• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1226-1232
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• 2010

A door can serve as a feature for place classification and localization for navigation of a mobile robot in indoor environments. This paper proposes a door detection method based on the recognition of various door handles using the general Hough transform (GHT) and support vector machine (SVM). The contour and color histogram of a door handle extracted from the database are used in GHT and SVM, respectively. The door recognition scheme consists of four steps. The first step determines the region of interest (ROI) images defined by the color information and the environment around the door handle for stable recognition. In the second step, the door handle is recognized using the GHT method from the ROI image and the image patches are extracted from the position of the recognized door handle. In the third step, the extracted patch is classified whether it is the image patch of a door handle or not using the SVM classifier. The door position is probabilistically determined by the recognized door handle. Experimental results show that the proposed method can recognize various door handles and detect doors in a robust manner.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.369-376
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• 2021

One of the most active areas of research in the field of affective computing and signal processing is emotion recognition. This paper proposes emotion recognition of low-resource (Sindhi) language. This work's uniqueness is that it examines the emotions of languages for which there is currently no publicly accessible dataset. The proposed effort has provided a dataset named MAVDESS (Mehran Audio-Visual Dataset Mehran Audio-Visual Database of Emotional Speech in Sindhi) for the academic community of a significant Sindhi language that is mainly spoken in Pakistan; however, no generic data for such languages is accessible in machine learning except few. Furthermore, the analysis of various emotions of Sindhi language in MAVDESS has been carried out to annotate the emotions using line features such as pitch, volume, and base, as well as toolkits such as OpenSmile, Scikit-Learn, and some important classification schemes such as LR, SVC, DT, and KNN, which will be further classified and computed to the machine via Python language for training a machine. Meanwhile, the dataset can be accessed in future via https://doi.org/10.5281/zenodo.5213073.

• 한국정보통신학회논문지
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• 제24권11호
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• pp.1528-1533
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• 2020

최근 Wi-Fi 전파 지문을 이용한 실내 위치 인식 기술이 다양한 산업 분야 및 공공 서비스에서 적용되어 운영되고 있다. 기계학습 기술의 관심과 함께 단말 주변의 무선 신호 데이터를 사용한 기계학습 기반의 위치 인식 기술이 빠르게 발전하고 있다. 이때 기계학습에 필요한 무선 신호 데이터의 수집 과정에서 왜곡되거나 학습에 적합하지 않은 데이터가 포함되어 위치 인식의 정확도가 낮아지는 결과가 발생한다. 또한 특정 위치에서 수집된 데이터를 기반의 위치 인식을 수행하는 경우 학습에 포함되지 않은 주변 위치에서의 위치 인식에 문제가 발생한다. 본 논문에서는 수집된 학습 데이터의 전처리 과정을 통해 향상된 위치 인식 결과를 얻기 위한 학습 데이터 전처리 기법을 제안한다.

• IEIE Transactions on Smart Processing and Computing
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• 제1권1호
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• pp.1-7
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• 2012

This paper presents a system used to automatically recognize the road traffic control gestures of police officers. In this approach,the control gestures of traffic police officers are captured in the form of depth images.A human skeleton is then constructed using a kinematic model. The feature vector describing a traffic control gesture is built from the relative angles found amongst the joints of the constructed human skeleton. We utilize Support Vector Machines (SVMs) to perform the gesture recognition. Experiments show that our proposed method is robust and efficient and is suitable for real-time application. We also present a testbed system based on the SVMs trained data for real-time traffic gesture recognition.

• Journal of Multimedia Information System
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• 제7권1호
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• pp.33-40
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• 2020

Machine learning consists of supervised and unsupervised learning among which supervised learning is used for the speech recognition objectives. Supervised learning is the Data mining task of inferring a function from labeled training data. Speech recognition is the current trend that has gained focus over the decades. Most automation technologies use speech and speech recognition for various perspectives. This paper demonstrates an overview of major technological standpoint and gratitude of the elementary development of speech recognition and provides impression method has been developed in every stage of speech recognition using supervised learning. The project will use DNN to recognize speeches using magnitudes with large datasets.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.125-139
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• 2023

Facial expression recognition, a topical problem in the field of computer vision and pattern recognition, is a direct means of recognizing human emotions and behaviors. This paper first summarizes the datasets commonly used for expression recognition and their associated characteristics and presents traditional machine learning algorithms and their benefits and drawbacks from three key techniques of face expression; image pre-processing, feature extraction, and expression classification. Deep learning-oriented expression recognition methods and various algorithmic framework performances are also analyzed and compared. Finally, the current barriers to facial expression recognition and potential developments are highlighted.

• 인터넷정보학회논문지
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• 제21권4호
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• pp.127-135
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• 2020

The purpose of this study is to develop a human activity recognition (HAR) system, which distinguishes 13 activities, including five activities commonly dealt with in conventional HAR researches and eight activities from the Eastern-Asian culture. The eight special activities include floor-sitting/standing, chair-sitting/standing, floor-lying/up, and bed-lying/up. We used a 3-axis accelerometer sensor on the wrist for data collection and designed a machine learning model for the activity classification. Data clustering through preprocessing and feature extraction/reduction is performed. We then tested six machine learning algorithms for recognition accuracy comparison. As a result, we have achieved an average accuracy of 99.7% for the 13 activities. This result is far better than the average accuracy of current HAR researches based on a smartwatch (89.4%). The superiority of the HAR system developed in this study is proven because we have achieved 98.7% accuracy with publically available 'pamap2' dataset of 12 activities, whose conventionally met the best accuracy is 96.6%.

• 한국지능시스템학회논문지
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• 제17권1호
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• pp.52-57
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• 2007

본 논문은 실시간 음성인식명령으로 구동되는 지능형 이동기제어시스템을 제안한다. 제안된 이동기제어시스템은 메인모듈, 음성인식모듈, 모터구동모듈, 센서모듈로 구성된다. 80C196KC로 구현된 메인모듈에서 퍼지논리가 적용된 지능형 제어시스템을 제안한다. 사용자의 몸무게 및 여러 가지 주변 환경요인들에 의한 비선형성을 개선하기 위해서 피드백제어가 가능한 모터구동모듈과 센서모듈이 구현된다. 또한 제안된 시스템에서 이동로봇의 제어를 위한 9개의 단어를 사용하여 동작을 테스트하였고, 제어입력으로 음성명령과 조이스틱 사용 시 이동로봇의 성능을 평가하였다.

• 한국정밀공학회지
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• 제5권4호
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• pp.32-38
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• 1988

Apaptive cintrol of machine tool is aimed to change cutting state satis- factorily without aid of a machine operator, if the cuting state is abnomal such as formation of tangled ribbon type chip, built-up edge and generation of chattering and so on. Among these the recognition of chip type is one of the most important since it has imlications relate to : 1. Safety of operator 2. Stoppage of work due to entanglment in tool and workpiece of chip 3. Problem of producted chip control In this paper the chip type is discriminatied by the pattern recognition technique. It is found that the power spectrum of cutting force for each chip type has it's own special pattern. Linear discriminant function for the recognition of the chip type is obtained by learning process. The discriminant function can be the basis of adaptive control for the rate of success of recognition by pattern recognition technique is at leasthigher than 83%.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.223-228
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• 2009

We propose an on-line machine learning approach for object recognition, where new images are continuously added and the recognition decision is made without delay. Random forest (RF) classifier has been extensively used as a generative model for classification and regression applications. We extend this technique for the task of building incremental component-based detector. First we employ object descriptor model based on bag of covariance matrices, to represent an object region then run our on-line RF learner to select object descriptors and to learn an object classifier. Experiments of the object recognition are provided to verify the effectiveness of the proposed approach. Results demonstrate that the propose model yields in object recognition performance comparable to the benchmark standard RF, AdaBoost, and SVM classifiers.