99 resultados para Vehicle Parts.


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A pneumatic vehicle is provided with a first sub-assembly with a chassis, part of the vehicle body, a pair of B-pillars, a pair of rear rails, wheels, an elongate aluminum compressed load bearing air tank oriented longitudinally in the chassis, side panels connected to the tank and the wheels, a heat exchanger to heat the compressed air, and an air motor driven by the heated, compressed air and connected to a wheel. A ventilation system has a restrictive solenoid valve for directing air to the heat exchanger. The air tank is provided with a carbon filament reinforced plastic layer, and a fiberglass and aramid-fiber layer. A second sub-assembly includes part of the vehicle body bonded to the first-sub-assembly using a structural adhesive, a pair of A-pillars, and a pair of roof rails. Seating includes inflatable components for adjustment.

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A pneumatic vehicle is provided with a chassis, wheels, a compressed air tank, a heat exchanger to heat the compressed air, and an air motor driven by the heated air and connected to at least one wheel. A pneumatic vehicle is provided with a chassis, wheels, a compressed air tank, and an air motor driven by the compressed air and connected to a wheel. The vehicle also has a ventilation system for the passenger compartment, a heat exchanger, and a restrictive solenoid valve for directing ventilation system air to the heat exchanger. A pneumatic vehicle is provided with a chassis, wheels, an aluminum compressed air tank, a carbon filament reinforced plastic layer over the tank, a fiberglass and aramid-fiber layer over the carbon filament reinforced plastic layer, and an air motor driven by the compressed air and connected to at least one wheel.

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A pneumatic vehicle is provided with a first and second sub-assembly. The first sub-assembly has a chassis supporting an air tank. The second sub-assembly has part of the vehicle body and is bonded to the first-sub-assembly using a structural adhesive. Another pneumatic vehicle is provided with a first and second sub-assembly. The first sub-assembly has a chassis, air tank, part of the vehicle body, a pair of B-pillars, and a pair of rear rails. The second sub-assembly has a part of the vehicle body, a pair of A-pillars, and a pair of roof rails. A vehicle seat is provided with a seat base and upright seat back providing a seating area. A bladder located in the central region of the seating area inflates to provide two bucket seats, and deflates to provide a bench seat. The vehicle seat also has inflatable inserts to provide cushioning.

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A pneumatic vehicle is provided with a first sub- assembly with a chassis, part of the vehicle body, a pair of B-pillars, a pair of rear rails, wheels, an elongate aluminum compressed load bearing air tank oriented longitudinally in the chassis, side panels connected to the tank and the wheels, a heat exchanger to heat the compressed air, and an air motor driven by the heated, compressed air and connected to a wheel. A ventilation system has a restrictive solenoid valve for directing air to the heat exchanger. The air tank is provided with a carbon filament reinforced plastic layer, and a fiberglass and aramid-fiber layer. A second sub-assembly includes part of the vehicle body bonded to the first-sub-assembly using a structural adhesive, a pair of A-pillars, and a pair of roof rails. Seating includes inflatable components for adjustment.

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This paper evaluates six commonly available parts-of-speech tagging tools over corpora other than those upon which they were originally trained. In particular this investigation measures the performance of the selected tools over varying styles and genres of text without retraining, under the assumption that domain specific training data is not always available. An investigation is performed to determine whether improved results can be achieved by combining the set of tagging tools into ensembles that use voting schemes to determine the best tag for each word. It is found that while accuracy drops due to non-domain specific training, and tag-mapping between corpora, accuracy remains very high, with the support vector machine-based tagger, and the decision tree-based tagger performing best over different corpora. It is also found that an ensemble containing a support vector machine-based tagger, a probabilistic tagger, a decision-tree based tagger and a rule-based tagger produces the largest increase in accuracy and the largest reduction in error across different corpora, using the Precision-Recall voting scheme.

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This paper presents parts-of-speech tagging as a first step towards an autonomous text-to-scene conversion system. It categorizes some freely available taggers, according to the techniques used by each in order to automatically identify word-classes. In addition, the performance of each identified tagger is verified experimentally. The SUSANNE corpus is used for testing and reveals the complexity of working with different tagsets, resulting in substantially lower accuracies in our tests than in those reported by the developers of each tagger. The taggers are then grouped to form a voting system to attempt to raise accuracies, but in no cases do the combined results improve upon the individual accuracies. Additionally a new metric, agreement, is tentatively proposed as an indication of confidence in the output of a group of taggers where such output cannot be validated.

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his paper evaluates six commonly available parts-of-speech tagging tools over corpora other than those upon which they were originally trained. In particular this investigation measures the performance of the selected tools over varying styles and genres of text without retraining, under the assumption that domain specific training data is not always available. An investigation is performed to determine whether improved results can be achieved by combining the set of tagging tools into ensembles that use voting schemes to determine the best tag for each word. It is found that while accuracy drops due to non-domain specific training, and tag-mapping between corpora, accuracy remains very high, with the support vector machine-based tagger, and the decision tree-based tagger performing best over different corpora. It is also found that an ensemble containing a support vector machine-based tagger, a probabilistic tagger, a decision-tree based tagger and a rule-based tagger produces the largest increase in accuracy and the largest reduction in error across different corpora, using the Precision-Recall voting scheme.

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This paper focuses on a parallel hybrid electric vehicle. It first develops a model for the vehicle using the backward-looking approach where the flow of energy starts from wheels and spreads towards engine and electric motor. Next, a fuzzy logic-based strategy is developed to control the operation of the vehicle. The objectives of the controller include managing the energy flow from engine and electric motor, controlling transmission ratio, adjusting speed, and sustaining battery's state of charge. The controller examines current vehicle speed, demand torque, slope difference, state of charge of battery, and engine and electric motor rotation speeds. Then, it determines the best values for continuous variable transmission ratio, speed, and torque. A slope window scheme is also developed to take into account the look-ahead slope information and determine the best vehicle speed for better fuel economy. The developed model and control strategy are simulated. The simulation results are presented and discussed. It is shown that the use of the proposed fuzzy controller reduces fuel consumption.

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Whilst air conditioning systems increase thermal comfortableness in vehicles, they also raise the energy consumption of vehicles. Achieving thermal comfort in an energy-efficient way is a difficult task requiring good coordination between engine and the air conditioning system. This paper presents a coordinated energy management system to reduce the energy consumption of the vehicle air conditioning system while maintaining the thermal comfortableness. The system coordinates and manages the operation of evaporator, blower, and fresh air and recirculation gates to provide the desired comfort temperature and indoor air quality, under the various ambient and vehicle conditions, the energy consumption can then be optimized. Three simulations of the developed coordinated energy management system are performed to demonstrate its energy saving capacity.

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Cold start driving cycles exhibit an increase in friction losses due to the low temperatures of metal and media compared to normal operating engine conditions. These friction losses are responsible for up to 10% penalty in fuel economy over the official drive cycles like the New European Drive Cycle (NEDC), where the temperature of the oil even at the end of the 1180 s of the drive cycle is below the fully warmed up values of between 100°C and 120°C. At engine oil temperatures below 100°C the water from the blow by condensates and dilutes the engine oil in the oil pan which negatively affects engine wear. Therefore engine oil temperatures above 100°C are desirable to minimize engine wear through blow by condensate. The paper presents a new technique to warm up the engine oil that significantly reduces the friction losses and therefore also reduces the fuel economy penalty during a 22°C cold start NEDC. Chassis dynamometer experiments demonstrated fuel economy improvements of over 7% as well as significant emission reductions by rapidly increasing the oil temperature. Oil temperatures were increased by up to 60°C during certain parts of the NEDC. It is shown how a very simple sensitivity analysis can be used to assess the relative size or efficiency of different heat transfer passes and the resulting fuel economy improvement potential of different heat recovery systems system. Due to its simplicity the method is very fast to use and therefore also very cost effective. The method demonstrated a very good correlation for the fuel consumption within ±1% compared to measurements on a vehicle chassis roll.

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In online role plays, students are asked to engage with a story that serves as a metaphor for real-life experience as they learn and develop skills. However, practitioners rarely examine the characteristics and management of this story as factors in the students' engagement in and learning from the activity. In this paper I present findings from a recent case study which examines these factors in an online role play that has been named as an exemplar and has been run for 19 years in Australian and international universities to teach Middle East politics and journalism. Online role plays are increasingly popular in tertiary education, in forms ranging from simple text-based role plays to virtual learning environment activities and e-simulations. The role play I studied required students to communicate in role via simulated email messages and draw on real-life resources and daily simulated online newspaper publications produced by the journalism students rather than rely on information or automated interactions built into an interface. This relatively simple format enabled me to observe clearly the impact of the technique's basic design elements. I studied both the story elements of plot, character and setting and the non-story elements of assessment, group work and online format. The data collection methods include analysis of student emails in the role play, a questionnaire, a focus group, interviews and the journal I kept as a participant-observer in the role play. In evaluating the qualities and impact of story elements I drew upon established aesthetic principles for drama and poststructuralist drama education.

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Improving fuel efficiency in vehicles can reduce the energy consumption concerns associated with operating the vehicles. This paper presents a model for a parallel hybrid electric vehicle. In the model, the flow of energy starts from wheels and spreads toward engine and electric motor. A fuzzy logic based control strategy is implemented for the vehicle. The controller manages the energy flow from the engine and the electric motor, controlling transmission ratio, adjusting speed, and sustaining battery's state of charge. The controller examines the vehicle speed, demand torque, slope difference, state of charge of battery, and engine and electric motor rotation speeds. It then determines the best values for continuous variable transmission ratio, speed, and torque. A slope window method is formed that takes into account the look-ahead slope information, and determines the best vehicle speed. The developed model and control strategy are simulated using real highway data relating to Nowra-Bateman Bay in Australia, and SAE Highway Fuel Economy Driving Schedule. The simulation results are presented and discussed. It is shown that the use of the proposed fuzzy controller reduces the fuel consumption of the vehicle.

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