Optimization of STM/FIM nanotip aspect ratio based on the Taguchi method

This paper presents optimization of electrochemical etching parameters to achieve the optimum aspect ratio of the scanning tunneling microscopy/field ion microscopy tungsten nanotip by using Taguchi method. The combination of optimum level of process parameters was obtained by using the analysis of signal-to-noise ratio. The level of importance of the process parameters on the nanotip aspect ratio was determined by using analysis of variance. It was found that the optimum level of process parameters are electrolyte concentration of 2 M/lit, wire immersion length of 2.5 mm, cathode tube inner diameter of 40 mm, and voltage of 3.5 V. Within the range of experiments and the process parameters in terms of impact significance were found to be electrolyte concentration, process voltage, wire immersion length, and inner diameter of cathode tube, respectively. By using the optimum level of the process parameters, the nanotip aspect ratio was enhanced by 263% in comparison to the mean value of the experimental results. The nanotip aspect ratio of up to 163:1 was obtained in the present research.     

Scheduling∕Cost Optimization and Neural Dynamics Model for Construction

A general mathematical formulation is presented for the scheduling of construction projects and is applied to the problem of highway construction scheduling. Repetitive and nonrepetitive tasks, work continuity constraints, multiple-crew strategies, and the effects of varying job conditions on the performance of a crew can be modeled. An optimization formulation is presented for the construction project scheduling problem, with the goal of minimizing the direct construction cost. The nonlinear optimization is then solved by the neural dynamics model developed recently by Adeli and Park. For any given construction duration, the model yields the optimum construction schedule for minimum construction cost automatically. By varying the construction duration, one can solve the cost-duration trade-off problem and obtain the global optimum schedule and the corresponding minimum construction cost. The new construction scheduling model provides the capabilities of both the critical path method (CPM) and linear scheduling method (LSM) approaches. In addition, it provides features desirable for repetitive projects, such as highway construction, and allows schedulers greater flexibility. It is particularly suitable for studying the effects of change order on the construction cost. This research provides the mathematical foundation for development of a new generation of more general, flexible, and accurate construction scheduling systems.     

A High Gain and Low Flicker Noise CMOS Mixer with Low Flicker Noise Corner Frequency Using Tunable Differential Active Inductor

This paper presents the design of a high conversion gain and low flicker noise down conversion CMOS double balanced Gilbert cell mixer using \(0.18\,\upmu \hbox {m}\) CMOS technology. The high conversion gain and low flicker noise mixer is implemented by using a differential active inductor (DAI) circuit and cross-coupled current injection technique within the conventional double-balanced Gilbert cell mixer. A cross-coupled current bleeding circuit is used to inject the current to the switching stage to decrease the flicker noise. Instead of spiral inductor, a DAI with high tunability of the inductor and quality factor is used to tune out the parasitic capacitance effect and decrease the leakage current that has a harmonic component and produce the flicker noise. By tuning the DAI, the flicker noise corner frequency is reduced to 150 Hz. The proposed circuit is simulated with Cadence Spectra and the simulation results shows the NF of 11.2 dB, conversion gain of 23.7 dB and IIP3 of \(-6\)  dB for an RF frequency of 2.4 GHz. The excellent LO-RF, LO-IF, RF-LO and RF-IF isolations of \(-60, -110, -52\) and \(-64\)  dB are achieved respectively. The total power consumption is 10.5 mW from a 1.8 V DC power supply.     

Study on automatic control of arc gap in robotic TIG welding

This paper presents a method for automatic control of arc length in tungsten inert gas (TIG) welding process using the arc voltage. By using this method, the role of operator in arc length control is played by an automatic control system based on a predefined arc voltage value for any special welding operation. A dynamic model for feed-rate mechanism and the relation between variations of arc length and voltage are described in details. Using a proportional-integral controller, variations of arc length in welding path is compensated with an automatic feed-rate mechanism in a normal direction to the welding path. By keeping the voltage constant during the process, a stable weld with higher quality and better appearance is obtained specially in welding of uneven surfaces. Theoretical and practical investigations show that the suggested method is able to control the TIG welding process successfully. Test results show that an accurate weld is obtained without the interference of the operator, and by comparing the predefined values of arc voltage with what is practically obtained, the welding gap is automatically adjusted.     

Recurrent Neural Network for Approximate Earthquake Time and Location Prediction Using Multiple Seismicity Indicators

Abstract:  A computational approach is presented for predicting the location and time of occurrence of future moderate-to-large earthquakes in an approximate sense based on neural network modeling and using a vector of eight seismicity indicators as input. Two different methods are explored. In the first method, a large seismic region is subdivided into several small subregions and the temporal historical earthquake record is divided into a number of small equal time periods. Seismicity indicators are computed for each subregion for each time period and their relationship to the magnitude of the largest earthquake occurring in that subregion during the following time-period is studied using a recurrent neural network. In the second more direct approach, the temporal historical earthquake record is divided into a number of unequal time periods where each period is defined as the time between large earthquakes. Seismicity indicators are computed for each time-period and their relationship to the latitude and longitude of the epicentral location, and time of occurrence of the following major earthquake is studied using a recurrent neural network.     

Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection.

A novel principal component analysis (PCA)-enhanced cosine radial basis function neural network classifier is presented. The two-stage classifier is integrated with the mixed-band wavelet-chaos methodology, developed earlier by the authors, for accurate and robust classification of electroencephalogram (EEGs) into healthy, ictal, and interictal EEGs. A nine-parameter mixed-band feature space discovered in previous research for effective EEG representation is used as input to the two-stage classifier. In the first stage, PCA is employed for feature enhancement. The rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network (RBFNN) employed in the second stage significantly. The classification accuracy and robustness of the classifier are validated by extensive parametric and sensitivity analysis. The new wavelet-chaos-neural network methodology yields high EEG classification accuracy (96.6%) and is quite robust to changes in training data with a low standard deviation of 1.4%. For epilepsy diagnosis, when only normal and interictal EEGs are considered, the classification accuracy of the proposed model is 99.3%. This statistic is especially remarkable because even the most highly trained neurologists do not appear to be able to detect interictal EEGs more than 80% of the times.