scholarly journals Research on the prediction of quay crane resource hour based on Ensemble Learning

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Gaoshen Wang ◽  
Yi Ding
Keyword(s):  
Ensemble Learning ◽  
Historical Data ◽  
Influence Factors ◽  
Estimation Method ◽  
Wait Time ◽  
Container Terminals ◽  
Estimation Model ◽  
Empirical Estimation ◽  
Preparation Time ◽  
Adaboost Algorithm

In Container terminals, a quay crane's resource hour is affected by various complex nonlinear factors, and it is not easy to make a forecast quickly and accurately. Most ports adopt the empirical estimation method at present, and most of the studies assumed that accurate quay crane’s resource hour could be obtained in advance. Through the ensemble learning (EL) method, the influence factors and correlation of quay crane’s resources hour were analyzed based on a large amount of historical data. A multi-factor ensemble learning estimation model based quay crane’s resource hour was established. Through a numerical example, it is finally found that Adaboost algorithm has the best effect of prediction, with an error of 1.5%. Through the example analysis, it comes to a conclusion: the error is 131.86% estimated by the experience method. It will lead that subsequent shipping cannot be serviced as scheduled, increasing the equipment wait time and preparation time, and generating additional cost and energy consumption. In contrast, the error based Adaboost learning estimation method is 12.72%. So Adaboost has better performance.

DEMANDS ESTIMATION OF NEW TELECOMMUNICATION SERVICES IN FUZZY ENVIRONMENT

2003 ◽  
Vol 02 (02) ◽  
pp. 333-347 ◽  
Author(s):  
LONG-SHUH LIN
Keyword(s):  
Historical Data ◽  
Estimation Method ◽  
Solution Procedure ◽  
Influential Factors ◽  
Estimation Model ◽  
Telecommunication Services ◽  
Fuzzy Environment ◽  
Proposed Model ◽  
Phone Service ◽  
One Year

With the uncertain influential factors of demands and the lack of required historical data, demand estimation for new telecommunication services have generally relied just on marketing survey and analysis. However, the data collected from marketing survey are usually expressed in human linguistic forms and hence are fuzzy in nature. That means the estimation method derived from traditional sampling theory cannot fully represent such fuzzy data and thus biased consequences caused often. Therefore, in this study, to completely capture the uncertainty of the surveyed data, we adopt a series of analytical methods based on fuzzy set theory to construct a fuzzy estimation model. Based on the proposed model, a solution procedure is developed to aid users to acquire the demands of new telecommunication services. Finally, the solution procedure is employed to estimate demands of mobile phone service within one year in Taiwan with satisfactory results.

Normalized Sprint Estimation

2019 ◽  
Vol 7 (2) ◽  
pp. 41-58
Author(s):  
Shailesh Kumar
Keyword(s):  
Dynamic Characteristics ◽  
Success Factors ◽  
Historical Data ◽  
Estimation Method ◽  
Accurate Estimation ◽  
Functional Requirements ◽  
Estimation Model ◽  
Software Projects ◽  
Project Estimation

Accurate estimation of software projects is quintessential for overall success of the project. Estimation of agile projects adopted in most of the modern software projects is challenging due to lack of historical data and due to dynamic characteristics of the agile projects. In this paper we introduce “Normalized Sprint Estimation” method which factors in dynamic characteristics of the agile projects such as non-functional requirements, sprint success factors and such. The author applied the normalized sprint estimation method to 14 sprints from three digital projects and the predicted estimation values had Pred (0.3), more than 80%. Though the normalized sprint estimation model is tested for digital projects, the same methodology can be applied for software projects from other domains as well.

Estimation of Forest Biomass Energy Resources Based on Bottom-up Approach in China

2015 ◽  
Vol 8 (1) ◽  
pp. 272-275
Author(s):  
Lan Zhang ◽  
Dan Yu ◽  
Caihong Zhang ◽  
Weidong Zhang
Keyword(s):  
Forest Biomass ◽  
Estimation Method ◽  
Energy Resource ◽  
Biomass Energy ◽  
Energy Resources ◽  
Theoretical Ground ◽  
Estimation Model ◽  
Bottom Up ◽  
Rational Development ◽  
Calculation Results

Currently, the forest biomass energy development is at an initial stage and the estimation method for the forest biomass energy resource reserve is to be unified and refined although there is a great value and potential in the development and utilization of forest biomass energy in China. Based on the existing studies, the present paper analyzes the origins and types of forest biomass energy resources in the perspective of sustainable forestry management, constructs the estimation model using a bottom-up approach, and estimates the total existing forest biomass energy resource reserve in China based on the data of the 7th Forest Resource Survey. The estimation method and the calculation results provide the important theoretical ground for promoting the rational development of forest biomass energy in China.

scholarly journals Estimation of resistance force at steady-state sinkage for cylindrical wheel-typed lunar/planetary exploration rovers with function of push–pull locomotion

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Daisuke Fujiwara ◽  
Naoki Tsujikawa ◽  
Tetsuya Oshima ◽  
Kojiro Iizuka
Keyword(s):  
Steady State ◽  
Estimation Method ◽  
Planetary Exploration ◽  
Experimental Result ◽  
Resistance Force ◽  
Estimation Model ◽  
Loose Soil ◽  
Key Factor ◽  
The Relationship ◽  
Resistance Forces

Abstract Planetary exploration rovers have required a high traveling performance to overcome obstacles such as loose soil and rocks. Push-pull locomotion rovers is a unique scheme, like an inchworm, and it has high traveling performance on loose soil. Push-pull locomotion uses the resistance force by keeping a locked-wheel related to the ground, whereas the conventional rotational traveling uses the shear force from loose soil. The locked-wheel is a key factor for traveling in the push-pull scheme. Understanding the sinking behavior and its resistance force is useful information for estimating the rover’s performance. Previous studies have reported the soil motion under the locked-wheel, the traction, and the traveling behavior of the rover. These studies were, however, limited to the investigation of the resistance force and amount of sinkage for the particular condition depending on the rover. Additionally, the locked-wheel sinks into the soil until it obtains the required force for supporting the other wheels’ motion. How the amount of sinkage and resistance forces are generated at different wheel sizes and mass of an individual wheel has remained unclear, and its estimation method hasn’t existed. This study, therefore, addresses the relationship between the sinkage and its resistance force, and we analyze and consider this relationship via the towing experiment and theoretical consideration. The results revealed that the sinkage reached a steady-state value and depended on the contact area and mass of each wheel, and the maximum resistance force also depends on this sinkage. Additionally, the estimation model did not capture the same trend as the experimental results when the wheel width changed, whereas, the model captured a relatively the same trend as the experimental result when the wheel mass and diameter changed.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

scholarly journals Advertisement Click-Through Rate Prediction Based on the Weighted-ELM and Adaboost Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Sen Zhang ◽  
Qiang Fu ◽  
Wendong Xiao
Keyword(s):  
Real Time ◽  
Prediction Accuracy ◽  
Historical Data ◽  
User Profile ◽  
Imbalanced Learning ◽  
Sample Distribution ◽  
The Real ◽  
Adaboost Algorithm ◽  
Click Through Rate ◽  
Prediction Approach

Accurate click-through rate (CTR) prediction can not only improve the advertisement company’s reputation and revenue, but also help the advertisers to optimize the advertising performance. There are two main unsolved problems of the CTR prediction: low prediction accuracy due to the imbalanced distribution of the advertising data and the lack of the real-time advertisement bidding implementation. In this paper, we will develop a novel online CTR prediction approach by incorporating the real-time bidding (RTB) advertising by the following strategies: user profile system is constructed from the historical data of the RTB advertising to describe the user features, the historical CTR features, the ID features, and the other numerical features. A novel CTR prediction approach is presented to address the imbalanced learning sample distribution by integrating the Weighted-ELM (WELM) and the Adaboost algorithm. Compared to the commonly used algorithms, the proposed approach can improve the CTR significantly.

scholarly journals Generalizability and Accuracy of Site Index Estimation Model with Ensemble Learning

2009 ◽  
Vol 42 (1) ◽  
pp. 53-67
Author(s):  
Yasushi MINOWA ◽  
Norifumi SUZUKI ◽  
Kazuhiro TANAKA
Keyword(s):  
Ensemble Learning ◽  
Site Index ◽  
Estimation Model ◽  
Index Estimation

The DMT Power Line Channel Sparse Bayesian Regression Estimation Based on Communication System

2014 ◽  
Vol 960-961 ◽  
pp. 1308-1311
Author(s):  
Yi Pei Huang ◽  
Ya Jun Han ◽  
Bao Fan Chen
Keyword(s):  
Channel Estimation ◽  
Bayesian Learning ◽  
Estimation Method ◽  
Power Line ◽  
Previous Method ◽  
Bayesian Regression ◽  
Estimation Model ◽  
Power Line Communications ◽  
Learning Framework ◽  
Performance Curves

This paper introduces the power line communications channel estimation method based on sparse Bayesian regression, it is through the use of Bayesian learning framework that provides a sparse model in the presence of noise accurate channel estimation model. Improved channel estimation using the power line for the system to consider the frequency domain equalization (FREQ) transmitter and receiver, the bit error rate and comparing the two methods for generating various channel estimation techniques, and (BER) performance curves simulation the results show that the performance of the method is better than the previous method of least squares technique.

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