• Journal of information and communication convergence engineering
• /
• v.5 no.1
• /
• pp.59-63
• /
• 2007

In this paper, a CLB-based CPLD low-power technology mapping algorithm considered a Trade-off is proposed. To perform low-power technology mapping for CPLDs, a given Boolean network has to be represented in a DAG. The proposed algorithm consists of three steps. In the first step, TD(Transition Density) calculation has to be performed. Total power consumption is obtained by calculating the switching activity of each node in a DAG. In the second step, the feasible clusters are generated by considering the following conditions: the number of inputs and outputs, the number of OR terms for CLB within a CPLD. The common node cluster merging method, the node separation method, and the node duplication method are used to produce the feasible clusters. In the final step, low-power technology mapping based on the CLBs packs the feasible clusters. The proposed algorithm is examined using SIS benchmarks. When the number of OR terms is five, the experiment results show that power consumption is reduced by 30.73% compared with TEMPLA, and by 17.11 % compared with PLA mapping.

• Journal of the Institute of Convergence Signal Processing
• /
• v.16 no.1
• /
• pp.9-14
• /
• 2015

Outlier detection and removal is a crucial step needed for various image processing applications such as image registration. Random Sample Consensus (RANSAC) is known to be the best algorithm so far for the outlier detection and removal. However RANSAC requires a cosiderable computation time. To drastically reduce the computation time while preserving the comparable quality, a outlier detection and removal method based on modified K-means is proposed. The original K-means was conducted first for matching point pairs and then cluster merging and member exclusion step are performed in the modification step. We applied the methods to various images with highly repetitive patterns under several geometric distortions and obtained successful results. We compared the proposed method with RANSAC and showed that the proposed method runs 3~10 times faster than RANSAC.

• Journal of Information Processing Systems
• /
• v.14 no.3
• /
• pp.727-739
• /
• 2018

This study evaluates the viewpoints of user focus incidents using microblog sentiment analysis, which has been actively researched in academia. Most existing works have adopted traditional supervised machine learning methods to analyze emotions in microblogs; however, these approaches may not be suitable in Chinese due to linguistic differences. This paper proposes a new microblog sentiment analysis method that mines associated microblog emotions based on a popular microblog through user-building combined with spectral clustering to analyze microblog content. Experimental results for a public microblog benchmark corpus show that the proposed method can improve identification accuracy and save manually labeled time compared to existing methods.

• Journal of Information Processing Systems
• /
• v.15 no.3
• /
• pp.616-631
• /
• 2019

With the rapid increase of information on the World Wide Web, finding useful information on the internet has become a major problem. The recommendation system helps users make decisions in complex data areas where the amount of data available is large. There are many methods that have been proposed in the recommender system. Collaborative filtering is a popular method widely used in the recommendation system. However, collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we propose a movie recommendation system by using social network analysis and collaborative filtering to solve this problem associated with collaborative filtering methods. We applied personal propensity of users such as age, gender, and occupation to make relationship matrix between users, and the relationship matrix is applied to cluster user by using community detection based on edge betweenness centrality. Then the recommended system will suggest movies which were previously interested by users in the group to new users. We show shown that the proposed method is a very efficient method using mean absolute error.

• Biomedical Science Letters
• /
• v.25 no.3
• /
• pp.275-281
• /
• 2019

This study was performed to characterize the chromosomal metallo-${\beta}$-lactamases (MBLs) of Chryseobacterium indologenes isolated from Korea and to propose a clustering method of IND MBLs based on their amino acid similarities. Chromosomal MBL genes were amplified by PCR from 31 clinical isolates of E. indologenes. Nucleotide sequencing was performed by the dideoxy chain termination method using these PCR products. Antimicrobial susceptibilities were determined by the agar dilution method. PCR experiments showed that all 31 E. indologenes isolates contained all $bla_{IND}$ genes. DNA sequence analysis revealed that E. indologenes isolates possessed ten types of $bla_{IND}$ gene, including seven novel variants ($bla_{IND-8}$ to $bla_{IND-14}$). The most common combination of MBL was IND-2 (n = 18). Minimum inhibitory concentrations of imipenem and meropenem for the isolates harboring novel IND MBLs were ${\geq}16{\mu}g/mL$. IND MBLs were grouped in three clusters, based on amino acid similarities.

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.354-357
• /
• 2021

Software vulnerabilities are becoming more and more increasing, their role is to harm the computer systems of companies, governmental organizations and agencies. The main objective of this paper is to propose a method that will cluster future software vulnerabilities that may spread. This method is developed by combining the Multiple Correspondence Analysis (MCA), the Elbow procedure and the Kmeans Algorithm. A simulation was done on a dataset of 15713 observations. This simulation allowed us to identify families of future vulnerabilities. This model was evaluated using the silhouette index.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.16 no.3
• /
• pp.113-117
• /
• 2021

The data gathering delay and the network lifetime are important indicators to measure the service quality of wireless sensor and actuator networks (WSANs). This study proposes a dynamically cluster head (CH) selection strategy and automatic scheduling scheme of collectors for prolonging the network lifetime and shorting data gathering delay in WSAN. First the monitoring region is equally divided into several subregions and each subregion dynamically selects a sensor node as CH. These can balance the energy consumption of sensor node thereby prolonging the network lifetime. Then a task allocation method based on genetic algorithm is proposed to uniformly assign tasks to actuators. Finally the trajectory of each actuator is optimized by ant colony optimization algorithm. Simulations are conducted to evaluate the effectiveness of the proposed method and the results show that the method performs better to extend network lifetime while also reducing data delay.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.11
• /
• pp.5005-5011
• /
• 2011

The purpose of this work is to know the validity of a new stimulation method in cognitive functional imaging using custom-designed images correspond to words or syllables improving the shortcomings of existing method using text. From March 2011 to May five Subjects in need of language related functional MRI scanning were selected and both of text stimulating method and image stimulating method sacanning were carried out three times each. Using 3.0T Philps MRI machine and Invivo Co's Eloquence system, data acquisition was performed with EPI-BOLD technique. Post processing was performed with SPM 99 while the activated signals were determined within 95 percent confidence level.The number of activation clusters and the activation ratio inside ROI were compared. As as result, all of the subject showed activation inside Broca area but it did not have statistical significance. In conclusion, the image sitimulation method has potential because image itself is a common means of recognition and it can be recognised easily even if there language barrier. This stimulation method can be applied to replacing the exising scanning method especially in the elderly, infants, foerigners who may not fully understand about the examination.

• Journal of Korean Society of Forest Science
• /
• v.105 no.1
• /
• pp.122-131
• /
• 2016

This study was conducted to analyze the effect of climatic variables on tree-ring growth of Larix leptolepis distributed in Korea by dendroclimatological method. For this, annual tree-ring growth data of Larix leptolepis collected by the $5^{th}$ National Forest Inventory were first organized to analyze yearly growth patterns of the species. To explain the relationship between tree-ring growth of Larix leptolepis and climatic variables, monthly temperature and precipitation data from 1950 to 2010 were compared with tree-ring growth data for each county. When tree-ring growth data were analyzed through cluster analysis based on similarity of climatic conditions, six clusters were identified. In addition, index chronology of Larix leptolepis for each cluster was produced through cross-dating and standardization procedures. The adequacy of index chronologies was tested using basic statistics such as mean sensitivity, auto correlation, signal to noise ratio, and expressed population signal of annual tree-ring growth. Response function analysis was finally conducted to reveal the relationship between tree-ring growth and climatic variables for each cluster. The results of this study are expected to provide valuable information necessary for estimating local growth characteristics of Larix leptolepis and for predicting changes in tree growth patterns caused by climate change.

• Journal of KIISE
• /
• v.42 no.3
• /
• pp.307-319
• /
• 2015

Current ontology studies use the Hadoop distributed storage framework to perform map-reduce algorithm-based reasoning for scalable ontologies. In this paper, however, we propose a novel approach for scalable Web Ontology Language (OWL) Horst Lite ontology reasoning, based on distributed cluster memories. Rule-based reasoning, which is frequently used for scalable ontologies, iteratively executes triple-format ontology rules, until the inferred data no longer exists. Therefore, when the scalable ontology reasoning is performed on computer hard drives, the ontology reasoner suffers from performance limitations. In order to overcome this drawback, we propose an approach that loads the ontologies into distributed cluster memories, using Spark (a memory-based distributed computing framework), which executes the ontology reasoning. In order to implement an appropriate OWL Horst Lite ontology reasoning system on Spark, our method divides the scalable ontologies into blocks, loads each block into the cluster nodes, and subsequently handles the data in the distributed memories. We used the Lehigh University Benchmark, which is used to evaluate ontology inference and search speed, to experimentally evaluate the methods suggested in this paper, which we applied to LUBM8000 (1.1 billion triples, 155 gigabytes). When compared with WebPIE, a representative mapreduce algorithm-based scalable ontology reasoner, the proposed approach showed a throughput improvement of 320% (62k/s) over WebPIE (19k/s).