Recent Patents on Electrical & Electronic Engineering (Formerly Recent Patents on Electrical Engineering) - Volume 6, Issue 3, 2013

This work presents review of the recent patents and applied researches in the field of ultrasonic processing of the semiconductor materials and devices. The ultrasound demonstrates selective and, simultaneously, complex character of the effect on semiconductors. In contrast to the thermal or light energy, uniformly absorbed over semiconductor volume, the acoustic wave energy is mainly absorbed by the crystal lattice defects. The peculiarities of this interaction results in practical applications of the ultrasonication of semiconductors for electronic properties design. It was shown that US processing has found technological niche as supporting operation during ion implantation process and growth of semiconductors. The use of an inhomogeneous stress and piezoelectric harmonic potential produced by surface acoustic waves in low dimensional heterostructures to improves the efficiency of available optoelectronic and nanoelectronic devices as well as create new ones. The phenomenon of acoustic cavitation that underlies at the basis of such technological processes as cleaning and sonochemical synthesis is discussed separately.

In current nanometer circuits, energy consumptions of the high-performance chips have become a critical concern. Adiabatic computing reduces energy dissipations by using AC power supplies to recycle the energy stored in node capacitances of the circuits. Adiabatic logic circuits only using a single-phase power clock do not need multiple powerclock generators and complicated clock trees, and thus are easily implemented. In this paper, recent patents and progress on adiabatic computing are reviewed. The structure and operation of a single-phase adiabatic logic named as SCPTAL (single-phase clock pass transistor adiabatic logic) are presented in detail. The design methods of the logic cells based on SCPTAL, such as basic gates, full adder, 5-2 compressor, and flip-flops, are also addressed. The energy comparisons between the SCPTAL and static CMOS cells are carried out. The results show that the SCPTAL circuits have large energy savings over a wide range of frequencies, as compared with conventional static CMOS logic ones, which can be a good candidate for ultra-low power applications.

Prediction of dissolved gases content in power transformer oil is very significant to detect incipient failures of transformer early. A hybrid model of RVM and PSO (PRVM) is applied to predict dissolved gases content in transformer oil in this paper, and particle swarm optimization is applied to choose the appropriate embedded dimension m because the choice of the embedded dimension has a great influence on its generalization performance. In this study, traditional support vector machine is used in comparison with the proposed PRVM method. In order to testify the superiority of PRVM compared with the traditional support vector machine fully, single-step prediction mode and multi-step prediction mode are employed respectively. The experimental results indicate that the prediction ability of PRVM is more excellent than that of SVM in single-step and multi-step prediction. The article also refers some recent patents on a hybrid model of RVM and PSO.

Fault diagnosis for motor bearing has a great economic and social significance, the patents on motor bearing fault detection and diagnosis method have been presented in the past years. However, there are some shortcomings including local extremum or over-fitting in the fault diagnosis method of these patents. Relevance vector machine (RVM), requires much fewer kernel functions compared with support vector machine (SVM), and provides the posterior distributions rather than just a point estimate. Thus, fault pattern recognition for motor bearing based on multi-layer RVM classifier was proposed in the paper, and two multi-class combination patterns of RVM classifiers including ‘binary tree’ and ‘one-against-one’ were used, among which ‘binary tree’ pattern is used to distinguish the fault state from the normal state, ‘one-against-one’ pattern was used to recognize the fault types. The fault pattern recognition results for motor bearing among RVM classifier, SVM classifier and RBF neural network (ANN) classifier were analyzed, and the experimental results indicate that RVM classifier has the better fault pattern recognition ability than SVM classifier or RBFNN classifier.

In this paper, the problem of passive filtering for sampled-data system has been studied which contains nonlinear perturbation and long time delay. The nonlinear perturbation is the relationship term of state and time, and it satisfies the Lipschitz constraints. Furthermore, the direct distribution method is applied to deal with long time-delay and to relax the dimension of decision variables. The proposed passive condition assures asymptotic stability for the filtering error, which is discussed using linear matrix inequalities (LMIs). Additionally, the non-fragile filter based on passivity is proposed and a numerical example is presented to verify the feasibility and effectiveness. The article also refers some recent patents on sampled-data system design.

In this paper, first order Sugeno-fuzzy logic-based models are constructed using the cutting parameters as an input data and the surface roughness (roundness) as an output data. Mapping between the input-output spaces is constructed to find the effect of the input cutting parameters on the output surface quality in terms of roundness error which is an indication of the surface roughness. Hence, the best cutting conditions in the lathe machine are selected to improve the output. A method is proposed to accurately establish the relationship between the different cutting parameters and the resulting surface irregularities which form the surface roughness using subtractive clustering technique based first order Sugeno fuzzy model. Model having minimum errors with fewer numbers of rules for each surface irregularity is obtained through enumerative search of the clustering parameters. The resulted model with optimum clustering parameters is then tuned by using adaptive neuro-fuzzy inference system (ANFIS). Consequently, the proposed model is successfully used to predict and minimize the roughness error using different cutting parameters involved in this investigation. Consequently, the machinist will decide beforehand which tool he should use before conducting the experiment, which will save time, effort and money. Hence, the best parameters can be picked up to achieve a reduced roundness error. The first model considers two cutting parameters as an input to the process. The second model will include tool usage as a third input. It shows that the tool usage should be included due to its effect in deteriorating the surface quality. In the second model, minimum roundness equals to 0.998 µm with 21.1 Hz, 0.0944 mm/rev, and 3.08 for workpiece frequency, feed rate and tool usage, respectively. The purpose of this study is to provide an overview about the use of first order Sugeno-fuzzy logic-based models in machining processes by addressing recent patents and scholarly articles on the subject of design, control and application of such systems.

The comprehensive quantified evaluation of power quality is one of the main foundations in power quality appraisal. This paper presents the improved principal component analysis (PCA) method to evaluate steady state power quality of grid-connected photovoltaic system synthetically. Some improvements are made to traditional PCA method for the purpose of taking subjective factors into account and normalizing the final assessment results. Furthermore, the assessment results are graded on the basis of the Chinese national standard. The result shows that this method is simple, practical and efficient on power quality comprehensive evaluation. This method is the basis of a number of patents on power quality evaluation.