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Article
Publication date: 16 July 2021

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…

921

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.

Details

Aircraft Engineering and Aerospace Technology, vol. 93 no. 6
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 10 September 2024

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…

49

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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Article
Publication date: 15 February 2021

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…

382

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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Article
Publication date: 27 January 2025

Jing Xia, Siqi Zhu, XinYuan He, Junfu Shen, XiaoPan Li, YiYun Kong and Chun Yao

This paper aims to explore how thermal activation enhances the oxidation complexation of the titanium alloy, aiming to enhance surface quality and processing efficiency.

4

Abstract

Purpose

This paper aims to explore how thermal activation enhances the oxidation complexation of the titanium alloy, aiming to enhance surface quality and processing efficiency.

Design/methodology/approach

The titanium alloys were chemically mechanically polished under various temperatures. The removal rate and surface roughness were characterized using a three-dimensional topography tester. The surface composition, content and valence state were characterized by X-ray photoelectron spectroscopy. The abrasion performance of the surface reaction layers was conducted using a friction wear testing machine.

Findings

The thermal activation temperature can enhance the chemical-mechanical polishing effect of titanium alloy. The thermal activation temperature can enhance the oxidation complexation synergistic effect of K2S2O8 and KF on titanium alloy, thereby improving the polishing effect. With the increase in temperature, the wear resistance of titanium alloy decreases after oxidation corrosion, making it more susceptible to removal through friction. By promoting the oxidation and corrosion of K2S2O8 and KF on the titanium alloy, higher temperatures can facilitate the formation of easily removable film layers on the surface, thereby enhancing the polishing effect.

Practical implications

This research contributes to enriching the theoretical framework of precision machining of titanium alloy and enhancing surface quality and machining efficiency.

Originality/value

With this statement, the authors hereby certify that the manuscript is the result of their own effort and ability. They have indicated all quotes, citations and references. Furthermore, the authors have not submitted any essay, paper or thesis with similar content elsewhere. No conflict of interest exists in the submission of this manuscript.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0167/

Details

Industrial Lubrication and Tribology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0036-8792

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Article
Publication date: 27 August 2024

Haitao 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.

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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

Industrial Robot: the international journal of robotics research and application, vol. 52 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

Available. Content available
Book part
Publication date: 21 May 2007

Abstract

Details

Aspects of Worker Well-Being
Type: Book
ISBN: 978-1-84950-473-7

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Book part
Publication date: 21 May 2007

Solomon W. Polachek and Oliver Bargain

Understanding how worker well-being is distributed across the population is of paramount importance. With such knowledge policy makers can devise efficient strategies to improve…

Abstract

Understanding how worker well-being is distributed across the population is of paramount importance. With such knowledge policy makers can devise efficient strategies to improve social welfare. This volume contains 13 chapters on topics enhancing our comprehension of inequality across workers. The issues addressed deal directly with the economic institutions that affect individual and family earnings distributions. The themes explored include job training, worker and firm mobility, minimum wages, wage arrears, unions, collective bargaining, unemployment insurance, and schooling. Among the questions answered are: To what extent do greater work hours of women mitigate the widening family earnings distribution? To what extent does deunionization widen the distribution of earnings? Do computers really cause a widening of the earnings distribution? How would the Russian wage distribution change if one accounted for wage arrears? How much of job creation and job destruction comes about because of business relocation? To what extent does maternal education increase children's education? Why do increases in the minimum wage fail to substantially decrease employment as economic theory would predict? And, to what extent do job skills matter for low-income workers?

Details

Aspects of Worker Well-Being
Type: Book
ISBN: 978-1-84950-473-7

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Article
Publication date: 29 May 2024

Ramesh P Natarajan, Kannimuthu S and Bhanu D

The existing traditional recommendations based on content-based filtering (CBF), collaborative filtering (CF) and hybrid approaches are inadequate for recommending practice…

51

Abstract

Purpose

The existing traditional recommendations based on content-based filtering (CBF), collaborative filtering (CF) and hybrid approaches are inadequate for recommending practice challenges in programming online judge (POJ). These systems only consider the preferences of the target users or similar users to recommend items. In the learning environment, recommender systems should consider the learning path, knowledge level and ability of the learner. Another major problem in POJ is the learners don't give ratings to practice challenges like e-commerce and video streaming portals. This purpose of the proposed approach is to overcome the abovementioned shortcomings.

Design/methodology/approach

To achieve the context-aware practice challenge recommendation, the data preparation techniques including implicit rating extraction, data preprocessing to remove outliers, sequence-based learner clustering and utility sequence pattern mining approaches are used in the proposed approach. The approach ensures that the recommender system considers the knowledge level, learning path and learning goals of the learner to recommend practice challenges.

Findings

Experiments on practice challenge recommendations conducted using real-world POJ dataset show that the proposed system outperforms other traditional approaches. The experiment also demonstrates that the proposed system is recommending challenges based on the learner's current context. The implicit rating extracted using the proposed approach works accurately in the recommender system.

Originality/value

The proposed system contains the following novel approaches to address the lack of rating and context-aware recommendations. The mathematical model was used to extract ratings from learner submissions. The statistical approach was used in data preprocessing. The sequence similarity-based learner clustering was used in transition matrix. Utilizing the rating as a utility in the USPAN algorithm provides useful insights into learner–challenge relationships.

Details

Data Technologies and Applications, vol. 58 no. 5
Type: Research Article
ISSN: 2514-9288

Keywords

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