Software quality assessment can cut costs significantly from early development stages. On the other hand quality assessment helps in taking development decisions, checking the effect of fault corrections, estimating m...
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In the petrochemical technological processes, the synthesis of 2 ethyl-hexanol-oxo-alcohol is of highest importance, being achieved through catalytic hydrogenation of 2 ethyl-hexenal. The hydrogenation reaction is hig...
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This paper describes a new holistic approach to integrate remote and virtual laboratories in the educational process. Aim of the integration is to test students' knowledge not only on lower levels as usual multipl...
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In the context of software engineering, quality assessment is not straightforward. Generally, quality assessment is important since it can cut costs in the product life-cycle. A software quality assessment model based...
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Most of the WSN applications need the number of sensor nodes deployed to be in order of hundreds, thousands or more to monitor certain phenomena and capture measurements over a long period of time. The large volume of...
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Most of the WSN applications need the number of sensor nodes deployed to be in order of hundreds, thousands or more to monitor certain phenomena and capture measurements over a long period of time. The large volume of sensor networks would generate continuous streams of raw events1 in case of centralized architecture, in which the sensor data captured by all the sensor nodes is sent to a central entity. In this paper, we describe the design and implementation of a system that carries out complex event detection queries inside wireless sensor nodes. These queries filter and remove undesirable events. They can detect complex events and meaningful information by combining raw events with logical and temporal relationship, and output this information to external monitoring application for further analysis. This system reduces the amount of data that needs to be sent to the central entity by avoiding transmitting the raw data outside the network. Therefore, it can dramatically reduce the communication burden between nodes and improve the lifetime of sensor networks. We have implemented our approach for the TinyOS Operating System, for the TelosB and Mica2 platforms. We conducted a performance evaluation of our method comparing it with a naive method. Results clearly confirm the effectiveness of our approach.
Our vision is, that an online lab will no longer be seen as a collection of monolithically constructed experiments but as a collection of laboratory devices that communicate with each other. Nowadays, many interested ...
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This paper describes a solution for improving the functional and structural testing capabilities of an electronic product built around one or more programmable devices (i.e. microcontrollers). This can be achieved by ...
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Transportation systems have emerged into a critical underlying structure, heavily sustaining all industrial sectors. Along them, monitoring systems were developed in order to increase the quality of the provided servi...
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Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. T...
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ISBN:
(数字)9798331501488
ISBN:
(纸本)9798331501495
Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.
Remote process control and supervision applications developed over the Internet require special communication models and techniques, which can guarantee the real-time and safety restrictions inherent to automation sys...
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