• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.55-62
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• 2010

Monitoring systems are able to report certain events at region of interest(ROI) and to take an appropriate action. From industrial product line full of robots to fire detection, intrusion detection, smart grid application, environmental pollution alarm system, monitoring system has widely used in diverse industry sector. Recently, due to advance of wireless communication technology and availability of low cost sensors, intelligent and/or smart monitoring systems such as sensor networks has been developed. Several deployment methods are introduced to meet various monitoring needs and deployment performance criteria are also summarized to be used to identify weak point and be useful at designing monitoring systems. Both efficiency during deployment and usefulness after the deployment should be assessed. Efficiency factors during deployment are elapsed time, energy required, deployment cost, safety, sensor node failure rate, scalability. Usefulness factors after deployment are ROI coverage, connectivity, uniformity, target density similarity, energy consumption rate per unit time and so on.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7B
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• pp.1034-1042
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• 2010

As many researchers have been interested in Green IT, Energy Efficient Ethernet(EEE) with rate adaptation has recently begun to receive many attention. However, the rate adaptation scheme can have different energy efficiency and delay according to the characteristics of various traffic patterns. Therefore, in this paper, we analyze the impact of different traffic patterns on the energy efficiency and delay in Ethernet with rate adaptation. To do this, firstly we design a rate adaptation simulator which consists of Poisson based traffic generator, Pareto distribution based ON-OFF generator and Ethernet node with rate adaptation by using OPNET Modeler. Using this simulator, we perform the simulation in view of the total number of switching, transmission rate reduction, energy saving ratio and average queueing delay. Simulation results show that IP traffic patterns with high self-similarity affect the number of switching, rate reduction and energy saving ratio. Additionally, the transition overhead is caused due to the high self-similar traffic.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1183-1190
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• 2017

With the development of network and IT technology, people are searching and purchasing items they want, not bounded by places. Therefore, there are various studies on how to solve the scalability problem due to the rapidly increasing data in the recommendation system. In this paper, we propose an item-based collaborative filtering method using Tag weight and a recommendation technique using MapReduce method, which is a distributed parallel processing method. In order to improve speed and efficiency, the proposed method classifies items into categories in the preprocessing and groups according to the number of nodes. In each distributed node, data is processed by going through Map-Reduce step 4 times. In order to recommend better items to users, item tag weight is used in the similarity calculation. The experiment result indicated that the proposed method has been more enhanced the appropriacy compared to item-based method, and run efficiently on the large amounts of data.

• The Korean Journal of Cytopathology
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• v.14 no.2
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• pp.76-81
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• 2003

Apocrine carcinoma of the breast is a very rare subtype. Although it has no clinical differences from usual ductal carcinoma of the breast, it should be categorized as a subtype of breast carcinoma because the cells of apocrine carcinoma reveal characteristic abundant eosinophillic cytoplasms with intraductal apical snouting as well as round or oval nuclei and central macronucleoli. On fine needle aspiration cytology, the cells of apocrine carcinoma have a lot of similarity to benign or reactive apocrine cells of the breast. Therefore, it is difficult to make a differential diagnosis of apocrine carcinoma from mammary neoplasms with similar cytologic findings unless the subtle cytologic differences are recognized. We report the cytologic and histologic findings of a case of apocrine carcinoma in the breast of a 40-year-old female patient. After the fine needle aspiration cytology, she received the lumpectomy and lymph node dissection. The cellularity was moderate to high. The cytoplasmic borders of tumor cells of three-dimensional clusters were relatively distinctive, and the cytoplasm was abundant, eosinophilic, and granular. Although the nuclear/cytoplasmic ratio was low, the nuclei of the cells were variable in size and shape with prominent macronucleoli. Histologically, it was a typical invasive apocrine carcinoma, showing numerous cytoplasmic lysosomes and mitochondriae on electron microscopy.

• 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 Industrial and Systems Engineering
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• v.39 no.2
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• pp.129-137
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• 2016

To identify the cause of the error and maintain the health of system, an administrator usually analyzes event log data since it contains useful information to infer the cause of the error. However, because today's systems are huge and complex, it is almost impossible for administrators to manually analyze event log files to identify the cause of an error. In particular, as OpenStack, which is being widely used as cloud management system, operates with various service modules being linked to multiple servers, it is hard to access each node and analyze event log messages for each service module in the case of an error. For this, in this paper, we propose a novel message-based log analysis method that enables the administrator to find the cause of an error quickly. Specifically, the proposed method 1) consolidates event log data generated from system level and application service level, 2) clusters the consolidated data based on messages, and 3) analyzes interrelations among message groups in order to promptly identify the cause of a system error. This study has great significance in the following three aspects. First, the root cause of the error can be identified by collecting event logs of both system level and application service level and analyzing interrelations among the logs. Second, administrators do not need to classify messages for training since unsupervised learning of event log messages is applied. Third, using Dynamic Time Warping, an algorithm for measuring similarity of dynamic patterns over time increases accuracy of analysis on patterns generated from distributed system in which time synchronization is not exactly consistent.

• Journal of Plant Biotechnology
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• v.30 no.3
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• pp.221-226
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• 2003

Genetic variation of Perilla germplasms was investigated using RAPD markers. Forty-two Perilla frutescens lines and cultivars collected form locals were subjected to RAPD analysis using 220 primers. Among them only 13 primers showed polymorphic bands and these 13 primers provided a total of 144 bands, consist of 115 polymorphic and 29 monomorphic ones. The polymorphic bands were subjected to phylogenetic analysis using UPGMA and maximum parsimony (MP) methods. In the UPGMA method, similarity coefficiency of 42 Perilla frutescens lines and cultivars ranged from 0 to 0.7842. The dendrogram of 42 lines and cultivars obtained through UPGMA method resulted in two major groups, and the similar clustering pattern was found by MP method, suggesting Perilla germplasms utilized in this study truly can be divided into two major groups. Although the two major groups were consistent roughly with their phenotypes (under of node, weight of 1,000 grains, and oil content), in detail, much inconsistency also was present.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.185-195
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• 2013

In this paper, we propose a video summarization technique on a smartphone, based on spatiotemporal saliency. The proposed technique detects scene changes by computing the difference of the color histogram, which is robust to camera and object motion. Then the similarity between adjacent frames, face region, and frame saliency are computed to analyze the spatiotemporal saliency in a video clip. Over-segmented hierarchical tree is created using scene changes and is updated iteratively using mergence and maintenance energies computed during the analysis procedure. In the updated hierarchical tree, segmented frames are extracted by applying a greedy algorithm on the node with high saliency when it satisfies the reduction ratio and the minimum interval requested by the user. Experimental result shows that the proposed method summaries a 2 minute-length video in about 10 seconds on a commercial smartphone. The summarization quality is superior to the commercial video editing software, Muvee.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.486-492
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• 2003

The vigilance threshold of conventional fuzzy ART algorithm decide whether to permit the mismatch between any input pattern and stored pattern. If the vigilance threshold was large, despite of little difference among input and stored patterns, the input pattern may be classified to new category. On the other hand, if the vigilance threshold was small, the similarity between two patterns may be accepted in spite of lots of difference and the input pattern are classified to category of the stored pattern. Therefore, the vigilance threshold for the image recognition must be experientially set for the good result. Moreover, it may occur in the fuzzy ART algorithm that the information of stored patterns is lost in the weight-adjusting process and the rate of pattern recognition is dropped. In this paper, I proposed the enhanced fuzzy ART algorithm that supports the dynamical setting of the vigilance threshold using the generalized intersection operator of fuzzy logic and the weight value being adaptively set in proportional to the current weight change and the previous weight by reflecting the frequency of the selection of winner node. For the performance evaluation of the proposed method, we applied to the recognition of container identifiers from shipping container images. The experiment showed that the proposed method produced fewer clusters than conventional ART2 and fuzzy ART algorithm. and had tile higher recognition rate.