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이지섭,박재범 한국운동생리학회 2022 운동과학 Vol.31 No.2
PURPOSE: To investigate the effect of muscle vibration on performance accuracy and multi-muscle coordination pattern during voluntary isometric knee extension torque production. METHODS: The subjects were tested under two conditions of external vibration frequencies (90 Hz vibration (VIB)&no-vibration (NVIB)) with three levels of torque magnitudes of 20% (MVT20), 40% (MVT40), and 60% of maximal voluntary torque (MVT60). The subjects were instructed to perform a submaximal isometric ramp task and matched the produced torque with the torque template shown in the screen as accurately as possible. External vibration was applied to the rectus femoris (RF). RESULTS: The performance error (RMSENORM) was reduced in 60% of MVT (MVT60) in both ramp and SS phases, and the iEMGAGO was significantly reduced by vibration under the same torque conditions in the SS phase. In addition, the muscle-mode (M-mode) composition was found to be different in the VIB and NVIB in the SS phase. We found that the VIB condition showed co-contraction M-modes and mixed M-modes. However, there was no significant difference in the ramp phase under all conditions. CONCLUSIONS: The neurophysiological changes due to muscle vibration may positively affect the task characteristics and steps that require accurate torque generation and provide information for the quantitative understanding of multi-muscle coordination of vibration.
이지섭,강수영,김승주 한국정보보호학회 2018 정보보호학회논문지 Vol.28 No.6
The AI speaker is a simple operation that provides users with useful functions such as music playback, online search,and so the AI speaker market is growing at a very fast pace. However, AI speakers always wait for the user's voice,which can cause serious problems such as eavesdropping and personal information exposure if exposed to security threats. Therefore, in order to provide overall improved security of all AI speakers, it is necessary to identify potential securitythreats and analyze them systematically. In this paper, security threat modeling is performed by selecting four products with high market share. Data FlowDiagram, STRIDE and LINDDUN Threat modeling was used to derive a systematic and objective checklist for vulnerabilitychecks. Finally, we proposed a method to improve the security of AI speaker by comparing the vulnerability analysis resultsand the vulnerability of each product. AI 스피커는 간단한 동작으로 음악재생, 온라인 검색 등 사용자에게 유용한 기능을 제공하고 있으며, 이에 따라AI 스피커 시장은 현재 매우 빠른 속도로 성장하고 있다. 그러나 AI 스피커는 항시 사용자의 음성을 대기하고 있어보안 위협에 노출될 경우 도청, 개인정보 노출 등 심각한 문제가 발생할 수 있다. 이에 모든 AI 스피커의 전반적으로 향상된 보안을 제공하기 위해 발생 가능한 보안 위협을 식별하고 체계적인 취약점 점검을 위한 방안이 필요하다. 본 논문에서는 점유율이 높은 제품 4개를 선정하여 보안위협모델링을 수행하였다. Data Flow Diagram,STRIDE, LINDDUN 위협모델링을 통해 체계적이고 객관적인 취약점 점검을 위한 체크리스트를 도출하였으며,이후 체크리스트를 이용하여 실제 기기에 대한 취약점 점검을 진행하였다. 마지막으로 취약점 점검 결과 및 각 제품에 대한 취약점 비교·분석을 통해 AI 스피커의 보안성을 향상시킬 수 있는 방안을 제안하였다.