• 전기학회논문지
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• 제65권12호
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• pp.2197-2210
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• 2016

Recently, the trend of zero emissions has increased in automotive engineering because of environmental problems and regulations. Therefore, the development of battery electric vehicles (EVs), hybrid/plug-in hybrid electric vehicles (HEVs/PHEVs), and fuel cell electric vehicles (FCEVs) has been mainstreamed. In particular, for light-duty electric vehicles, improvement in electric motor performance is directly linked to driving range and driving performance. In this paper, using an improved design for the interior permanent magnet synchronous motor (IPMSM), the EV driving range for the light-duty EV was extended. In the electromagnetic design process, a 2D finite element method (FEM) was used. Furthermore, to consider mechanical stress, ANSYS Workbench was adopted. To conduct a vehicle simulation, the vehicle was modeled to include an electric motor model, energy storage model, and regenerative braking. From these results, using the advanced vehicle simulator (ADVISOR) based on MATLAB Simulink, a vehicle simulation was performed, and the effects of the improved design were described.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.158-168
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• 2024

There is hardly any person in modern times who has not taken soft drinks instead of drinking water. The rate of people taking soft drinks being surprisingly high, researchers around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases and so on. Therefore, in this work an image-based tool is developed to monitor the nutritional information of soft drinks by using deep convolutional neural network with transfer learning. At first, visual saliency, mean shift segmentation, thresholding and noise reduction technique, collectively known as 'pre-processing' are adopted to extract the location of drinks region. After removing backgrounds and segment out only the desired area from image, we impose Discrete Wavelength Transform (DWT) based resolution enhancement technique is applied to improve the quality of image. After that, transfer learning model is employed for the classification of drinks. Finally, nutrition value of each drink is estimated using Bag-of-Feature (BoF) based classification and Euclidean distance-based ratio calculation technique. To achieve this, a dataset is built with ten most consumed soft drinks in Bangladesh. These images were collected from imageNet dataset as well as internet and proposed method confirms that it has the ability to detect and recognize different types of drinks with an accuracy of 98.51%.