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The purpose of this paper is to develop a coiling robot in the production of coated elevator compensation chains to replace the manual coiling operations and improve the quality of compensation chains.

This paper introduces both mechanical and servo control system designs of the coiling robot. The structure of two friction wheels stabilizes the conveying speed of compensation chain, so the chain speed matches with the car speed. A centering mechanism pushes the chain to its original position. Seven servo motors are integrated into the system, and they are controlled by a servo control system based on programmable logic controller, positioning controller, analog output block and touch screen.

The results of the project show that the coiling robot can both greatly reduce the number of workers and the intensity of the work and improve the quality of the chain. The chain lid by the robot is not only neat, but also uniform in its inner stress.

When the output speed of the compensation chain from the rear friction wheel does not match the coiling speed, the coiling operation has to be halted. Then, the operator adjusts the chain speed and restarts the coiling operation.

The coiling robot is proven working. It has been adopted by a leading company manufacturing compensation chains.

This is the first coiling robot which is practically used in a production line of compensation chains. Its design, mechanism and control systems are of great reference values to people.

Today’s business environment has the nature that challenges the decision-making processes of businesses with its many different factors including technological developments and changing consumer structures. Previous research focused on specific contexts of the business environment regarding marketing and innovation issues, however, an integrative approach can be helpful for understanding and taking actions. This study aims to present a comprehensive framework which employs sensemaking from management literature as an approach to evaluate the market environment. Following a conceptual approach for the methodology, a framework consisting of three stages: discovery, sensemaking, and prediction is included in the study. Proposed framework identifies the stages for the sensemaking of consumers and can guide marketing decision-makers for competitiveness in the digital world.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems.

This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating.

Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment.

This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languages.

This paper proposes an improved fatigue life analysis method for optimal design of electric multiple units (EMU) gear, which aims at defects of traditional Miner fatigue cumulative damage theory.

A fatigue life analysis method by modifying S–N curve and considering material difference is presented, which improves the fatigue life of EMU gear based on shape modification optimization. A corrected method for stress amplitude, average stress and S–N curve is proposed, which considers low stress cycle, material difference and other factors. The fatigue life prediction of EMU gear is carried out by corrected S–N curve and transient dynamic analysis. Moreover, the gear modification technology combined with intelligent optimization method is adopted to investigate the approach of fatigue life analysis and improvement.

The results show that it is more corresponded to engineering practice by using the improved fatigue life analysis method than the traditional method. The function of stress and modification amount established by response surface method meets the requirement of precision. The fatigue life of EMU gear based on the intelligent algorithm for seeking the optimal modification amount is significantly improved compared with that before the modification.

The traditional fatigue life analysis method does not consider the influence of working condition and material. The life prediction results by using the method proposed in this paper are more accurate and ensure the safety of the people in the EMU. At the same time, the combination of intelligent algorithm and gear modification can improve the fatigue life of gear on the basis of accurate prediction, which is of great significance to the portability of EMU maintenance.

A large number of data have proved that under the same von Mises equivalent strain condition, the fatigue life under multiaxial non-proportional loading is often much lower than the life under multiaxial proportional loading. This is mainly due to the influence of the non-proportional loading path and the additional hardening effect, which lead to a sharp decrease in life.

The modulus attenuation effect is used to modify the static hardening coefficient, and the predicted value obtained is closer to the additional hardening coefficient obtained from the experiment. A fatigue life model can consider non-proportional paths, and additional hardening effects are proposed. And the model uses multiaxial fatigue test data to verify the validity and adaptability of the new model. The life prediction accuracy and material application range are satisfactory.

Because loading path and additional hardening of the material affect fatigue life, a new multiaxis fatigue life model based on the critical plane approach is proposed. And introducing a non-proportional additional damage coefficient, the joint influence of the load path and the additional hardening can be considered. The model's life prediction accuracy and material applicability were verified with multiaxial fatigue test data of eight materials and nine loads compared with the prediction accuracy of the Kandil–Brown–Miller (KBM) model and Fatemi–Socie (FS) model.

The physical meaning of the new model is clear, convenient for practical engineering applications.

The paper aims to propose a new type of three-phase quasi-Z-source indirect matrix converter (QZSIMC) to extend the voltage gain for application in the induction motor drives.

A unique H-shape quasi-Z-source network is connected between the three-phase voltage source and traditional indirect matrix converter to achieve the voltage boost and buck in a single-stage power conversion. The complete space vector modulation (SVM) method is proposed to control the proposed QZSIMC. The output voltage amplitude of quasi-Z-source network can be boosted by the shoot-through of the front-end rectifier, so the whole system's voltage gain is extended. Meanwhile, the QZSIMC modeling and quasi-Z-source impedance parameter design are developed by using the state space averaging method. The design-oriented analysis based on small signal model is used to investigate the quasi-Z-source impedance parameter's impact on the QZSIMC's dynamic performance. A simulated application example employs a 4-kW induction motor drive to verify the proposed QZSIMC, the developed modulation method and parameter design method.

The proposed QZSIMC can achieve high voltage gain larger than one and also can fulfill buck function, which widens the induction motor drive's operation range. The simulation results verify the proposed QZSIMC and SVM and also validate the quality performance of the proposed induction motor drive and all theoretical analysis and parameter design method.

The proposed QZSIMC effectively overcomes the limitation of traditional indirect matrix converter, through extending the voltage gain larger than one. The systematic principle, analysis, parameter design, and simulation verification provide the proposed QZSIMC with a feasible approach in practical induction motor drive applications.

This paper aims to study the influence degree of three factors affecting the vibration amplitude of aerostatic spindle and optimizes each factor.

The vibration amplitude of the spindle is characterized according to internal structure and operating characteristics of aerostatic spindle. The radial and axial vibration models of aerostatic spindle were established by the spring-damper system. The influence degree of main influencing factors on the spindle vibration amplitude was investigated through correlation analysis.

The results indicate that the crucial factor is aerostatic spindle speed and experiments validated that increasing spindle speed can enhance spindle stability. The influence of three factors on radial vibration is greater than that on axial vibration. Finally, the values of optimal working parameters were obtained by genetic algorithm.

The method in this article can effectively predict aerostatic spindle vibration amplitude and perfect the stability of aerostatic spindle.

The purpose of this study is to develop a review rating prediction method based on a supervised text mining approach for unrated customer reviews.

Using 2,851 hotel comment card (HCC) reviews, this paper manually labeled positive and negative comments with seven aspects (dining, cleanliness, service, entertainment, price, public, room) that emerged from the content of said reviews. After text preprocessing (tokenization, eliminating punctuation, stemming, etc.), two classifier models were created for predicting the reviews’ sentiments and aspects. Thus, an aggregate rating scale was generated using these two classifier models to determine overall rating values.

A new algorithm, Comment Rate (CRate), based on supervised learning, is proposed. The results are compared with another review-rating algorithm called location based social matrix factorization (LBSMF) to check the consistency of the proposed algorithm. It is seen that the proposed algorithm can predict the sentiments better than LBSMF. The performance evaluation is performed on a real data set, and the results indicate that the CRate algorithm truly predicts the overall rating with ratio 80.27%. In addition, the CRate algorithm can generate an overall rating prediction scale for hotel management to automatically analyze customer reviews and understand the sentiment thereof.

The review data were only collected from a resort hotel during a limited period. Therefore, this paper cannot explore the effect of independent variables on the dependent variable in context of larger period.

This paper provides a novel overall rating prediction technique allowing hotel management to improve their operations. With this feature, hotel management can evaluate guest feedback through HCCs more effectively and quickly. In this way, the hotel management will be able to identify those service areas that need to be developed faster and more effectively. In addition, this review rating prediction approach can be applied to customer reviews posted via online platforms for detecting review and rating reliability.

Manually analyzing textual information is time-consuming and can lead to measurement errors. Therefore, the primary contribution of this study is that although comment cards do not have rating values, the proposed CRate algorithm can predict the overall rating and understand the sentiment of the reviews in question.