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Han Sol,Song Sung Wook,Hong Hansol,김우정,강영준,Park Chang Bae,Kang Jeong Ho,Bu Ji Hwan,이성근,Ko Seo Young,이수훈,Kang Chul-Hoo 대한응급의학회 2023 Clinical and Experimental Emergency Medicine Vol.10 No.2
Objective: This study investigated the hospital diagnoses and characteristics of uncooperative prehospital patients suspected of acute stroke who could not undergo a prehospital stroke screening test (PHSST). Methods: This retrospective observational study was conducted at a single academic hospital with a regional stroke center. We analyzed three scenario-based prehospital stroke screening performances using the final hospital diagnoses: (1) a conservative approach only in patients who underwent the PHSST, (2) a real-world approach that considered all uncooperative patients as screening positive, and (3) a contrapositive approach that all uncooperative patients were considered as negative. Results: Of the 2,836 emergency medical services (EMS)-transported adult patients who met the prehospital criteria for suspicion of acute stroke, 486 (17.1%) were uncooperative, and 570 (20.1%) had a confirmed final diagnosis of acute stroke. The diagnosis in the uncooperative group did not differ from that in the cooperative group (22.0% vs. 19.7%, P=0.246). The diagnostic performances of the PHSST in the conservative approach were as follows: 79.5% sensitivity (95% confidence interval [CI], 75.5%–83.1%), 90.2% specificity (95% CI, 88.8%–91.6%), and 0.849 area under the receiver operating characteristic curve (AUC; 95% CI, 0.829–0.868). The sensitivity and specificity were 83.3% (95% CI, 80.0%–86.3%) and 75.2% (95% CI, 73.3%–76.9%), respectively, in the real-world approach and 64.6% (95% CI, 60.5%–68.5%) and 91.9% (95% CI, 90.7%–93.0%), respectively, in the contrapositive approach. No significant difference was evident in the AUC between the real-world approach and the contrapositive approach (0.792 [95% CI, 0.775–0.810] vs. 0.782 [95% CI, 0.762–0.803], P>0.05). Conclusion: We found overestimation (false positive) and underestimation (false negative) in the uncooperative group depending on the scenario-based EMS stroke screening policy for uncooperative prehospital patients suspected of acute stroke.
Hansol Seo,Sung-Chul Jun,Dukyoung Jung,Jae Soo Hong,Chang-Hyung Lee,김한성,Dohyung Lim 한국정밀공학회 2020 International Journal of Precision Engineering and Vol.21 No.6
The characteristics of ankle joint motions in the elderly that arise from a wide range of activities of daily living (ADLs) have not been adequately assessed using a quantitative and objective pattern recognition approach. The current study aims to analyze the characteristics of ankle joint motions for 12 diff erent ADLs in the elderly through the pattern recognition approach; this study also aims to identify whether this analysis technique is eff ective, quantitative and objective in understanding the characteristics of ankle joint motions. Fifty elderly participants performed 12 ADLs that were selected based on Katz’s ADL indicators. Inertial measurement units were used to measure the ankle joint motions, and their patterns and similarities were analyzed using the pattern recognition approach. The results identifi ed the inherent ankle joint motion features for each ADL. The similarities of the patterns of ADLs related to walking were very low ( p < 0.25) for the ankle joint motions even though the range of motion and pattern shapes were similar to one another. The similarities of the patterns of ADLs related to sitting/rising were particularly high ( p > 0.9) for dorsi/plantar fl exion and low ( p < 0.5) for abduction/adduction. The similarities of the patterns of ADLs related to lying/rising were high, particularly for dorsi/plantar fl exion and inversion/eversion. The results suggest that applying a pattern recognition approach with a conventional kinematic analysis may be eff ective, quantitative, and objective in understanding the kinematic characteristics of ankle joints.
Hansol Kim,Cheolwoong Kim,Hong Kyoung Seong,Jeonghoon Yoo IEEE 2015 IEEE transactions on magnetics Vol.51 No.12
<P>This paper suggests an optimal structure design to enhance the magnetic actuator performance. During the operation of the actuator, heat generated by coils reduces the device efficiency. Therefore, the design objective must be set to decrease the coil temperature as well as to increase the magnetic actuating force. A C-shaped actuator was selected as a design target and the structural optimization scheme based on the phase field method was adopted to obtain the optimal shape. The simulation and optimization process was performed using the commercial package COMSOL associated with the MATLAB programming. Design results were also verified by the experiments focused on the effect in the magnetic field.</P>
커버리지 달성 성능 향상을 위한 반복 횟수를 제한하는 Concolic 테스팅 경로 탐색 기법
최한솔(Hansol Choe),홍신(Shin Hong) Korean Institute of Information Scientists and Eng 2021 정보과학회논문지 Vol.48 No.2
This paper proposes a loop-bounded search strategy for effective and efficient coverage achievement in concolic testing. In selecting a new path to explore, a loop-bounded search strategy limits the number of iterations in a loop to a certain loop-bound, so that the concolic testing is guided to explore various program behaviors within a limited range. In addition, to extend the range of path exploration gradually, this search strategy increments the loop-bound over test executions based on their coverage achievement rates. We implemented three versions of loop-bounded search strategies based on three existing concolic search strategies of CREST. The experiments with 4 real-world target programs (Vim, Grep, Busybox Awk, and Busybox Sed) showed that CREST achieves a higher branch coverage more quickly when the loop-bounded search strategies are applied.
이한솔(Hansol Lee),김영관(Younggwan Kim),홍지만(Jiman Hong) 한국스마트미디어학회 2019 스마트미디어저널 Vol.8 No.2
최근 컴퓨터 비전을 활용한 사물인식 기술이 센서 기반 사물인식 기술을 대체할 기술로 주목을 받고 있다. 센서 기반 사물인식 기술은 일반적으로 고가의 센서를 필요로 하기 때문에 기술이 상용화되기 어렵다는 문제가 있었다. 반면 컴퓨터 비전을 활용한 사물인식 기술은 고가의 센서 대신 비교적 저렴한 카메라를 사용할 수 있다. 동시에 CNN이 발전하면서 실시간 사물인식이 가능해진 이후 IoT, 자율주행자동차 등 타 분야에 활발하게 도입되고 있다. 그러나 사물 인식 모델을 상황에 알맞게 선택하고 학습시키기 위해서는 딥러닝에 대한 전문적인 지식을 요구하기 때문에 비전문가가 사물 인식 모델을 사용하기에는 어려움이 따른다. 따라서 본 논문에서는 딥러닝 기반 사물인식 모델들의 구조와 성능을 분석하고, 사용자가 원하는 조건의 최적의 딥러닝 기반 사물 인식 모델을 스스로 선정할 수 있는 플랫폼을 제안한다. 또한 통계에 기반한 사물 인식 모델 선정이 필요한 이유를 실험을 통해 증명한다. Recently, object recognition technology using computer vision has attracted attention as a technology to replace sensor-based object recognition technology. Sensor-based object recognition technology has a problem that it is difficult to commercialize the technology because an expensive sensor is required. On the other hand, since object recognition technology using computer vision can replace sensors with inexpensive cameras. Moreover, Real-time object recognition becomes possible because of the development of CNN, it is actively introduced into other fields such as IoT and autonomous vehicles. However, using the object recognition model requires expert knowledge on deep learning to select and learn the model, it is difficult for non-experts to use it. Therefore, in this paper, we analyze the structure of deep - learning - based object recognition models, and propose a platform that can automatically select a deep - running object recognition model based on a user s desired condition. We also show the reason why we need to select the object recognition model based on the statistics through experiments on the models.