Search results

The rotor in screw motor is driven to rotate by highly pressure difference of drilling fluid (DF), while rotor drives drill bit to break rocks. DF works in the volume cavity (VC) which exists between the stator and rotor (SAR), these process realizes the conversion from hydraulic energy to mechanical energy finally. In order to assure seal performance and output power reliability of VC in common hypocycloid screw motor (CHSM), it’s essential to survey SAR end-face profile.

In this article, based on the internal and external cycloid method given for SAR end-face of φ172 7/8-head LZ type CHSM, the interference among SAR is established based on the meshing model through theoretical equilibrium method (TEM). Last, the reasonable design value of SAR interference in TEM is verified with the hydraulic parameters test results.

The profile optimization that top-root part of rotor end-face profiles is replaced by elliptical-circular arcs (ECA) makes the transition area of tooth-top and tooth-root connect smoother than before. The reasonable interference of SAR in TEM is almost 0.16mm~0.22mm to ensure better sealing performance. Through the hydraulic test, the interference positive fluctuation or the number of SAR head reduces increase (starting-pressure-drop) SPD while negative fluctuations by contraries. Meanwhile, DF penetration also decreases the revolution speed with the SAR interference decreases. The less SAR head revolution speed is always below the more with the constant driving power and DF hydraulic drop. Ultimately, decreasing in overall-efficiency occurs for larger fluctuation of interference or or less interference among SAR.

The line type optimization and analysis in TEM for CHSM improves the motor seal and output performance, also has important application values simultaneously.

To investigate two explanations for how variations in social network structure might produce differences in cognitive and perceptual orientation. One explanation is that the extent to which structures lead people to feel strong social bonds encourages holism. The other is that the extent to which a network leads individuals to be concerned about distal network relations leads to holistic thinking.

An experimental study in which participants interacted in three-person networks of negotiated (with or without a one-exchange rule), generalized, or productive exchange before being administered the framed-line test, a common measure of cognitive and perceptual orientation.

Participants in network structures more likely to lead participants to be concerned about what was happening in relationships in the network of which they were not part performed relatively more holistically on the framed-line test. However, these effects did not extend to both modules of the test, and a check on the ordering of networks as reflecting concern with distal network relationships failed.

The experimental design was structured such that only one of the presented explanations could possibly be supported, whereas they both could be correct. Nevertheless, results do indicate that cognitive orientation did respond to variations in network structure.

Explanations for cultural differences typically implicate social structure, although the explanations often cannot be directly tested. Results show that social structure can produce effects that mirror differences thought to reflect profound cultural variations.

This paper aims to inspect the defects of solder joints of printed circuit board in real-time production line, simple computing and high accuracy are primary consideration factors for feature extraction and classification algorithm.

In this study, the author presents an ensemble method for the classification of solder joint defects. The new method is based on extracting the color and geometry features after solder image acquisition and using decision trees to guarantee the algorithm’s running executive efficiency. To improve algorithm accuracy, the author proposes an ensemble method of random forest which combined several trees for the classification of solder joints.

The proposed method has been tested using 280 samples of solder joints, including good and various defect types, for experiments. The results show that the proposed method has a high accuracy.

The author extracted the color and geometry features and used decision trees to guarantee the algorithm's running executive efficiency. To improve the algorithm accuracy, the author proposes using an ensemble method of random forest which combined several trees for the classification of solder joints. The results show that the proposed method has a high accuracy.

The purpose of this paper is to propose a method for hand measurement based on image and marker watershed algorithm, and combine the data to analyze the shape and characteristics of the hand.

A portable hand image capturing instrument was designed and manufactured, and the hand images and dimensions of 328 young men in Zhejiang area were obtained. The outer contour curve of the hand and the key points of finger root, fingertip, wrist and knuckle position were extracted. Then, the size of each hand part was calculated. The hand data obtained from the two-dimensional image was compared with the manual measurement data. Finally, the hands were classified according to the measurement data, and the relationship between hand control size and hand length, hand width and the relationship between hand length and height were explored.

The data comparison results show that the two measurement methods have high data consistency and are replaceable. In addition, analyzing the data obtained four major characteristic factors that affect the shape of the hand, divided the hands of young men in Zhejiang into five categories, and obtained the regression equations of basic hand size, hand length and hand width, and obtained the regression equation of hand length and height.

The method proposed in this study to obtain hand size based on the image and mark watershed algorithm has lower requirements on the external environment and testers, conforms to the development trend of applying artificial intelligence to anthropometric engineering and provides a useful reference value for data collection of gloves specification design. In addition, the results of data analysis can provide a valuable reference basis for consumer hand shape predictions, which can be used to guide the research and production of hand instruments, the design of specifications series and the purchase of hand products.

By studying the green development efficiency (GDE) of 33 cities in the provinces of Jiangsu, Zhejiang, and Fujian in China, this study strives to conduct an analysis of the sustainable practices implemented in these developed regions, and derive valuable insights that can foster the promotion of green transformation.

First, the urban green development system (GDS) was decomposed into the economic benefit subsystem (EBS), social benefit subsystem (SBS), and pollution control subsystem (PCS). Then, a mixed network SBM model was proposed to evaluate the GDE during 20152020, with Moran’s I and Bootstrap truncated regression model subsequently applied to measure the spatial characteristics and driving factors of efficiency.

Subsystem efficiency presents a distribution trend of PCS > EBS > SBS. There is a particular spatial aggregation effect in EBS efficiency, whereas SBS and PCS efficiencies have no significant spatial autocorrelation. Furthermore, urbanization level contributes significantly to the efficiency of all subsystems; industrial structure, energy consumption, and technological innovation play a crucial role in EBS and SBS; external openness is a pivotal factor in SBS; and environmental regulation has a significant effect on PCS.

This study further decomposes the black box of GDS into subsystems including the economy, society, and environment. Additionally, by employing a mixed network SBM model and Bootstrap truncated regression model to investigate efficiency and its driving factors from the subsystem perspective, it endeavors to derive more detailed research conclusions and policy implications.

The purpose of this paper is to study the impact of government supervision and market environment on farmers' pesticide application behavior, as well as the intermediary effect of farmers' literacy, and investigate the substitution effect between government supervision and market environment.

In this paper, logit and Poisson regression models were used to investigate the comprehensive impact of government supervision and market environment on farmers' pesticide application behavior, and the intermediary effect model is used to examine the intermediary effect of farmers' literacy.

Government supervision is an important constraint for the formation of individual behavior paradigm, but it has both positive and negative effects, depending on different instruments. The market subject constraint and market incentive are two important ways that the market environment affects Chinese farmers' pesticide application behavior. Farmers' literacy plays a partial mediating role in the influencing mechanism of government and market factors. The government supervision and market environment, two different constraint forces, have substitution effects in the process of regulating farmers' pesticide application behavior.

In the influence mechanism, farmers' literacy, such as values, responsibilities and skill requirement related to scientific pesticide use, was included into the analysis framework as intermediary variables. The authors found that government supervision and market environment not only directly affect farmers' pesticide application behavior but also indirectly affect farmers' pesticide application behavior through farmers' literacy.

The purpose of this paper is to propose a defect detection method of bare printed circuit boards (PCB) with high accuracy.

First, bilateral filtering of the PCB image was performed in the uniform color space, and the copper-clad areas were segmented according to the color difference among different areas. Then, according to the chaotic characteristics of the spatial distribution and the gradient direction of the edge pixels on the boundary of the defective areas, the feature vector, which evaluates quantitatively the significant degree of the defect characteristics by using the gradient direction information entropy and the uniform local binary patterns, was constructed. Finally, support vector machine classifier was used for the identification and localization of the PCB defects.

Experimental results show that the proposed algorithm can accurately detect typical defects of the bare PCB, such as short circuit, open circuit, scratches and voids.

Considering the limitations of describing all kinds of defects on bare PCB by using single kind of feature, the gradient direction information entropy and the local binary patterns were fused to build a feature vector, which evaluates quantitatively the significant degree of the defect features.

The main purpose is to construct the mapping relationship between garment flat and pattern. Particle swarm optimization–least-squares support vector machine (PSO-LSSVM), the data-driven model, is proposed for predicting the pattern design dimensions based on small sample sizes by digitizing the experience of the patternmakers.

For this purpose, the sleeve components were automatically localized and segmented from the garment flat by the Mask R-CNN. The sleeve flat measurements were extracted by the Douglas–Peucker algorithm. Then, the PSO algorithm was used to optimize the LSSVM parameters. PSO-LSSVM was trained by utilizing the experience of patternmakers.

The experimental results demonstrated that the PSO-LSSVM model can effectively improve the generation ability and prediction accuracy in pattern design dimensions, even with small sample sizes. The mean square error could reach 1.057 ± 0.06. The fluctuation range of absolute error was smaller than the others such as pure LSSVM, backpropagation and radial basis function prediction models.

By constructing the mapping relationship between sleeve flat and pattern, the problems of the garment flat objective recognition and pattern design dimensions accurate prediction were solved. Meanwhile, the proposed method overcomes the problem that the parameters are determined by PSO rather than empirically. This framework could be extended to other garment components.

The authors propose a solder joint recognition method based on eigenspace technology.

The original solder joint image is transformed into a small set of feature subspace called “eigensolder”, which is the eigenvector of the training set and can represent a solder joint well. Then, the eigensolder feature is extracted by projecting the new solder joint image into the subspace, and the Euclidean distance measure is used to classify the solder joint.

The experimental results show that the proposed method is superior to the traditional classification method in solder joint recognition, and it can achieve 96.43 per cent recognition rate using only 15 eigenvalue images. It is suitable for the classification with small samples.

Traditional classification method like neural network and statistical method cost long time. Here, Eigensolder method is used to extract feature. Eigensolder method is more efficient, as it uses the principal component analysis method to reduce the feature dimension of input image and only measure the distance to classify.

Most industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. This is a tedious and time‐consuming task that requires some technical expertise, and hence new approaches to robot programming are required. The purpose of this paper is to present a robotic system that allows users to instruct and program a robot with a high‐level of abstraction from the robot language.

The paper presents in detail a robotic system that allows users, especially non‐expert programmers, to instruct and program a robot just showing it what it should do, in an intuitive way. This is done using the two most natural human interfaces (gestures and speech), a force control system and several code generation techniques. Special attention will be given to the recognition of gestures, where the data extracted from a motion sensor (three‐axis accelerometer) embedded in the Wii remote controller was used to capture human hand behaviours. Gestures (dynamic hand positions) as well as manual postures (static hand positions) are recognized using a statistical approach and artificial neural networks.

It is shown that the robotic system presented is suitable to enable users without programming expertise to rapidly create robot programs. The experimental tests showed that the developed system can be customized for different users and robotic platforms.

The proposed system is tested on two different robotic platforms. Since the options adopted are mainly based on standards, it can be implemented with other robot controllers without significant changes. Future work will focus on improving the recognition rate of gestures and continuous gesture recognition.

The key contribution of this paper is that it offers a practical method to program robots by means of gestures and speech, improving work efficiency and saving time.

This paper presents an alternative to the typical robot teaching process, extending the concept of human‐robot interaction and co‐worker scenario. Since most companies do not have engineering resources to make changes or add new functionalities to their robotic manufacturing systems, this system constitutes a major advantage for small‐ to medium‐sized enterprises.