Effects of CCEP and Sc on superplasticity of Al–5.6Mg–0.7Mn alloys

Trace amount (0.3?wt%) of scandium is added to Al–5.6Mg–0.7Mn alloy to form uniformly distributed Al₃Sc precipitates for producing a fine-grained and stable microstructure at high temperature through cross-channel extrusion process. Superplasticity and hot workability of the Sc-containing Al–5.6Mg–0.7Mn alloy, after extrusion, are also examined. The result indicates that Al–5.6Mg–0.7Mn alloys with and without 0.3?wt% Sc after extrusion of six passes at 300°C, fine-grained structures were observed with grain sizes of 1–2?µm and improvement of mechanical properties. Furthermore, Al₃Sc phase can effectively retard recrystallization to increase the thermal stability and remain equiaxed. The elongation of Al–5.6Mg–0.7Mn alloy with Sc addition to failure is extended to 873% maximum at high temperature of 450°C at strain rate of 1?×?10^?1?s^?1after six passes in the CCEP.     

Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment. The data were collected to verify the performance of the proposed models for wearable devices. Finally, the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58% and 99.16%, respectively, for the four strokes. The accuracy for five-fold cross validation was 99.87%. This result also shows that the multi-scale convolutional neural network has better robustness after five-fold cross validation.     

Optimization of screen-printing parameters of SN9000 ink for pinholes using Taguchi method in chip on film packaging

The influence of pinholes on the chip on film (COF) in screen-printing was studied in this paper. Yield improvement is the most challenging part of COF manufacturing in view of its processing complexity, mainly in the screen-printing process. The process parameters such as ink capacity, origin control distance, angle of the squeezer, method of mixing, freshness of ink, speed of printing, and speed of scraper are considered to improve the pinholes. In Taguchi method, a two level orthogonal array is used to determine the signal-to-noise (S/N) ratio. Analysis of variance and the F-test values are used to determine the most significant process parameters affecting the pinholes. Confirmation analysis tests with the optimal levels of process parameters are carried out. The results of the experiment show that Taguchi method is a very suitable approach with regard to solving quality problems related to such pinholes.     

Toward effective service composition for real-time SOA-based systems

Many application domains are increasingly leveraging service-oriented architecture (SOA) techniques to facilitate rapid system deployment. Many of these applications are time-critical and, hence, real-time assurance is an essential step in the service composition process. However, there are gaps in existing service composition techniques for real-time systems. First, admission control is an essential technique to assure the time bound for service execution, but most of the service composition techniques for real-time systems do not take admission control into account. A service may be selected for a workflow during the composition phase, but then during the grounding phase, the concrete service may not be able to admit the workload. Thus, the entire composition process may have to be repeated. Second, communication time is an important factor in real-time SOA, but most of the existing works do not consider how to obtain the communication latencies between services during the composition phase. It is clear that maintaining a full table of communication latencies for all pairs of services is infeasible. Obtaining communication latencies between candidate services during the composition phase can also be costly, since many candidate services may not be used for grounding. Thus, some mechanism is needed for estimating the communication latency for composite services. In this paper, we propose a three-phase composition approach to address the above issues. In this approach, we first use a highly efficient but moderately accurate algorithm to eliminate most of the candidate compositions based on estimated communication latencies and assured service response latency. Then, a more accurate timing prediction is performed on a small number of selected compositions in the second phase based on confirmed admission and actual communication latency. In the third phase, specific concrete services are selected for grounding, and admissions are actually performed. The approach is scalable and can effectively achieve service composition for satisfying real-time requirements. Experimental studies show that the three-phase approach does improve the effectiveness and time for service composition in SOA real-time systems. In order to support the new composition approach, it is necessary to effectively specify the needed information. In this paper, we also present the specification model for timing-related information and the extension of OWL-S to support this specification model.     

Extending fieldbus standards to food processing and packaging industry: A review

Several fieldbus systems based on different standards are in use since a decade. Bearing in mind that a dedicated fieldbus for food plant operation and management (FOAM) does not exist, this paper highlights their applications in this sector. In recent years, such pressures as operational flexibility, state-of-the-art technology, regulations, quality control, productivity, and last but not the least the environmental issues have motivated the food processing and packaging industry to search for strategic ways to fulfill factory-wide automation, control, and management requirements. However, the acceptance of fieldbus technology especially for the food processing and packaging applications has been minimal. Because, new processing plants small to large including renovation and modernization sites are emerging almost everyday, there is a pressing need to extensively disseminate the characteristic features, so that the fieldbus revolution can spread to serve the food industries in a bigger way. This paper critically reviews the characteristic features of some popular fieldbus systems while advocating modern networked automation tools and systems scenarios that can meet the FPOM (food plant operation and management) requirements. In particular, the characteristic features of some contemporary fieldbus standards have been comprehensively described. Some applications are highlighted. A suggestion is made to develop a dedicated fieldbus for the food industry. Conceptual specification of requirements framework is outlined.     

Improving the generalization performance of RBF neural networks using a linear regression technique

In this paper we present a method for improving the generalization performance of a radial basis function (RBF) neural network. The method uses a statistical linear regression technique which is based on the orthogonal least squares (OLS) algorithm. We first discuss a modified way to determine the center and width of the hidden layer neurons. Then, substituting a QR algorithm for the traditional Gram–Schmidt algorithm, we find the connected weight of the hidden layer neurons. Cross-validation is utilized to determine the stop training criterion. The generalization performance of the network is further improved using a bootstrap technique. Finally, the solution method is used to solve a simulation and a real problem. The results demonstrate the improved generalization performance of our algorithm over the existing methods.     

A two-stage approach in solving the state probabilities of the multi-queue M/G/1 model

The M/G/1 model is the fundamental basis of the queueing system in many network systems. Usually, the study of the M/G/1 is limited by the assumption of single queue and infinite capacity. In practice, however, these postulations may not be valid, particularly when dealing with many real-world problems. In this paper, a two-stage state-space approach is devoted to solving the state probabilities for the multi-queue finite-capacity M/G/1 model, i.e. q-M/G/1/K_i with K_i buffers in the ith queue. The state probabilities at departure instants are determined by solving a set of state transition equations. Afterward, an embedded Markov chain analysis is applied to derive the state probabilities with another set of state balance equations at arbitrary time instants. The closed forms of the state probabilities are also presented with theorems for reference. Applications of Little's theorem further present the corresponding results for queue lengths and average waiting times. Simulation experiments have demonstrated the correctness of the proposed approaches.