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Jaesub Shin,HyeonSeok Jang 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Domestic and foreign outdoor autonomous driving robots are being developed to be used for various purposes. Since outdoor autonomous driving requires different technologies from indoor autonomous driving, evaluation should be made based on appropriate evaluation methods. And some countries are demanding appropriate technical standards for outdoor autonomous robots to ensure proper performance when driving outdoors. In this paper, we intend to propose a technical criteria along with an appropriate autonomous driving evaluation methods for an outdoor autonomous driving robot that has not been developed in Korea.
한성민(Sungmin Han),신재섭(Jaesub Shin),조재욱(Jaewook Cho),장종문(Jongmoon Jang),최홍수(Hongsoo Choi),최지웅(Ji-Woong Choi) 한국통신학회 2013 韓國通信學會論文誌 Vol.38 No.12(융합기술)
인공와우는 와우의 물리적 또는 기능적 손상으로 유발된 청각장애를 가지고 있는 환자에게 청각기능을 회복하는 매우 효과적인 수단으로 알려져 있다. 하지만 현재까지 상용화된 인공와우 제품은 크기, 전력소모와 같은 측면에서 휴대성이 불편하여 아직 많은 한계점을 가지고 있다. 이러한 단점을 해결하기 위한 새로운 방식의 인체 삽입형 인공와우 개발이 요구되고 있다. 본 논문에서는 최근 연구 개발 중인 인체 삽입형 인공와우를 구성하는 센서부, 자극부, 무선통신부 등의 설계와 본 시스템에 탑재되는 통신시스템의 특징 및 시뮬레이션을 통한 평가결과 등을 기술하였다. Artificial cochlear implant system is known as the most efficient and widespread device to patients who have cochlear disorder. However, current commercialized artificial cochleas have inconveniences because of large volume size and high power consumption, requiring further research on improvements in terms of the size, power, and performance. In this paper, we will introduce our fully implantable artificial cochlear implant system, where small-size sensors and actuators are wirelessly connected, focusing on communication system design and its performance simulation.
Resource Allocation for H-FDD OFDMA Systems
Choi, Jihwan P.,Jaesub Shin,Jungwon Lee,Hui-Ling Lou,Ji-Woong Choi IEEE 2014 IEEE communications letters Vol.18 No.7
<P>Half-frequency-division duplexing (H-FDD) has recently attracted attention since it can serve as a cost-effective solution for machine-to-machine (M2M) communication networks. This letter proposes a method to jointly optimize resource allocation, mobile station grouping, and frame partitioning for H-FDD orthogonal frequency-division multiplexing access systems. We develop a near-optimal and low-complexity algorithm that is also applicable to frequency-division duplexing and time-division duplexing as special cases. Simulation results demonstrate that our algorithm with reduced computation can provide performance close to that of the optimal but time-consuming exhaustive search.</P>
Oh, Jooyoung,Cho, Dongrae,Park, Jaesub,Na, Se Hee,Kim, Jongin,Heo, Jaeseok,Shin, Cheung Soo,Kim, Jae-Jin,Park, Jin Young,Lee, Boreom IOP 2018 Physiological measurement Vol.39 No.3
<P> <I>Objective</I>: Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. <I>Approach</I>: Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. <I>Main results</I>: HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. <I>Significance</I>: Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.</P>
심박변이도를 이용한 중환자실 입원 환자의 자율신경계 일중변동성에 대한 예비연구
오주영,조동래,김종인,박재섭,허재석,김재진,라세희,신증수,이보름,박진영,Oh, Jooyoung,Cho, Dongrae,Kim, Jongin,Park, Jaesub,Heo, Jaeseok,Kim, Jae-Jin,Na, Se Hee,Shin, Cheung Soo,Lee, Boreom,Park, Jin Young 대한생물정신의학회 2017 생물정신의학 Vol.24 No.1
Objectives A normal circadian rhythm of autonomic nervous system function stands for the daily change of sympathetic and parasympathetic modulation, which can be measured by heart rate variability (HRV). Generally, patients in the intensive care unit (ICU) are prone to sleep-wake cycle dysregulation, therefore, it may have an influence on the circadian rhythm of autonomic nervous system. This study was designed to interpret possible dysregulation of autonomic nervous system in ICU patients by using HRV. Methods HRV was assessed every 3 hours in 21 ICU patients during a 7-minute period. The statistical differences of HRV features between the morning (AM 6 : 00-PM 12 : 00), and the afternoon (PM 12 : 00-PM 18 : 00) periods were evaluated in time domain and frequency domain. Results Patients showed significantly increased normalized power of low frequencey (nLF), absolute power of low frequencey (LF)/absolute power of high frequencey (HF) in the afternoon period as compared to the morning period. However, normalized power of high frequency (nHF) was significantly decreased in the afternoon period. There was no statistically significant difference between the morning period and the afternoon period in the time domain analysis. Conclusions The increased sympathetic tone in the afternoon period supports possible dysregulation in the circadian rhythm of autonomic nervous system in ICU patients. Future studies can help to interpret the association between autonomic dysregulation and negative outcomes of ICU patients.