical and pathological data. All statistical analysis were performed using GraphPad Prism version 3.0 software. Results: We found that female gender was associated with thyroid cancer risk (P<0.05). A positive relationship was identified between CYP1A1 mRNA levels and the presence of chronic disease, alcohol use, tumor size, metastasis and an advanced clinical stage (P<0.05). Conclusions: The results suggest that CYP1A1 gene expression could be used as a marker for thyroid cancer.

• Journal of Biomedical Engineering Research
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• v.23 no.6
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• pp.485-489
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• 2002

In this study. respiratory efforts were monitored by the change of pulse transit time (PTT) which is related with the arterial pressure PTT is the time interval between the peak of R wave in ECG and the maximal slope point of photoplethysmogram(PPG). Biosignals, ECG and finger photoplethysmogram(PPG), were converted to digital data, and PTT was evaluated in personal computer with every heart beat. Results were presented as a graph using spline interpolation. The software was implemented in C$\^$++/ as a window-based application program. PTT was periodically changed according to airflow in resting respiration. In the resting respiration, PTT was changed according to the respiratory cycle. The amplitude of PTT fluctuation was increased by deep respiration, and increased by partial airway obstruction. These results suggest that PTT is responsible to respiratory effort which could be evaluated by the pattern of PTT change. And it is expected that PTT could be applied in the monitoring of respiratory effort by noninvasive methods, and is very useful method for the evaluation of respiratory distress.