• Journal of the Society of Naval Architects of Korea
• /
• v.55 no.1
• /
• pp.9-21
• /
• 2018

Survivability of naval vessels is defined as the ability to perform functions and missions in a combat environment. Survivability has close relationship with the spatial layout of naval vessels. In order to maximize survivability, it must be considered from the early stage of design. However the existing concept of survivability was intended to be applied to unit vessels. So it was not suitable for assessment of spatial layout results at the early stage of design. In this paper, a simplified assessment method which can evaluate the spatial layout considering the survivability in the early stage of design has been proposed. For this, assessment layers were defined on survivability components such as susceptibility, vulnerability, and recoverability. Assessment layers of each component were overlapped to deduce a survivability layer of spatial layout alternatives. In addition, the proposed method and optimization algorithm were used to derive optimal spatial layout alternatives considering survivability.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.1
• /
• pp.1-6
• /
• 2014

In this study, we propose optimized RBFNNs based on night vision face recognition simulator with the aid of $(2D)^2$ PCA algorithm. It is difficult to obtain the night image for performing face recognition due to low brightness in case of image acquired through CCD camera at night. For this reason, a night vision camera is used to get images at night. Ada-Boost algorithm is also used for the detection of face images on both face and non-face image area. And the minimization of distortion phenomenon of the images is carried out by using the histogram equalization. These high-dimensional images are reduced to low-dimensional images by using $(2D)^2$ PCA algorithm. Face recognition is performed through polynomial-based RBFNNs classifier, and the essential design parameters of the classifiers are optimized by means of Differential Evolution(DE). The performance evaluation of the optimized RBFNNs based on $(2D)^2$ PCA is carried out with the aid of night vision face recognition system and IC&CI Lab data.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.18 no.6
• /
• pp.545-556
• /
• 2016

If a back analysis is used in various measurement information for the estimation of an operating subsea tunnel safety, it is possible to obtain the results within efficient error rate. With such a commercial geotechnical analysis program as FLAC3D, back analysis is performed with a DEA which was validated in previous studies. However, there is a problem that is relatively a time-consuming analysis. For this reason, beam-spring model-based FEM solver which takes shorter relative analysis time, was developed by Python language, and then combined with the built-DEA. In order to consider the assessment of safety of an operation tunnel near real-time, a program for longitudinal direction tunnel was developed due to its relative easy development for analysis solver engine.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.19 no.2
• /
• pp.195-212
• /
• 2017

When back analysis is used for the assessment of an operating subsea tunnel safety in various measurement information such as stress, water pressure and tunnel lining and ground stiffness degradation, the reliable results within tolerable error rate can be obtained. By utilizing a commercial geotechnical analysis program FLAC3D, back analysis can be performed with a DEA which has already been successfully validated in previous studies. However, relative more time-consumption is the drawback of this approach. For this reason, this study introduced beam-spring model-based on FEM solver which uses less analysis time relatively. Beam-spring program capable of structural analysis of a circular tunnel section was developed by using Python language and combined with the built-DEA. From the measurement datum, expected to estimate the stability of an operation tunnel close to real-time.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.3
• /
• pp.215-222
• /
• 2021

As the importance of human-computer interaction (Human Computer Interface) technology grows and research on HCI is progressing, it is inferred about the research emotion inference or the computer reaction according to the user's intention, not the computer reaction by the standard input of the user. Stress is an unavoidable result of modern human civilization, and it is a complex phenomenon, and depending on whether or not there is control, human activity ability can be seriously changed. In this paper, we propose an intelligent emotional recommendation system using music as a way to relieve stress after measuring heart rate variability (HRV) and acceleration photoplethymogram (APG) increased through stress as part of human-computer interaction. The differential evolution algorithm was used to extract reliable data by acquiring and recognizing the user's biometric information, that is, the stress index, and emotional inference was made through the semantic web based on the obtained stress index step by step. In addition, by searching and recommending a music list that matches the stress index and changes in emotion, an emotional recommendation system suitable for the user's biometric information was implemented as an application.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.1
• /
• pp.195-201
• /
• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.