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The paper aims to propose a method to build environmental constraint region online in complex-shaped peg-in-hole assembly tasks.

Compared with conventional way which using computer-aided design (CAD) models of assembly parts to construct the environmental constraint region offline, the paper provides an online approach that consists of three aspects: modeling assembly parts through visual recognition, decomposing complex shapes into multiple primitive convex shapes and a numerical algorithm to simulate the peg-in-hole insertion contact. Besides, a contrast experiment is performed to validate the feasibility and effectiveness of the method.

The experiment result indicates that online construction takes less time than the offline way under the same task conditions. Furthermore, due to the CAD models of the parts are not required to be known, the method proposed in the paper has a broader application in most assembly scenarios.

With the improvement of customization and complexity of manufactured parts, the assembly of complex-shaped parts has drawn greater attention of many researchers. The assembly methods based on attractive region in environment (ARIE) have shown great performance to achieve high-precision manipulation with low-precision systems. The construction of environmental constraint region serves as an essential part of ARIE-based theory, directly affect the formulation and application of assembly strategies.

To improve the power density and generation efficiency of the tubular permanent magnetic linear generators (TPMLGs) under realistic sea-stator condition, a TPMLG with 120° phase belt toroidal windings (120°-TPMLG) for wave energy conversion is proposed in this paper.

First, the structure of the 120°-TPMLG is introduced and its operation principle is analyzed. Second, the design process of the 120°-TPMLG is described. Meanwhile, the finite-element models of the 120°-TPMLG and the TPMLG with traditional fractional pitch windings (T-TPMLG) are established based on the similar overall dimensions. Then, the electromagnetic characteristics of the 120°-TPMLG are analyzed, such as air gap flux density, back electromotive force and load voltage. Finally, a comparative analysis of the magnetic flux density, flux linkage, load and no-load performance of the two generators are conducted.

The result shows that the 120°-TPMLG has higher power density and generation efficiency than the T-TPMLG.

This paper proposes a TPMLG with 120° phase belt toroidal windings (120°-TPMLG) to improve the power density and generation efficiency.

Employing the anti-corruption campaign as an exogenous political shock, this paper examines how political intervention shapes the impact of financial expert CEOs on firm investment decisions.

This paper uses a sample of 2,808 Chinese firms listed in the Shanghai and Shenzhen Stock Exchanges from 2003 to 2016. Panel data is used for conducting the analysis controlling for firm, industry, and year fixed effects.

The authors found that CEOs with financial expertise are sensitive to political intervention when making investment decisions. First, financial expert CEOs spend more on R&D expenditure in private-owned companies and they are associated with less R&D expenditure in state-owned enterprises (SOEs). Second, financial expert CEOs are associated with higher investment expenditure in general, but they become less likely to invest more in the post-anti-corruption period. The reduction in investment expenditure due to the anti-corruption campaign is more pronounced in SOEs than in private-owned companies. Third, the anti-corruption campaign promotes R&D investment in general, but in SOEs, expert CEOs tend to be less likely to invest more on R&D after the anti-corruption shock.

This paper enriches the growing literature on the impact of political intervention and the role of the anti-corruption campaign on corporate behaviour.

This paper aims to present a novel position sensorless control scheme with fault-tolerance ability for switched reluctance motor at low speed.

First, the detection pulses are injected in the freewheeling and idle intervals of each phase. Second, the aligned position of each phase can be detected by comparing the consecutive rise time of detection current. Third, the whole-region rotor position and real-time rotational speed can be updated four times for the improvement of detection accuracy. Finally, the fault-tolerant control strategy is performed to enhance the robustness and reliability of proposed sensorless scheme under faulty conditions.

Based on proposed sensorless control strategy, the estimated rotor position is in good agreement with the actual rotor position and the maximum rotor position error is 1.5°. Meanwhile, the proposed sensorless scheme is still effective when the motor with multiphase loss and the maximum rotor position error is 1.9°. Moreover, the accuracy of the rotor position estimation can be ensured even if the motor is in an accelerated state or decelerated state.

The proposed sensorless method does not require extensive memory, complicated computation and prior knowledge of the electromagnetic properties of the motor, which is easy to implement. Furthermore, it is suitable for different control strategies at low speed without negative torque generation.

This study aims to further the understanding of support structures and the likely impacts on maraging steel MS1 parts fabricated by selective laser melting (SLM) at 45°, 60° and 75° building angles.

Two groups of samples, one group with support structures and the other group without support structures, were designed with the same specifications and printed under the same conditions by SLM at 45°, 60° and 75° building angles. Differences in dimensional accuracy, surface roughness, Vickers microhardness, residual stress and microstructure were compared between groups.

The results showed that with support structures, more accurate dimension and slightly higher Vickers microhardness could be obtained. Larger compressive stress dominated and was more uniformly distributed on the supporting surface. Without support structures, the dimension became more precise as the building angle increased and alternating compressive and tensile stress was unevenly distributed on the supporting surface. In addition, the surface roughness of the outer surface decreased with the increase of the built angle, regardless of the support structures. Furthermore, whether the building angle was 45°, 60° or 75°, the observed microstructures revealed that the support structures altered the orientation of the molten pool and the direction of grain growth.

This paper studies the influence of support structures on the workpieces printed at different building angles. Support structures affect the residual stress distribution, heat dissipation rate and microstructure of the parts, and thus affecting the printing quality. Therefore, it is necessary to balance the support strategy and printing quality to better apply or design the support structures in SLM.

The purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.

The algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.

The first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.

Data points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

This paper aims to propose a novel method to identify the parameters of robotic manipulators using the torque exerted by the robot joint motors (measured by current sensors).

Previous studies used additional sensors like force sensor and inertia measurement unit, or additional payload mounted on the end-effector to perform parameter identification. The settings of these previous works were complicated. They could only identify part of the parameters. This paper uses the torque exerted by each joint while performing Fourier periodic excited trajectories. It divides the parameters into a linear part and a non-linear part, and uses linear least square (LLS) parameter estimation and dual-swarm-based particle swarm optimization (DPso) to compute the linear and non-linear parts, respectively.

The settings are simpler and can identify the dynamic parameters, the viscous friction coefficients and the Coulomb friction coefficients of two joints at the same time. A SIASUN 7-Axis Flexible Robot is used to experimentally validate the proposal. Comparison between the predicted torque values and ground-truth values of the joints confirms the effectiveness of the method.

The proposed method identifies two joints at the same time with satisfying precision and high efficiency. The identification errors of joints do not accumulate.

This paper seeks to discuss the genealogical sources for Chinese immigrants as well as the settlement of Chinese in the USA and the historical evolution of Chinese names, their origins, arrangement and development. It aims to cover the origins of various classes of Chinese surnames, followed by the content description of a traditional genealogical book for jiapu.

The paper researches the various ways that a Chinese person can find out about their ancestry.

The paper reveals the roles of libraries, including serving the needs of patrons interested in genealogical research, preserving and interpreting information through oral and family history projects and collaborating with other libraries through interlibrary loan, document delivery, library consortia, collection management and international resource‐sharing.

The study provides information on where and how to locate the genealogical resources for researching the genealogy of a Chinese family.

The paper analyzes the value of genealogical research as a documentary source for population history, life expectancy in a clan, marriages and family connections, as well as lineage organizations and inter‐lineage relations.

Music sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.

A CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.

The model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.

The dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.

The purpose of this study is to investigate the moderating role of knowledge management (KM) strategies in developing the effect of intellectual capital (IC) on innovation for small- and medium-sized enterprises (SMEs). Specifically, the current study explores how different interactions between IC and KM strategies lead to more powerful innovation in SMEs.

This study analyzes survey responses from 170 owners/managers of SMEs in Iran. The study uses partial least square structural equation modeling methods within Smart PLS software.

This study reveals that first IC has an excellent level of engagement with both incremental and radical types of innovation, but its engagement level with radical innovation is higher than that for incremental innovation. Second, the human capital component of IC has a direct positive impact on radical innovation although it has no significant impact on incremental innovation. Third, the personalization strategy of KM positively moderates the impact of human capital on both incremental and radical innovation.

This paper is an empirical attempt in SMEs to combine IC and KM strategies to strengthen innovation. It presents research community for SMEs of a developing country that has been investigated in a limited way compared to large firms of developed nations and provides valuable insights into further research.