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.     

Fraud detection using fraud triangle risk factors

The objective of this study is to identify the financial statement fraud factors and rank the relative importance. First, this study reviews the previous studies to identify the possible fraud indicators. Expert questionnaires are distributed next. After questionnaires are collected, Lawshe’s approach is employed to eliminate these factors whose CVR (content validity ratio) values do not meet the criteria. Further, the remaining 32 factors are reviewed by experts to be the measurements suitable for the assessment of fraud detection. The Analytic Hierarchy Process (AHP) is utilized to determine the relative weights of the individual items. The result of AHP shows that the most important dimension is Pressure/Incentive and the least one is Attitude/rationalization. In addition, the top five important measurements are “Poor performance”, “The need for external financing”, “Financial distress”, “Insufficient board oversight”, and “Competition or market saturation”. The result provides a significant advantage to auditors and managers in enhancing the efficiency of fraud detection and critical evaluation.     

An effective neuro-fuzzy paradigm for machinery condition healthmonitoring

An innovative neuro-fuzzy network appropriate for fault detection and classification in a machinery condition health monitoring environment is proposed. The network, called an incremental learning fuzzy neural (ILFN) network, uses localized neurons to represent the distributions of the input space and is trained using a one-pass, on-line, and incremental learning algorithm that is fast and can operate in real time. The ILFN network employs a hybrid supervised and unsupervised learning scheme to generate its prototypes. The network is a self-organized structure with the ability to adaptively learn new classes of failure modes and update its parameters continuously while monitoring a system. To demonstrate the feasibility and effectiveness of the proposed network, numerical simulations have been performed using some well-known benchmark data sets, such as the Fisher's Iris data and the Deterding vowel data set. Comparison studies with other well-known classifiers were performed and the ILFN network was found competitive with or even superior to many existing classifiers. The ILFN network was applied on the vibration data known as Westland data set collected from a U.S. Navy CH-46E helicopter test stand, in order to assess its efficiency in machinery condition health monitoring. Using a simple fast Fourier transform (FFT) technique for feature extraction, the ILFN network has shown promising results. With various torque levels for training the network, 100% correct classification was achieved for the same torque Levels of the test data.