• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.27-38
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• 2017

Packet classification is one of the essential functionalities of Internet routers in providing quality of service. Since the arrival rate of input packets can be tens-of-millions per second, wire-speed packet classification has become one of the most challenging tasks. While traditional packet classification only reports a single matching result, new network applications require multiple matching results. Ternary content-addressable memory (TCAM) has been adopted to solve the multi-match classification problem due to its ability to perform fast parallel matching. However, TCAM has a fundamental issue: high power dissipation. Since TCAM is designed for a single match, the applicability of TCAM to multi-match classification is limited. In this paper, we propose a cost- and energy-efficient multi-match classification architecture that combines TCAM with a tuple space search algorithm. The proposed solution uses two small TCAM modules and requires a single-cycle TCAM lookup, two SRAM accesses, and several Bloom filter query cycles for multi-match classifications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1322-1329
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• 2010

As body sensor network (BSN) research becomes mature, the need for managing power consumption of sensor nodes has become evident since most of the applications are designed for continuous monitoring. Real time Electrocardiograph (ECG) analysis on sensor nodes is proposed as an optimal choice for saving power consumption by reducing data transmission overhead. Smart sensor nodes with the ability to categorize lately detected ECG cycles communicate with base station only when ECG cycles are classified as abnormal. In this paper, ECG classification algorithms are described, which categorize detected ECG cycles as normal or abnormal, or even more specific cardiac diseases. Our Euclidean distance (ED) based classification method is validated to be most power efficient and very accurate in determining normal or abnormal ECG cycles. A close comparison of power efficiency and classification accuracy between our ED classification algorithm and generalized linear model (GLM) based classification algorithm is provided. Through experiments we show that, CPU cycle power consumption of ED based classification algorithm can be reduced by 31.21% and overall power consumption can be reduced by 13.63% at most when compared with GLM based method. The accuracy of detecting NSR, APC, PVC, SVT, VT, and VF using GLM based method range from 55% to 99% meanwhile, we show that the accuracy of detecting normal and abnormal ECG cycles using our ED based method is higher than 86%.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.436-443
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• 2011

Recognition of structures in urban environments is a fundamental ability for unmanned ground vehicles. In this paper we propose the geometrical featured voxel which has not only 3-D coordinates but also the type of geometrical properties of point cloud. Instead of dealing with a huge amount of point cloud collected by range sensors in urban, the proposed voxel can efficiently represent and save 3-D urban structures without loss of geometrical properties. We also provide an urban structure classification algorithm by using the proposed voxel and machine learning techniques. The proposed method enables to recognize urban environments around unmanned ground vehicles quickly. In order to evaluate an ability of the proposed map representation and the urban structure classification algorithm, our vehicle equipped with the sensor system collected range data and pose data in campus and experimental results have been shown in this paper.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• Culinary science and hospitality research
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• v.6 no.2
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• pp.199-225
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• 2000

It is desirable that the management of human resources, as a strategy for the competition, should be the necessity for the hotel industries to survive in the rapid change and continuous development In other words, the management of enterprise provides the foundation to form human relationship, just as the hospitality industry operates with human relationship. Here by, all the problems out hotels have faced are that our hotels should look for a new human resources. The control of human resources in hotels means that if does not only satisfy hotels, employees, and guests but improves the personal ability. also it is important for the method of hotel operation as a management. Therefor, hotel managers have to get a good human resources, at the same time, improve the potential ability from them in order to get development for industries and a person. This study in the effective project for the human resources in hotels is relation to the organization of hotel sand he factor of human resources. This research if focused on the property of classification each factor in the control of human resources. according to the classification, the relationship and an effect are commonly made in groups of properties and are named respectively.

• Archives of design research
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• v.16 no.3
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• pp.81-90
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• 2003

The purpose of the study is to propose a new way of classification for products and to forecast the future of products through the physical factor and the mental factor as human natures. For the purpose of the study, the research was carried out in three ways. Firstly, the study considered the evolutional process of products through human natures. At this stage, the study defined that the physical ability and the mental ability of human are the cores of the product's evolution. Secondly, for understanding human evolution, the study set up two types of future humans . Finally, the study classified products by the physical factor and the mental factor as human natures with the aspect of embryology. As the results, the study illustrated two different species of products and their futures.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.1
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• pp.56-62
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• 2011

Terrain recognition ability is crucial to the performance of legged robots in an outdoor environment. For instance, a robot will not easily walk and it will tumble or deviate from its path if there is no information on whether the walking surface is flat, rugged, tough, and slippery. In this study, the ground surface recognition ability of robots is discussed, and to enable walking robots to recognize the surface state and changes, a central moment method was used. The values of the sensor signals (load cell) of robots while walking were detected in the supported section and were analyzed according to signal variance, skewness, and kurtosis. Based on the results of such analysis, the surface state was detected and classified.

• Journal of Physiology & Pathology in Korean Medicine
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• v.19 no.2
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• pp.561-567
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• 2005

We statistically analyzed the relationship between the constitution and the thickness and stiffness of skin depending on sex and age, using 1079 clinical data registered to SCIB(Sasang constitution Information Bank), and the following results are obtained : The thickness of skin has big discrimination ability in classification of Taeeumin and Soyangin, especially in women and in ages 21 or more. The stiffness of skin also has big discrimination ability in classification of Taeeumin and Soeumin, especially in Taeumin women and Soeumin man and in ages 21-60. The differences stated above have been proved to be meaningful enough by Chi-square test.

• Journal of Physiology & Pathology in Korean Medicine
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• v.19 no.2
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• pp.536-543
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• 2005

We statistically analyzed the relationship between the constitution and the refineness and tactile of skin depending on sex and age, using 1079 clinical data registered to SCIB(Sasang constitution Information Bank), and the following results are obtained: The thickness of skin has big discrimination ability in classification of Taeeumin and Soyangin, especially in women and in ages 21 or more. The stiffness of skin also has big discrimination ability in classification of Taeeumin and Soeumin, especially in Taeumin women and Soeumin man and in ages 21-60. The differences stated above have been proved to be meaningful enough by Chi-square test.

• Journal of Sensor Science and Technology
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• v.22 no.2
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• pp.162-173
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• 2013

The purpose of this paper is to classify VOC gases by emulating the characteristics found in biological olfaction. For this purpose, we propose new signal processing method based a polymeric chemical sensor array consisting of 4096 sensors which is created by NEUROCHEM project. To remove unstable sensors generated in the manufacturing process of very large scaled chemical sensor array, we used discrete wavelet transformation and cosine similarity. And, to remove the supernumerary redundancy, we proposed the method of selecting candidates of representative sensor representing sensors with similar features by Fuzzy c-means algorithm. In addition, we proposed an improved algorithm for selecting representative sensors among candidates of representative sensors to better enhance classification ability. However, Classification for very large scaled sensor array has a great deal of time in process of learning because many sensors are used for learning though a redundancy is removed. Throughout experimental trials for classification, we confirmed the proposed method have an outstanding classification ability, at transient state as well as steady state.