Yuxia Ji, Li Chen, Jun Zhang, Dexin Zhang and Xiaowei Shao
The purpose of this paper is to investigate the pose control of rigid spacecraft subject to dead-zone input, unknown external disturbance and parametric uncertainty in space…
Abstract
Purpose
The purpose of this paper is to investigate the pose control of rigid spacecraft subject to dead-zone input, unknown external disturbance and parametric uncertainty in space maneuvering mission.
Design/methodology/approach
First, a 6-Degree of Freedom (DOF) dynamic model of rigid spacecraft with dead-zone input, unknown external disturbances and parametric uncertainty is derived. Second, a super-twisting-like fixed-time disturbance observer (FTDO) with strong robustness is developed to estimate the lumped disturbances in fixed time. Based on the proposed observer, a non-singular fixed-time terminal sliding-mode (NFTSM) controller with superior performance is proposed.
Findings
Different from the existing sliding-mode controllers, the proposed control scheme can directly avoid the singularity in the controller design and speed up the convergence rate with improved control accuracy. Moreover, no prior knowledge of lumped disturbances’ upper bound and its first derivatives is required. The fixed-time stability of the entire closed-loop system is rigorously proved in the Lyapunov framework. Finally, the effectiveness and superiority of the proposed control scheme are proved by comparison with existing approaches.
Research limitations/implications
The proposed NFTSM controller can merely be applied to a specific type of spacecrafts, as the relevant system states should be measurable.
Practical implications
A NFTSM controller based on a super-twisting-like FTDO can efficiently deal with dead-zone input, unknown external disturbance and parametric uncertainty for spacecraft pose control.
Originality/value
This investigation uses NFTSM control and super-twisting-like FTDO to achieve spacecraft pose control subject to dead-zone input, unknown external disturbance and parametric uncertainty.
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Fei Gao, Jia Miao, Xiaoming Han, Rong Fu and Jiguang Chen
Since the multi-component of powder metallurgy was dispersed, and each component sheared flow and tiered under the action of friction force, it was difficult to disclose the…
Abstract
Purpose
Since the multi-component of powder metallurgy was dispersed, and each component sheared flow and tiered under the action of friction force, it was difficult to disclose the evolution characteristics of each component. Meanwhile, third body mixing with particles of each component covered on the friction surface, which further increased the difficulty of understanding evolution of each component and the corresponding third body in the friction process. To solve this problem, this paper aims to propose a mechanical assembled method which compact several component sheets in order.
Design/methodology/approach
Pure copper, aluminum and artificial graphite sheets with thickness 0.5, 1 and 2 mm, respectively, were assembled into a jig by mechanical compact method. The relationship between arrangement patterns of the components and its friction coefficient was studied by using fixed speed friction test machine, the speed range from 200 to 2,000 r/min and the pressure range from 0.25 to 0.64 MPa.
Findings
The testing results showed that when the distribution of same components was congregated, friction coefficient dropped from 0.6 to 0.4. While the distribution of different components was dispersed, friction coefficient dropped from 0.6 to 0.25. The friction coefficient decline was caused by performances changes of third body fluidity. The sufficiently mixed third body made third body adhesion weaker and increased third body fluidity. That provoked friction coefficient decreasing obviously at high speed. On the contrary, with the high congregation of same components, strong third body adhesion led to a rougher surface which contributed to a higher friction coefficient.
Originality/value
By means of the mechanical-assembled multi-layer components to reveal the influence mechanism of every component on friction properties, will provide a new test approach for tribology.
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David Leece, Tony Berry, Jia Miao and Robert Sweeting
The purpose of this paper is to identify the key characteristics of the post‐investment relationship between the venture capital firm and its investee companies.
Abstract
Purpose
The purpose of this paper is to identify the key characteristics of the post‐investment relationship between the venture capital firm and its investee companies.
Design/methodology/approach
The research is a case study of a major UK venture capital firm using qualitative research to determine the key characteristics of the post‐investment relationship. The study is based on interviews with parties on both sides of the relationship.
Findings
While the results reflect the findings of the entrepreneurship and venture capital literature they also point to the importance of network growth and development for organizational learning in the venture capital industry, professionalization of investee firms and as a context in which the selection of the entrepreneur and the post investment relationship are set.
Research limitations/implications
The research has the limitation of most case studies that the results cannot readily be generalized, in this case to the wider population of venture capital firms. Confidentiality issues also limited the extent to which a longitudinal study could be conducted.
Practical implications
A better understanding of the post‐investment relationship can inform entrepreneurs in their pitch for funds and in their anticipation of the post investment relationship. This understanding can also assist venture capital firms in the management of this relationship.
Originality/value
The case study uses data from rare access to a venture capital firm. It also differs by interviewing both parties to the post‐investment relationship, that is venture capitalist and investee firm.
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Mingxia Jia, Yuxiang Chris Zhao, Xiaoyu Zhang and Dawei Wu
In the era of digital intelligence, individuals are increasingly interacting with digital information in their daily lives and work, and a growing phenomenon known as digital…
Abstract
Purpose
In the era of digital intelligence, individuals are increasingly interacting with digital information in their daily lives and work, and a growing phenomenon known as digital hoarding is becoming more prevalent. Prior research suggests that humanities researchers have unique and longstanding information interaction and management practices in the digital scholarship context. This study therefore aims to understand how digital hoarding manifests in humanities researchers’ behavior, identify the influencing factors associated with it, and explore how they perceive and respond to digital hoarding behavior.
Design/methodology/approach
Qualitative research methods enable us to acquire a rich insight and nuanced understanding of digital hoarding practices. In this study, semi-structured interviews were conducted with 20 humanities researchers who were pre-screened for a high propensity for digital hoarding. Thematic analyses were then used to analyze the interview data.
Findings
Three main characteristics of digital hoarding were identified. Further, the research paradigm, digital affordance, and personality traits and habits, collectively influencing the emergence and development of digital hoarding behaviors, were examined. The subtle influence of traditional Chinese culture was encountered. Interestingly, this study found that humanists perceive digital hoarding as a positive expectation (associated with inspiration, aesthetic pursuit, and uncertainty avoidance). Meanwhile, humanists' problematic perception of this behavior is more widely observed — they experience what we conceptualize as an “expectation-perception” gap. Three specific information behaviors related to avoidance were identified as aggravating factors for digital hoarding.
Originality/value
The findings deepen the understanding of digital hoarding behaviors and personal information management among humanities researchers within the LIS field, and implications for humanities researchers, digital scholarship service providers, and digital tool developers are discussed.
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The purpose of this paper is to develop a holistic approach to the assessment of dynamic capabilities (DCs). Holistic refers to incorporating all DCs of an organisation relevant…
Abstract
Purpose
The purpose of this paper is to develop a holistic approach to the assessment of dynamic capabilities (DCs). Holistic refers to incorporating all DCs of an organisation relevant for determining and executing the firm's strategy.
Design/methodology/approach
A two-phase study was conducted. First, secondary sources, such as media, industry and annual reports, are being used to initially assess CASE (connected, autonomous, shared and electric) and implications for incumbent car manufacturers in a structured way. Second, semi-structured interviews with automotive managers and further automotive stakeholders offer in-depth insights into CASE, as well as incumbents' strategies and the underlying rationale.
Findings
The proposed framework for assessing DCs offers a holistic approach and provides new angles of analysis. First, the time dimension is considered using scenarios since timing is vital in strategy and implementation. Second, capabilities are broken down into technological and non-technological, sharpening strategic decision-making of automakers. Third, the analysis considers external VUCA (volatility, uncertainty, complexity and ambiguity) as they interplay with internal DCs.
Research limitations/implications
Further testing of the proposed DC assessment approach offers a promising opportunity for future research. This paper focuses on the automotive industry, but it is worth investigating the extent to which the approach can be used in other dynamic industries, such as finance or retail.
Originality/value
The approach proposed highlights the importance and nuances of considering external perspectives in the DC assessment and the relevance of non-technological capabilities in the automotive industry. Thereby, it contributes to the literature on capability assessments and the operationalisability of the DC lens.
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Saadet Güler, Ahmet Yavaş, Berk Özler and Ahmet Çagri Kilinç
Three-dimensional (3D) printing is popular for many applications including the production of photocatalysts. This paper aims to focus on developing of 3D-printed…
Abstract
Purpose
Three-dimensional (3D) printing is popular for many applications including the production of photocatalysts. This paper aims to focus on developing of 3D-printed photocatalyst-nano composite lattice structure. Digital light processing (DLP) 3D printing of photocatalyst composites was performed using photosensitive resin mixed with 0.5% Wt. of TiO2 powder and varying amounts (0.025% Wt. to 0.2% Wt.) of graphene nanoplatelet powder. The photocatalytic efficiency of DLP 3D-printed photocatalyst TiO2 composite was investigated, and the effects of nano graphite powder incorporation on the photocatalytic activity, thermal and mechanical properties were investigated.
Design/methodology/approach
Methods involve 3D computer-aided design modeling, printing parameters and comprehensive characterization techniques such as structural equation modeling, X-ray diffraction, thermogravimetric analysis, Fourier-transform infrared (FTIR) and mechanical testing.
Findings
Results highlight successful dispersion and characteristics of TiO2 and graphene nanoplatelet (GNP) powders, intricate designs of 3D-printed lattice structures, and the influence of GNPs on thermal behavior and mechanical properties.
Originality/value
The study suggests applicability in wastewater treatment and environmental remediation, showcasing the adaptability of 3 D printing in designing effective photocatalysts. Future research should focus on practical applications and the long-term durability of these 3D-printed composites.
Graphical abstract
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Haolin Fei, Ziwei Wang, Stefano Tedeschi and Andrew Kennedy
This paper aims to evaluate and compare the performance of different computer vision algorithms in the context of visual servoing for augmented robot perception and autonomy.
Abstract
Purpose
This paper aims to evaluate and compare the performance of different computer vision algorithms in the context of visual servoing for augmented robot perception and autonomy.
Design/methodology/approach
The authors evaluated and compared three different approaches: a feature-based approach, a hybrid approach and a machine-learning-based approach. To evaluate the performance of the approaches, experiments were conducted in a simulated environment using the PyBullet physics simulator. The experiments included different levels of complexity, including different numbers of distractors, varying lighting conditions and highly varied object geometry.
Findings
The experimental results showed that the machine-learning-based approach outperformed the other two approaches in terms of accuracy and robustness. The approach could detect and locate objects in complex scenes with high accuracy, even in the presence of distractors and varying lighting conditions. The hybrid approach showed promising results but was less robust to changes in lighting and object appearance. The feature-based approach performed well in simple scenes but struggled in more complex ones.
Originality/value
This paper sheds light on the superiority of a hybrid algorithm that incorporates a deep neural network in a feature detector for image-based visual servoing, which demonstrates stronger robustness in object detection and location against distractors and lighting conditions.
Details
Keywords
Yahao Wang, Yanghong Li, Zhen Li, HaiYang He, Sheng Chen and Erbao Dong
Aiming at the problem of insufficient adaptability of robot motion planners under the diversity of end-effector constraints, this paper proposes Transformation Cross-sampling…
Abstract
Purpose
Aiming at the problem of insufficient adaptability of robot motion planners under the diversity of end-effector constraints, this paper proposes Transformation Cross-sampling Framework (TC-Framework) that enables the planner to adapt to different end-effector constraints.
Design/methodology/approach
This work presents a standard constraint methodology for representing end-effector constraints as a collection of constraint primitives. The constraint primitives are merged sequentially into the planner, and a unified constraint input interface and constraint module are added to the standard sampling-based planner framework. This approach enables the realization of a generic planner framework that avoids the need to build separate planners for different end-effector constraints.
Findings
Simulation tests have demonstrated that the planner based on TC-framework can adapt to various end-effector constraints. Physical experiments have also confirmed that the framework can be used in real robotic systems to perform autonomous operational tasks. The framework’s strong compatibility with constraints allows for generalization to other tasks without modifying the scheduler, significantly reducing the difficulty of robot deployment in task-diverse scenarios.
Originality/value
This paper proposes a unified constraint method based on constraint primitives to enhance the sampling-based planner. The planner can now adapt to different end effector constraints by opening up the input interface for constraints. A series of simulation tests were conducted to evaluate the TC-Framework-based planner, which demonstrated its ability to adapt to various end-effector constraints. Tests on a physical experimental system show that the framework allows the robot to perform various operational tasks without requiring modifications to the planner. This enhances the value of robots for applications in fields with diverse tasks.
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Adrian Ramirez-Nafarrate, Luis Antonio Moncayo-Martinez and Gerardo Steve Munguía-Williams
This paper aims to propose an alternate, efficient and scalable modeling framework to simulate large-scale bike-sharing systems using discrete-event simulation. This study uses…
Abstract
Purpose
This paper aims to propose an alternate, efficient and scalable modeling framework to simulate large-scale bike-sharing systems using discrete-event simulation. This study uses this model to evaluate several initial bike inventory policies inspired by the operation of the bike-sharing system in Mexico City, which is one of the largest around the world. The model captures the heterogeneous demand (in time and space) and this paper analyzes the trade-offs between the performance to take and return bikes. This study also includes a simulation-optimization algorithm to determine the initial inventory and present a method to deal with the bias caused by dynamic rebalancing on observed demand.
Design/methodology/approach
This paper is based on the analysis of an alternate and efficient discrete-event simulation modeling framework. This framework captures the heterogeneity of demand and allows one to experiment with large-scale models. This study uses this model to test several initial bike inventory policies and also combined them with an optimization engine. The results, provide valuable insights not only for the particular system that motivated the study but also for the administrators of any bike-sharing system.
Findings
The findings of this paper include: most of the best policies use a ratio of bikes: docks near to 1:2; however, it is important the way they are initially allocated; a policy that contradicts the demand profile of the stations can lead to poor performance, regardless the quick and dynamic changes of bike locations during the morning period; the proposed simulation-optimization algorithm achieves the best results.
Research limitations/implications
The findings are limited to the initial inventory of the system under study. The model assumes a homogeneous probability distribution function for the travel time. This assumption seems reasonable for the system under study. This paper limits the tested inventory policies to simple practical rules. There might be other sophisticated methods to obtain better solutions, but they might be system-specific.
Practical implications
The insights of this paper are valuable for operators of bike-sharing systems because this study focuses on the analysis of the impact of the initial inventory assuming that dynamic rebalancing may not be existing during the morning peak-time. This paper finds that initial inventory has a great impact on the performance, regardless of how quickly the bikes are dispersed across the system. This study also provides insights into the effect of dynamic rebalancing on observed demand.
Social implications
Increasing knowledge about the operation of the bike-sharing system has a positive effect on society because more cities around the world could consider implementing these systems as a public transportation mode. Furthermore, delivering suggestions on how to increase the user service level could incentivize people to adopt bikes as a mobility option, which would contribute to improve their health and also reduce air pollution caused by motorized vehicles.
Originality/value
This paper considers that the contributions of this work to existing literature are the following: this study proposes a novel efficient and scalable simulation framework to evaluate initial bike inventory policies; the analysis presented in the paper includes an approach to deal with the bias in the observed demand caused by dynamic rebalancing and the analysis includes the value of demand information to determine an effective initial bike inventory policy.
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Chao Li, Weimin Zhai, Weiming Fu, Jiahu Qin and Yu Kang
This study aims to introduce a method for predicting the remaining useful life (RUL) of bearings based on parallel feature extraction. The proposed model provides prior knowledge…
Abstract
Purpose
This study aims to introduce a method for predicting the remaining useful life (RUL) of bearings based on parallel feature extraction. The proposed model provides prior knowledge and removes redundant handcrafted feature information, additionally, which focuses on the important features at different time scales.
Design/methodology/approach
Distinct from traditional parallel feature extraction methods, which can lead to information redundancy, a one-dimensional convolutional autoencoder is introduced to process selected indicators to remove redundancy and retain useful feature information. To fully capture the important degradation information within different stages in the feature sequences, a novel multi-scale attention feature fusion module is proposed to extract degradation features at different time scales. Considering the impact of degradation modes on RUL prediction, a dual-task prediction module based on no degradation mode labels is designed to obtain accurate RUL.
Findings
Comparative experiments and ablation studies on the PHM2012 bearing dataset verified the effectiveness of the proposed method. Furthermore, the rationality of the selected parameters is confirmed through model parameter analysis.
Originality/value
The novelty of the proposed method is that it not only provides prior knowledge but also further removes redundant information from prior knowledge. In addition, the distribution differences between the original features and their multi-scale convolution results are measured through Kullback–Leibler divergence as the attention scores, which allows the proposed method to focus on important information at different time scales.