Adaptive CUSUM and EWMA charts with auxiliary information and variable sampling intervals for monitoring the process mean

The variable sampling interval (VSI) feature enhances the sensitivity of a control chart that is based on fixed sampling interval (FSI). In this paper, we enhance the sensitivities of the auxiliary information-based (AIB) adaptive Crosier cumulative sum (CUSUM) (AIB-ACC) and adaptive exponentially weighted moving average (EWMA) (AIB-AE) charts using the VSI feature when monitoring a mean shift which is expected to lie within a given interval. The Monte Carlo simulations are used to compute zero-state and steady-state run length properties of these control charts. It is found that the AIB-ACC and AIB-AE charts with VSI feature are uniformly more sensitive than those based on FSI feature. Real datasets are also considered to demonstrate the implementation of these control charts.     

An efficient double exponentially weighted moving average Benjamini‐Hochberg control chart to control false discovery rate

A control chart based on double exponentially weighted moving average and Benjamini‐Hochberg multiple testing procedure is proposed that controls the false discovery rate (FDR). The proposed control chart is based on probabilities (or P values) to accept or reject the null hypothesis of the underlying process is in control. To make a decision, instead of using only the current probability, previous “m” probabilities are considered. The performance of the control chart is evaluated in terms of average run length (ARL) using Monte Carlo simulations. Procedure for estimation of parameters used in the control chart is also discussed. The proposed control chart is compared with previous control charts and found to be more efficient in controlling the false discovery rate.     

A fast hybrid computer vision technique for real-time embedded bus passenger flow calculation through camera

Bus passenger flow calculation system is a critical part of the smart public transportation framework. Bus passenger flow information can help to make data statistics report of the passenger at a bus station which can be used by public transport operator to evaluate the quality of the transportation. Statistics report of crowded passengers in the bus station help managers to understand the bus transit operations, can provide the database for the intelligent transportation scheduling, help to provide more and better services for passengers, overall data statistics of passengers has important practical significance to improve public transport environment. This paper presents a passenger counting algorithm based on hybrid machine learning approach. In the first step, an advanced method is used to extract the Histogram of oriented gradients (HOG) feature of passenger’s heads. Classification of head features is done by using support vector machine (SVM) as a classifier for the liner model. Heads are detected successfully after performing all steps. In next step Kanade-Lucas-Tomasi (KLT) is used to reality head tracking, the multiple target tracking is achieved and the head motion trajectory of passenger target is captured stably. At last, the trajectory is analyzed and the automatic counting of bus passenger flow is realized. In the last step, the proposed algorithm is move to embedded system for practical implementation. In this paper, the algorithm intends to use ADSP-BF609 embedded platform for transplantation. The experimental results demonstrate that the statistical accuracy of the proposed algorithm is enhanced successfully; especially during the daytime with the good illustration, the effective counting of the passenger flow is achieved and the inward and outward passenger counting can be realized. In this paper three feature extraction models are used namely local binary patterns, histograms of oriented gradients and binarized statistical image in order to get accurate features. Furthermore, three common classification techniques including naïve bayes classifier, boosted tress and support vector machines are used for fine classification of extracted vectors obtained from different features extractors model. 94.50% accuracy is achieved when support vector machine (SVM) classifies the features extracted using Histogram of oriented gradients (HOG). SVM surpasses the accuracy obtained by Boosted tree namely 81.30% using Histogram of oriented gradients (HOG) features.

     

Effects of Dissimilar Alumina Particulates on Microstructure and Properties of Cold-Sprayed Alumina/A380 Composite Coatings

In this study,alumina/A380 composite coatings were fabricated by cold spray.The influence of alumina particulates' morphology(spherical and irregular) and content on the deposition behavior of the coatings(including surface roughness,surface residual stress,cross-sectional microstructure and microhardness) was investigated.Results revealed that the spherical alumina mainly shows micro-tamping effect during deposition,which result in remarkable low surface roughness and porosity of the coatings.In addition,very low deposition efficiency and good interfacial bonding between the coating and the substrate were achieved.For irregular alumina particles,the embedding of ceramic particulates in the coating was dominant during deposition process,resulting in high retention in the final deposit.However,it showed limited influence on porosity,surface roughness and interfacial bonding of the deposit.The coatings containing irregular alumina particulates exhibited much higher microhardness than those containing spherical alumina due to the higher load-bearing capacity of deposited alumina.     

Single-Shot Characterization of Enzymatic Reaction Constants K(m) and k(cat) by an Acoustic-Driven, Bubble-Based Fast Micromixer

In this work we present an acoustofluidic approach for rapid, single-shot characterization of enzymatic reaction constants K(m) and k(cat). The acoustofluidic design involves a bubble anchored in a horseshoe structure which can be stimulated by a piezoelectric transducer to generate vortices in the fluid. The enzyme and substrate can thus be mixed rapidly, within 100 ms, by the vortices to yield the product. Enzymatic reaction constants K(m) and k(cat) can then be obtained from the reaction rate curves for different concentrations of substrate while holding the enzyme concentration constant. We studied the enzymatic reaction for β-galactosidase and its substrate (resorufin-β-D-galactopyranoside) and found K(m) and k(cat) to be 333 ± 130 μM and 64 ± 8 s(-1), respectively, which are in agreement with published data. Our approach is valuable for studying the kinetics of high-speed enzymatic reactions and other chemical reactions.     

Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation

This paper describes a relational database system for semi-generative process planning for sheet metal parts that emulates expert system capabilities. The system integrates a feature-based relational database for the parts, a forward chaining rule-based strategy for machine selection, both global and feature-specific execution of the rules and a graph theoretic cost optimization model for optimal process plan selection. This system, which is currently being developed for a sheet metal fabrication company, suggests that, using the experience of shopfloor personnel, an efficient integration of feature-based process planning and expert system strategies can be accomplished.     

Antioxidant activity,phenolic, carotenoid and color changes in packaged fresh carrots stored under refrigeration temperature

Carrots of uniform size were wrapped tightly within a plastic cling film and stored under refrigeration conditions for a period of 24 days. The roots were analyzed for changes in phenolics, carotenoids, antioxidant activity, texture and optical properties at every 6th day. Total carotenoid levels decreased from 12.68 to 8.17 mg/100 gm during the storage and a similar change in phenolics was observed with values decreasing from 41.63 to 32.31 mg GAE/100 gm. Collective losses of both carotenoids and phenolics negatively affected antioxidant activity in carrots with figures decreasing from 16.50 to 11.07%. The refrigeration storage of carrots also had an influence on its optical and textural characteristics at significant levels (p?<?0.5). Texture change in carrots was more profound in core sections of root during the storage. A strong positive correlation was observed for antioxidants with carotenoids, phenolics, L* and a* values.     

Biomolecular conjugation inside synthetic polymer nanopores via glycoprotein-lectin interactions

We demonstrate the supramolecular bioconjugation of concanavalin A (Con A) protein with glycoenzyme horseradish peroxidase (HRP) inside single nanopores, fabricated in heavy ion tracked polymer membranes. Firstly, the HRP-enzyme was covalently immobilized on the inner wall of the pores using carbodiimide coupling chemistry. The immobilized HRP-enzyme molecules bear sugar (mannose) groups available for the binding of Con A protein. Secondly, the bioconjugation of Con A on the pore wall was achieved through its biospecific interactions with the mannose residues of the HRP enzyme. The immobilization of biomolecules inside the nanopore leads to the reduction of the available area for ionic transport, and this blocking effect can be exploited to tune the conductance and selectivity of the nanopore in aqueous solution. Both cylindrical and conical nanopores were used in the experiments. The possibility of obtaining two or more conductance states (output), dictated by the degree of nanopore blocking resulted from the different biomolecules in solution (input), as well as the current rectification properties obtained with the conical nanopore, could also allow implementing information processing at the nanometre scale. Model simulations based on the transport equations further verify the feasibility of the sensing procedure that involves concepts from supramolecular chemistry, molecular imprinting, recognition, and nanotechnology.