A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.
Lowering the voltage of the RF sources can reduce the cost of future accelerator systems. This can be accomplished using multiple beam guns or guns with sheet beam in tubes creating high RF power. However, the optical design is almost impossible without 3D analysis, since the devices are no longer axis-symmetric. A new approach for 3D analysis of the electron gun and beam optics utilizes a combination of 3D MAFIA and TOPAZ computer programs. An algorithm based on perturbation theory provides a 3D correction to the 2D, self-consistent field solutions. This information is used to study propagated charged particles through the problem domain. Applications of this technique to the design of a high power multiple beam guns is discussed.
A fluorescent screen was used to monitor relative beam position and spot size of a 56-MEV electron beam in the linac test stand. A chromium doped alumina ceramic screen inserted into the beam was monitored by a video camera. The resulting image was capturedusing a frame grabber and storedintomemory. Reconstruction and analysis ofthestoredimage was performedusingPV-WAVE i. This paperwilldiscuss the hardware and softwareimplementationofthefluorescent screenand imaging system. Proposed improvements forthe APS linacfluorescent screensand image processing will also be discussed.
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