Hybrid techniques to enhance solar thermal: the way forward

Solar is one of the pillars for clean and environment friendly energy. The drawback of the solar is the interruption during the night and cloudy and rainy weather. This paper presents the author’s experience on enhancing the solar thermal systems by integration techniques with either other energy resources or thermal energy storages (TES). The present works includes the hybrid solar drying through integration with thermal backup unit. The experimental results on hybrid drying showed enhancement of 64.1% for Empty Fruit Bunch, and 61.1% for chili pepper, compared with open solar mode drying. Secondly, solar water heating was proved to be sufficient to supply hot water during the day and night time by integration with TES. The experimented system was able to maintain the water hot up to the next morning. On large scale and industrial application, experimental results on modified inclined solar chimney had shown enhancement via integration with wasted flue gas. By this technique, the system was developed to operate 24 hours a day. The efficiency was enhanced by 100% in case of hybrid operation compared with solar mode operation. The research results are demonstrating that the integration techniques can contribute effectively in enhancing the performance of the thermal solar systems.The author acknowledges Universiti Teknologi PETRONAS for providing the financial, technical and logistic support to execute the solar hybrid program. The program is sponsored under many internal research funds, e.g. STIRF no. 24/07.08, STIRF no. 44/08.09, URIF 19/2012 and URIF 22/2013. Ministry of Higher Education of Malaysia is acknowledged for providing the research fund of the solar hybrid drying program under PRGS scheme

GA/SA-based hybrid techniques for the scheduling of generator maintenance in power systems

YesProposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problem

A proposal for preprocessing, reduction, and selection of visual information in airborne flight simulation

A noncritical empirical detail-by-detail transformation of environmental information into flight display by purely physical methods characterizes the textured visual simulation as indiscernibly identical with the real scene. This method uses image storage, readout, image transformation, and graphic display to present a true color visual flight image to the pilot onboard aircraft in a purely electronic manner, employing analog-hybrid techniques

A Survey on Hybrid Techniques Using SVM

Support Vector Machines (SVM) with linear or nonlinear kernels has become one of the most promising learning algorithms for classification as well as for regression. All the multilayer perceptron (MLP),Radial Basic Function(RBF) and Learning Polynomials are also worked efficiently with SVM. SVM is basically derived from statistical Learning Theory and it is very powerful statistical tool. The basic principal for the SVM is structural risk minimization and closely related to regularization theory. SVM is a group of supervised learning techniques or methods, which is used to do for classification or regression. In this paper discussed the importance of Support Vector Machines in various areas. This paper discussing the efficiency of SVM with the combination of other classification techniques

Hybrid circuit modules for motor commutation and control

Thick film hybrid techniques are used to develop circuitry for a brushless dc motor commutator. The power commutator contains the driving circuit and an amplifier that controls the armature current. A position decoder contains digital integrated circuits which receive the signals from the armature position sensors and generate the driving signals for the power commutator in the proper sequence. These units drive motors with stall currents up to about 400 mA

Generation scheduling using genetic algorithm based hybrid techniques

The solution of generation scheduling (GS) problems involves the determination of the unit commitment (UC) and economic dispatch (ED) for each generator in a power system at each time interval in the scheduling period. The solution procedure requires the simultaneous consideration of these two decisions. In recent years researchers have focused much attention on new solution techniques to GS. This paper proposes the application of a variety of genetic algorithm (GA) based approaches and investigates how these techniques may be improved in order to more quickly obtain the optimum or near optimum solution for the GS problem. The results obtained show that the GA-based hybrid approach offers an effective alternative for solving realistic GS problems within a realistic timeframe

The Robo-AO-2 facility for rapid visible/near-infrared AO imaging and the demonstration of hybrid techniques

We are building a next-generation laser adaptive optics system, Robo-AO-2, for the UH 2.2-m telescope that will deliver robotic, diffraction-limited observations at visible and near-infrared wavelengths in unprecedented numbers. The superior Maunakea observing site, expanded spectral range and rapid response to high-priority events represent a significant advance over the prototype. Robo-AO-2 will include a new reconfigurable natural guide star sensor for exquisite wavefront correction on bright targets and the demonstration of potentially transformative hybrid AO techniques that promise to extend the faintness limit on current and future exoplanet adaptive optics systems.Comment: 15 page

Misclassification analysis for the class imbalance problem

In classification, the class imbalance issue normally causes the learning algorithm to be dominated by the majority classes and the features of the minority classes are sometimes ignored. This will indirectly affect how human visualise the data. Therefore, special care is needed to take care of the learning algorithm in order to enhance the accuracy for the minority classes. In this study, the use of misclassification analysis is investigated for data re-distribution. Several under-sampling techniques and hybrid techniques using misclassification analysis are proposed in the paper. The benchmark data sets obtained from the University of California Irvine (UCI) machine learning repository are used to investigate the performance of the proposed techniques. The results show that the proposed hybrid technique presents the best performance in the experiment

Qualified Two-Hybrid Techniques by DWT Output to Predict Fault Location

The power transmission system is essential for the power scheme to transfer the energy from generators to consumers. The short circuit problem repeatedly occurs in the transmission system, and the main problem is to separate the sources from users. This research has applied two hybrid techniques to predict fault location. The first hybrid technique has involved the Discrete Wavelet Transformation (DWT) and Adaptive Neuro-Fuzzy Inference System (ANFIS), while the second hybrid technique is for DWT grouping and Support Vector Machine (SVM). These hybrid techniques are intended to estimate the fault location of each fault category in a transmission system. The DWT was applied to both D8 and D9 level at the 50 kHz sample frequency. The root mean square (RMS) values of the D8 and D9 coefficients were used for training using ANFIS and SVM techniques. After that, ANFIS and SVM were utilised to detect faults in the phase and ground lines. Several types of fault have been simulated, i.e. fault location, fault resistance, and original point of view. The RMS results from the two hybrid techniques were compared to find the best results. The tests of error estimation were performed for the three bus systems. The comparison of error estimation of the two methods shows that both hybrid techniques can be applied to predict fault locations

Improved Classification of Breast Cancer Data using Hybrid Techniques

Breast cancer is the second leading cancer for women in developed countries including India. Many new cancer detection and treatment approaches were developed. The most effective way to reduce breast cancer deaths is detect it earlier. The frequent occurrence of breast cancer and its serious consequences have attracted worldwide attention in recent years. Problems such as low rate of accuracy and poor self-adaptability still exist in traditional diagnosis. In order to solve these problems, an Ada Boost-SVM classification algorithm, Combined with k-means is proposed in this research for the early diagnosis of breast cancer. The effectiveness of the proposed methods are examined by calculating its accuracy, confusion matrix which give important clues to the physicians for early diagnosis of breast cancer