Assessment of underground cable ratings based on distributed temperature sensing

Numerous power utilities have installed advanced distributed temperature sensing (DTS) systems to measure the temperature of underground cables. This paper presents a general procedure for real-time assessment of underground cable ratings based on DTS. After the identification and location of hot spots on a cable, the cable installation data, load, and DTS-recorded temperature over a period of time are used to estimate soil thermal parameters and assess cable ratings under normal and overloading conditions. In order to accurately estimate soil thermal parameters, a combined technique of the finite-element method and gradient-based optimization is used. The technique was validated in the laboratory and applied to a rating assessment of a 230-kV/500-MVA cable circuit. The assessed continuous and emergency ratings are helpful in improving the operation of this circuit.     

Exploratory analysis of brain connectivity with ICA.

Covariance-based methods of exploration of functional connectivity of the brain from functional magnetic resonance imaging (fMRI) experiments, such as principal component analysis (PCA) and structural equation modeling (SEM), require a priori knowledge such as an anatomical model to infer functional connectivity. In this research, a hybrid method, combining independent component analysis (ICA) and SEM, which is capable of deriving functional connectivity in an exploratory manner without the need of a prior model is introduced. The spatial ICA (SICA) derives independent neural systems or sources involved in task-related brain activation, while an automated method based on the SEM finds the structure of the connectivity among the elements in independent neural systems. Unlike second-order approaches used in earlier studies, the task-related neural systems derived from the ICA provide brain connectivity in the complete statistical sense. The use and efficacy of this approach is illustrated on two fMRI datasets obtained from a visual task and a language reading task.     

炉壁集束碳氧枪在电炉中的应用

介绍了鄂钢电炉厂70t consteel 电炉使用炉壁集束碳氧枪后对经济指标的改善,以及在生产实践中的冶金效果.     

A Real-World Application of Lane-Guidance Technologies—Automated Snowblower

This paper describes the development process and the initial field test results of an automated snowblower, focusing on one of the more difficult snow removal operations: blowing snow off the freeway alongside a guardrail without the snowblower touching the guardrail. The development process includes transforming this highway winter maintenance operation into a control problem, modeling a snowblower, designing control algorithms, devising a human-machine interface, and equipping a 20-ton snowblower with sensors and an actuator. Specific challenges include modeling the low-speed tire-induced oscillation, designing high-gain automatic control on front wheels while keeping rear steering under driver control, and implementing such a system under practical limitations. A new dynamic deflection tire model is incorporated into a bicycle model to account for the additional lateral dynamics. A low-order controller was first generated based on the understanding of the specific control problem and, then, refined and tuned iteratively using linear-matrix-inequality optimization. The initial winter field tests were successfully conducted with embedded magnetic markers along the guardrails installed on the shoulders of Interstate-80 in the Sierra Mountain region close to Donner Summit, CA, during the winter of 2005.     

Halogen-induced charge transfer polymerization of pyrrole in aqueous media

Simultaneous polymerization and doping of pyrrole have been carried out in the presence of a halogenic electron acceptor, bromine (Br₂) or iodine (I₂), in aqueous dispersion or in a two-phase solvent system. The morphology of the polypyrrole (PPY) so produced is granular and porous. The electrical conductivity of the PPY-I₂ charge transfer (CT) complex is of the order of 10¹ ohm⁻¹ cm⁻¹ while that of the PPY-Br₂ complex is about one order of magnitude less. Both complexes are stable in the atmosphere. The physicochemical properties of the PPY-I₂ and PPY-Br₂ CT complexes prepared under various experimental conditions are examined in detail.     

Development of a thermal hydraulic analysis code for gas-cooled reactors with annular fuels

Gas-cooled reactors have been highlighted as a promising option for next generation reactor technology. A thermal hydraulic analysis code for gas-cooled reactors has been developed with a heat transfer model of a block element, which is solved implicitly with the helium energy equation. Validation was carried out through comparison with both experimental and analytical results. A computation module for annular fuel rods has been coupled to the code for comparative analyses of an annular fuel-based block element. At normal operation, the annular fuel shows 80 °C lower peak temperature than the solid fuel for the same power in Japan's high temperature engineering test reactor (HTTR), even though the pressure drop is higher in the annular fuel.     

Rapid determination of chlorophylls in vegetable oils by laser-based fluorometry

The presence of chlorophylls in vegetable oils is considered undesirable due to their adverse effects on oxidative deterioration, bleachability and hydrogenation. The usual methods for determination of chlorophylls in vegetable oils are absorption spectrophotometry, fluorometry and liquid chromatography using fluorescence detectors. This paper outlines a fluorometric method using a laser as the excitation source. The chlorophylls in crude palm oil, rapeseed oil, soya bean oil and olive oil were determined directly by laser-based fluorometry using (a) a helium-neon laser (632-8 nm, 5 mW) as the excitation source and an optical-multichannel analyser (OMA) with an intensified diode array as the detector, and (b) a helium-neon laser and a photomultiplier tube as the detector. The method is especially suitable for the detection of chlorophylls in crude palm oil since no pre-extraction step is required. The levels of chlorophylls measured in vegetable oils using the laser technique was also compared against values obtained by conventional spectrophotometric methods. The simplicity and rapid quantification of laser based fluorometry suggest that this technique can be readily applied to the detection or a wide variety of fluorescing species in vegetable oils.     

Explicit robust schemes for implementation of a class of principal value-based constitutive models: Symbolic and numeric implementation

The issue of developing effective and robust schemes to implement a class of the Ogden-type hyperelastic constitutive models is addressed. To this end, special purpose functions (running under MACSYMA) are developed for the symbolic derivation, evaluation, and automatic FORTRAN code generation of explicit expressions for the corresponding stress function and material tangent stiffness tensors. These explicit forms are valid over the entire deformation range, since the singularities resulting from repeated principal-stretch values have been theorectically removed. The required computational algorithms are outlined, and the resulting FORTRAN computer code is presented.     

Classification and prediction of the critical heat flux using fuzzy theory and artificial neural networks

A new method to predict the critical heat flux (CHF) is proposed, based on the fuzzy clustering and artificial neural network. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulting clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanism. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods.     

A neural network control system with parallel adaptive enhancementsapplicable to nonlinear servomechanisms

In this paper, we present a technique for using an additional parallel neural network to provide adaptive enhancements to a basic fixed neural network-based nonlinear control system. This proposed parallel adaptive neural network control system is applicable to nonlinear dynamical systems of the type commonly encountered in many practical position control servomechanisms. Properties of the controller are discussed, and it is shown that if Gaussian radial basis function networks are used for the additional parallel neural network, uniformly stable adaptation is assured and the approximation error converges to zero asymptotically. In the paper, the effectiveness of the proposed parallel adaptive neural network control system is demonstrated in real-time implementation experiments for position control in a servomechanism with asymmetrical loading and changes in the load