Uncontrolled Variations Research Articles

Electrophoresis-Based Adaptive Manipulation of Nanowires in Fluid Suspension

Automated, highly precise online manipulation of nanoscale and microscale objects is essential to achieve scalable nanomanufacturing. However, nanoparticles and microparticles exhibit as yet unpredictable and, therefore, uncontrolled variations in their structures or compositions that can limit their functions and properties. In this article, we present an electric field-based adaptive manipulation scheme to precisely control the motion of individual nanowires, online estimate their unknown mobilities, and rapidly plan the desired trajectories with the estimated mobilities. The input saturation is considered in the adaptive controller, and the closed-loop system is proved to be asymptotically stable. The proposed motion planners (MPs), ${{\tt Bi}}$ - ${{\tt iSST}}$ s, are built on and extended by the stable sparse rapidly exploring random tree ( ${{\tt SST}}$ )-based kinodynamic motion-planning algorithms. Two sparse trees are maintained to increase the probability of finding a solution. The ${{\tt Bi}}$ - ${{\tt iSST}}$ algorithms use the workspace information, heuristics, and optimization to effectively guide the search process. When compared with the state-of-the-art algorithms, a ${{\tt Bi}}$ - ${{\tt iSST}}$ variant, the ${{\tt opt}}$ - ${{\tt Bi}}$ - ${{\tt iSST}}$ algorithm, quickly updates feasible solutions with the online estimated mobilities of multiple nanowires and converges to a near-optimal, minimum-time solution to increase the efficiency of simultaneous manipulation of the nanowires. Therefore, without complex characterization of each nanowire’s mobility, the nanowires can be steered simultaneously and efficiently to achieve precisely controlled positions without collisions. Simulation and experimental results confirm that the proposed integrated adaptive manipulation scheme precisely, independently, and simultaneously manipulates the motion of multiple nanowires.

A novel Gait-Appearance-based Multi-Scale Video Covariance Approach for pedestrian (re)-identification

In order to handle the complex databases of acquired images in the security area, a robust and adaptive framework for Video Surveillance Data Mining as well as for multi-shot pedestrian (re)-identification is required. The pedestrian’s signature must be invariant and robust against the noise and uncontrolled variation. In this paper a new fast Gait-Appearance-based Multi-Scale Video Covariance (GAMS-ViCov) unsupervised approach was proposed to efficiently describe any image-sequence, on streaming or stored in the database, of a pedestrian into a compact and fixed size signature while exploiting the whole relevant spatiotemporal information. The proposed model is based on multi-scale features extracted from a novel data structure called ‘Two-Half-Video-Tree’ (THVT) which represents the pedestrians and allows discarding the uncontrolled variations. THVT can efficiently model the gait and appearance of the upper and lower parts of the person’s silhouette into trees of multi-scale features. THVT can thus model the video data to new structured forms through a fast algorithm. Furthermore, GAMS-ViCov approach can also be competitive as a technique of dynamic video summarization using k-means clustering to model the signatures extracted from the image-sequences of each person into a cluster center. For each person’s cluster, the image-sequence that its signature is nearest to the centroid is selected and stored as the key image-sequence of this person. The proposed approach was evaluated for the person (re)-identification with i-LIDS and PRID databases. The experimental results show that GAMS-ViCov outperforms the most of unsupervised approaches.

SUSTAINABILITY IN ADDITIVE MANUFACTURING

Additive Manufacturing (AM) opens new opportunities for the economy and the society and the global market of this technology is growing rapidly. However, quality assurance remains the main barrier for a broader integration of AM in the industrial sector. Most quality-related problems of AM are caused by uncontrolled variations in the production chain. By identifying the key controlling parameters or the Key Characteristics (KC) and introducing the proper process control protocol for these parameters, the effect of these variations can be limited and expensive monitoring, rework, repair and quality-related problems can often be avoided. The work presented in this paper reviews the recent literature related to sustainability in AM and proposes a new approach into how the key characteristics, which are normally used to reduce variations in production, can give an insight to a sustainable AM.

Adaptive Tube Model Predictive Control for Manipulating Multiple Nanowires with Coupled Actuation in Fluid Suspension

Automated, highly precise online manipulation of multiple nano and microscale objects is essential to achieve scalable nanomanufacturing. One of the biggest limitations of the wireless external actuation is its global and coupled influence in the workspace, which limits the capability to robustly control multiple nano and microparticles independently and simultaneously. Another challenge for the highly precise manipulation of nanoparticles is due to their uncontrolled variations in structures or compositions that result in different dynamic behaviors. In this paper, we present an adaptive tube model predictive control scheme for the simultaneous manipulation of multiple nanowires under coupled electric fields in fluid suspension. The proposed strategy estimates the unknown mobilities of the individual nanowires online, formulates dynamic tubes that update based on the online estimated mobilities and nonlinear dynamics, and addresses the coupled actuation from the global electric field with dynamic separated tubes constructed for each nanowire. Simulations results show that as the number of simultaneously manipulated nanowires increases, the manipulation time increases and the maximum disturbance the system could reject decreases rapidly.

Evaluating the Risk of Corona Discharge in Superalloy Vacuum Induction Melting Furnace Applications

Corona discharge, or gas breakdown, is undesired in superalloy vacuum induction melting (VIM) applications as it can damage insulation, produce ozone and subsequently oxidize and contaminate the melt. Gas breakdown also introduces uncontrolled variations to the casting process and increases the risk of needing to scrap the end product. To reduce the risk of corona, a rule-of-thumb of 150 to 1000 micron (150 mTorr to 1 Torr) was developed in the past as a No-Go Pressure Zone. Operating within this zone has been observed to create an unwanted, readily ionizable environment. An analytical model is developed within this work to evaluate the historical rule-of-thumb. The analytical model incorporates the following elements: Paschen’s law, gas compressibility, gas and insulation permittivity, and electric field non-uniformity. The model is then applied to a sampling of VIM furnace designs to evaluate the shifts of the No-Go Pressure Zone due to variations in configuration and other operating conditions. In the end, the objective is to improve VIM furnace design and operation to reduce the risk of corona discharge—thereby improving the casting process, practice of alloy melting and quality of the end product.

Home (not so) sweet home

PurposeThis paper aims to elaborate on the crucial effects that a seemingly detrimental policy change in Spain has had on the international entrepreneurial activities of domestic renewable energy (RE) firms.Design/methodology/approachPrimary data were collected from nine RE companies in Spain and then triangulated with secondary data and interviews from informants in other local institutions.FindingsDomestic RE firms, due to an institutional scape driver action, reacted to an increasingly uncertain and generally more adverse renewable energy policy framework in this country by preferring to internationalise towards foreign markets that had lower political uncertainty than the domestic one.Research limitations/implicationsThis paper complements previous research primarily on firm-specific factors that enhance internationalising firms’ survival and growth through a focus on the impact of a changing institutional-political environment at the home country-level.Practical implicationsPractitioners in the RE sector should analyse the risk of focusing only on the home market, as it can be too dependent on uncontrolled variations in domestic energy policy.Social implicationsThe findings indicate that a more stable and supportive, long-term perspective in the domestic RE policy is essential for the sustained growth and development of this emerging industry.Originality/valueTo analyse the strategy by which a number of purposefully selected companies were able to use international expansion as a survival-seeking strategy against a drastic policy-level change in the domestic RE market.

Classification and Diagnosis of Bioprocess Cell Growth Productions Using Early-Stage Data

Many industrial pharmaceutical manufacturing processes are composed of multiple-step batch operations. However, uncontrolled variations often occur during operations that affect the cell growth per...

The role of Si in GaN/AlN/Si(111) plasma assisted molecular beam epitaxy: polarity and inversion

The microstructure, polarity and Si distribution in AlN/GaN layers grown by plasma assisted molecular beam epitaxy on Si(111) was assessed by scanning transmission electron microscopy. Samples grown under both metal- and nitrogen-rich conditions contained defects at the AlN/Si interface which suggest formation of an Al-Si eutectic. Correlated with this, interfacial segregation of Si was found in the samples. It is proposed that Si is dissolved in a eutectic layer floating on the AlN surface under metal-rich conditions. This Si is then incorporated into the film if the growth becomes nitrogen-rich, either intentionally or due to plasma source transients. These Si-rich layers appear to induce inversion of the nitride from nitrogen- to metal-polarity, and uncontrolled variations in the Si concentration cause occasional nonuniformity in the resulting inversion.

Cascaded asymmetric local pattern: a novel descriptor for unconstrained facial image recognition and retrieval

Feature description is one of the most frequently studied areas in the expert systems and machine learning. Effective encoding of the images is an essential requirement for accurate matching. These encoding schemes play a significant role in recognition and retrieval systems. Facial recognition systems should be effective enough to accurately recognize individuals under intrinsic and extrinsic variations of the system. The templates or descriptors used in these systems encode spatial relationships of the pixels in the local neighbourhood of an image. Features encoded using these hand crafted descriptors should be robust against variations such as; illumination, background, poses, and expressions. In this paper a novel hand crafted cascaded asymmetric local pattern (CALP) is proposed for retrieval and recognition facial image. The proposed descriptor uniquely encodes relationship amongst the neighbouring pixels in horizontal and vertical directions. The proposed encoding scheme has optimum feature length and shows significant improvement in accuracy under environmental and physiological changes in a facial image. State of the art hand crafted descriptors namely; LBP, LDGP, CSLBP, SLBP and CSLTP are compared with the proposed descriptor on most challenging datasets namely; Caltech-face, LFW, and CASIA-face-v5. Result analysis shows that, the proposed descriptor outperforms state of the art under uncontrolled variations in expressions, background, pose and illumination.

Focus variation measurement and advanced analysis of volumetric loss at the femoral head taper interface of retrieved modular replacement hips in replica

This paper offers a technique for non-contact assessment of material volume loss at the femoral head taper interface of modular replacement hips with novel use of the focus variation principle. A novel 3D to areal data conversion technique also allows powerful areal data analysis techniques to be applied to point cloud (3D) measurements for volume loss optimisation. Accurate characterisation of retrieved femoral head tapers is important to assess their in vivo performance particularly with respect to the bio toxicity of wear and corrosion products.A cohort of 8 retrieved femoral heads showing a wide range of degradation (0.5mm3 < volume loss < 12mm3) [1] was selected for the development of this technique. Using this common cohort of retrieved hips, the current technique was benchmarked against the well proven roundness measurement machine (RMM) method. This existing technique generates areal (2.5D) data and exploits a range of existing areal analysis techniques to optimise volume loss assessment. For benchmarking continuity and to exploit the same areal techniques, volume loss analysis for the current technique was carried out using the software written for the existing RMM method.The focus variation instrument’s integrated language was used to write script to convert (un-wrap) the taper surface 3D data into areal format.The current method shows a mean absolute difference in volume loss of 14% (-12% signed) from that of the benchmark with a range of 1% to 27%. The spread of measured values is significantly higher for the current method than for the benchmark. However, it is noted that replication can offer the advantage of capturing the whole taper surface on some taper types where physical access is limited for a stylus based roundness method.The current technique is also compared to the existing Redlux™ technique in which replicated female tapers are measured using a confocal instrument. The current technique is shown to have comparable performance to the Redlux™ technique but offers a more sophisticated methodology for volume loss analysis. In addition the current technique offers new instrumentation and analysis tools to the field.Small uncontrolled casting variations are noted in the current technique, resulting in poorer performance with small volume loss samples where the influence of this effect is most pronounced. However, given the simplified assumptions of the volume loss calculation where results may be skewed by deposits, some uncertainty will be evident with any approach.

A principled multiresolution approach for signal decomposition

One of the common issues with the Structural Health Monitoring (SHM) of civil infrastructure, is that signals measured from structures are nonstationary. The nonstationarity is the result of uncontrolled variations in the environmental and operational variations (EOVs) in the structure, which result from the fact that monitoring can only be carried out in situ. Unfortunately, the EOVs prove to be a problem for damage detection, as they can mask the onset of damage, itself a nonstationary process. The aim of the current paper is to demonstrate a principled, but simple, decomposition method, which can separate signals into stationary and nonstationary components; the stationary component can then be used as a feature for SHM. The method is based on a wavelet/multiresolution analysis of the signal of interest, followed by a sequence of wavelet level-by-level tests for stationary using the ADF (Augmented Dickey-Fuller) test statistic. The decomposition is illustrated using data from the famous SHM campaign on the Z24 Bridge. The paper should be of interest, not just to engineers, but to econometricians, or anyone concerned with nonstationary signal processing.

Deep Low-Resolution Person Re-Identification

Person images captured by public surveillance cameras often have low resolutions (LR) in addition to uncontrolled pose variations, background clutters and occlusions. This gives rise to the resolution mismatch problem when matched against the high resolution (HR) gallery images (typically available in enrolment), which adversely affects the performance of person re-identification (re-id) that aims to associate images of the same person captured at different locations and different time. Most existing re-id methods either ignore this problem or simply upscale LR images. In this work, we address this problem by developing a novel approach called Super-resolution and Identity joiNt learninG (SING) to simultaneously optimise image super-resolution and person re-id matching. This approach is instantiated by designing a hybrid deep Convolutional Neural Network for improving cross-resolution re-id performance. We further introduce an adaptive fusion algorithm for accommodating multi-resolution LR images. Extensive evaluations show the advantages of our method over related state-of-the-art re-id and super-resolution methods on cross-resolution re-id benchmarks.

Environmental Performance and Key Characteristics in Additive Manufacturing: A Literature Review

Additive Manufacturing (AM) opens new opportunities for the economy and the society and the global market of this technology is growing rapidly. However, quality assurance remains the main barrier for a broader integration of AM in the industrial sector. Most quality-related problems of AM are caused by uncontrolled variations in the production chain. By identifying the key controlling parameters or the “Key Characteristics (KC)” and introducing the proper process control protocol for these parameters, the effect of these variations can be limited and expensive monitoring, rework, repair and quality-related problems can often be avoided. In addition, these KCs can also be used to achieve sustainability in AM. In addition of providing a product with a quality that satisfies the client's requirements, the manufacturing resources usage (energy and material) can be minimized. The work presented in this paper reviews the recent literature related to sustainability in AM and proposes a new approach into how the key characteristics, which are normally used to reduce variations in production, can give an insight to a sustainable AM.

Estimation of anthracnose dynamics by nonlinear filtering

In this work, we apply the nonlinear filtering theory to the estimation of the partially observed dynamics of anthracnose which is a phytopathology. The signal here is the inhibition rate and the observations are the fruit volume and the rotted volume. We propose stochastic models based on deterministic models studied previously in the literature, in order to represent the noise introduced by uncontrolled variations on parameters and errors on the measurements. Under the assumption of Brownian noises, we prove the well-posedness of the models in either they take into account the space variable or not. The filtering problem is solved for the nonspatial model giving Zakai and Kushner–Stratonovich equations satisfied respectively by the unnormalized and the normalized conditional distribution of the signal with respect to the observations. A prevision problem and a discrete filtering problem are also studied for the realistic cases of discrete and possibly incomplete observations. We illustrate the filter behavior through figures displaying the average estimation relative error and a 95% confidence region obtained after a hundred of numerical simulations with initial conditions taken randomly with respect to uniform law.

Centre symmetric quadruple pattern: A novel descriptor for facial image recognition and retrieval

Facial features are defined as the local relationships that exist amongst the pixels of a facial image. Hand-crafted descriptors identify the relationships of the pixels in the local neighborhood defined by the kernel. Kernel is a two dimensional matrix which is moved across the facial image. Distinctive information captured by the kernel with limited number of pixel achieves satisfactory recognition and retrieval accuracies on facial images taken under constrained environment (controlled variations in light, pose, expressions, and background). To achieve similar accuracies under unconstrained environment local neighborhood has to be increased, in order to encode more pixels. Increasing local neighborhood also increases the feature length of the descriptor. In this paper we propose a hand-crafted descriptor namely Centre Symmetric Quadruple Pattern (CSQP), which is structurally symmetric and encodes the facial asymmetry in quadruple space. The proposed descriptor efficiently encodes larger neighborhood with optimal number of binary bits. It has been shown using average entropy, computed over feature images encoded with the proposed descriptor, that the CSQP captures more meaningful information as compared to state of the art descriptors. The retrieval and recognition accuracies of the proposed descriptor has been compared with state of the art hand-crafted descriptors (CSLBP, CSLTP, LDP, LBP, SLBP and LDGP) on bench mark databases namely; LFW, Color-FERET, and CASIA-face-v5. Result analysis shows that the proposed descriptor performs well under controlled as well as uncontrolled variations in pose, illumination, background and expressions.

Validated Simulations of Heat Transfer From a Vertical Heated-Rod Array to a Helium-Filled Isothermal Enclosure

Measurements of heat transfer from an array of vertical heater rods to the walls of a square, helium-filled enclosure are performed for a range of enclosure temperatures, helium pressures, and rod heat generation rates. This configuration is relevant to a used nuclear fuel assembly within a dry storage canister. The measurements are used to assess the accuracy of computational fluid dynamics (CFD)/radiation simulations in the same configuration. The simulations employ the measured enclosure temperatures as boundary conditions and predict the temperature difference between the rods and enclosure. These temperature differences are as large as 72 °C for some experiments. The measured temperature of rods near the periphery of the array is sensitive to small, uncontrolled variations in their location. As a result, those temperatures are not as useful for validating the simulations as measurements from rods near the array center. The simulated rod temperatures exhibit random differences from the measurements that are as large as 5.7 °C, but the systematic (average) error is 1 °C or less. The random difference between the simulated and measured maximum array temperature is 2.1 °C, which is less than 3% of the maximum rod-to-wall temperature difference.

Deep adaptive feature embedding with local sample distributions for person re-identification

Person re-identification (re-id) aims to match pedestrians observed by disjoint camera views. It attracts increasing attention in computer vision due to its importance to surveillance systems. To combat the major challenge of cross-view visual variations, deep embedding approaches are proposed by learning a compact feature space from images such that the Euclidean distances correspond to their cross-view similarity metric. However, the global Euclidean distance cannot faithfully characterize the ideal similarity in a complex visual feature space because features of pedestrian images exhibit unknown distributions due to large variations in poses, illumination and occlusion. Moreover, intra-personal training samples within a local range which are robust to guide deep embedding against uncontrolled variations cannot be captured by a global Euclidean distance. In this paper, we study the problem of person re-id by proposing a novel sampling to mine suitable positives (i.e., intra-class) within a local range to improve the deep embedding in the context of large intra-class variations. Our method is capable of learning a deep similarity metric adaptive to local sample structure by minimizing each sample’s local distances while propagating through the relationship between samples to attain the whole intra-class minimization. To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep feature embedding. This attains local discriminations by selecting local-ranged positive samples, and the learned features are robust to dramatic intra-class variations. Experiments on benchmarks show state-of-the-art results achieved by our method.

Local quadruple pattern: A novel descriptor for facial image recognition and retrieval

In this paper a novel hand-crafted local quadruple pattern (LQPAT) is proposed for facial image recognition and retrieval. Most of the existing hand-crafted descriptors encode only a limited number of pixels in the local neighborhood. Under unconstrained environment the performance of these descriptors tends to degrade drastically. Major problem in increasing the local neighborhood is that, it also increases the feature length of the descriptor. The proposed descriptor tries to overcome these problems by defining an efficient encoding structure with optimal feature length. The proposed descriptor encodes relations amongst the neighbors in quadruple space. Two micro patterns are computed from the local relationships to form the descriptor. The retrieval and recognition accuracies of the proposed descriptor has been compared with state of the art hand crafted descriptors on bench mark databases namely; Caltech-face, LFW, Color-FERET, and CASIA-face-v5. Result analysis shows that the proposed descriptor performs well under uncontrolled variations.

Quantitative analysis based on spectral shape deformation: A review of the theory and its applications

Most of the commonly used calibration methods in quantitative spectroscopic analysis are established or derived from the assumption of a linear relationship between the concentrations of the analytes of interest and corresponding absolute spectral intensities. They are not applicable for heterogeneous samples where the potential uncontrolled variations in optical path length due to the changes in samples' physical properties undermine the basic assumption behind them. About a decade ago, a unique calibration strategy was proposed to extract chemical information from spectral data contaminated with multiplicative light scattering effects. From then on, this calibration strategy has been attentively examined, modified, and used by its developers. After more than 10 years of development, some important features of the calibration strategy have been identified. It has been proved that the calibration strategy can solve many complex problems in quantitative spectroscopic analysis. But, because of the relatively low awareness of the calibration strategy among chemometrics society, its potential has not been fully exploited yet. This paper reviews the theory of the calibration strategy and its applications with a view to introducing the unique and powerful calibration strategy to a wider audience.

Accounting for Uncontrolled Variations in Low-Speed Turbine Experiments

It is common to assume that the performance of low-speed turbines depends only on the flow coefficient and Reynolds number. As such, the required operating point is achieved by controlling the values of these two nondimensional quantities by, for example, appropriate choices for the mass flow rate and applied brake torque. However, when the turbine has an atmospheric inlet and uses unconditioned air, variations in ambient pressure, temperature, and humidity are introduced. While it is still possible to maintain the required values for the flow coefficient and Reynolds number, the ambient variations affect additional nondimensional quantities which are related to the blade speed and gas properties. Generally, the values of these additional nondimensional quantities cannot be controlled and, consequently, they affect the turbine performance. In addition, thermal effects, which are exacerbated by the use of plastic blades, can cause changes in the blade row seal clearance and these also affect the performance. Therefore, to obtain measurements with greater accuracy and repeatability, the changes in the uncontrolled nondimensional quantities must be accounted. This paper contains four parts. First, it is described how suitable data acquisition parameters can be determined to eliminate short time scale facility unsteadiness within the measurements. Second, by the analysis of models, the most appropriate forms for the additional nondimensional quantities that influence turbine performance are obtained. Since the variations in the uncontrolled nondimensional quantities affect repeatability, the size of the effect on the turbine performance is quantified. Third, a best-fit accounting methodology is described, which reduces the effects of the uncontrolled nondimensional quantities on turbine performance, provided sufficient directly related measurements are available. Finally, the observations are generalized to high-speed turbomachines.