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Smart Thermostat based on Machine Learning and Rule Engine
Tran Quoc Bao Huy,정선태 한국멀티미디어학회 2020 멀티미디어학회논문지 Vol.23 No.2
In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat’s temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants’ comfort while users’ preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants’ comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.
Application of computer vision for rapid measurement of seed germination
( Tran Quoc Huy ),( Collins Wakholi ),( Byoung-kwan Cho ) 한국농업기계학회 2017 한국농업기계학회 학술발표논문집 Vol.22 No.1
Root is an important organ of plant that typically lies below the surface of the soil. Root surface determines the ability of plants to absorb nutrient and water from the surrounding soil. This study describes an application of image processing and computer vision which was implemented for rapid measurement of seed germination such as root length, surface area, average diameter, branching points of roots. A CCD camera was used to obtain RGB image of seed germination which have been planted by wet paper in a humidity chamber. Temperature was controlled at approximately 250C and 90% relative humidity. Pre-processing techniques such as color space, binarized image by customized threshold, removal noise, dilation, skeleton method were applied to the obtained images for root segmentation. The various morphological parameters of roots were estimated from a root skeleton image with the accuracy of 95% and the speed of within 10 seconds. These results demonstrated the high potential of computer vision technique for the measurement of seed germination.
임베디드 시스템을 위한 멀티태스킹 딥러닝 학습 기반 경량화 성별/연령별 추정
( Huy-tran Quoc Bao ),정선태 ( Chung Sun-tae ) 한국정보처리학회 2020 한국정보처리학회 학술대회논문집 Vol.27 No.1
Age estimation and gender classification for human is a classic problem in computer vision. Almost research focus just only one task and the models are too heavy to run on low-cost system. In our research, we aim to apply multitasking learning to perform both task on a lightweight model which can achieve good precision on embedded system in the real time.
Development of image processing techniques for the detection of occluded fruits
( Tran Quoc Huy ),( Byoung-kwan Cho ) 한국농업기계학회 2016 한국농업기계학회 학술발표논문집 Vol.21 No.2
Fruits detection is an important task for the estimation of fruits yield before harvest. In this study image processing techniques were explored to enhance the detection accuracy for occluded fruits in natural environment. Several image processing techniques, such as OTSU dynamic threshold segmentation method, characterizing color features in different color spaces, image noise removal methods and edge detection methods were combined. The overlapped fruits and severely occluded fruits by the branches and leaves were separated from the main group to follow different image processing procedures. Respective image processing procedures for the three conditions of non-occluded, overlapped and severely occluded fruits were developed.
빠른 영역-합성곱 신경망을 이용한 다중 스케일 보행자 검출 방법
잔꾸억후이(Tran, Quoc Huy),김응태(Kim, Eung Tae) 한국방송·미디어공학회 2019 한국방송공학회 학술발표대회 논문집 Vol.2019 No.6
최근에 딥러닝 기술을 적용한 보행자 검출 연구가 활발히 진행되고 있다. 연구자들은 딥러닝 네트워크를 이용하여 보행자 오검출율을 낮추는 방법에 대해 지속적으로 연구하여 성능을 꾸준히 상승시켰다. 그러나 대부분의 연구는 다중 스케일 보행자가 분포되는 저해상도 영상에서 보행자를 제대로 검출하지 못하는 어려움이 존재한다. 따라서 본 연구에서는 기존의 Faster R-CNN 구조를 기반으로 하여 새로운 다중 특징 융합 레이어와 다중 스케일 앵커 박스를 적용하여 보행자 오검출율을 줄이는 MS-FRCNN(Multi-scaleF aster R-CNN) 구조를 제안한다. 제안된 방식의 성능 검증을 위해 Caltech 데이터세트를 이용하여 실험한 결과, 제안된 MS-FRCNN 방식이 기존의 다른 보행자 검출 방식보다 다중 스케일 보행자 검출에서 medium 조건하에 5%, all 조건하에 3.9% 나아짐을 알 수 있었다.
( Tran Thi Nguyet ),( Nguyen Vu Quoc Huy ),( Yunmi Kim ) 한국여성건강간호학회 2021 여성건강간호학회지 Vol.27 No.4
Purpose: This study aimed to examine the effect of a newborn care education program using ubiquitous learning (UL-NCEP) on exclusive breastfeeding and maternal role confidence of first-time mothers in Vietnam. Methods: This quasi-experimental study with a nonequivalent control group design was conducted at a university hospital in Hue city, Vietnam, between June and July 2018. Eligible first-time mothers were conveniently allocated to the experimental (n=27) and the control group (n=25). Mothers in the control group received only routine care, whereas mothers in the experimental group received UL-NCEP through tablet personal computers in addition to routine care in the hospital. Then, the educational content was provided to mothers by their smartphone for reviewing at home. UL-NCEP was developed based on the World Health Organization’s “Essential Newborn Care Course” guidelines. The exclusive breastfeeding rate and maternal role confidence level after birth and at 4 weeks postpartum were assessed in both groups to assess the effect of UL-NCEP. Results: At 4 weeks postpartum, the experimental group showed a significantly higher level than the control, for exclusive breastfeeding rate (p<.05) as well as mean maternal role confidence (p<.05). Conclusion: UL-NCEP was a feasible and effective intervention in increasing first-time Vietnamese mothers’ exclusive breastfeeding rate and maternal role confidence level. This program may be integrated into routine care for postpartum mothers to promote mother and infant health among first-time mothers in Vietnam.
Vu Quoc Huy,Tran Ngoc Binh 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.4
Nonlinear systems with certain parameters afected by sudden and uncertain disturbance are identifed by systematic state errors from the reference model. The identifcation algorithm is used to design an adaptive terminal sliding mode controller. The coefcient of the switching control component is automatically adjusted according to the deviation of the system state from the sliding surface. The proposed control method makes the state of the system converge to zero but not to converge to the state of the reference model. Simulation results have proved the efectiveness of the algorithm
Machine Vision Technique for Rapid Measurement of Soybean Seed Vigor
Lee, Hoonsoo,Huy, Tran Quoc,Park, Eunsoo,Bae, Hyung-Jin,Baek, Insuck,Kim, Moon S.,Mo, Changyeun,Cho, Byoung-Kwan Korean Society for Agricultural Machinery 2017 바이오시스템공학 Vol.42 No.3
Purpose: Morphological properties of soybean roots are important indicators of the vigor of the seed, which determines the survival rate of the seedlings grown. The current vigor test for soybean seeds is manual measurement with the human eye. This study describes an application of a machine vision technique for rapid measurement of soybean seed vigor to replace the time-consuming and labor-intensive conventional method. Methods: A CCD camera was used to obtain color images of seeds during germination. Image processing techniques were used to obtain root segmentation. The various morphological parameters, such as primary root length, total root length, total surface area, average diameter, and branching points of roots were calculated from a root skeleton image using a customized pixel-based image processing algorithm. Results: The measurement accuracy of the machine vision system ranged from 92.6% to 98.8%, with accuracies of 96.2% for primary root length and 96.4% for total root length, compared to manual measurement. The correlation coefficient for each measurement was 0.999 with a standard error of prediction of 1.16 mm for primary root length and 0.97 mm for total root length. Conclusions: The developed machine vision system showed good performance for the morphological measurement of soybean roots. This image analysis algorithm, combined with a simple color camera, can be used as an alternative to the conventional seed vigor test method.