Experimental studies on a two stage pulse tube cryocooler reaching 2.5K

A two stage Pulse Tube Cryocooler (PTC) is designed and fabricated which reaches a no-load temperature of 2.5K in the second stage and similar to 60 K in the first stage respectively. The system provides a cooling power of similar to 250 mW at 5K in the second stage. Stainless steel meshes (size 200) and lead (Pb) granules are used as the first stage regenerator materials and combination of Pb with Er3Ni / HoCu2 are used as the second stage regenerator materials. The system operates at 1.6 Hz using a 6 kW water cooled helium compressor. Studies conducted by varying the dimensions of Pulse Tubes and regenerators show that the dimensions of the Pulse Tubes are more critical to the performance of the Cryocooler than those of the regenerators. Experimental studies show that the optimum volume ratios of Er3Ni to Pb and HoCu2 to Pb in the second stage regenerator should be 3:2 and 2:3 respectively for the best performance. Further, systems with HoCu2 performed better than those with Er3Ni. The theoretical analysis of the system has been carried out using a simple isothermal model. The experimentally measured cooling powers are in good agreement with the theoretical predictions.

Concrete-filled double skin tube columns subjected to lateral impact loading: Experimental and numerical study

This research treats the lateral impact behaviour of composite columns, which find increasing use as bridge piers and building columns. It offers (1) innovative experimental methods for testing structural columns, (2) dynamic computer simulation techniques as a viable tool in analysis and design of such columns and (3) significant new information on their performance which can be used in design. The research outcomes will enable to protect lives and properties against the risk of vehicular impacts caused either accidentally or intentionally.

Experimental study on behaviour of concrete filled double skin tube columns under lateral impact loading

This paper presents an experimental investigation on the lateral impact response of axially loaded concrete filled double skin tube (CFDST) columns. A total of four test series are being conducted at Queensland University of Technology using a novel horizontal impact-testing rig. The test results reported in this paper are from the first test series, where the columns are pinned at both ends and impacted at mid-span. In the next three series, effects of support conditions, impact location and repeated impact will be treated. The main objectives of the current paper are to describe the innovative testing procedure and provide some insight into the lateral impact behavior and failure of simply supported axially pre-loaded CFDST columns. The results include time histories of impact forces, reaction forces, axial force and global lateral deflection. Based on the test data, the failure mode, peak impact force, peak reaction forces, maximum deflection and residual deflection, with and without axial load, are analyzed and discussed. The findings of this study will serve as a benchmark reference for future analysis and design of CFDST columns.

LOX separation studies using cryogenic vortex tube

In-flight collection of air, pre-cooling, liquefaction and separation of liquid oxygen (LOX) are key technologies for futuristic launch vehicles, Vortex tube technology is one of the few potential technologies for this application. Extensive studies have been carried out on straight and conical vortex tubes for developing vortex tube technology for high purity LOX separation. Studies show that 12mm. diameter conical vortex tube with L/D of 10 could achieve LOX purity of similar to 96% with separation efficiency of similar to 14% indicating that it is not possible to obtain both high LOX purity and high separation efficiency simultaneously in a single vortex tube. However, it is possible to achieve both high LOX purity and separation efficiency by staging of vortex tubes. LOX purity of 96% and separation efficiency of similar to 73.5% has been achieved for second stage vortex tube supplied with pre-cooled air having 60% oxygen purity. LOX purity has been further increased to 97% by applying controlled heating power over liquid oxygen flowing discharge surface of the vortex tube.

Primary Fallopian tube carcinoma:occurrence, risk and prognostic factors

The aim of the study was to clarify the occurrence, and etiological and prognostic factors of primary fallopian tube carcinoma (PFTC). We studied the sociodemographic determinants of the incidence of PFTC in Finland and the role of chlamydial infections and human papillomavirus infections as risk factors for PFTC. Serum tumor markers were studied as prognostic factors for PFTC. We also evaluated selected reproductive factors (parity, sterilization and hysterectomy) as risk or protective factors of PFTC. The risks of second primary cancers after PFTC were also studied. The age-adjusted incidence of PFTC in Finland increased to 5.4 / 1,000,000 in 1993 97. The incidence rate was higher in the cities, but the relative rise was higher in rural areas. Women in the two highest social classes showed a 1.8 fold incidence compared with those in the lowest. Women in agriculture and those not working outside the home showed only half the PFTC incidence of those in higher socioeconomic occupations. Pretreatment serum concentrations of hCGβ, CA125 and TATI were evaluated as prognostic markers for PFTC. Elevated hCGβ values (above the 75th percentile, 3.5 pmol/L; OR 2.49, 95% CI 1.22 5.09), stage and histology were strong independent prognostic factors for PFTC. The effects of parity, sterilization and hysterectomy on the risk of PFTC were studied in a case control-study with 573 PFTC cases from the Finnish Cancer Registry. In multivariate analysis parity was the only significant protective factor as regards PFTC, with increasing protection associated with increasing number of deliveries. In univariate analysis sterilization gave borderline protection against PFTC and the protective effect increased with time since the operation. In multivariate analysis the protection did not reach statistical significance. Chlamydial and human papillomavirus (HPV) infections were studied in two separate seroepidemiological case-control studies with 78 PFTC patients. The incidence of women with positive HPV or chlamydial serology was the same in PFTC patients and in the control group and was not found to be a risk factor for PFTC. Finally, the possible risk of a second primary cancer after diagnosis and treatment of PFTC in a cohort of 2084 cases from 13 cancer registries followed for second primary cancers within the period 1943 2000 was studied. In PFTC patients, second primary cancers were 36% more common than expected (SIR 1.36, 95% CI 1.13 1.63). In conclusion, the incidence of PFTC has increased in Finland, especially in higher social classes and among those in certain occupations. Elevated serum hCGβ reflect a worsened prognosis. Parity is a clear protective factor, as is previous sterilization. After PFTC there is a risk of second primary cancers, especially colorectal, breast, lung and bladder cancers and non-lymphoid leukemia. The excess of colorectal and breast cancers after PFTC may indicate common effects of earlier treatments, or they could reflect common effects of lifestyle or genetic, immunological or environmental background.

Rotorcraft parameter estimation using radial basis function neural network

Increased emphasis on rotorcraft performance and perational capabilities has resulted in accurate computation of aerodynamic stability and control parameters. System identification is one such tool in which the model structure and parameters such as aerodynamic stability and control derivatives are derived. In the present work, the rotorcraft aerodynamic parameters are computed using radial basis function neural networks (RBFN) in the presence of both state and measurement noise. The effect of presence of outliers in the data is also considered. RBFN is found to give superior results compared to finite difference derivatives for noisy data. (C) 2010 Elsevier Inc. All rights reserved.

Search for the Higgs Boson Using Neural Networks in Events with Missing Energy and b-Quark Jets in pp̅ Collisions at √s=1.96 TeV

We report on a search for the standard model Higgs boson produced in association with a $W$ or $Z$ boson in $p\bar{p}$ collisions at $\sqrt{s} = 1.96$ TeV recorded by the CDF II experiment at the Tevatron in a data sample corresponding to an integrated luminosity of 2.1 fb$^{-1}$. We consider events which have no identified charged leptons, an imbalance in transverse momentum, and two or three jets where at least one jet is consistent with originating from the decay of a $b$ hadron. We find good agreement between data and predictions. We place 95% confidence level upper limits on the production cross section for several Higgs boson masses ranging from 110$\gevm$ to 150$\gevm$. For a mass of 115$\gevm$ the observed (expected) limit is 6.9 (5.6) times the standard model prediction.

Search for standard model Higgs boson production in association with a W boson using a neural network discriminant at CDF

We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector that correspond to an integrated luminosity of approximately 1.9 inverse fb. We select events consistent with a signature of a single charged lepton, missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method, a jet probability tagging method, and a neural network filter. We use kinematic information in an artificial neural network to improve discrimination between signal and background compared to previous analyses. The observed number of events and the neural network output distributions are consistent with the standard model background expectations, and we set 95% confidence level upper limits on the production cross section times branching fraction ranging from 1.2 to 1.1 pb or 7.5 to 102 times the standard model expectation for Higgs boson masses from 110 to $150 GeV/c^2, respectively.

Investigations of a two-stage pulse tube cryocooler operating down to 2.5K

A two-stage pulse tube cryocooler (PTC) which produces a no-load temperature of similar to 2.5 K in its second stage at an operating frequency of 1.6 Hz has been designed and fabricated. The second stage of the system provides a refrigeration power of similar to 250 mW at 5.0 K. The system uses stainless steel meshes (mesh size 200) along with lead (Pb) granules and combinations of Pb, Er3Ni, and HoCu2 as the first and second stage regenerator materials, respectively. Experimental studies have been carried out on different pulse tube configurations by varying the dimensions of the pulse tubes and regenerators to arrive at the best one, which leads to the lowest no-load second stage cold head temperature. Using this configuration, detailed experimental studies have been conducted by varying the volume percentage ratios of the second stage regenerator materials such as HoCu2, Er3Ni, and Pb (with an average grain size of similar to 250 mu m). This article presents the results of our experimental studies on cryocoolers with the regenerator material arranged in layered structures. Comparative studies have also been presented for specific cases where the regenerator materials are arranged as a homogeneous mixture in the second stage. The experimental results clearly indicate that the design of PTCs should use only layered structures of regenerator materials and not homogenous mixtures.

Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.

Neural network approach for fault location in unbalanced distribution networks with limited measurements

This paper presents an Artificial Neural Network (ANN) approach for locating faults in distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses only limited measurements. Faults are located according to the impedances of their path using a Feed Forward Neural Networks (FFNN). Various practical situations in distribution systems, such as protective devices placed only at the substation, limited measurements available, various types of faults viz., three-phase, line (a, b, c) to ground, line to line (a-b, b-c, c-a) and line to line to ground (a-b-g, b-c-g, c-a-g) faults and a wide range of varying short circuit levels at substation, are considered for studies. A typical IEEE 34 bus practical distribution system with unbalanced loads and with three- and single- phase laterals and a 69 node test feeder with different configurations are considered for studies. The results presented show that the proposed approach of fault location gives close to accurate results in terms of the estimated fault location.

Robust/optimal temperature profile control using neural networks

An approximate dynamic programming (ADP) based neurocontroller is developed for a heat transfer application. Heat transfer problem for a fin in a car's electronic module is modeled as a nonlinear distributed parameter (infinite-dimensional) system by taking into account heat loss and generation due to conduction, convection and radiation. A low-order, finite-dimensional lumped parameter model for this problem is obtained by using Galerkin projection and basis functions designed through the 'Proper Orthogonal Decomposition' technique (POD) and the 'snap-shot' solutions. A suboptimal neurocontroller is obtained with a single-network-adaptive-critic (SNAC). Further contribution of this paper is to develop an online robust controller to account for unmodeled dynamics and parametric uncertainties. A weight update rule is presented that guarantees boundedness of the weights and eliminates the need for persistence of excitation (PE) condition to be satisfied. Since, the ADP and neural network based controllers are of fairly general structure, they appear to have the potential to be controller synthesis tools for nonlinear distributed parameter systems especially where it is difficult to obtain an accurate model.

Design of a FIR filter for image restoration using principal component neural networks

The neural network finds its application in many image denoising applications because of its inherent characteristics such as nonlinear mapping and self-adaptiveness. The design of filters largely depends on the a-priori knowledge about the type of noise. Due to this, standard filters are application and image specific. Widely used filtering algorithms reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design a finite impulse response filter based on principal component neural network (PCNN) is proposed in this study for image filtering, optimized in the sense of visual inspection and error metric. This algorithm exploits the inter-pixel correlation by iteratively updating the filter coefficients using PCNN. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions. Further, the number of unknown parameters is very few and most of these parameters are adaptively obtained from the processed image.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

A neural network based CBIR system using STI features and relevance feedback

Multimedia mining primarily involves, information analysis and retrieval based on implicit knowledge. The ever increasing digital image databases on the Internet has created a need for using multimedia mining on these databases for effective and efficient retrieval of images. Contents of an image can be expressed in different features such as Shape, Texture and Intensity-distribution(STI). Content Based Image Retrieval(CBIR) is an efficient retrieval of relevant images from large databases based on features extracted from the image. Most of the existing systems either concentrate on a single representation of all features or linear combination of these features. The paper proposes a CBIR System named STIRF (Shape, Texture, Intensity-distribution with Relevance Feedback) that uses a neural network for nonlinear combination of the heterogenous STI features. Further the system is self-adaptable to different applications and users based upon relevance feedback. Prior to retrieval of relevant images, each feature is first clustered independent of the other in its own space and this helps in matching of similar images. Testing the system on a database of images with varied contents and intensive backgrounds showed good results with most relevant images being retrieved for a image query. The system showed better and more robust performance compared to existing CBIR systems