Han-Seok Ko

Effectiveness Analysis for a Lightweight Torpedo Considering Evasive Maneuvering and TACM of a Target

In the development phase of a torpedo, the effectiveness analysis is carried out to predict the performance and to learn how to use the torpedo. In order to obtain reliable data, it is required to model the tactical situation closely to the actual one. Because the submarine is a target of a lightweight torpedo, the anti-torpedo countermeasures of a submarine such as evasive maneuvering and TACM (Torpedo Acoustic Counter Measure) should be modeled in detail. In this paper, the evasive maneuvering is modeled reflecting the movement characteristics of the submarine. Furthermore various TACMs such as a floating-type decoy, a self-propelled decoy and jammers are also modeled. Then, effectiveness of a lightweight torpedo is measured and analyzed using the simulation program which is developed through the above modeling procedure.

Visual Voice Activity Detection and Adaptive Threshold Estimation for Speech Recognition

In this paper, we propose an algorithm for achieving robust Visual Voice Activity Detection (VVAD) for enhanced speech recognition. In conventional VVAD algorithms, the motion of lip region is found by applying an optical flow or Chaos inspired measures for detecting visual speech frames. The optical flow-based VVAD is difficult to be adopted to driving scenarios due to its computational complexity. While invariant to illumination changes, Chaos theory based VVAD method is sensitive to motion translations caused by driver`s head movements. The proposed Local Variance Histogram (LVH) is robust to the pixel intensity changes from both illumination change and translation change. Hence, for improved performance in environmental changes, we adopt the novel threshold estimation using total variance change. In the experimental results, the proposed VVAD algorithm achieves robustness in various driving situations.