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Automatic defect detection is a fundamental and vital topic in the research field of industrial intelligence. In this work, the authors develop a more flexible deep learning method for the industrial defect detection.

The authors propose a unified framework for detecting defects in industrial products or planar surfaces based on an end-to-end learning strategy. A lightweight deep learning architecture for blade defect detection is specifically demonstrated. In addition, a blade defect data set is collected with the dual-arm image collection system.

Numerous experiments are conducted on the collected data set, and experimental results demonstrate that the proposed system can achieve satisfactory performance over other methods. Furthermore, the data equalization operation helps for a better defect detection result.

An end-to-end learning framework is established for defect detection. Although the adopted fully convolutional network has been extensively used for semantic segmentation in images, to the best knowledge of the authors, it has not been used for industrial defect detection. To remedy the difficulties of blade defect detection which has been analyzed above, the authors develop a new network architecture which integrates the residue learning to perform the efficient defect detection. A dual-arm data collection platform is constructed and extensive experimental validation are conducted.

For robots to more actively interact with the surrounding environment in object manipulation tasks or walking, they must understand the physical attributes of objects and surface materials they encounter. Dynamic tactile sensing can effectively capture rich information about material properties. Hence, methods that convey and interpret this tactile information to the user can improve the quality of human–machine interaction. This paper aims to propose a visual-tactile cross-modal retrieval framework to convey tactile information of surface material for perceptual estimation.

The tactile information of a new unknown surface material can be used to retrieve perceptually similar surface from an available surface visual sample set by associating tactile information to visual information of material surfaces. For the proposed framework, the authors propose an online low-rank similarity learning method, which can effectively and efficiently capture the cross-modal relative similarity between visual and tactile modalities.

Experimental results conducted on the Technischen Universität München Haptic Texture Database demonstrate the effectiveness of the proposed framework and the method.

This paper provides a visual-tactile cross-modal perception method for recognizing material surface. By the method, a robot can communicate and interpret the conveyed information about the surface material properties to the user; it will further improve the quality of robot interaction.

The purpose of this paper is to present a novel data glove which can capture the motion of the arm and hand by inertial and magnetic sensors. The proposed data glove is used to provide the information of the gestures and teleoperate the robotic arm-hand.

The data glove comprises 18 low-cost inertial and magnetic measurement units (IMMUs) which not only make up the drawbacks of traditional data glove that only captures the incomplete gesture information but also provide a novel scheme of the robotic arm-hand teleoperation. The IMMUs are compact and small enough to wear on the upper arm, forearm, palm and fingers. The calibration method is proposed to improve the accuracy of measurements of units, and the orientations of each IMMU are estimated by a two-step optimal filter. The kinematic models of the arm, hand and fingers are integrated into the entire system to capture the motion gesture. A positon algorithm is also deduced to compute the positions of fingertips. With the proposed data glove, the robotic arm-hand can be teleoperated by the human arm, palm and fingers, thus establishing a novel robotic arm-hand teleoperation scheme.

Experimental results show that the proposed data glove can accurately and fully capture the fine gesture. Using the proposed data glove as the multiple input device has also proved to be a suitable teleoperating robotic arm-hand system.

Integrated with 18 low-cost and miniature IMMUs, the proposed data glove can give more information of the gesture than existing devices. Meanwhile, the proposed algorithms for motion capture determine the superior results. Furthermore, the accurately captured gestures can efficiently facilitate a novel teleoperation scheme to teleoperate the robotic arm-hand.

This paper aims to find an objects representation scheme with high precision and to compute the objects’ separation distance effectively in final analysis. Proximity queries have been used widely in robot trajectory planning, automatic assembly planning, virtual surgery and many other applications. The core of proximity query is the precise computation of (minimum) separation distance in narrow phase, and specific object representation scheme corresponds to different methods of separation distance computation.

In this paper, a second-order cone programming (SOCP)-based (minimum) separation distance computation algorithm was proposed. It treats convex superquadrics, descriptive primitives of complex object as the study objects. The separation distance between two convex superquadrics was written as a general nonlinear programming (NLP) problem with superquadric constraints and then transformed into an SOCP problem with the method of conic formulation of superquadric constraints. Finally, a primal-dual interior point method embedded in MOSEK was used for solving the SOCP problem.

The proposed algorithm achieved exact separation distance computation between convex superquadrics, with a relative error of 10-6. It is particularly suitable for proximity queries in narrow phase of static collision detection algorithms. Further, the proposed algorithm achieved continuous collision detection between rectilinear translation superquadrics.

The proposed algorithm in narrow phase of static collision detection algorithms makes objects’ separation distance effectively computed. Proximity queries are easy and more accurate to perform in this way.

Multi-level intake structures are used to take the surface water of reservoirs. The changed boundary conditions will certainly make the water hammer phenomenon more complicated. This paper aims to find out the influence and law of the water hammer pressure after setting the stop log gates.

The authors use the computational fluid dynamics method with the adaptive grid technology to stimulate the water hammer phenomenon of the multi-level intake hydropower station. In the analysis, we set several different heights of stop log gates and two representative times in the starting up and shutdown processes to reflect the impact of multi-level intake structures.

The authors find that the setting of the stop log gates will reduce the pressure during the normal operation and will increase the period and amplitude of the water hammer wave, but will not necessarily increase the maximum water hammer pressure during the shutdown process. The relationship between the height of the stop log gates and the amplitude of the water hammer wave is affected by the shutdown time. After setting stop log gates, the depression depth and wave height of the water level in front of the dam increase when the load changes.

The authors study in this paper the water pressure of the multi-level intake hydropower station that has never been studied before and obtain some laws.

The purpose of this paper is to study the tribology behavior of steel–steel contact under the lubrication of water-based drilling mud with different oleic acid-filled microcapsules as lubricant additives.

A ball-on-disc tribometer was used to evaluate the lubrication properties of the steel–steel contact. The wear tracks of the worn surfaces were observed by a scanning electron microscope.

Results show that the dependence of both friction and wear on the category of additives shares a consistent pattern. In contrast to oleic acid and empty microcapsules, oleic acid-filled microcapsules achieve the best tribological performance which is related to the lubricant effect of oleic acid and the isolation and rolling abilities of microcapsules.

This study provides a helpful method of encapsulated lubricant additives to prolong lubrication performance for steel–steel contact.

This study has applied microcapsules to improve the tribological properties of drilling mud.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2019-0320/

Since China began its market-oriented economic reform in 1979, government budget deficits have been a fact of life. On one hand, the share of resources owned or controlled by the government must be shrunk in order to create a favorable environment for the development of the market economy. On the other hand, the government still performs many responsibilities required by the traditional planed economy. After calculating government budget deficits and analyzing their causes, the article assesses the impacts of government budget deficits and predicts the future of deficits in China.

Since China began its market-oriented economic reform in 1979, government budget deficits have been a fact of life. On one hand, the share of resources owned or controlled by the government must be shrunk in order to create a favorable environment for the development of the market economy. On the other hand, the government still performs many responsibilities required by the traditional planed economy. After calculating government budget deficits and analyzing their causes, the article assesses the impacts of government budget deficits and predicts the future of deficits in China.

Social commerce sites offer fertile ground for users to communicate product information. Given that such sites have the potential to transform the way of doing business, it is clearly important for academics to understand user information sharing on social commerce sites. Existing research has considered motivations as core elements of user information sharing in online communities. The purpose of this paper is to advance the theoretical understanding of user information sharing by investigating differences in motivations between the different user types of social commerce sites: sellers and buyers, and the impact on social commerce.

The authors conducted an empirical study by analyzing data from a social commerce site in China, including panel data (n=892) and survey data (n=913).

This research showed that user type (i.e. sellers and buyers) plays an important moderating role in user information sharing: sellers exert a positive moderating effect on utilitarian and social motivations, while buyers are found to have a positive moderating effect on hedonic motivation.

This study contributes to existing literature, not only by exploring the antecedents of user information sharing on social commerce sites from utilitarian, hedonic and social dimensions, but also by providing an evaluation of user types (i.e. sellers and buyers). The authors believe that the results of this study offer important and interesting insights for IS research and practice.

This study will enhance social commerce site managers’ understanding of better features for information sharing and differences in motivation between sellers and buyers. This could improve the effectiveness of encouraging strategies and help social commerce sites be more sustainable in the highly competitive contemporary environment.

Based on social exchange theory and motivation theory, this paper takes user types into account, and postulate that user type (i.e. sellers and buyers) plays a moderating role in the relationships between motivations (i.e. utilitarian, hedonic and social motivation) and user information sharing intention on social commerce sites.