A steel surface defect inspection approach towards smart industrial monitoring

Journal of Intelligent Manufacturing - With the advance in Industry 4.0, smart industrial monitoring has been proposed to timely discover faults and defects in industrial processes. Steel is widely...     

Customer knowledge discovery from online reviews

The explosive growth of Chinese electronic market has made it possible for companies to better understand consumers?? opinion towards their products in a timely fashion through their online reviews. This study proposes a framework for extracting knowledge from online reviews through text mining and econometric analysis. Specifically, we extract product features, detect topics, and identify determinants of customer satisfaction. An experiment on the online reviews from a Chinese leading B2C (Business-to-Customer) website demonstrated the feasibility of the proposed method. We also present some findings about the characteristics of Chinese reviewers.     

Evaluation of contrast metrics for liquid‐crystal displays under different viewing conditions

Abstract— A psychophysical experiment was carried out to assess the perceptual contrast on a large‐sized liquid‐crystal display (LCD) under nine phases of viewing conditions. Based on the results, six contrast models employing different color attributes or the index of just‐noticeable difference (JND) were developed. Their performances were also tested by the visual data of the nine phases, which indicated that the model C_Q employing CIECAM02 brightness gives the best performance in predicting visual data under different viewing conditions among those models. A preprocessing step was also proposed to utilize the contrast model C_Q conveniently in the practical contrast evaluation of LCDs.     

A best possible deterministic on-line algorithm for minimizing makespan on parallel batch machines

We study on-line scheduling on parallel batch machines. Jobs arrive over time. A batch processing machine can handle up to B jobs simultaneously. The jobs that are processed together form a batch and all jobs in a batch start and are completed at the same time. The processing time of a batch is given by the processing time of the longest job in the batch. The objective is to minimize the makespan. We deal with the unbounded model, where B is sufficiently large. We first show that no deterministic on-line algorithm can have a competitive ratio of less than 1+(?{m²+4}-m)/21+(\sqrt{m^{2}+4}-m)/2 , where m is the number of parallel batch machines. We then present an on-line algorithm which is the one best possible for any specific values of m.     

Modeling of the condyle elements within a biomechanical knee model

The development of a computational multibody knee model able to capture some of the fundamental properties of the human knee articulation is presented. This desideratum is reached by including the kinetics of the real knee articulation. The research question is whether an accurate modeling of the condyle contact in the knee will lead to reproduction of the complex combination of flexion/extension, abduction/adduction, and tibial rotation observed in the real knee. The model is composed by two anatomic segments, the tibia and the femur, whose characteristics are functions of the geometric and anatomic properties of the real bones. The biomechanical model characterization is developed under the framework of multibody systems methodologies using Cartesian coordinates. The type of approach used in the proposed knee model is the joint surface contact conditions between ellipsoids, representing the two femoral condyles, and points, representing the tibial plateau and the menisci. These elements are closely fitted to the actual knee geometry. This task is undertaken by considering a parameter optimization process to replicate experimental data published in the literature, namely that by Lafortune and his coworkers in 1992. Then kinematic data in the form of flexion/extension patterns are imposed on the model corresponding to the stance phase of the human gait. From the results obtained, by performing several computational simulations, it can be observed that the knee model approximates the average secondary motion patterns observed in the literature. Because the literature reports considerable inter-individual differences in the secondary motion patterns, the knee model presented here is also used to check whether it is possible to reproduce the observed differences with reasonable variations of bone shape parameters. This task is accomplished by a parameter study, in which the main variables that define the geometry of condyles are taken into account. It was observed that the data reveal a difference in secondary kinematics of the knee in flexion versus extension. The likely explanation for this fact is the elastic component of the secondary motions created by the combination of joint forces and soft tissue deformations. The proposed knee model is, therefore, used to investigate whether this observed behavior can be explained by reasonable elastic deformations of the points representing the menisci in the model.     

Automatic stress exaggeration by prosody modification to assist language learners perceive sentence stress

In this paper, we propose a set of automatic stress exaggeration methods that can enlarge the differences between stressed and unstressed syllables. Our stress exaggeration methods can be used in computer-aided language learning systems to assist second language learners perceive stress patterns. The intention of our automatic stress exaggeration methods is to support hyper-pronunciation training which is commonly used in classrooms by teachers. In hyper-pronunciation training, exaggeration is used to help learners increase their awareness of acoustic features and effectively apply these features into their pronunciation. Duration, pitch and intensity have been claimed to be the main acoustic features that are closely related to stress in English language. Thus, four stress exaggeration methods are proposed in this paper: (i) duration-based stress exaggeration, (ii) pitch-based stress exaggeration, (iii) intensity-based stress exaggeration, and (iv) a combined stress exaggeration method that integrates the duration-based, pitch-based and intensity-based exaggeration methods. Our perceptual experimental results show that resynthesised stimuli by our proposed stress exaggerated methods can help learners of English as a Second Language (ESL) better perceive English stress patterns significantly.     

3-D face recognition method based on optimum 3-D image measurement technology

Most face recognition systems employ 2-D color or gray-scale images. However, face recognition based on 2-D images is adversely affected by 3-D movement, variable lighting, and the use of cosmetics. 3-D image measurement technology has the potential to overcome these limitations of face recognition based on 2-D images since it can perform geometric analysis. We propose a method that is capable of recognizing a person from a 3-D facial image obtained using a 3-D shape measurement system by employing a technique that optimizes the intensity-modulation pattern projection. This face recognition method is based on the iterative closest point algorithm. It is robust to changes in reflectivity and color. Since the 3-D facial information can be registered, this method can estimate rotations and translations to compensate for different positions or directions. In order to prove the validity of the proposed technique, a verification experiment was conducted which used 105 sample 3-D images obtained from 15 subjects. It achieved a detection rate of 96% when heads were turned at an angle of 20° or less relative to the camera.     

Centroid Iteration algorithm for image tracking

A simple and elegant tracking algorithm called Centroid Iteration algorithm is proposed. It employs a new Background-Weighted similarity measure which can greatly reduce the influence from the pixels shared by the target template and background on localization. Experiments demonstrated the Background-Weighted measure performs much better than the other similarity measures like Kullback–Leibler divergence, Bhattacharyya coefficient and so on. It has been proved that this measure can compute the similarity value contribution of each pixel in the target candidate, based on which, a new target search method called Centroid Iteration is constructed. The convergence of the method has been demonstrated. Theory analysis and visual experiments both validated the new algorithm.     

Intelligent control of doubly‐fed induction generator systems using PIDNNs

An intelligent control for a stand‐alone doubly‐fed induction generator (DFIG) system using a proportional‐integral‐derivative neural network (PIDNN) is proposed in this study. This system can be applied as a stand‐alone power supply system or as the emergency power system when the electricity grid fails for all sub‐synchronous, synchronous, and super‐synchronous conditions. The rotor side converter is controlled using field‐oriented control to produce 3‐phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field‐oriented control, is primarily implemented to maintain the magnitude of the DC‐link voltage. Furthermore, the intelligent PIDNN controller is proposed for both the rotor and grid side converters to improve the transient and steady‐state responses of the DFIG system for different operating conditions. Both the network structure and online learning algorithm are introduced in detail. Finally, the feasibility of the proposed control scheme is verified through experimentation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society