Hand Gesture Recognition using Infrared Proximity Sensor Array

Ganbayar Batchuluun(간바야르 바트촐롱),Bayanmunkh Odgerel(바잉뭉흐 어드게렐),Chang Hoon Lee(이창훈) 한국지능시스템학회 2014 한국지능시스템학회 학술발표 논문집 Vol.24 No.2

Hand Gesture Recognition Using an Infrared Proximity Sensor Array

Ganbayar Batchuluun,Bayanmunkh Odgerel,Chang Hoon Lee 한국지능시스템학회 2015 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.15 No.3

Hand gesture is the most common tool used to interact with and control various electronic devices. In this paper, we propose a novel hand gesture recognition method using fuzzy logic based classification with a new type of sensor array. In some cases, feature patterns of hand gesture signals cannot be uniquely distinguished and recognized when people perform the same gesture in different ways. Moreover, differences in the hand shape and skeletal articulation of the arm influence to the process. Manifold features were extracted, and efficient features, which make gestures distinguishable, were selected. However, there exist similar feature patterns across different hand gestures, and fuzzy logic is applied to classify them. Fuzzy rules are defined based on the many feature patterns of the input signal. An adaptive neural fuzzy inference system was used to generate fuzzy rules automatically for classifying hand gestures using low number of feature patterns as input. In addition, emotion expression was conducted after the hand gesture recognition for resultant human-robot interaction. Our proposed method was tested with many hand gesture datasets and validated with different evaluation metrics. Experimental results show that our method detects more hand gestures as compared to the other existing methods with robust hand gesture recognition and corresponding emotion expressions, in real time.

Ayurveda in Mongolia from Antiquity to 1937

Ganbayar, Ya.,Tumurbaatar, N. The Society Of Sasang Constitutional Medicine 2007 사상체질의학회지 Vol.19 No.3

We have studied the history of the introduction of Ayurveda medicine in Mongolia. During the periods of the Hunnu (400 BC-200 AD), Ikh Nirun (400-600 AD), and Uigur Dynasty (800-1,000 AD), Ayurveda (Indian Medicine) was introduced to Mongolia along with Buddhism from the Middle Asian countries Kushan, Khotan, Sogd and Uigar. Ayurveda was fully introduced to Mongolia under the deep influence of Tibetan Buddhism from the 13th century. Mongolia's first Medical School, following the Tibetan tradition, was established in 1662. In Mongolia more than 40 Medical Schools were established from 1662-1937. 26 Ayurvedic treatises were translated into the Mongolian language and published in 1742-1749. Since the $14^{th}$ century Mongols have been translating Tibetan Medical books into the Mongolian language, of which we have today found more than ten. Over the centuries, Mongolian scholars have written many commentaries to these medical texts.

Hand Gesture Recognition Using an Infrared Proximity Sensor Array

Batchuluun, Ganbayar,Odgerel, Bayanmunkh,Lee, Chang Hoon Korean Institute of Intelligent Systems 2015 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.15 No.3

Hand gesture is the most common tool used to interact with and control various electronic devices. In this paper, we propose a novel hand gesture recognition method using fuzzy logic based classification with a new type of sensor array. In some cases, feature patterns of hand gesture signals cannot be uniquely distinguished and recognized when people perform the same gesture in different ways. Moreover, differences in the hand shape and skeletal articulation of the arm influence to the process. Manifold features were extracted, and efficient features, which make gestures distinguishable, were selected. However, there exist similar feature patterns across different hand gestures, and fuzzy logic is applied to classify them. Fuzzy rules are defined based on the many feature patterns of the input signal. An adaptive neural fuzzy inference system was used to generate fuzzy rules automatically for classifying hand gestures using low number of feature patterns as input. In addition, emotion expression was conducted after the hand gesture recognition for resultant human-robot interaction. Our proposed method was tested with many hand gesture datasets and validated with different evaluation metrics. Experimental results show that our method detects more hand gestures as compared to the other existing methods with robust hand gesture recognition and corresponding emotion expressions, in real time.

Hand Motion Recognition Using Optic Sensor

Batchuluun Ganbayar(바트촐롱 간바야르),Chang Hoon Lee(이창훈) 한국지능시스템학회 2014 한국지능시스템학회 학술발표 논문집 Vol.24 No.1

Effectiveness of Transcutaneous Bilirubin Measurement in Managing Neonatal Jaundice

( Enkhjin Ganbayar ),( Undraa Ishgeedei ),( Dojkhand Tuvden ),( Purev Ganbaatar ),( Dulguun Batsaikhan ) 대한간학회 2020 춘·추계 학술대회 (KASL) Vol.2020 No.1

Aims: Neonatal jaundice is a common cause of concern in immediate newborn period for parents. Obtaining blood bilirubin samples is a painful procedure; it predisposes the baby to infections and requires skilled health personnel. Moreover, laboratory tests are costly and time consuming, leading to unnecessary delays in commencing phototherapy and discharge from hospital. Transcutaneous bilirubinometer has been in use since 2017 as screening tool in postnatal wards. Methods: Ninety newborns with jaundice were referred to the postnatal ward of Dornod Medical center from 2017 august to 2019 December. For patients, we used breastfeeding, intravenous fluid and phototherapy. Before and after the treatment, transcutaneous bilirubinometer were cheked. Results: From the 210 participants of the age (day) 1-35 (mean 17), male were 123 (58.9%), female were 87 (41.1%), body mass were 1.1-4.9 kg (mean 3.7). Phototheraphy and nursing care had significantly decreased bilirubin level from 129.0- 469.0 (mean 295.1) mmol/l to 97-298 (mean 173.4) mmol/l (T test, P≤0.05). Conclusions: In conclusion, specially of transcutaneous bilirubin measurement is safe and effective in neonatal jaundice.

Body-movement-based human identification using convolutional neural network

Batchuluun, Ganbayar,Naqvi, Rizwan Ali,Kim, Wan,Park, Kang Ryoung Elsevier 2018 expert systems with applications Vol.101 No.-

<P><B>Abstract</B></P> <P>Biometric technology based on human gait identifies humans at a far distance even if the individual's face is covered, hidden, or not visible to cameras in dark environments. Previous studies based on human gait were conducted considering both bright and dark environments for human identification in surveillance systems. The studies conducted in low-illumination environments (dark environments) are based on side view images (horizontal walking) of subjects. However, there are cases in which people only show the front and back views of their bodies while they are walking in low-illumination corridors. In these views, it is difficult to identify humans by using conventional features such as cycle, cadence, stride length of walking, and distance between points (ankle, knee, and hip). Additionally, the cases of problems such as people carrying cellphones and/or small personal items (a purse, bag, clothes, etc.) have critical effects on the accuracy of human identification. To overcome these problems, we propose a new human identification technique, which is based on the front and back view images of a human, captured by using a thermal camera sensor. Our technique uses movements of the human body for identification, particularly movement of the head, shoulders, and legs. We have used a convolutional neural network for feature extraction and classification in this study. Five datasets were compiled by collecting data of 80 people including men and women in both bright and dark environments. The experimental results with our collected data and open database showed a higher performance by using our method compared to those of previous studies.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Our method is body movement-based human identification using front and back view. </LI> <LI> Our identification method is robust to the cases of people carrying items or with various poses. </LI> <LI> Deep learning method using thermal difference image-based three-channel inputs is used. </LI> <LI> Our collected database and trained CNN are public to other researchers. </LI> </UL> </P>

몽골인민공화국 역사교과서 연구 - 중학교 역사 교과서를 중심으로 -

조복현(Cho, Bok Hyeon),Ganbayar Munkhdelger 한국사회과교육연구학회 2020 사회과교육 Vol.59 No.3

몽골에서는 역사교육이 오랜 전통을 가지고 있는데 16세기에 티벳 불교가 전해지면서 불교사적 시각에서 역사서술이 이루어지고 티벳 불교 승려들에 의한 역사 교육이 이루어졌다. 그러다가 독립과 인민혁명을 거치면서 민족사적 시각에서 역사를 교육했지만 1930년대 이래로 민주혁명이 이루어지기까지 오랜 기간 동안 마르크스-레닌주의에 입각한 사회주의 이념에 따라 역사를 교육하였다. 그러나 민주혁명 이후로는 역사교육의 목표를 몽골의 역사와 애국심을 함양하기 위한 몽골사를 기반으로 하면서 칭키스 칸에 대한 시각도 크게 달라졌다. 현재 몽골에서는 교육시간의 측면에서 지리나 사회 과목보다는 역사 과목을 중시하하면서 교육의 목표와 역사 교육에서 요구하는 실력, 역사 교육이 추구하는 지식과 능력 및 태도, 역사 교과의 교육 방법이나 환경 및 평가 방법 등에 대해서 상세히 규정하고 있다. 역사 교육의 구체적인 내용은, 초등학교 5학년에서 고대부터 현대까지 통사적으로 간략하게 몽골의 역사를 학습하고 중학교에서는 몽골사와 세계사를 체계적으로 학습하고, 고등학교에서는 역사과목이 선택과목으로 분류되고 중학교 과정에서 학습한 내용을 반복해서 복습함으로서 비효율적인 측면도 있지만 교육 목적에 맞는 인재를 양성할 수 있다는 장점도 있다. History education of Mongolia has a long tradition. Tibetan Buddhism was introduced in the 16th century. History writing was conducted from a Buddhist historical perspective and history education was performed by Tibetan Buddhist monks. Through the independence and the People’s Revolution, history education was carried out from a national historical perspective. From the 1930s to the democratic revolution, Mongolia has taught history according to the Marxist-Leninist socialist ideology. After the democratic revolution, however, the view of Genghis Khan has changed greatly, with the goal of history education based on Mongolian history to foster Mongolian history and patriotism. Currently, Mongolia emphasizes history subjects rather than geography or social subjects in terms of education time. It specifies in detail goals and skills required by history education, knowledge, abilities, and attitudes pursued by history education, and methods of education or environment and evaluation of history classes. Specific details of history education include the ability to train talented people suitable for educational purposes, although it is inefficient to briefly learn Mongolian history from the fifth grade of elementary school to the present, to systematically learn Mongolian history and world history in middle school, and to review contents of middle school courses repeatedly.

Pedestrian detection based on faster R-CNN in nighttime by fusing deep convolutional features of successive images

Kim, Jong Hyun,Batchuluun, Ganbayar,Park, Kang Ryoung Elsevier 2018 expert systems with applications Vol.114 No.-

<P><B>Abstract</B></P> <P>Existing studies using visible-light cameras have mainly focused on methods of pedestrian detection during daytime. However, these studies found it difficult to detect pedestrians during nighttime with low external light. The NIR illuminator has limitations in terms of illumination angle and distance, and the illuminator's power needs to be adjusted depending on whether an object is near or distant. Although, thermal cameras were used for nighttime pedestrian detection, thermal cameras are currently expensive and thus difficult to install in many places. To solve these problems, attempts have been made to use visible-light cameras for nighttime pedestrian detection. However, most of these attempts considered an indoor environment where the distance to the object was short. This study proposes a method of pedestrian detection at nighttime using a visible-light camera and faster region-based convolutional neural network (R-CNN). In addition, as pedestrians cannot be reliably detected from a single nighttime image, we combined deep convolutional features in successive frames.</P> <P>Using Korea advanced institute of science and technology (KAIST) open database, we conducted experiments and observed that the proposed method performed better than the baseline methods at all times (day and night). In addition, through the experiments with national ICT Australia Ltd. (NICTA) open database, we confirm that the proposed method is effective for pedestrian detection at all times. Finally, we present theoretical grounds for the proposed fusion.</P> <P><B>Hightlights</B></P> <P> <UL> <LI> CNN training with augmented data was found effective in improving detection accuracy. </LI> <LI> Fusion of convolutional features in successive images enhanced detection accuracy. </LI> <LI> Effectiveness of our method of fusing successive-frame features is theoretically proved. </LI> <LI> Our analysis of complex faster R-CNN architecture helps other researchers for understanding. </LI> <LI> We open the trained CNN model, algorithm, and generated images to other researchers. </LI> </UL> </P>

낙엽기반 펠릿 내수성 강화 연구

고성민(Seong-Min Go),이명호(Myeong Ho Lee),이창현(Chang Hyeon Lee),주관(Kuan Zhou),간바야르(Ganbayar),성용주(Yong Joo Sung) 한국펄프·종이공학회 2021 한국펄프·종이공학회 학술발표논문집 Vol.2021 No.10