Search results

1 – 10 of over 38000
Article
Publication date: 16 August 2022

Xin Lai, Dan Wu, Di Wu, Jia He Li and Hang Yu

The purpose of this study is to solve the problems of poor stability and high energy consumption of the dynamic window algorithm (DWA) for the mobile robots, a novel…

Abstract

Purpose

The purpose of this study is to solve the problems of poor stability and high energy consumption of the dynamic window algorithm (DWA) for the mobile robots, a novel enhanced dynamic window algorithm is proposed in this paper.

Design/methodology/approach

The novel algorithm takes the distance function as the weight of the target-oriented coefficient, and a new evaluation function is presented to optimize the azimuth angle.

Findings

The jitter of the mobile robot caused by the drastic change of angular velocity is reduced when the robot is closer to the target point. The simulation results show that the proposed algorithm effectively optimizes the stability of the mobile robot during operation with lower angular velocity dispersion and less energy consumption, but with a slightly higher running time than DWA.

Originality/value

A novel enhanced dynamic window algorithm is proposed and verified. According to the experimental result, the proposed algorithm can reduce the energy consumption of the robot and improves the efficiency of the robot.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 8 December 2022

Chunming Tong, Zhenbao Liu, Wen Zhao, Baodong Wang, Yao Cheng and Jingyan Wang

This paper aims to propose an online local trajectory planner for safe and fast trajectory generation that combines the jerk-limited trajectory (JLT) generation algorithm

Abstract

Purpose

This paper aims to propose an online local trajectory planner for safe and fast trajectory generation that combines the jerk-limited trajectory (JLT) generation algorithm and the particle swarm optimization (PSO) algorithm. A trajectory switching algorithm is proposed to improve the trajectory tracking performance. The proposed system generates smooth and safe flight trajectories online for quadrotors.

Design/methodology/approach

First, the PSO algorithm method can obtain the optimal set of target points near the path points obtained by the global path searching. The JLT generation algorithm generates multiple trajectories from the current position to the target points that conform to the kinetic constraints. Then, the generated multiple trajectories are evaluated to pick the obstacle-free trajectory with the least cost. A trajectory switching strategy is proposed to switch the unmanned aerial vehicle (UAV) to a new trajectory before the UAV reaches the last hovering state of the current trajectory, so that the UAV can fly smoothly and quickly.

Findings

The feasibility of the designed system is validated through online flight experiments in indoor environments with obstacles.

Practical implications

The proposed trajectory planning system is integrated into a quadrotor platform. It is easily implementable onboard and computationally efficient.

Originality/value

The proposed local planner for trajectory generation and evaluation combines PSO and JLT generation algorithms. The proposed method can provide a collision-free and continuous trajectory, significantly reducing the required computing resources. The PSO algorithm locally searches for feasible target points near the global waypoint obtained by the global path search. The JLT generation algorithm generates trajectories from the current state toward each point contained by the target point set. The proposed trajectory switching strategy can avoid unnecessary hovering states in flight and ensure a continuous and safe flight trajectory. It is especially suitable for micro quadrotors with a small payload and limited onboard computing power.

Details

Aircraft Engineering and Aerospace Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 12 December 2022

Iman Mohammadi, Hamzeh Mohammadi Khoshouei and Arezoo Aghaei Chadegani

In this study, to maximize returns and minimize investment risk, an attempt was made to form an optimal portfolio under conditions where the capital market has a price…

Abstract

Purpose

In this study, to maximize returns and minimize investment risk, an attempt was made to form an optimal portfolio under conditions where the capital market has a price bubble. According to the purpose, the research was of the applied type, in terms of data, quantitative and postevent, and in terms of the type of analysis, it was of the descriptive-correlation type. Sequence, skewness and kurtosis tests were used to identify the months with bubbles from 2015 to 2021 in the Tehran Stock Exchange. After identifying the bubble courses, artificial bee colony meta-heuristic and invasive weed algorithms were used to optimize the portfolio. The purpose of this paper is to address these issues.

Design/methodology/approach

The existence of bubbles in the market, especially in the capital market, can prevent the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. However, due to the goal of investors to achieve a portfolio of high returns with the least amount of risk, there is need to pay attention to these markets increases.

Findings

The results identify 14 periods of price bubbles during the study period. Additionally, stock portfolios with maximum returns and minimum risk were selected for portfolio optimization. According to the results of using meta-heuristic algorithms to optimize the portfolio, in relation to the obtained returns and risk, no significant difference was observed between the returns and risk of periods with price bubbles in each of the two meta-heuristic algorithms. This study can guide investors in identifying bubble courses and forming an optimal portfolio under these conditions.

Research limitations/implications

One of the limitations of this research is the non-generalizability of the findings to stock exchanges of other countries and other time periods due to the condition of the price bubble, as well as other companies in the stock market due to the restrictions considered for selecting the statistical sample.

Originality/value

This study intends to form an optimal stock portfolio in a situation where the capital market suffers from a price bubble. This study provides an effective and practical solution for investors in the field of stock portfolio optimization.

Details

Managerial Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 8 December 2022

Jonathan S. Greipel, Regina M. Frank, Meike Huber, Ansgar Steland and Robert H. Schmitt

To ensure product quality within a manufacturing process, inspection processes are indispensable. One task of inspection planning is the selection of inspection…

Abstract

Purpose

To ensure product quality within a manufacturing process, inspection processes are indispensable. One task of inspection planning is the selection of inspection characteristics. For optimization of costs and benefits, key characteristics can be defined by which the product quality can be checked with sufficient accuracy. The manual selection of key characteristics requires substantial planning effort and becomes uneconomic if many product variants prevail. This paper, therefore, aims to show a method for the efficient determination of key characteristics.

Design/methodology/approach

The authors present a novel Algorithm for the Selection of Key Characteristics (ASKC) based on an auto-encoder and a risk analysis. Given historical measurement data and tolerances, the algorithm clusters characteristics with redundant information and selects key characteristics based on a risk assessment. The authors compare ASKC with the algorithm Principal Feature Analysis (PFA) using artificial and historical measurement data.

Findings

The authors find that ASKC delivers superior results than PFA. Findings show that the algorithms enable the cost-efficient selection of key characteristics while maintaining the informative value of the inspection concerning the quality.

Originality/value

This paper fills an identified gap for simplified inspection planning with the method for the efficient selection of key features via ASKC.

Details

International Journal of Quality & Reliability Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 23 November 2022

Ibrahim Karatas and Abdulkadir Budak

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by…

Abstract

Purpose

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Design/methodology/approach

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Findings

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R2, were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R2 are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R2 are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

Research limitations/implications

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

Originality/value

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 11 November 2022

Gang Shi and Honglei Shang

Traditional algorithms require at least two complete vector observations to estimate orientation parameters. However, sensor faults and disturbances may cause some…

Abstract

Purpose

Traditional algorithms require at least two complete vector observations to estimate orientation parameters. However, sensor faults and disturbances may cause some components of vector observations unavailable. This paper aims to propose algorithms to realize orientation estimation using vector observations with one or two components lost.

Design/methodology/approach

The fundamental of the proposed method is using norm equation and dot product equation to estimate the lost components, then, using an improved TRIAD to calculate attitude matrix. Specific algorithms for one and two lost components cases are constructed respectively, and the nonuniqueness of orientation estimation is analyzed from a geometric point of view. At last, experiments are performed to test the proposed algorithms.

Findings

The loss of components results in the loss of orientation information. The introduction of the norm equation and dot product equation can partially compensate for the loss of information. Experiment results and analysis show that the proposed algorithms can provide effective orientation estimation, and in vast majority of applications, the proposed algorithms can provide a unique solution in one lost component case and double solutions in two lost components case.

Originality/value

The proposed method addresses the problem of orientation estimation when one or two components of vector observations are unavailable. The introduction of the norm equation and dot product equation makes the calculation cost low, while the analyses from a geometric point of view makes the study of nonuniqueness more intuitive.

Details

Sensor Review, vol. 42 no. 6
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 3 November 2022

Reza Edris Abadi, Mohammad Javad Ershadi and Seyed Taghi Akhavan Niaki

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large…

Abstract

Purpose

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.

Design/methodology/approach

Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.

Findings

This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.

Research limitations/implications

In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.

Originality/value

Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.

Details

Information Discovery and Delivery, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2398-6247

Keywords

Article
Publication date: 6 March 2017

Shyang-Jye Chang and Ray-Hong Wang

The motion vector estimation algorithm is very widely used in many image process applications, such as the image stabilization and object tracking algorithms. The…

Abstract

Purpose

The motion vector estimation algorithm is very widely used in many image process applications, such as the image stabilization and object tracking algorithms. The conventional searching algorithm, based on the block matching manipulation, is used to estimate the motion vectors in conventional image processing algorithms. During the block matching manipulation, the violent motion will result in greater amount of computation. However, too large amount of calculation will reduce the effectiveness of the motion vector estimation algorithm. This paper aims to present a novel searching method to estimate the motion vectors effectively.

Design/methodology/approach

This paper presents a novel searching method to estimate the motion vectors for high-resolution image sequences. The searching strategy of this algorithm includes three steps: the larger area searching, the adaptive directional searching and the small area searching.

Findings

The achievement of this paper is to develop a motion vector searching strategy to improve the computation efficiency. Compared with the conventional motion vector searching algorithms, the novel motion vector searching algorithm can reduce the motion matching manipulation effectively by 50 per cent.

Originality/value

This paper presents a novel searching strategy to estimate the motion vectors effectively. From the experimental results, the novel motion vector searching algorithm can reduce the motion matching manipulation effectively, compared with the conventional motion vector searching algorithms.

Details

Engineering Computations, vol. 34 no. 1
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 1 March 1993

S.R.P. MEDEIROS, P.M. PIMENTA and P. GOLDENBERG

A new algorithm for reducing the profile and root‐mean‐square wavefront of sparse matrices with a symmetric structure is presented. Our numerical experiments show an…

Abstract

A new algorithm for reducing the profile and root‐mean‐square wavefront of sparse matrices with a symmetric structure is presented. Our numerical experiments show an overall better performance than the widely used reverse Cuthill‐McKee, Gibbs‐King and Sloan algorithms. The new algorithm is fast, simple and useful in engineering analysis where it can be employed to derive efficient orderings for both profile and frontal solution schemes.

Details

Engineering Computations, vol. 10 no. 3
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 6 March 2009

Ik Sang Shin, Sang‐Hyun Nam, Rodney Roberts and Seungbin Moon

The purpose of this paper is to provide a minimum time algorithm to intercept an object on a conveyor belt by a robotic manipulator. The goal is that the robot is able to…

Abstract

Purpose

The purpose of this paper is to provide a minimum time algorithm to intercept an object on a conveyor belt by a robotic manipulator. The goal is that the robot is able to intercept objects on a conveyor line moving at a given speed in minimum time.

Design/methodology/approach

In order to formulate the problem, the robot and object‐arrival time functions were introduced, and conclude that the optimal point occurs at the intersection of these two functions. The search algorithm for finding the intersection point between the robot and object arrival time functions are also presented to find the optimal point in real‐time.

Findings

Simulation results show that the presented algorithm is well established for various initial robot positions.

Practical implications

A trapezoidal velocity profile was employed which is used in many industrial robots currently in use. Thus, it is believed that robot travel time algorithm is readily implemented for any commercially available robots.

Originality/value

The paper considers exhaustive cases where robot travel time functions are dependent upon initial positions of robotic end‐effectors. Also presented is a fast converging search algorithm so that real time application is more feasible in many cases.

Details

Industrial Robot: An International Journal, vol. 36 no. 2
Type: Research Article
ISSN: 0143-991X

Keywords

1 – 10 of over 38000