http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Scale Invariant Single Face Tracking Using Particle Filtering With Skin Color
Perdana Adhitama,Soo Hyung Kim,In Seop Na 한국콘텐츠학회(IJOC) 2013 International Journal of Contents Vol.9 No.3
In this paper, we will examine single face tracking algorithms with scaling function in a mobile device. Face detection and tracking either in PC or mobile device with scaling function is an unsolved problem. Standard single face tracking method with particle filter has a problem in tracking the objects where the object can move closer or farther from the camera. Therefore, we create an algorithm which can work in a mobile device and perform a scaling function. The key idea of our proposed method is to extract the average of skin color in face detection, then we compare the skin color distribution between the detected face and the tracking face. This method works well if the face position is located in front of the camera. However, this method will not work if the camera moves closer from the initial point of detection. Apart from our weakness of algorithm, we can improve the accuracy of tracking.
Scale Invariant Single Face Tracking Using Particle Filtering With Skin Color
Adhitama, Perdana,Kim, Soo Hyung,Na, In Seop The Korea Contents Association 2013 International Journal of Contents Vol.9 No.3
In this paper, we will examine single face tracking algorithms with scaling function in a mobile device. Face detection and tracking either in PC or mobile device with scaling function is an unsolved problem. Standard single face tracking method with particle filter has a problem in tracking the objects where the object can move closer or farther from the camera. Therefore, we create an algorithm which can work in a mobile device and perform a scaling function. The key idea of our proposed method is to extract the average of skin color in face detection, then we compare the skin color distribution between the detected face and the tracking face. This method works well if the face position is located in front of the camera. However, this method will not work if the camera moves closer from the initial point of detection. Apart from our weakness of algorithm, we can improve the accuracy of tracking.
( Doan Perdana ),( Ray-guang Cheng ),( Riri Fitri Sari ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.1
The most challenging issues in the multi-channel MAC of the IEEE 1609.4 standard is how to handle the dynamic vehicular traffic condition with a high mobility, dynamic topology, and a trajectory change. Therefore, dynamic channel coordination schemes between CCH and SCH are required to provide the proper bandwidth for CCH/SCH intervals and to improve the quality of service (QoS). In this paper, we use a Markov model to optimize the interval based on the dynamic vehicular traffic condition with high mobility nodes in the multi-channel MAC of the IEEE 1609.4 standard. We evaluate the performance of the three-dimensional Markov chain based on the Poisson distribution for the node distribution and velocity. We also evaluate the additive white Gaussian noise (AWGN) effect for the multi-channel MAC coordination scheme of the IEEE 1609.4 standard. The result of simulation proves that the performance of the dynamic channel coordination scheme is affected by the high node mobility and the AWGN. In this research, we evaluate the model analytically for the average delay on CCHs and SCHs and also the saturated throughput on SCHs.
인도네시아 케둥 케리스 마을의 지속가능한 산림경영이 소규모 산주에게 미치는 생태적·사회경제적 편익 분석
아디타페르다나푸트라 ( Aditya Perdana Putra ),도나페르마나페위 ( Dona Permana Pewi ),쥬엘안도 ( Jewel Andoh ),이요한 ( Yohan Lee ) 한국산림경제학회 2018 산림경제연구 Vol.25 No.2
지속가능한 산림경영 인증은 열대림 감소나 황폐화에 대한 인간활동에 의한 처방의 형태로 최근 상당 기간 존재해 왔다. 이 산림인증시스템은 지속가능한 산림경영을 활성화하기 위한 목적으로 2006년 이후에 인도네시아 케둥 케리스 마을에 소규모 산주에 의해 받아들여 졌다. 그러나 아직까지 이러한 인증시스템의 생태적, 경제적 편익에 대한 연구는 밝혀지지 않았다. 따라서 본 연구는 인도네시아 케둥 케리스 마을의 소규모 산주들이 지속가능한 산림경영 인증을 통해서 얻은 효과를 연구하기 위해 총 35명의 소규모 산주와 3명의 전문가에게 설문조사를 실시하였다. 그 결과, 지속가능한 산림경영인증이 수질개선, 야생동물보호, 목재가격상승 및 소득증대에 있어서 직간접적인 편익을 주는 것으로 나타났다. Sustainable forest management certification (SFMC) has existed over decades as a form of anthropogenic remedy for tropical deforestation and forest degradation. This forest certification system, since 2006 to date, has been adopted by small forest holders (SFHs) in Kedung Keris village, Indonesia to promote sustainable forest management and improve their livelihoods. However, an empirical assessment of the ecological and socioeconomic benefits of the SFMC in the Kedung Keris village is missing in the literature. Thus, we assessed the impact of the SFMC on SFHs management in the Kedung Keris village. We interviewed 35 SFHs and 3 informants on the ecological and socioeconomic benefits of the SFMC with a semi-structured questionnaire. We observed that the SFMC has led to some ecological and socioeconomic benefits to the SFHs management in the village such as improved water supply, wildlife conditions, and an increase of timber price and revenue.
Ridho Hendra Yoga Perdana(리드호 헨드라 요가 페르다나),Beongku An(안병구) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
This paper studies the deep learning-based joint power allocation and phase shift in multiuser multi-intelligent reflecting surface (IRS)-aided massive MIMO systems. The signal-to-noise-plus noise ratio is formulated to determine the spectral efficiency problem. Particularly, we design a deep neural network (DNN) to learn the relation between the position of every user within cell with the optimal power allocation and phase shift policies. The simulation results show that the suggested idea achieves good performance in predict the power allocation and phase shift with accuracy 97% compared to the conventional method while it reduces the computation complexity.
Ridho Hendra Yoga Perdana,Toan-Van Nguyen,Beongku An 한국통신학회 2023 ICT Express Vol.9 No.2
In this paper, we propose a deep learning approach for solving power allocation problems in massive MIMO networks. We use signal-to-interference-plus-noise-ratio (SINR) and signal-to-leak-plus-noise ratio (SLNR) criteria for linear precoder design to define the max–min and max-prod power allocation challenges. The power allocation process to each user equipment in the base station coverage takes a long time and is inefficient, hence numerous base stations are deployed to serve multiple user equipments. As a result, we develop a deep neural network (DNN) framework in which the user’s equipment position is utilized to train the deep model, which is then used to forecast the ideal power distribution depending on the user’s location. Compared to the traditional optimization approach, the DNN design helps to obtain the optimal solution of the power allocation problem within a short time via a quick-inference process. Simulation results show that the SINR criterion outperforms the SLNR one. Meanwhile, deep learning achieves excellent results in forecasting power allocation with an accuracy of 85% for the max–min strategy and 99% for the max-product approach.