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1 – 4 of 4Haitao Liu, Junfu Zhou, Guangxi Li, Juliang Xiao and Xucang Zheng
This paper aims to present a new trajectory scheduling method to generate a smooth and continuous trajectory for a hybrid machining robot.
Abstract
Purpose
This paper aims to present a new trajectory scheduling method to generate a smooth and continuous trajectory for a hybrid machining robot.
Design/methodology/approach
The trajectory scheduling method includes two steps. First, a G3 continuity local smoothing approach is proposed to smooth the toolpath. Then, considering the tool/joint motion and geometric error constraints, a jerk-continuous feedrate scheduling method is proposed to generate the trajectory.
Findings
The simulations and experiments are conducted on the hybrid robot TriMule-800. The simulation results demonstrate that this method is effectively applicable to machining trajectory scheduling for various parts and is computationally friendly. Moreover, it improves the robot machining speed and ensures smooth operation under constraints. The results of the S-shaped part machining experiment show that the resulting surface profile error is below 0.12 mm specified in the ISO standard, confirming that the proposed method can ensure the machining accuracy of the hybrid robot.
Originality/value
This paper implements an analytical local toolpath smoothing approach to address the non-high-order continuity problem of the toolpath expressed in G code. Meanwhile, the feedrate scheduling method addresses the segmented paths after local smoothing, achieving smooth and continuous trajectory generation to balance machining accuracy and machining efficiency.
Details
Keywords
Zhongjun Tang, Tingting Wang, Junfu Cui, Zhongya Han and Bo He
Because of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the common…
Abstract
Purpose
Because of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the common needs of all consumers for a kind of experiential product with short life cycle (EPSLC). Methods for predicting TSV of long-life-cycle products may not be suitable for EPSLC. Furthermore, point prediction cannot obtain satisfactory prediction results because information available before production is inadequate. Thus, this paper aims at proposing and verifying a novel interval prediction method (IPM).
Design/methodology/approach
Because interval prediction may satisfy requirements of preproduction investment decision-making, interval prediction was adopted, and then the prediction difficult was converted into a classification problem. The classification was designed by comparing similarities in attribute relationship patterns between a new EPSLC and existing product groups. The product introduction may be written or obtained before production and thus was designed as primary source information. IPM was verified by using data of crime movies released in China from 2013 to 2017.
Findings
The IPM is valid, which uses product introduction as input, classifies existing products into three groups with different TSV intervals, mines attribute relationship patterns using content and association analyses and compares similarities in attribute relationship patterns – to predict TSV interval of a new EPSLC before production.
Originality/value
Different from other studies, the IPM uses product introduction to mine attribute relationship patterns and compares similarities in attribute relationship patterns to predict the interval values. It has a strong applicability in data content and structure and may realize rolling prediction.
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Keywords
Junfu Xiao, Siying Chen, Zhixiong Tan, Yanyu Chen, Jiayi Wang and Han Jingwei
Given the inevitable transition to renewable resource utilization and the urgent need to reduce carbon emissions, this study conducted quasi natural experiments to assess the…
Abstract
Purpose
Given the inevitable transition to renewable resource utilization and the urgent need to reduce carbon emissions, this study conducted quasi natural experiments to assess the impact of renewable resource utilization on carbon emissions based on the national “urban mining” demonstration bases (NUMDB).
Design/methodology/approach
This study uses panel data from 275 prefecture-level cities in China from 2006 to 2019. The paper selects NUMDB as the proxy variable and conducts a quasi-natural experiment using a multi-period differences-in-differences model. We examine the impact of NUMDB on reducing carbon emissions, and then deeply explore its mechanism and spatial spillover effect.
Findings
This study found that: (1) the construction of NUMDB can significantly decrease the carbon emission in the host cities; (2) NUMDB’s construction has more significantly reduced the carbon emission in regions with higher levels of circular economy development, green technology innovation, regional environmental pollution, digital economy development and financial development; (3) by means of green technology innovation, optimized energy structure, and high-quality talent aggregation, NUMDB reduces urban carbon emissions; (4) NUMDB construction positively affects the carbon reduction efficiency of neighboring regions.
Research limitations/implications
We propose corresponding policy suggestions to further promote the carbon emission reduction effect of NUMDB and develop the renewable resources industry in China based on the research findings.
Practical implications
The contributions of this paper are as follows. Our study contributes to expanding the research scope on the environmental impact of the renewable resource industry, as there are few quantitative studies in this area.
Social implications
We further consider the spatial heterogeneity of policies and analyze the carbon reduction effect of the NUMDB from the city level, which is beneficial to exploring more targeted and operable carbon reduction paths.
Originality/value
This study on identifying the causal relationship between renewable resource utilization and carbon emission reduction helps to explore the sustainable development path of renewable resource more comprehensively. Meanwhile, this paper provides a reference for other countries to improve the utilization of renewable resource and effectively reduce carbon emissions.
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Keywords
Junfu Chen, Xiaodong Zhao and Dechang Pi
The purpose of this paper is to ensure the stable operation of satellites in orbit and to assist ground personnel in continuously monitoring the satellite telemetry data and…
Abstract
Purpose
The purpose of this paper is to ensure the stable operation of satellites in orbit and to assist ground personnel in continuously monitoring the satellite telemetry data and finding anomalies in advance, which can improve the reliability of satellite operation and prevent catastrophic losses.
Design/methodology/approach
This paper proposes a deep auto-encoder (DAE) satellite anomaly advance warning framework for satellite telemetry data. Firstly, this study performs grey correlation analysis, extracts important feature attributes to construct feature vectors and builds the variational auto-encoder with bidirectional long short-term memory generative adversarial network discriminator (VAE/BLGAN). Then, the Mahalanobis distance is used to measure the reconstruction score of input and output. According to the periodic characteristic of satellite operation, a dynamic threshold method based on periodic time window is proposed. Satellite health monitoring and advance warning are achieved using reconstruction scores and dynamic thresholds.
Findings
Experiment results indicate DAE methods can probe that satellite telemetry data appear abnormal, trigger a warning before the anomaly occurring and thus allow enough time for troubleshooting. This paper further verifies that the proposed VAE/BLGAN model has stronger data learning ability than other two auto-encoder models and is sensitive to satellite monitoring data.
Originality/value
This paper provides a DAE framework to apply in the field of satellite health monitoring and anomaly advance warning. To the best of the authors’ knowledge, this is the first paper to combine DAE methods with satellite anomaly detection, which can promote the application of artificial intelligence in spacecraft health monitoring.
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