Search results

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

When college entrance examinations act as gatekeepers to modern-sector jobs, the entire education system then becomes oriented toward these examinations. This occurs at the expense of learning for the sake of learning and other aspects of education that address the holistic development and well-being of students. In recent years in China, there has been growing concern that examination competition has compromised the quality of classroom teaching and learning and is detrimental to the development of skills necessary for the global knowledge economy. These concerns have given rise to a far-reaching set of education reforms known as the New Curriculum reforms which have aimed to move students to the center of teaching and learning and to transform teaching and learning so as to foster such capacities as creativity, innovation, collaboration, self-expression, engagement, enjoyment of learning, inquiry skills, problem-solving abilities, and ability to apply knowledge in practice. In this chapter, we use videotaped high school New Curriculum demonstration lessons to examine teaching and learning practices that are regarded as exemplary in the current reform context. We investigate how teachers are negotiating the competing demands of preparing students for the examinations and addressing the aims of the New Curriculum reforms. The nature of student participation in the classroom emerges in the analysis as a key indicator of the success of this negotiation.

The purpose of this paper is to investigate problems in performing stable lane changes and to find a solution to reduce energy consumption of autonomous electric vehicles.

An optimization algorithm, model predictive control (MPC) and Karush–Kuhn–Tucker (KKT) conditions are adopted to resolve the problems of obtaining optimal lane time, tracking dynamic reference and energy-efficient allocation. In this paper, the dynamic constraints of vehicles during lane change are first established based on the longitudinal and lateral force coupling characteristics and the nominal reference trajectory. Then, by optimizing the lane change time, the yaw rate and lateral acceleration that connect with the lane change time are limed. Furthermore, to assure the dynamic properties of autonomous vehicles, the real system inputs under the restraints are obtained by using the MPC method. Based on the gained inputs and the efficient map of brushless direct-current in-wheel motors (BLDC IWMs), the nonlinear cost function which combines vehicle dynamic and energy consumption is given and the KKT-based method is adopted.

The effectiveness of the proposed control system is verified by numerical simulations. Consequently, the proposed control system can successfully achieve stable trajectory planning, which means that the yaw rate and longitudinal and lateral acceleration of vehicle are within stability boundaries, which accomplishes accurate tracking control and decreases obvious energy consumption.

This paper proposes a solution to simultaneously satisfy stable lane change maneuvering and reduction of energy consumption for autonomous electric vehicles. Different from previous path planning researches in which only the geometric constraints are involved, this paper considers vehicle dynamics, and stability boundaries are established in path planning to ensure the feasibility of the generated reference path.

This empirical study aims to test the impact of four types of entrepreneurial context on the growth and success rates of new ventures in China and related the findings to the theory and practice of entrepreneurship dating back 2,500 years to ancient China.

After describing the business guidelines given by Fan Li, an entrepreneurial merchant selling Chinese medicines in ancient times, a conceptual framework was extracted as the basis for a discussion of the relationship between entrepreneurial context and entrepreneurial activity. Entrepreneurial context was conceptualized as being composed of family, social, business and institutional components. Five hypotheses about the influence of these different context variables on entrepreneurial activities were developed. From data compiled from the sampling of 239 business entrepreneurs in Beijing, a hierarchical regression was formed and the hypotheses tested.

The impact of entrepreneurial context on entrepreneurial activity can be divided into two layers, internal factors (e.g. family context) which are similar to “yin” (?) in the traditional Chinese philosophy while external factors (e.g. business, social and institutional contexts) were like “yang” (?). The two factors play different roles in entrepreneurial activities, while different contexts mediate and moderate each other in complex ways.

Research limitations pertain to the size and locale of the sample. A larger sample that involved subjects from different regions would facilitate a wider understanding of the effects of entrepreneurial context upon the entrepreneurial process.

The theory of entrepreneurial context is in its beginning stages, and the paper completed a systematic study of entrepreneurial context through theoretical model building using large-sample empirical research. In addition, the paper is the first ever to relate the theory and practice of entrepreneurship back 2,500 years. Through a multi-research methodology, the study clearly shows the critical importance of integrating Chinese history into the development of management theory.