http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Marcel Maghiar (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Abbas Rashidi,Mohamad Hoseyn Sigari,Marcel Maghiar,David Citrin 대한토목학회 2016 KSCE JOURNAL OF CIVIL ENGINEERING Vol.20 No.4
Digital images and video clips collected at construction jobsites are commonly used for extracting useful information. Exploring new applications for image processing techniques within the area of construction engineering and management is a steady growing field of research. One of the initial steps for various image processing applications is automatically detecting various types of construction materials on construction images. In this paper, the authors conducted a comparison study to evaluate the performance of different machine learning techniques for detection of three common categorists of building materials: Concrete, red brick, and OSB boards. The employed classifiers in this research are: Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM). To achieve this goal, the feature vectors extracted from image blocks are classified to perform a comparison between the efficiency of these methods for building material detection. The results indicate that for all three types of materials, SVM outperformed the other two techniques in terms of accurately detecting the material textures in images. The results also reveals that the common material detection algorithms perform very well in cases of detecting materials with distinct color and appearance (e.g., red brick); while their performance for detecting materials with color and texture variance (e.g., concrete) as well as materials containing similar color and appearance properties with other elements of the scene (e.g., ORB boards) might be less accurate.
-
Productivity Estimation of Bulldozers using Generalized Linear Mixed Models
A. Rashidi,H. Rashidi Nejad,Marcel Maghiar 대한토목학회 2014 KSCE JOURNAL OF CIVIL ENGINEERING Vol.18 No.6
The productivity estimation of construction machinery is a significant challenge faced by many earthmoving contractors. Traditionally, contractors have used manufacturers’ catalogues or have simply relied on the site personnel’s experiences to estimatethe equipment production rates. However, various studies have demonstrated that typically, there are large differences between theestimated and real values. In the construction research domain, linear regression and neural network methods have been consideredas popular tools for estimating the productivity of equipment. However, linear regression cannot provide very accurate results, whileneural network methods require an immense volume of historical data for training and testing. Hence, a model that works with asmall dataset and provides results that are accurate enough is required. This paper proposes a generalized linear mixed model as apowerful tool to estimate the productivity of Komatsu D-155A1 bulldozers that are commonly used in many earthmoving job sites indifferent countries. The data for the numerical analysis are collected from actual productivity measurements of 65 bulldozers. Theoutputs of the proposed model are compared with the results obtained by using a standard linear regression model. In this manner, thecapabilities of the proposed method for accurate estimations of productivity rates are demonstrated.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
