Abstracts:The study presents a new time evolving clustering method for tracing the evolution of magnetic field ranges emitted from laptops over time. It is an extension of K-Medians algorithm for which cluster set found at current time point depends not only on the current measured values but also on the cluster sets found at the previous time points. The method is employed on the measured magnetic field radiation of the laptops in the extremely low frequency range at multiple frequencies of TCO standard. Specifically, a new measurement methodology of the magnetic field to which the users of the laptop are exposed in the office environment is proposed. The level of the magnetic field variation in time is measured. Results show that method can be employed to detect the most dangerous magnetic field ranges in laptop areas. Also, it is usable by manufacturers for advancing the safe laptop design or by laptop users for safer and more appropriate use.

Abstracts:For an ARMA model with an observed noise, the observed noise variance estimation is not only a part of the model identification, but also its estimation accuracy affects the following MA parameter estimation accuracy directly. However, it is difficult to improve the estimation accuracy of the observed noise variance, especially when the observed noise variance is small. In this paper, two new methods are proposed to estimate the observed noise variance accurately. In the first method, the lower lags of the auto-covariance function are used to estimate the observed noise variance with high estimation accuracy, but it is valid only when the AR order is greater than the MA order. In the second method, the ARMA model is approximated as a high-order AR model so that it is effective even though the AR order is equal to or less than the MA order. If the observed noise variance is too small, its estimation error may be too large to valid the estimate. An empirical criterion is proposed to judge the necessity of estimating the observed noise variance. The proposed methods are verified by simulations and applied to the random noise modeling for gyroscopes tentatively.

Abstracts:An integrated analytical approach to improve the performance of adaptive sub-ranging analog-to-digital converters (ADCs) in both pre-threshold phase and post-threshold phase of their operation is presented and discussed. The differentia specifica of the adaptive ADCs, discriminating them from conventional sub-ranging ADCs, is the method of forming output codes of converted samples. The adaptive ADCs employ simple internal digital processing units (DPUs) to compute the output codes, which creates the conditions for optimization of the conversion algorithm and improvement of their effective number of bits (ENOB) beyond ENOB of their conventional counterparts. Previous researches led to development of two main groups of conversion algorithms used in adaptive ADCs, however none fully satisfactory.

Abstracts:This paper aims at developing an effective methodology to find the optimum inserting conditions that maximize the pullout strength of joints produced by ultrasonic insertion process using response surface methodology (RSM) and genetic algorithm (GA). Experiments were planed as per central composite design of experiments. The second order mathematical model for pullout strength was developed using response surface methodology with pressure, inserting time and holding time as process parameters. The developed RSM model is then coupled with GA, to determine the optimal inserting conditions. The results of this study revealed that the proposed RSM – GA integration technique managed to find the optimum inserting conditions, which leads to the maximum value of inserting performance when compared to the RSM results.

Abstracts:The toe box design is a crucial element for footwear comfort in high-heeled shoes. Their shoe lasts, however, are developed based on foot measurements taken under a foot-flat condition. There lacks investigations on whether the forefoot shape changes with elevated heels.

Abstracts:A Polymethyl methacrylate (PMMA) microfiber loop resonator (PMLR) coated with zinc oxide (ZnO) nanorods is proposed and demonstrated for the monitoring of relative humidity (RH) of the surrounding air. Relative humidity refers to the amount of moisture in the air relative to the total amount of moisture that the air can contain. The PMLR has a loop diameter of 42μm and was fabricated using a direct drawing technique using an 8μm diameter PMMA microfiber. The results showed an increase in the sensitivity of the sensor with the additional coating of the ZnO nanorods on the surface of the PMMA fiber. The working mechanism of the device is based on the observed increment in the transmission of the sensor that was carefully placed in a sealed chamber. As the relative humidity level increases from 50%RH to 80%RH, the output power of the PMLR with ZnO nanorods coating decreases linearly from −37.77dBm to −52.99dBm with a maximum sensitivity, linearity and resolution of 0.5221dBm/%RH, more than 99% and 0.4094%RH, respectively. This work is highly beneficial as it combines the high sensitivity of traditional silica fibers with the ruggedness and flexibility of PMMA fibers, making them highly suitable for continuous monitoring of relative humidity in compact areas.

Abstracts:Our study aims at developing an efficient quality monitoring system in small scale resistance spot welding based on dynamic resistance. The dynamic resistance variation was related to weld nugget formation process. An initial resistance peak caused by asperity heating was detected. The second peak in dynamic resistance and single peak in dynamic voltage could be attributed to bulk material heating. An obvious interrelationship could be found between end resistance and weld quality. The features extracted from dynamic resistance curve were mainly influenced by welding current. The overall resistance level was dropped as welding current enlarged. The multiple linear regression analysis and back propagation neural network were then used to estimate the weld quality in combination with extracted features. Result of the regression analysis for quality prediction was basically satisfactory. The proposed neural network model showed a better performance than regression analysis regarding the maximum estimation error and root mean square error. Accuracy of the neural network based quality estimation could be further improved combining quality level classification strategy. Combination of the dynamic resistance measurement with neural network model was supposed effective to achieve the quality monitoring purpose in small scale resistance spot welding.

Abstracts:The evaluation of geotechnical parameters of geo-materials are essential part of every geotechnical project. But sometimes, it is not possible to determine the all required parameter in the laboratory. Therefore, scientist and engineers used the statistical and empirical relation to determine the crucial parameters. The present study focused on the determination of parameters like uniaxial compressive strength (UCS), tensile strength (TS), point load index (PLI) and Young’s modulus (YM) from very easily determinable physical parameters viz. density (DEN), porosity (PORO) and compressional wave velocity (P-WV) using multiple variable regression analysis (MVRA) and adaptive neuro-fuzzy inference system (ANFIS). The various ANFIS structures and MVRA models were tried for prediction of desired parameters and best one was considered based on variance account for (VAF), root mean square error (RMSE) and correlation coefficient (R²). ANFIS structure not only depends on the input parameters and rules, but also on the output parameter as observed in case of PLI.

Abstracts:The strength, hardness, wear and corrosion resistance are the most important mechanical properties of the tribological components. Martensitic stainless steel plays very important role to represent these characteristics. The AISI-420 stainless steel widely used to manufacture the hydraulic and gas turbine components. The present work deals with microstructure and slurry erosive wear studies of cobalt based clad developed through microwave energy at a frequency of 2.45GHz. The scanning electron microscope (SEM) and energy dispersive X-ray spectroscopy (EDS) analysis result reveals the presence of cobalt and chromium rich solid solution in the intercellular substances. The Taguchi orthogonal array was used to gauge the factors affecting the wear significantly. The influence of various factors such as speed, particle size and impingement angle on wear behavior was evaluated by Means and ANOVA (analysis of variance). It was observed that the slurry speed significantly influenced the more mass loss on unclad substrate. The wear mechanism of the worn surface was studied through a scanning electronic microscope. It shows the mixed mode of erosion in the form of microcutting, ploughing, and fractures appeared are major wear mechanisms.

Abstracts:A novel and sensitive phenazopyridine sensor has been fabricated by electrodepositing of Au nanoparticles on layered double hydroxide/reduced graphene oxide modified glassy carbon. Study of layer formation on the electrode surface was followed by electrochemical impedance spectroscopy and cyclic voltammetry using redox couples Fe(CN)6 ^3−/4− species. The nanocomposite was characterized by field emission scanning electron microscopy, transmission electron microscopy, Fourier transform infrared and energy dispersive X-ray spectroscopy. The fabricated sensor exhibits excellent electrochemical catalytic activities toward the oxidation of phenazopyridine due to the synergistic effects between layered double hydroxide, reduced graphene oxide and Au nanoparticles. The oxidation peak current was proportional to the concentration of phenazopyridine from 0.08 to 405.0μmolL⁻¹ with a detection limit of 0.009μmolL⁻¹ at signal to noise ratio of 3. The capability of the proposed sensor for determination of phenazopyridine was examined by the standard addition method in real samples.