The axes response and resonance identification for a machine tool

The X-Y axes, distributed-lumped model, for a machine tool system are derived. Model validations using measured results are presented. Adaptation of the model accommodating thereby the Z axis transmission is provided. The combined X-Y-Z axes response, for a cylindrical cutting operation, enabling an assessment of machining integrity, is detailed. Resonance conditions and the identification of adverse cutting frequencies are investigated. Dynamic amplification and the time domain performance of the system is determined.     

Sound source localization using compressive sensing-based feature extraction and spatial sparsity

In this paper, we propose a source localization algorithm based on a sparse Fast Fourier Transform (FFT)-based feature extraction method and spatial sparsity. We represent the sound source positions as a sparse vector by discretely segmenting the space with a circular grid. The location vector is related to microphone measurements through a linear equation, which can be estimated at each microphone. For this linear dimensionality reduction, we have utilized a Compressive Sensing (CS) and two-level FFT-based feature extraction method which combines two sets of audio signal features and covers both short-time and long-time properties of the signal. The proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. In comparison to the state-of-the-art methods, the proposed method improves the accuracy while the complexity is reduced in some cases.     

A learning framework for the optimization and automation of document binarization methods

Almost all binarization methods have a few parameters that require setting. However, they do not usually achieve their upper-bound performance unless the parameters are individually set and optimized for each input document image. In this work, a learning framework for the optimization of the binarization methods is introduced, which is designed to determine the optimal parameter values for a document image. The framework, which works with any binarization method, has a standard structure, and performs three main steps: (i) extracts features, (ii) estimates optimal parameters, and (iii) learns the relationship between features and optimal parameters. First, an approach is proposed to generate numerical feature vectors from 2D data. The statistics of various maps are extracted and then combined into a final feature vector, in a nonlinear way. The optimal behavior is learned using support vector regression (SVR). Although the framework works with any binarization method, two methods are considered as typical examples in this work: the grid-based Sauvola method, and Lu’s method, which placed first in the DIBCO’09 contest. The experiments are performed on the DIBCO’09 and H-DIBCO’10 datasets, and combinations of these datasets with promising results.     

Human eye sclera detection and tracking using a modified time-adaptive self-organizing map

This paper investigates the use of time-adaptive self-organizing map (TASOM)-based active contour models (ACMs) for detecting the boundaries of the human eye sclera and tracking its movements in a sequence of images. The task begins with extracting the head boundary based on a skin-color model. Then the eye strip is located with an acceptable accuracy using a morphological method. Eye features such as the iris center or eye corners are detected through the iris edge information. TASOM-based ACM is used to extract the inner boundary of the eye. Finally, by tracking the changes in the neighborhood characteristics of the eye-boundary estimating neurons, the eyes are tracked effectively. The original TASOM algorithm is found to have some weaknesses in this application. These include formation of undesired twists in the neuron chain and holes in the boundary, lengthy chain of neurons, and low speed of the algorithm. These weaknesses are overcome by introducing a new method for finding the winning neuron, a new definition for unused neurons, and a new method of feature selection and application to the network. Experimental results show a very good performance for the proposed method in general and a better performance than that of the gradient vector field (GVF) snake-based method.     

Multidisciplinary design optimization of a small solid propellant launch vehicle using system sensitivity analysis

Multidisciplinary design optimization approaches have significant effects on aerospace vehicle design methodology. In designing next generation of space launch systems, MDO processes will face new and greater challenges. This study develops a system sensitivity analysis method to optimize multidisciplinary design of a two-stage small solid propellant launch vehicle. Suitable design variables, technological, and functional constraints are considered. Appropriate combinations of disciplines such as propulsion, weight, geometry, and trajectory simulation are used. A generalized sensitivity equation is developed and solved. These results are basis for optimization. Comparison of the developed approach with gradient optimization methods reveals that developed approach requires less computation time.     

Strategic reasoning about business models: a conceptual modeling approach

Strategic reasoning about business models is an integral part of service design. In fast moving markets, businesses must be able to recognize and respond strategically to disruptive change. They have to answer questions such as: what are the threats and opportunities in emerging technologies and innovations? How should they target customer groups? Who are their real competitors? How will competitive battles take shape? In this paper we define a strategic modeling framework to help understand and analyze the goals, intentions, roles, and the rationale behind the strategic actions in a business environment. This understanding is necessary in order to improve existing or design new services. The key component of the framework is a strategic business model ontology for representing and analyzing business models and strategies, using the i* agent and goal oriented methodology as a basis. The ontology introduces a strategy layer which reasons about alternative strategies that are realized in the operational layer. The framework is evaluated using a retroactive example of disruptive technology in the telecommunication services sector from the literature.     

Operating Environment-Based Availability Importance Measures for Mining Equipment (Case Study: Sungun Copper Mine)

When a system’s performance is inadequate, the concept of availability importance can be used to improve it. The availability of an item depends on the combined aspects of its reliability and maintainability. In a system consisting of many subsystems, the availability of some subsystems is more important to system performance than others. The availability measure determines the priority of availability across subsystems. Most researchers only consider operation time and ignore the influence of the operating environment; therefore, their estimations are not accurate enough. In contrast to previous research, we focus on the influence of the operating environment on the system/subsystem’s characteristics with a view to prioritizing them based on the importance of availability. The paper considers part of the mining fleet system of Sungun copper mine, including the wagon drill, loader, bulldozer, and dump truck subsystems. We identify an ordered list of possibilities for availability improvement and suggest changes or remedial actions for each item to either reduce its failure rate or reduce the time required to repair it.     

Electrohydrodynamic‐Induced SERS Immunoassay for Extensive Multiplexed Biomarker Sensing

Cancer diagnosis and patient monitoring require sensitive and simultaneous measurement of multiple cancer biomarkers considering that single biomarker analysis present inadequate information on the underlying biological transformations. Thus, development of sensitive and selective assays for multiple biomarker detection might improve clinical diagnosis and expedite the treatment process. Herein, a microfluidic platform for the rapid, sensitive, and parallel detection of multiple cancer‐specific protein biomarkers from complex biological samples is presented. This approach utilizes alternating current electrohydrodynamic‐induced surface shear forces that provide exquisite control over fluid flow thereby enhancing target–sensor interactions and minimizing non‐specific binding. Further, the use of surface‐enhanced Raman scattering‐based spectral encoding with individual barcodes for different targets enables specific and simultaneous detection of captured protein biomarkers. Using this approach, the specific and sensitive detection of clinically relevant biomarkers including human epidermal growth factor receptor 2 (HER2); Mucin 1, cell surface associated (MUC1); epidermal growth factor receptor; and Mucin 16, cell surface associated (MUC16) at concentrations as low as 10 fg mL^?1 in patient serum is demonstrated. Successful target detection from patient samples further demonstrates the potential of this current approach for the clinical diagnosis, which envisages a clinical translation for a rapid and sensitive appraisal of clinical samples in cancer diagnostics.     

Improved electrochemical performance of nitrocarburised stainless steel by hydrogenated amorphous carbon thin films for bone tissue engineering

In this study, hydrogenated amorphous carbon thin films, structurally similar to diamond‐like carbon (DLC), were deposited on the surface of untreated and plasma nitrocarburised (Nitrocarburizing‐treated) stainless steel medical implants using a plasma‐enhanced chemical vapour deposition method. The deposited DLC thin films on the nitrocarburising‐treated implants (CN+DLC) exhibited an appropriate adhesion to the substrates. The results clearly indicated that the applied DLC thin films showed excellent pitting and corrosion resistance with no considerable damage on the surface in comparison with the other samples. The CN+DLC thin films could be considered as an efficient approach for improving the biocompatibility and chemical inertness of metallic implants.Inspec keywords: tissue engineering, bone, biomedical materials, electrochemistry, amorphous state, carbon, hydrogen, thin films, plasma CVD, adhesion, corrosion resistance, surface hardeningOther keywords: electrochemical performance, plasma nitrocarburised stainless steel medical implants, hydrogenated amorphous carbon thin films, bone tissue engineering, plasma‐enhanced chemical vapour deposition method, adhesion, corrosion resistance, biocompatibility, chemical inertness, metallic implants, C:H     

Change Point Estimation in the Mean of Multivariate Linear Profiles with No Change Type Assumption via Dynamic Linear Model

Change point estimation is a useful concept that helps quality engineers to effectively search for assignable causes and improve quality of the process or product. In this paper, the maximum likelihood approach is developed to estimate change point in the mean of multivariate linear profiles in Phase II. After the change point, parameters are estimated through filtering and smoothing approaches in dynamic linear model. The proposed change point estimator can be applied without any prior knowledge about the change type against existing estimators which assume change type is known in advance. Besides, sporadic change point can be identified as well. Simulation results show the effectiveness of the proposed estimators to estimate step, drift and monotonic, as well as sporadic changes in small to large shifts. In addition, effect of different values of the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart smoothing coefficient on the performance of the proposed estimator is investigated presenting that the smoothing estimator has more uniform performance. Copyright © 2015 John Wiley & Sons, Ltd.