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THE electrical supply system is designed to provide both 200 volt a.c. and 28 volt d.c. services for operation of the aircraft. Although electrical power is required for the operation of the auto‐stabiliser system, this is not essential for flight and the flying controls are non‐electric. The aircraft does not therefore, depend on the integrity of the electrical system for flight. Loads, such as undercarriage lowering selection etc., required to operate if a total generation failure has occurred, are all d.c. and are fed by the main aircraft batteries. As a further precaution basic flight and communication facilities can also be powered in an emergency from a standby battery which is not charged in flight and is therefore independent of any of the normal aircraft services.

The purpose of this paper is to present and apply a parasitic extraction approach for the calculation of DC busbar inductances.

A computational approach based on the finite integration technique and computed magnetic energy is developed to extract parasitic inductances. The finite integration analysis is conducted via the magnetoquasistatic solver of CST EM Studio^® capturing the 3D geometrical effects of the design, as well as the skin and proximity effects.

The method is applied successfully to evaluate the leakage inductances of two printed circuit boards structures; a backplane sample for the verification purpose and a real DC bus employed in a three‐phase pulse width modulation inverter.

The paper demonstrates that the method calculates the loop inductances accurately. It does not, however, verify the used technique to split loop inductances into partial inductances.

The extraction method is easy‐to‐use and able to handle complex geometries within acceptable computation time and accuracy.

The paper introduces a way to compute the parasitic inductances from the results of a numerical electromagnetic field solver.

This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks detection of multicrystalline solar cell surface based on machine vision is fast, economical, intelligent and easier for on-line detection. However, the generalization capability of feature extraction scheme adopted by existed methods is limited, which has become an obstacle for further improving the detection accuracy.

A novel micro-cracks detection method based on self-learning features and low-rank matrix recovery is proposed in this paper. First, the input image is preprocessed to suppress the noises and remove the busbars and fingers. Second, a self-learning feature extraction scheme in which the feature extraction templates are changed along with the input image is introduced. Third, the low-rank matrix recovery is applied to the decomposition of self-learning feature matrix for obtaining the preliminary detection result. Fourth, the preliminary detection result is optimized by incorporating the superpixel segmentation. Finally, the optimized result is further fine-tuned by morphological postprocessing.

Comprehensive evaluations are implemented on a data set which includes 120 testing images and corresponding human-annotated ground truth. Specifically, subjective evaluations show that the shape of detected micro-cracks is similar to the ground truth, and objective evaluations demonstrate that the proposed method has a high detection accuracy.

First, a self-learning feature extraction method which has good generalization capability is proposed. Second, the low-rank matrix recovery is combined with superpixel segmentation for locating the defective regions.

TEN THOUSAND AND NINETY gallons of usable fuel are carried in six integral type tanks, three in each wing, the individual tank capacities per wing being: No. 1 Tank (Inner) 2,865 gallons, No. 2 Tank (Centre) 1,750 gallons, No. 3 Tank (Outer) 430 gallons. Each tank is equipped with two booster pumps mounted in canisters which permit pump replacement without draining the tanks.

Walking-worker assembly lines can be regarded as an effective method to achieve the above-mentioned characteristics. In such systems, workers, following each other, travel workstations in sequence by performing all of the required tasks of their own product. As the eventual stage of assembly line design, efforts should be made for capacity adjustments to meet the demand in terms of allocating tasks to workers via assembly line balancing. In this context, the purpose of this study is to address the balancing problem for multi-model walking-worker assembly systems, with the aim of improving planning capability for such systems by means of developing an optimization methodology.

Two linear integer programming models are proposed to balance a multi-model walking-worker assembly line optimally in a sequential manner. The first mathematical programming model attempts to determine number of workers in each segment (i.e. rabbit chase loop) for each model. The second model generates stations in each segment to smooth workflow. What is more, heuristic algorithms are provided due to computational burden of mathematical programming models. Two segment generation heuristic algorithms and a station generation heuristic algorithm are provided for the addressed problem.

The application of the mathematical programming approach improved the performance of a tap-off box assembly line in terms of number of workers (9.1 per cent) and non-value-added time ratio (between 27.9 and 26.1 per cent for different models) when compared to a classical assembly system design. In addition, the proposed approach (i.e. segmented walking-worker assembly line) provided a more convenient working environment (28.1 and 40.8 per cent shorter walking distance for different models) in contrast with the overall walking-worker assembly line. Meanwhile, segment generation heuristics yielded reduction in labour requirement for a considerable number (43.7 and 49.1 per cent) of test problems. Finally, gaps between the objective values and the lower bounds have been observed as 8.3 per cent (Segment Generation Heuristic 1) and 6.1 (Segment Generation Heuristic 2).

The proposed study presents a decision support for walking-worker line balancing with high level of solution quality and computational performance for even large-sized assembly systems. That being the case, it contributes to the management of real-life assembly systems in terms of labour planning and ergonomics. Owing to the fact that the methodology has the potential of reducing labour requirement, it will present the opportunity of utilizing freed-up capacity for new lines in the start-up period or other bottleneck processes. In addition, this study offers a working environment where skill of the workers can be improved within reasonable walking distances.

To the best knowledge of the author, workload balancing on multi-model walking-worker assembly lines with rabbit chase loop(s) has not yet been handled. Addressing this research gap, this paper presents a methodology including mathematical programming models and heuristic algorithms to solve the multi-model walking-worker assembly line balancing problem for the first time.

Voltage ranking attempts to rank busbar voltage deviations from their normally accepted security margins based on a set of performance indices (PI), without performing a full load flow. Existing methods suffer from either masking effects or long computation time. In this paper, an artificial neural network method is proposed for voltage ranking. Counterpropagation network (CPN) has been employed to overcome the problems listed above. A variety of input features are used with the aim of lowering the dimension of the proposed ANN to make it applicable for large power systems. The method is tested on two example systems, a five‐bus system and a 71‐bus system with very encouraging results.

Automatic contingency selection aims to quickly predict the impact of a set of next contingencies on an electric power system without actually performing a full ac load flow. Artificial neural network methods have been employed to overcome the masking effects or slow execution associated with existing methods. However, the large number of input features for the ANN limits its applications to large power systems. In this paper, a novel feature selection method, named the Weak Nodes method, based on a heuristic approach is proposed for an ANN‐based automatic contingency selection for electric power system, especially for the voltage ranking problem. Pre‐contingency state variables of weak nodes in the power system are adopted as input features for the ANN. The method is tested on the 77 busbar NGC derived network by Counter‐propagation Method and it is proved that it reduces the input features for ANN dramatically without losing ranking accuracy.

Details of the Hydraulic, Engine Control, Electrical, Fire Protection, Flight Control, Air Conditioning and Cabin Pressurization, Ice and Rain Protection, Radio and Auxiliary Power Plant Systems. IN order that high utilization rates and low maintenance costs may be achieved it was necessary to match the fail‐safe, long‐life structure of the HS.748 to trouble‐free systems. The system design philosophy adopted on the Hawker Siddeley 748 was ‘avoid complication and use proven components wherever possible’. This approach together with a layout whereby external access is provided to all systems has produced easy and almost trouble‐free maintenance. The majority of the system components are installed under the cabin floor and are grouped as shown in Fig. 1. Access is through the landing gear doors or four large quick release elliptical access panels enabling all trades to work on the aircraft at the same time.

IN MANY cathodic protection installations, the use of automatic control of current is based on the premise of master and slave equipment. In this case, if the system is applied to a generating station cooling system, the water system is considered in convenient units (e.g. one turbo alternator unit). The most important item in the cooling system associated with the turbo alternator is then selected as the master unit, the usual choice being the main steam condenser. In this master unit are installed the sensing electrodes which will form the basis of the feedback circuit which directs the operation of the rectifier. The less important items of equipment in the cooling circuits are then fed from the distribution busbars via suitable trimming resistances. A typical installation is shown in Fig. 1. Although the supply to the minor items will be appropriately affected by changes in electrolyte, it is clear that as far as these minor items are concerned the mode of control cannot be regarded as automatic.

With the completion last year of a tropical marine site on the island of Aruba, Reynolds Metals Co., Richmond, Virginia, USA, now has long‐term atmospheric test facilities covering a wide range of natural environments. This report is compiled by the company's senior corrosion engineer. Testing locations include Richmond, Virginia; Phoenix, Arizona; Corpus Christi, Texas; Kure Beach, North Carolina; McCook, Illinois; Manila, Philippine Islands; and Denge Marsh and Widnes, England. Panels are on test also at Arenzano, Italy; Bohus‐Malmon, Sweden; and on a Gulf of Mexico's drill platform. Exposed materials include specimens of bare, painted and colour anodised wrought and extruded aluminium as well as screen wire, fencing, busbar and special products. Stressed and unstressed aluminium is exposed, together with zinc, steel and copper alloys, for calibration and comparison. The programme started in 1956 now includes over 20,000 test specimens. Data on changes in mechanical properties and corrosion rates derived from the tests are handled on a computer.