Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.     

SVM-based ontology matching approach

There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which are used in current multi-strategy ontology mapping. The semantic information is not included in them and a lot of manual intervention is also needed, so it leads to that some factual mapping relations are missed. Addressing this issue, the work presented in this paper puts forward an ontology matching approach, which uses multi-strategy mapping technique to carry on similarity iterative computation and explores both linguistic and structural similarity. Our approach takes different similarities into one whole, as a similarity cube. By cutting operation, similarity vectors are obtained, which form the similarity space, and by this way, mapping discovery can be converted into binary classification. Support vector machine (SVM) has good generalization ability and can obtain best compromise between complexity of model and learning capability when solving small samples and the nonlinear problem. Because of the said reason, we employ SVM in our approach. For making full use of the information of ontology, our implementation and experimental results used a common dataset to demonstrate the effectiveness of the mapping approach. It ensures the recall ration while improving the quality of mapping results.     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.     

机动车车牌识别中SVM的应用

支持向量机是一类新型机器学习方法,成功应用于各个领域,算法在分类性能上优于其它学习算法。该文在字符特征提取基础上,将支持向量机理论应用于车牌识别系统,提高识别率和识别速度,具有很好的推广价值。     

A copper/polyimide Metal-base packaging technology

A unique substrate MCPM (Mitsubishi Copper Polyimide Metal-base) technology has been developed by applying our basic copper/polyimide technology.¹ This new substrate technology MCPM is suited for a high-density, multi-layer, multi-chip, high-power module/package, such as used for a computer. The new MCPM was processed using a copper metal base (110 × 110 mm), full copper system (all layers) with 50-μm fine lines. As for pad metallizations for the IC assembly, we evaluated both Ni/Au for chip and wire ICs and solder for TAB ICs. The total number of assembled ICs is 25. To improve the thermal dispersion, copper thermal vias are simultaneously formed by electro-plating. This thermal via is located between the IC chip and copper metal base, and promotes heat dispersion. We employed one large thermal via (4.5 mm^?) and four small vias (1.0 mm^?) for each IC pad. The effect of thermal vias and/or base metal is simulated by a computer analysis and compared with an alumina base substrate. The results show that the thermal vias are effective at lowering the temperature difference between the IC and base substrate, and also lowering the temperature rise of the IC chip. We also evaluated the substrate’s reliability by adhesion test, pressure cooker test, etc.     

Parametric study of the electrical to thermal power conversion in an inductively coupledrf plasma torch

An inductively coupled (ic) radio-frequency (rf) stabilized argon plasma was generated in a quartz tube using a 7 MHzrf generator. The electrical and thermal powers were measured and the efficiency of energy conversion was computed. The results indicate that for a constant power input, there is an optimum argon flow rate for which maximum energy conversion efficiency occurs.     

Solid-liquid phase equilibria of benzene + 2-methyl-2-propanol system under high pressures

Solid-liquid phase equilibria of the benzene + 2-methyl-2-propanol system have been investigated at temperatures from 278 to 323 K and pressures up to 300 MPa using a high-pressure optical vessel. The uncertainties of the measurements of temperature, pressure and composition are within ±0.1 K, ±0.5 MPa, and ±0.001 mole fraction, respectively. The freezing pressure at a constant composition increases monotonously with pressure. The eutectic point shifts to a higher temperature and benzene-rich composition with increasing pressure. In order to describe the pressure-temperature-composition relation of high-pressure solid-liquid phase equilibria, a new simple equation has been proposed as follows:

The adsorption characteristics of two Mo sites on a sulfided MoO3/γ-Al2O3 surface

The surface structure of the oxidized and ammonia dissolved MoO₃/-Al₂O₃ samples and the adsorption characteristics of the sulfided samples were investigated by Laser Raman Spectroscopy (LRS) and Low Temperature Infrared Spectroscopy (LTIR) techniques.It was verified that there were two kinds of coordinated unsaturated Mo sites (denoted as Mo_A(CUS) and Mo_B(CUS)) on the surface and the precursors of Mo_A(CUS) and Mo_B(CUS) were Mo(O) and Mo(T), respectively. It was also concluded that Mo_A(CUS) sites could adsorb NO and CO, while Mo_B(CUS) could adsorb CO only. The surface concentration of Mo_A(CUS) might be far smaller than that of Mo_B(CUS).     

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本文把与井简直接连通的大裂缝及井筒定义为“裂缝—井筒系统”,通过气体状态方程的变换和续流总量的计算,探索出井底大裂缝的容积及其储量的计算方法。     

Thermophysical properties data on molten semiconductors

Thermophysical properties of molten semiconductors are reviewed. Published data for viscosity, thermal conductivity, surface tension, and other properties are presented. Several measurement methods often used for molten semiconductors are described. Recommended values of thermophysical properties are tabulated for Si, Ge, GaAs, InP, InSb, GaSb, and other compounds. This review shows that further measurements of thermophysical properties of GaAs and InP in the molten state are required. It is also indicated that a very limited amount of data on emissivity is available. Space experiments relating to thermophysical property measurements are described briefly.Nomenclature Density - C _p Specific heat - Kinematic viscosity - Dynamic viscosity= - Thermal diffusivity - Thermal conductivity=C_p - Volumetric thermal expansion coefficient - Surface tension - d/dT Temperature coefficient of surface tension - g Gravitational acceleration - T Temperature - T Temperature difference - L Characteristic dimension