Learning Probabilistic Automata and Markov Chains via Queries

We investigate the problem of learning probabilistic automata and Markov chains via queries in the teacher-student learning model. Probabilistic automata and Markov chains are probabilistic extensions of finite state automata and have similar structures. We discuss some natural oracles associated with probabilistic automata and Markov chains. We present polynomial-time algorithms for learning probabilistic automata and Markov Chains using these oracles.     

The application of fuzzy theory for the control of weld line positions in injection-molded part

This research proposes the fuzzy theory for the control of weld lines in plastic injection molding. The weld line occurs as a result of geometrical changes in molded parts in the injection molding process. The weld line is one of the defects present in plastic injection-molded parts; the line affects the quality of parts as well as the strength of the products. In the present study, fuzzy theory was applied in the design of injection molding. First, expert experiences were transformed into IF∼THEN∼ rules to establish the knowledge base for developing fuzzy inference rules. The rules were then used to adjust the molding parameters, which in turn were applied to control the weld line position in the injection molding process. The results indicate that fuzzy theory exhibited favorable applicability in the control of the weld line as well as decreased the simulation time, thereby accelerating the design process of injection molding.     

Dimensional and angular measurements using least squares and neural networks

In this study a machine vision approach is developed for dimensional and angular measurements of manufactured components comprising straight line segments. We aim at the measurements of distance between two parallel lines and angle between two intersecting lines using both least mean square (LMS) and artificial neural network (ANN) techniques. LMS models estimate the line parameters based on the sum of squared perpendicular distances, rather than the vertical distances, between the observed data points and the line. A set of 23 gauge blocks of varying sizes is used to evaluate the performance of the LMS line estimators. Experimental results show that the measurement errors of the LMS models are affected by the line length and orientation of digital images. ANN techniques are, therefore, used to adjust the measurement errors resulting from the LMS models. Two back-propagation neural networks are developed, one for measuring the distance between two parallel lines, and the other for measuring the angle between two intersecting lines. Experimental results show that the ANNs are very effective for correcting the measurement errors regardless of line lengths and orientations of digital images. A 90% improvement in measurement accuracy for the ANN compared to the LMS was achieved. By using the ANNs, the measurement accuracy and flexibility in manufacturing applications can be significantly improved.     

Using a d-optimal mixture design to study the thermal properties of short glass fiber- and polytetrafluoroethylene-reinforced polycarbonate composites

This study analyzed variations of thermal properties that depend on the injection molding techniques during the blending of short glass fiber (SGF)- and polytetrafluoroethylene (PTFE)-reinforced polycarbonate (PC) composites. The proposed planning of blending experiments is using a d-optimal mixture design (DMD). The thermal conductivity and thermal expansion coefficient were selected for discussion. Nine experimental runs, based on a DMD method, utilized to train the back-propagation neural network (BPNN), and then the simulated annealing algorithm (SAA) approach is applied to search for an optimal mixture ratio setting. In addition, the result of BPNN integrating SAA was also compared with response surface methodology (RSM) approach. The results of confirmation experiment show that DMD, RSM, and BPNN integrating SAA method are effective tools for the optimization of reinforced process. Furthermore, analysis of variance and response surface graphs were applied to identify the effect of mixture ratio of SGF- and PTFE-reinforced PC composites for the thermal conductivity and thermal expansion coefficient.     

Magnetic concentration of particles and cells in ferrofluid flow through a straight microchannel using attracting magnets

Concentrating particles to a detectable level is often necessary in many applications. Although magnetic force has long been used to enrich magnetic (or magnetically tagged) particles in suspensions, magnetic concentration of diamagnetic particles is relatively new and little reported. We demonstrate in this work a simple magnetic technique to concentrate polystyrene particles and live yeast cells in ferrofluid flow through a straight rectangular microchannel using negative magnetophoresis. The magnetic field gradient is created by two attracting permanent magnets that are placed on the top and bottom of the planar microfluidic device and held in position by their natural attractive force. The magnet–magnet distance is mainly controlled by the thickness of the device substrate and can be made small, allowing for the use of a dilute ferrofluid in the developed magnetic concentration technique. This advantage not only enables a magnetic/fluorescent label-free handling of diamagnetic particles, but also renders such handling biocompatible.     

Practical design and implementation of recognition assisted dynamic surveillance system

Open Service Gateway Initiative (OSGi) and Open-source Computer Vision (OpenCV) are widely used for developing applications. OSGi is constructed to provide a service platform with high application interoperability while OpenCV is used to provide many application programming interfaces (APIs) about image processing. In this paper, we design a recognition assisted surveillance system based on OSGi and OpenCV platforms. The system features dynamic monitoring by a camera carried by a robot and a Java 2 Micro-Edition (J2ME) viewer on a mobile phone. With the assistance of image recognition techniques, the captured frames are adaptively reproduced for handheld phones in a limited bandwidth environment. The proposed adaptive pause time control mechanism can efficiently improve the synchronization relationship between captured and viewed frames across heterogeneous networks. The evaluation results show that the proposed scheme can save power for the moveable camera and have a shorter time delay between the captured and viewed frames.     

An integrated MCDM technique combined with DEMATEL for a novel cluster-weighted with ANP method

Traditionally, most importance-assessing methods used to demonstrate the importance among criteria by preference weightings are based on the assumptions of additivity and independence. In fact, people have found that using such an additive model is not always feasible because of the dependence and feedback among the criteria to somewhat different degrees. To solve the issue the analytic network process (ANP) method is proposed by Saaty. The general method is easy and useful for solving the above-mentioned problem. However in ANP procedures, using average method (equal cluster-weighted) to obtain the weighted supermatrix seems to be irrational because there are different degrees of influence among the criteria. Therefore, we intended to propose an integrated multiple criteria decision making (MCDM) techniques which combined with the decision making trial and evaluation laboratory (DEMATEL) and a novel cluster-weighted with ANP method in this paper, in which the DEMATEL method is used to visualize the structure of complicated causal relationships between criteria of a system and obtain the influence level of these criteria. And, then adopt these influence level values as the base of normalization supermatrix for calculating ANP weights to obtain the relative importance. Additionally, an empirical study is illustrated to demonstrate that the proposed method is more suitable and reasonable. By the concept of ideal point, some important conclusions drawn from a practical application can be referred by practitioners.     

Optimisation of electron-beam surface hardening of cast iron for high wear resistance using the Taguchi method

The paper presents the application of the Taguchi method to develop an optimised electron-beam surface hardening of cast iron for high wear resistance. The experiments were conducted on both the ductile and grey cast iron. The factors investigated during the surface-hardened process included the material matrix, the accelerating voltage, the electrical current, the travel velocity, the melted width, the beam oscillation, and the post-heat treatment temperature. In this study, the L18 and L9 orthogonal arrays were introduced through the two-stage experimental designs and trials. Smaller-is-better was used as a quality characteristic to evaluate the experimental results by computing their signal-to-noise (S/N) ratios of the wear volume after wear tests.It was found that using the Taguchi method coupled with a two-round experimental design strategy is simple, effective and efficient in developing an optimised EB surface hardening process. The experimental results show that the most important process parameters identified are the accelerating voltage, the travel speed, the electrical current and post-heat treatment, respectively. The best wear resistance result obtained through the best combination of process parameters is 8.845×10⁸ kg-mm/mm³.