• 로봇학회논문지
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• 제7권3호
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• pp.171-181
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• 2012

Outdoor mobile robots are faced with various terrain types having different characteristics. To run safely and carry out the mission, mobile robot should recognize terrain types, physical and geometric characteristics and so on. It is essential to control appropriate motion for each terrain characteristics. One way to determine the terrain types is to use non-contact sensor data such as vision and laser sensor. Another way is to use contact sensor data such as slope of body, vibration and current of motor that are reaction data from the ground to the tire. In this paper, we presented experimental results on terrain classification using contact sensor data. We made a mobile robot for collecting contact sensor data and collected data from four terrains we chose for experimental terrains. Through analysis of the collecting data, we suggested a new method of terrain feature extraction considering physical characteristics and confirmed that the proposed method can classify the four terrains that we chose for experimental terrains. We can also be confirmed that terrain feature extraction method using Fast Fourier Transform (FFT) typically used in previous studies and the proposed method have similar classification performance through back propagation learning algorithm. However, both methods differ in the amount of data including terrain feature information. So we defined an index determined by the amount of terrain feature information and classification error rate. And the index can evaluate classification efficiency. We compared the results of each method through the index. The comparison showed that our method is more efficient than the existing method.

• Journal of Advanced Research in Ocean Engineering
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• 제2권4호
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• pp.202-208
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• 2016

In this paper, we propose novel seabed sediment classification algorithm using feature obtained by continuous wavelet transform (CWT). Contrast to previous researches using direct reflection coefficient of seabed which is function of frequency and is highly influenced by sediment types, we develop an algorithm using both direct reflection signal and backscattering signal. In order to obtain feature vector, we employ CWT of the signal and obtain histograms extracted from local binary patterns of the scalogram. The proposed algorithm also adopts principal component analysis (PCA) to reduce dimension of the feature vector so that it requires low computational cost to classify seabed sediment. For training and classification, we adopts K-means clustering algorithm which can be done with low computational cost and does not require prior information of the sediment. To verify the proposed algorithm, we obtain field data measured at near Jeju island and show that the proposed classification algorithm has reliable discrimination performance by comparing the classification results with actual physical properties of the sediments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권10호
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• pp.1751-1768
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• 2011

Falls are one of the most concerned accidents for elderly people and often result in serious physical and psychological consequences. Many researchers have studied fall detection techniques in various domain, however none released to a commercial product satisfying user requirements. We present a systematic modeling and evaluating procedure for best classification performance and then do experiments for comparing the performance of six procedures to get a statistical classifier based wrist-type fall detector to prevent dangerous consequences from falls. Even though the wrist may be the most difficult measurement location on the body to discern a fall event, the proposed feature deduction process and fall classification procedures shows positive results by using data sets of fall and general activity as two classes.

• 아동학회지
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• 제35권2호
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• pp.85-102
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• 2014

The purpose of this study was to examine the awareness of racial features, racial in-group classification and preference by Korean children. The study participants comprised 89 children aged 3-5 years. The children performed photograph description and choice tasks. The major findings were as follows: First, older children were significantly more likely than younger children to use racial feature and less likely to use general physical feature to describe the stimuli. Second, children tended to select the South-Asian person speaking in Korean language as a Korean, rather than the Korean person speaking in English. Third, children tended to select the person of Korean appearance speaking in English as a playmate. The result revealed the developmental features of racial awareness. Furthermore the correspondence of language plays an important role on the children's in-group classification whereas the correspondence of appearance plays an important role on the children's preference.

• 정보처리학회논문지B
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• 제14B권3호
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• pp.153-162
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• 2007

사상의학은 우리나라의 대표적인 의학으로 사람의 체질을 4가지로 분류하고 각 체질별로 처치방법을 달리하는 독특한 의학이다. 이러한 사상체질에서 가장 중요한 것은 사상체질의 분류이며 이를 정확히 감별하는 일은 매우 어려운 과제이다. 이를 위해 본 논문에서는 체질별 분류를 행할 수 있는 하이브리드 형태의 진단기기 개발을 목표로 하고 이 중 본 논문은 용모사기론에 입각하여 이목구비에 대한 형태학적인 특징을 파악하는 방법에 대해 제안하고자 한다. 본 논문에서는 1단계에서 QSCC II 프로그램을 통해 사상체질군을 분류하고 이를 검증하였으며, 2단계에서 이목구비에 대한 실측으로 각 체질 간 이목구비 계측치를 분석하여 보다 정확하고 편리하게 체질을 분류할 수 있는 방법을 제시하고 이를 검증하였다. 또한 3단계에서는 정면 얼굴과 측면 얼굴에 대한 체질 분류 기반의 주요 영역을 추출, 분석하고 검증하였다. 이와 같은 실험, 고찰 및 검증과정을 통해 정확한 사상체질 분류 진단기기 개발을 위한 정면 얼굴과 측면 얼굴에서 안면 색상 기반의 주요 얼굴 영역을 추출하는 방법을 제안하고자 한다.

• 한국전문물리치료학회지
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• 제28권2호
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• pp.123-131
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• 2021

Background: Only 2% of falls in older adults result in serious injuries (i.e., hip fracture). Therefore, it is important to differentiate injurious versus non-injurious falls, which is critical to develop effective interventions for injury prevention. Objects: The purpose of this study was to a. extract the best features of surface electromyography (sEMG) for classification of injurious falls, and b. find a best model provided by data mining techniques using the extracted features. Methods: Twenty young adults self-initiated falls and landed sideways. Falling trials were consisted of three initial fall directions (forward, sideways, or backward) and three knee positions at the time of hip impact (the impacting-side knee contacted the other knee ("knee together") or the mat ("knee on mat"), or neither the other knee nor the mat was contacted by the impacting-side knee ("free knee"). Falls involved "backward initial fall direction" or "free knee" were defined as "injurious falls" as suggested from previous studies. Nine features were extracted from sEMG signals of four hip muscles during a fall, including integral of absolute value (IAV), Wilson amplitude (WAMP), zero crossing (ZC), number of turns (NT), mean of amplitude (MA), root mean square (RMS), average amplitude change (AAC), difference absolute standard deviation value (DASDV). The decision tree and support vector machine (SVM) were used to classify the injurious falls. Results: For the initial fall direction, accuracy of the best model (SVM with a DASDV) was 48%. For the knee position, accuracy of the best model (SVM with an AAC) was 49%. Furthermore, there was no model that has sensitivity and specificity of 80% or greater. Conclusion: Our results suggest that the classification model built upon the sEMG features of the four hip muscles are not effective to classify injurious falls. Future studies should consider other data mining techniques with different muscles.

• 한국조경학회지
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• 제32권3호
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• pp.91-105
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• 2004

Recently the function of a department store has changed to the concept of a multi-functional center because of the alternative stores such as discount stores, home shopping, and internet shopping. This means that the front plaza of a department store is not a personal or private space any more, but a public space. This study focuses on the special character of public space through the classification and preference types of department store front plazas. The major results of this study can be summarized as follows: (1) Components of front plaza of department store are classified by three factors. The first factor, named "space limit", has 14 elements ; the second, named "space decoration" has 16 elements ; third, named "activity", has 2 elements. The first preferred element is easily- used and easily- serviced wide space. The second preferred element is the equipment that is placed linearly along the street. The third preferred element is cultural events. (2) The comparison between the frequency and preference shows that the plazas could not satisfy the user-needs. (3) Preference factors of front plazas were examined to three characters such as familiarity, peculiarity, and openness. Familiarity, peculiarity, openness have a positive correlation in all types. Peculiarity especially influences the other two space - preference factors.

• 한국정밀공학회지
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• 제21권12호
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• pp.100-108
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• 2004

In reverse engineering, data acquisition methodology can generally be categorized into contacting and non-contacting types. Recently, researches on hybrid or sensor fusion of the two types have been increasing. In addition, efficient construction of a geometric model from the measurement data is required, where considerable amount of user interaction to classify and localize regions of interest is inevitable. Our research focuses on the classification of each bounded region into a pre-defined feature shape fer a hybrid measuring scheme, where the overall procedures are described as fellows. Firstly, the physical model is digitized by a non-contacting laser scanner which rapidly provides cloud-of-points data. Secondly, the overall digitized data are approximated to a z-map model. Each bounding curve of a region of interest (featured area) can be 1.aced out based on our previous research. Then each confined area is systematically classified into one of the pre-defined feature types such as floor, wall, strip or volume, followed by a more accurate measuring step via a contacting probe. Assigned to each feature is a specific digitizing path topology which may reflect its own geometric character. The research can play an important role in minimizing user interaction at the stage of digitizing path planning.

• 전기전자학회논문지
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• 제25권1호
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• pp.212-217
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• 2021

Premature contraction arrhythmia is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Most of arrhythmia clasification methods have been developed with the primary objective of the high detection performance without taking into account the computational complexity. Also, personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Therefore it is necessary to design efficient method that classifies arrhythmia by analyzing the persons's physical condition and decreases computational cost by accurately detecting minimal feature point based on only QRS features. We propose method for personalized specific classification of premature contraction arrhythmia based on QRS features in smart healthcare environments. For this purpose, we detected R wave through the preprocessing method and SOM and selected abnormal signal sets.. Also, we developed algorithm to classify premature contraction arrhythmia using QRS pattern, RR interval, threshold for amplitude of R wave. The performance of R wave detection, Premature ventricular contraction classification is evaluated by using of MIT-BIH arrhythmia database that included over 30 PVC(Premature Ventricular Contraction) and PAC(Premature Atrial Contraction). The achieved scores indicate the average of 98.24% in R wave detection and the rate of 97.31% in Premature ventricular contraction classification.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.85-91
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• 1995

This paper presents a system which recognizes the Korean Sign Language(KSL) and translates into normal Korean speech. A sign language is a method of communication for the deaf-mute who uses gestures, especially both hands and fingers. Since the human hands and fingers are not the same in physical dimension, the same form of a gesture produced by two signers with their hands may not produce the same numerical values when obtained through electronic sensors. In this paper, we propose a dynamic gesture recognition method based on feature analysis for efficient classification of hand motions, and on a fuzzy min-max neural network for on-line pattern recognition.