• Journal of Electrical Engineering and Technology
• /
• 제13권1호
• /
• pp.38-50
• /
• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• Journal of Computational Design and Engineering
• /
• 제1권1호
• /
• pp.48-54
• /
• 2014

In this paper, the authors propose a system for assisting mold designers of plastic parts. With a CAD model of a part, the system automatically determines the optimal ejecting direction of the part with minimum undercuts. Since plastic parts are generally very thin, many rib features are placed on the inner side of the part to give sufficient structural strength. Our system extracts the rib features from the CAD model of the part, and determines the possible ejecting directions based on the geometric properties of the features. The system then selects the optimal direction with minimum undercuts. Possible ejecting directions are represented as discrete points on a Gauss map. Our new point distribution method for the Gauss map is based on the concept of the architectural geodesic dome. A hierarchical structure is also introduced in the point distribution, with a higher level "rough" Gauss map with rather sparse point distribution and another lower level "fine" Gauss map with much denser point distribution. A system is implemented and computational experiments are performed. Our system requires less than 10 seconds to determine the optimal ejecting direction of a CAD model with more than 1 million polygons.

• Smart Structures and Systems
• /
• 제31권4호
• /
• pp.421-436
• /
• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Structural Engineering and Mechanics
• /
• 제84권5호
• /
• pp.651-664
• /
• 2022

A precise prediction of the ultimate bond strength between rebar and surrounding concrete plays a major role in structural design, as it effects the load-carrying capacity and serviceability of a member significantly. In the present study, Gaussian models are employed for modelling bond strength of ribbed steel bars embedded in concrete. Gaussian models offer a non-parametric method based on Bayesian framework which is powerful, versatile, robust and accurate. Five different Gaussian models are explored in this paper-Gaussian Process (GP), Variational Heteroscedastic Gaussian Process (VHGP), Warped Gaussian Process (WGP), Sparse Spectrum Gaussian Process (SSGP), and Twin Gaussian Process (TGP). The effectiveness of the models is also evaluated in comparison to the numerous design formulae provided by the codes. The predictions from the Gaussian models are found to be closer to the experiments than those predicted using the design equations provided in various codes. The sensitivity of the models to various parameters, input feature space and sampling is also presented. It is found that GP, VHGP and SSGP are effective in prediction of the bond strength. For large data set, GP, VHGP, WGP and TGP can be computationally expensive. In such cases, SSGP can be utilized.

• 정보보호학회논문지
• /
• 제33권6호
• /
• pp.1067-1076
• /
• 2023

최근 변종 악성코드가 증가하면서 사이버 해킹 침해사고 규모가 확대되고 있다. 그리고 지능형 사이버 해킹 공격에 대응하기 위해 악성코드 패밀리를 효과적으로 분류하기 위한 기계학습 기반 연구가 활발히 진행되고 있다. 그러나 기존의 분류 모델은 데이터셋이 난독화되거나, 희소한 경우에 성능이 저하되는 문제가 있었다. 본 논문에서는 ASM 파일과 BYTES 파일에서 추출한 특징을 결합한 하이브리드 데이터셋을 제안하고, FNN을 사용하여 분류 성능을 평가한다. 실험 결과에 따르면 제안하는 방법은 단일 데이터셋에 비해 약 4% 향상된 성능을 보였으며, 특히 희소한 패밀리에 대해서는 약 30%의 성능 향상을 보였다.

• 한국항행학회논문지
• /
• 제27권2호
• /
• pp.190-196
• /
• 2023

최근 들어, 라이다 기술의 발전에 따라 정확한 거리 측정이 가능해지면서 라이다 기반의 3차원 객체 탐지 네트워크에 대한 관심이 증가하고 있다. 기존의 네트워크는 복셀화 및 다운샘플링 과정에서 공간적인 정보 손실이 발생해 부정확한 위치 추정 결과를 발생시킨다. 본 연구에서는 고수준 특징과 높은 위치 정확도를 동시에 획득하기 위해 어텐션 기반 융합 방식과 카메라-라이다 융합 시스템을 제안한다. 먼저, 그리드 기반의 3차원 객체 탐지 네트워크인 Voxel-RCNN 구조에 어텐션 방식을 도입함으로써, 다중 스케일의 희소 3차원 합성곱 특징을 효과적으로 융합하여 3차원 객체 탐지의 성능을 높인다. 다음으로, 거짓 양성을 제거하기 위해 3차원 객체 탐지 네트워크의 탐지 결과와 이미지상의 2차원 객체 탐지 결과를 결합하는 카메라-라이다 융합 시스템을 제안한다. 제안 알고리즘의 성능평가를 위해 자율주행 분야의 KITTI 데이터 세트를 이용하여 기존 알고리즘과의 비교 실험을 수행한다. 결과적으로, 차량 클래스에 대해 BEV 상의 2차원 객체 탐지와 3차원 객체 탐지 부분에서 성능 향상을 보였으며 특히 Voxel-RCNN보다 차량 Moderate 클래스에 대하여 정확도가 약 0.47% 향상되었다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2023년도 학술발표회
• /
• pp.168-168
• /
• 2023

The frequent occurrence of cyanobacterial harmful algal blooms (CHABs) in inland waters under climate change seriously damages the ecosystem and human health and is becoming a big problem in South Korea. Satellite remote sensing is suggested for effective monitoring CHABs at a larger scale of water bodies since the traditional method based on sparse in-situ networks is limited in space. However, utilizing a standalone variable of satellite reflectances in common CHABs dual-models, which relies on both chlorophyll-a (Chl-a) and phycocyanin or cyanobacteria cells (Cyano-cell), is not fully beneficial because their seasonal variation is highly impacted by surrounding meteorological and bio-environmental factors. Along with the development of Artificial Intelligence (AI), monitoring CHABs from space with analyzing the effects of environmental factors is accessible. This study aimed to investigate the potential application of AI in the dual-model strategy (Chl-a and Cyano-cell are output parameters) for monitoring seasonal dynamics of CHABs from satellites over Korean inland waters. The Sentinel-3 satellite was selected in this study due to the variety of spectral bands and its unique band (620 nm), which is sensitive to cyanobacteria. Via the AI-based feature selection, we analyzed the relationships between two output parameters and major parameters (satellite water-leaving reflectances at different spectral bands), together with auxiliary (meteorological and bio-environmental) parameters, to select the most important ones. Several AI models were then employed for modelling Chl-a and Cyano-cell concentration from those selected important parameters. Performance evaluation of the AI models and their comparison to traditional semi-analytical models were conducted to demonstrate whether AI models (using water-leaving reflectances and environmental variables) outperform traditional models (using water-leaving reflectances only) and which AI models are superior for monitoring CHABs from Sentinel-3 satellite over a Korean inland water body.

• 대한지리학회지
• /
• 제49권5호
• /
• pp.761-778
• /
• 2014

본 논문은 거래빈도가 낮아 지금껏 적극적으로 시도되지 못한 상업용 토지의 가격을 정확히 추정하고자 하였다. 서울시 상업용 토지 실거래가 자료를 대상으로 선형 결합 형태의 평균 구조(전역적 경향), 지수 형태의 공분산함수 그리고 순수 오차항을 구성요소로 하는 모형을 구축 및 적용하였다. 상권별로 가격수준이 차별적으로 형성되는 상업용 토지 가격의 특성을 감안하여 대표적 공간보간기법인 크리깅 방법을 적용함으로써 지가의 공간적 상관성을 명시적으로 고려하였다. 더 나아가 희소한 자료의 한계를 극복하기 위해 전문가 지식을 사전 확률분포의 형태로 모형에 반영할 수 있는 베이지안 크리깅 방법을 활용하였다. 적용한 모형의 성능은 적합 과정에 사용되지 않은 검증 자료를 대상으로 검토하였으며, 전문가 지식의 반영과 공간적 상관성의 명시적 고려를 통해 가격 추정의 정확성이 높아진 사실을 확인하였다. 본 논문은 베이지안 크리깅 기법을 토지 가격 추정에 적용하되, 전문가의 주관적 지식을 명시적으로 모형에 반영하였다는 점 등에서 기존 연구와 차별성을 갖는다. 본 논문의 결과는 거래 자료가 희소한 상황에서도 신뢰성 있게 부동산 가격을 추정해야하는 경우에 유용하게 활용될 수 있을 것으로 기대된다.