Bayesian Availability Using Gamma Distributed Priors

Classical statistical estimation techniques have limited usage in predicting system availability when a system, e.g., a computer or telecommunications system, is highly reliable. A Bayesian solution is suggested in this paper. Gamma priors, which are rich in shapes, are assigned to the scale parameters of a gamma distributed component on-time and off-time. Estimation of the gamma priors follows engineer's past experiences for reliability modeling. Both steady-state and instantaneous availabilities are derived using Bayes Theorem. An illustration highlights the assessment and evaluation of the proposed availability measure.     

Muscular fatigue and maximum endurance time assessment for male and female industrial workers

A single arm pushing experiment was conducted in an electronic factory in Yantai, China to assess muscular fatigue using the theoretical models of muscular strength and maximum endurance time (MET) developed by Ma et al. (2009). Seventy seven workers, including 38 males and 39 females, participated in the study. The muscular strength of pushing was measured after the subject pushed a stick, with a force of 2.5 kgf, for 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 min. Fatigue rate was determined based on a regression approach. In addition to the theoretical model, the MET for such a pushing task was also determined using four empirical models in the literature. The results indicated that females were more resistant to muscular fatigue than males in the pushing task. The results of the muscular strength prediction show that the predictability of the muscular strength model is acceptable. The prediction errors for muscular strength for female subjects were significantly lower than those of the male subjects. The predicted MET using the theoretical model, with a group constant k, was highly correlated with those using the empirical models compared in the current study.     

Perception of Hand Force in Power Grip for Females

Borg's rating of perceived exertion (RPE) and category ratio (CR‐10) scales are commonly used to quantify perceived muscular exertion for body segments. Twenty females participated in an experiment to study the power grip force at four perceived exertion levels using either dominant or nondominant hand under two posture conditions. It was found that the subjects tended to apply a higher power grip force (100% of perceived maximum voluntary contraction) than the levels they were requested to apply. The power grip forces between dominant and nondominant hands at low hand exertion levels were negligible. The grip forces between the two hands were significantly different when the exertion level was nearly maximal. Linear regression models were established for the subjects to link the relationship between the perceived hand exertion and measured grip force, hand used, and hand/arm posture. All the models were statistically significant (p < 0.0001) with R² values 0.97 or higher. These models provided better estimates in perceived hand exertion for dominant hand than for nondominant hand. A follow‐up experiment was conducted to measure the subjective rating of both the CR‐10 and RPE when a 98 N grip force was applied. It was found that the subjects reported higher exertion levels when they were using the CR‐10 scale than when they were using the RPE scale. © 2011 Wiley Periodicals, Inc.     

Automatically generated parallel treebanks and their exploitability in machine translation

Given much recent discussion and the shift in focus of the field, it is becoming apparent that the incorporation of syntax is the way forward for improvements to the current state-of-the-art in machine translation (MT). Parallel treebanks are a relatively recent innovation and appear to be ideal candidates for MT training material. However, until recently there has been no other means to build them than by hand. In this paper, we describe how we make use of new tools to automatically build a large parallel treebank and extract a set of linguistically-motivated phrase pairs from it. We show that adding these phrase pairs to the translation model of a baseline phrase-based statistical MT (PB-SMT) system leads to significant improvements in translation quality. Following this, we describe experiments in which we exploit the information encoded in the parallel treebank in other areas of the PB-SMT framework, while investigating the conditions under which the incorporation of parallel treebank data performs optimally. Finally, we discuss the possibility of exploiting automatically-generated parallel treebanks further in syntax-aware paradigms of MT.     

A hybrid approach for identification of concurrent control chart patterns

Control chart patterns (CCPs) are widely used to identify the potential process problems in modern manufacturing industries. The earliest statistical techniques, including chart and R chart, are respectively used for monitoring process mean and process variance. Recently, pattern recognition techniques based on artificial neural network (ANN) are very popular to be applied to recognize unnatural CCPs. However, most of them are limited to recognize simple CCPs arising from single type of unnatural variation. In other words, they are incapable to handle the problem of concurrent CCPs where two types of unnatural variation exist together within the manufacturing process. To facilitate the research gap, this paper presents a hybrid approach based on independent component analysis (ICA) and decision tree (DT) to identify concurrent CCPs. Without loss of generality, six types of concurrent CCPs are used to validate the proposed method. Experimental results show that the proposed approach is very successful to handle most of the concurrent CCPs. The proposed method has two limitations in real application: it needs at least two concurrent CCPs to reconstruct their source patterns and it may be incapable to handle the concurrent pattern incurred by two correlated process (“upward trend” and “upward shift”).     

Physiological and psychophysical responses in handling maximum acceptable weights under different footwear--floor friction conditions

A study on combined manual materials-handling tasks performed on floors under three friction levels was conducted. Eight male subjects participated in the study. The maximum acceptable weight of handling, including lifting, carrying for 3m, lowering, and walking 3m back at twice per minute was determined. The subject then performed the same tasks for 10 min. Heart rate, Vo2, energy efficiency, perceived sense of slip, and rating of perceived exertion for whole body strain were measured. The results showed that the effects of friction level on the maximum acceptable weights of handling, perceived sense of slip, Vo2, and energy efficiency were statistically significant (p相似文献   

The effect of shoe sole tread groove depth on the friction coefficient with different tread groove widths, floors and contaminants

Slipping and falling are common phenomena in both workplaces and our daily activities. The risks associated with slipping and falling are related to the materials of footwear/floor, contamination condition, and geometric design of the sole. Shoe soles of various tread design are very common. Tread pattern of the shoe affects friction especially under liquid-contaminated conditions. Verification of the effects of tread groove depth is significant in assisting designers in designing proper footwear for workers exposed to slippery floor conditions. In this study, we measured the friction coefficients using the Neolite footwear pads on the terrazzo, steel, and vinyl floors under three liquid-contaminated conditions. A Brungraber Mark II slipmeter was used. The footwear pads had tread grooves with a width of either 3 or 9 mm. The depth of the tread grooves ranged from 1 to 5 mm. The results showed that tread groove depth affected the friction coefficients significantly. Higher friction values were recorded for footwear pads with deeper tread grooves on wet and water–detergent-contaminated floors. The averaged coefficient of friction (COF) gain per tread groove depth increase in millimeter under these two surface conditions ranged from 0.018 to 0.108, depending on the tread groove width, floor, and contaminant.     

Detection and classification of defect patterns on semiconductor wafers

The detection of process problems and parameter drift at an early stage is crucial to successful semiconductor manufacture. The defect patterns on the wafer can act as an important source of information for quality engineers allowing them to isolate production problems. Traditionally, defect recognition is performed by quality engineers using a scanning electron microscope. This manual approach is not only expensive and time consuming but also it leads to high misidentification levels. In this paper, an automatic approach consisting of a spatial filter, a classification module and an estimation module is proposed to validate both real and simulated data. Experimental results show that three types of typical defect patterns: (i) a linear scratch; (ii) a circular ring; and (iii) an elliptical zone can be successfully extracted and classified. A Gaussian EM algorithm is used to estimate the elliptic and linear patterns, and a spherical-shell algorithm is used to estimate ring patterns. Furthermore, both convex and nonconvex defect patterns can be simultaneously recognized via a hybrid clustering method. The proposed method has the potential to be applied to other industries.