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다수의 스피커를 사용하는 선형 배열 시스템에서 기하학적 접근 방법을 통한 스윗 스팟 분석
양훈민(Hunmin Yang),박영진(Youngjin Park),박윤식(Youn-sik Park) 한국소음진동공학회 2013 한국소음진동공학회 논문집 Vol.23 No.11
This paper describes techniques used to analyze the sweet spot of sound field reproduced by ear-level linear arrays of loudspeakers by geometrical approach method. Previous researches have introduced various sweet spot definitions in their own way. In general, sweet spot is defined as an area whose stereophonic sound effect is valid. Its size is affected by the geometrical arrangement of the system. In this paper, a case when plane waves are generated by linear arrays of loudspeakers in the horizontal plane is considered. So the sweet spot is defined as an area in which the listener can perceive the desired azimuth angle. Because there are many loudspeakers, impulse responses at listener’s ears are in the form of pulse-train and the time-duration of the pulse-train affects the localization performance of the listener. So we calculated the maximum time duration of pulse-train by geometrical approach method and identified with the results of impulse response simulation. This paper also includes parameter analysis with respect to aperture size, so it suggests a tool for sound engineers to expect the sweet spot size and listener’s sound perception.
Hypokalemic periodic paralysis; two different genes responsible for similar clinical manifestations
Hunmin Kim,Hee Hwang,Hae Il Cheong,Hye Won Park 대한소아청소년과학회 2011 Clinical and Experimental Pediatrics (CEP) Vol.54 No.11
Primary hypokalemic periodic paralysis (HOKPP) is an autosomal dominant disorder manifesting as recurrent periodic flaccid paralysis and concomitant hypokalemia. HOKPP is divided into type 1 and type 2 based on the causative gene. Although 2 different ion channels have been identified as the molecular genetic cause of HOKPP, the clinical manifestations between the 2 groups are similar. We report the cases of 2 patients with HOKPP who both presented with typical clinical manifestations, but with mutations in 2 different genes (CACNA1S p.Arg528His and SCN4A p.Arg672His). Despite the similar clinical manifestations, there were differences in the response to acetazolamide treatment between certain genotypes of SCN4A mutations and CACNA1S mutations. We identified p.Arg672His in the SCN4A gene of patient 2 immediately after the first attack through a molecular genetic testing strategy. Molecular genetic diagnosis is important for genetic counseling and selecting preventive treatment.
Improving Instance Segmentation using Synthetic Data with Artificial Distractors
Kanghyun Park,Hyeongkeun Lee,Hunmin Yang,Se-Yoon Oh 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10
Despite the advances in deep learning, training instance segmentation models like convolutional neural networks still tend to depend on enormous training data that are expensive and require labor to annotation. To avoid labor-intensive procedure, synthetic data can be an alternative because it is easy to generate and automatically segmented. However, it is challenging to train instance segmentation model that perform well at real world using only synthetic data because of domain gap. It is wrong direction to put a lot of effort into solving these problems by making synthetic data more photorealistic. In this paper, we suggest how to learn the instance segmentation model using synthetic data with artificial distractors. The performance has been improved about 7% by adding flying distractors compared to original synthetic data.
Training Deep Neural Networks with Synthetic Data for Off-Road Vehicle Detection
Eunchong Kim,Kanghyun Park,Hunmin Yang,Se-Yoon Oh 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10
In tandem with growing deep learning technology, vehicle detection using convolutional neural network is now become a mainstream in the field of autonomous driving and ADAS. Taking advantage of this, lots of real image datasets have been produced in spite of the painstaking work of data collection and ground truth annotation. As an alternative, virtually generated images are introduced. This makes data collection and annotation much easier, but a different kind of problem called ‘domain gap’ is announced. For instance, in off-road vehicle detection, there is a difficulty in producing off-road image dataset not only by collecting real images, but also by synthesizing images sidestepping the domain gap. In this paper, focusing on the off-road army tank detection, we introduce a synthetic image generator using domain randomization on off-road scene context. We train a deep learning model on synthetic dataset using low level features form feature extractor pre-trained on real common object dataset. With proposed method, we improve the model accuracy to 0.86 AP@0.5IOU, outperforming naïve domain randomization approach.