• Wind and Structures
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• 제38권6호
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• pp.461-475
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• 2024

A reliable wind speed forecasting method is crucial for the applications in wind engineering. In this study, the generalized S-transform (GST) is innovatively applied for wind speed forecasting to uncover the time-frequency characteristics in the non-stationary wind speed data. The improved grey wolf optimizer (IGWO) is employed to optimize the adjustable parameters of GST to obtain the best time-frequency resolution. Then a hybrid method based on IGWO-optimized GST is proposed to validate the effectiveness and superiority for multi-step non-stationary wind speed forecasting. The historical wind speed is chosen as the first input feature, while the dynamic time-frequency characteristics obtained by IGWO-optimized GST are chosen as the second input feature. Comparative experiment with six competitors is conducted to demonstrate the best performance of the proposed method in terms of prediction accuracy and stability. The superiority of the GST compared to other time-frequency analysis methods is also discussed by another experiment. It can be concluded that the introduction of IGWO-optimized GST can deeply exploit the time-frequency characteristics and effectively improving the prediction accuracy.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• Computers and Concrete
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• 제29권 6호
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• pp.375-391
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• 2022

This paper examined the impact of the cross-sectional structure on the structural results under different loading conditions of reinforced concrete (RC) members' management limited in Carbon Fiber Reinforced Polymers (CFRP). The mechanical properties of CFRC was investigated, then, totally 32 samples were examined. Test parameters included the cross-sectional shape as square, rectangular and circular with two various aspect rates and loading statues. The loading involved concentrated loading, eccentric loading with a ratio of 0.46 to 0.6 and pure bending. The results of the test revealed that the CFRP increased ductility and load during concentrated processing. A cross sectional shape from 23 to 44 percent was increased in load capacity and from 250 to 350 percent increase in axial deformation in rectangular and circular sections respectively, affecting greatly the accomplishment of load capacity and ductility of the concentrated members. Two Artificial Intelligence Models as Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) were used to estimating the tensile and flexural strength of specimen. On the basis of the performance from RMSE and RSQR, C-Shape CFRC was greater tensile and flexural strength than any other FRP composite design. Because of the mechanical anchorage into the matrix, C-shaped CFRCC was noted to have greater fiber-matrix interfacial adhesive strength. However, with the increase of the aspect ratio and fiber volume fraction, the compressive strength of CFRCC was reduced. This possibly was due to the fact that during the blending of each fiber, the volume of air input was increased. In addition, by adding silica fumed to composites, the tensile and flexural strength of CFRCC is greatly improved.

• 대한원격탐사학회지
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• 제36권5_3호
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• pp.1053-1066
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• 2020

이산화황(SO₂)은 대기 중 화학 반응을 통해 2차 대기오염물질을 생성하는 전구체로, 주로 산업활동이나 주거 및 교통 활동 등을 통해 배출된다. 장기간 노출 시 호흡기 질환이나 심혈관 질환 등을 유발하여 인체 건강에 부정적인 영향을 미칠 수 있기 때문에 이에 대한 지속적인 모니터링이 필요하다. 우리나라에서는 SO₂에 대해 관측소 기반의 모니터링이 수행되고 있으나 이는 공간적으로 연속적인 정보를 제공하는 데에 한계가 있다. 따라서, 본 연구에서는 위성자료와 수치모델 자료를 융합하여 일별 13시를 타겟으로 하는 1 km의 고해상도로 공간적으로 연속적인 SO₂ 지상농도를 산출하였다. 2015년 1월부터 2019년 4월까지의 기간 동안 남한 지역에 대하여 스태킹 앙상블 기법을 이용하여 SO₂ 지상농도 추정 모델을 개발하였다. 스태킹 앙상블 기법이란 여러가지 기계학습 기법을 두 단계로 쌓는 방식으로 융합하여 단일 모델 대비 더 향상된 성능을 도출하는 방법이다. 본 연구에서는 베이스 모델로는 RF (Random Forest)와 XGB (eXtreme Gradient BOOSTing) 기법이, 메타 모델로는 MLR (Multiple Linear Regression) 기법이 사용되었다. 구축된 모델의 교차검증 결과 메타 모델은 상관계수(R) = 0.69와 root-mean-squared-error(RMSE) = 0.0032 ppm의 결과를 보였으며 이는 베이스 모델의 평균 대비 약 25% 향상된 안정성을 보였다. 또한 모델 구축에 사용되지 않은 기간에 대한 예측 검증을 수행하여 모델의 일반화 가능성을 평가하였다. 구축된 모델을 이용하여 남한 지역의 SO₂ 지상농도 공간분포를 분석한 결과 일반적인 계절성과 배출원의 변화를 잘 반영하는 패턴을 보임을 확인하였다.