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Mid-priced cars (segment B) are attracting the attention of middle-income families in Vietnam. They often consider choosing one of three vehicles from three different manufacturers, consisting of Hyundai Accent 1.4AT, Toyota Vios 1.5G, and Honda City 1.5L. This study was carried out to rank those three cars. Twelve criteria for rating each car provided by the dealer were used. These criteria are both qualitative and quantitative, and also fall into all three types, including max, min and another (“Yes”, “No”). The importance of each criterion was determined by experts in a survey. They are all knowledgeable about cars. Two multi-criteria decision making (MCDM) methods including R (A simple ranking method for multi-attribute decision making in the industrial environment) and CURLI (Collaborative Unbiased Rank List Integration) method were applied for ranking. This is the first work that has used both methods mentioned above. The result revealed that the rank of the alternatives is the same when both the methods were used. This result gives us a certain confidence when choosing a car. Accordingly, Honda City 1.5L is ranked first. R and CURLI not only succeeded in ranking cars in this study, but also promise to be successful when used in other situations. Moreover, other criteria for evaluating the vehicle options that have not been surveyed in this study are mentioned in the last section of this paper. They need to be further considered to include in other next studies for car selection

The short form of the Experiences in Close Relationships Scale–Revised Child version (ECR-RC) is a promising self-report measure of anxious and avoidant attachment in Western adolescents, yet little is known about its psychometric properties across cultures. More importantly, little is known about attachment styles across cultures, child gender, and parental gender. The present study aims to address these limitations by studying the psychometric properties and measurement invariance of the ECR-RC in a sample of 1,232 Belgian and Vietnamese adolescents (45.9% boys, Mage = 12.3, SD = 1.20, range = 9.0–15.0; 61.36% Vietnamese adolescents). Results indicated that the factor structure of the mother-oriented ECR-RC was replicated across a Belgian and a Vietnamese sample and that the scale was invariant across both cultures and across gender and age. Vietnamese adolescents were more avoidantly and anxiously attached to their mothers compared to their Belgian counterparts. Boys were more avoidantly and anxiously attached compared to girls for the total sample. Considering two countries separately, boys were found to be more avoidantly attached, not anxiously attached compared to girls. Furthermore, with increasing age, more anxious and avoidant attachment was reported, except in Belgian adolescents where anxious and avoidant attachment did not differ over age. Focusing solely on the Vietnamese data, results revealed that the ECRRC is a reliable measure to assess Vietnamese adolescents’ anxious and avoidant attachment to both parents. Vietnamese adolescents did not differ in their levels of anxious attachment toward both parents but showed higher avoidant attachment to fathers compared to mothers.

The paper presents an intelligent control system for self-adjusting on-line cutting condition for high speed machining (self-HSM) with considering the tool-wear amount to keep the machined product’s quality in allowable limit. For realizing the self-HSM, the empirical analysis of variance (ANOVA) and artifical neural network (ANN) are used. The ANOVA is used for generating the empirical functions which are used as the boundary condition as well as constraint evaluation. The ANN is used for generating the new optimal cutting condition. Then, the self-HSM updates this cutting condition on the real machine — HS Super MC500. The new optimal cutting parameter is sent to the controller for updating the new machining condition to keep the machined part’s quality. The integration of the empirical analysis and ANN enables generating the optimal cutting parameters correctly and efficiently for high-speed milling.

This paper presents a study on analysis of influences of the surface roughness in Electrical Discharge Machining 90CrSi tablet shape punches with the use of copper electrode. In this paper, 9 experimental runs were designed and conducted by using Taguchi method. In addition, 4 process parameters including the gap voltage, the pulse current, the pulse on time and the pulse off time were investigated. The influences of these input parameters on the surface roughness were evaluated by analysing variance. Also, from the experimental results, optimum values of the input parameters for getting the minimum surface roughness were proposed.

This study aims to propose a method to calculate optimum exchanged grinding wheel diameter to minimize grinding cost in grinding surface process for stainless steel. The interactions of the grinding cost and optimum exchanged grinding wheel diameter have been analyzed and presented in mathematical formulae. To obtain the minimal grinding cost, the optimal exchanged grinding wheel diameter has been determined by investigating six two-level grinding process parameters including initial grinding wheel diameter, total dressing depth, radial grinding wheel wear per dress, wheel life, machine tool hour and grinding wheel cost. Based on 26 experiments conducted in computational program, the optimal exchanged grinding wheel diameter is presented as a function of these grinding parameters. This developed model has been verified and proven by experimental results. The proposed method of calculation of the optimum exchanged grinding wheel diameter can be applied in practice to reduce the grinding cost.

The molecular dynamics is the research method which is used to simulate the liquid ejection in this paper. The factors of 200000 time step (fs) total ejective time of total 200 000 time steps (fs) and various compressible forces of 9.0 × 10−10, 10.0 × 10−10 and 11.0 × 10−10 Newton (N) are main investigated in this research. These influences have action to the moveable direction of fluid jet in the whole ejective process. The research result indicates that liquid jet is prepared to separate out from nozzle plate's surface to form up droplets at the ejective time of 140 000 fs for the nozzle diameter size of 27.5 Angstrom (Å), system temperature of 310 Kelvin (K) and compressible force magnitude of 9.0 × 10−10 (N). However, when increasing the ejective time or compressible force magnitude, the jets were not only non-destruction from nozzle's surface to produce the droplets but also downward movement to go back the nozzle's surface. Meanwhile, with the nozzle diameter size of 40 Å, the droplets are not only production but also movement up to go away the nozzle under same research condition. Those prove that the ejective time and compressible force magnitude have the influences to the moveable direction of fluid jet in the whole ejective process.

The paper proposed a new efficient diagnostic method for the various fault statuses of gear. This method identifies the gear fault exactly by an optimal classification model based on our relevant features. Firstly, the empirical mode decomposition (EMD) method is used to decompose a non-linear and non-stationary vibration signal into intrinsic mode functions (IMFs). The statistical characteristics of the first several IMFs are calculated to form a feature-matrix. Secondly, the meaningful singular values are exploited by the singular value decomposition (SVD) method, which represent as an important feature vector of the original vibration signal, named EMD-SVD. The obtained EMD-SVD feature is the necessary input to serve the classifier model. Finally, a least square support vector machine model (LSSVM) is optimally trained in the EMD-SVD feature, learning to identify the respective fault pattern with the various status of gear. In the formation of the optimal classifier model, the genetic algorithm is used to find out the best parameter pair of the LSSVM classifier model, commonly called GA-LSSVM. We experimented on gear vibration dataset, and the achieved results demonstrated that the proposed diagnostic method was effective in terms of identification accuracy in the short time.

In this paper, based on deep learning method for the high-dimensional feature representation of vibration signal and optimal machine learning model, a new diagnosis technique is proposed for identifying the big vibration data of multi-level fault of roller bearing. Firstly, a deep learning network based on stacked autoencoders (SAE) with hidden layers is exploited for vibration feature representation (VFR) of roller bearing data, named as VFR-SAE, in which the unsupervised learning algorithm is used to reveal the significant properties in the data such as nonlinear, non-station properties. The represented features can provide good discriminability for fault diagnosis task. Secondly, an optimal classifier model based on least square support vector machine (LSSVM) classifier and chemical reaction optimization (CRO) algorithm, named as LSSVM-CRO, is formed to perform supervised fine-turning and classification. In this work, the transfer learning performance can be especially tuned in this diagnosis technique. That is, the features of target vibration data will be extracted by the learning of feature representation which depends on the weight matrix of hidden layers of VFR-SAE method. The experimental results by analyzing the roller bearing vibration signals with multi-status of fault have demonstrated that the VFR-SAE based feature extraction in conjunction with the LSSVM-CRO classifier model can achieve higher accuracies than the other popular classifier models.

Predicting the quality of the machined components is the grandiose signification in industrial manufacturing. Calculation of the surface roughness is one of the most important elements to predict the quality of machined parts. In this paper, the surface roughness was modeled in the face-end mill process by using general rhombu insert. In the proposed mathematical model, with the stable milling condition, the surface roughness is formulated by a function of feed rate, and insert geometry such as nose radius and nose angle. An experimental method is proposed based on the stable milling condition to estimate the surface roughness. The developed surface roughness calculation model has been successfully verified by both simulation and experiment with very promising results. The average difference between predicted and experimental results is 7.59%. The proposed model can be applied to predict the surface roughness in face-end mill processes by using general insert at stable cutting condition.

In transit condition, Jack-up rig is a type of floating marine structure with large dimensions. The current standards have just regulated calculating in the equivalent models according to the Finite Element Method (FEM), without consideration of structure’s simultaneous work as in the overall model. Wave loads included diffracted, radiated wave loads acting on large-sized Jack-up Rig in the transit condition are usually determined by the Boundary Element Method (BEM).

In this paper, an application of an embedded system running on the Android operating system to a behavioral control system of mobile robot is presented. In the behavioral control system, the behaviors concerning collision-free and goal-oriented movement are functionally classified into two distinct modules and smoothly cooperated based on a fuzzy logic system. The application is successfully tested through experiments in a real environment.

Ship autopilots are usually designed based on the PD and PID controllers because of simplicity, reliability and easy to construct. However their performance in various environmental conditions is not as good as desired. This advantage can be overcome by adjusting works or constructing adaptive controllers. But those methods are complex and not easy. This paper presents a new method for constructing ship autopilots based on combination of fuzzy logic control and linear control. The new ship autopilot has the advantages of both the PID and FLC control methodologies: easy to construct, optimal control laws can be established based on ship masters knowledge. Therefore, the new ship autopilot can be well adapted with parameter variations and strong environment effects. Simulation using MATLAB software for a ship with real parameters in course keeping and course changing maneuvers shows high effectiveness of the new autopilot in comparison with ordinary PID ship autopilots.