The Impact of Driver Reaction in Cooperative Vehicle Safety Systems

Cooperative Vehicular Safety (CVS) has recently been widely studied in the field of automated vehicular systems. CVS systems help decrease the rates of accidents. However, implementing and testing CVS applications in real world is very costly and risky. Hence, most of the related research studies on CVS applications have relied mainly on simulations. In simulated CVS systems, it is important to consider all critical aspects of used models, and how these models affect one another. The movement model is a key component in the simulation study of CVS systems, which controls the mobility of vehicles (nodes) and responses to the continually changing acquiredinformation. However, existing mobility models are not created to take action(s) in response to hazardous situations (identified by situational awareness component). Integrating the reaction(s) to a hazardous alert is a missing element in current CVS system simulations. Hence to rectify this deficiency, this work is to incorporate a Driver’s Reaction Model (DReaM) that react and respond to hazard alerts, and studies the effect of main components of CVS system including the added model. We examined a simulation modeling framework that describes cooperative vehicle safety system as one unified model. The studied framework is powered by cooperation and communication between vehicles. Investigated elements are communication model, movement model, warning generation, and driver response to warning indicating an emergency of near to crash situation

DC Microgrid based on Battery, Photovoltaic, and fuel Cells; Design and Control

Microgrids offer flexibility in power generation in a way of using multiple renewable energy sources. In the past few years, microgrids become a very active research area in terms of design and control strategies. Most of the microgrids use DC/DC converters to connect renewable energy sources to the load. In this paper, the simulation model of a DC microgrid with three different energy sources (Lithium-ion battery (LIB), photovoltaic (PV) array, and fuel cell) and external variant power load is built with MATLAB/Simulink and the simulative results show that the stability of DC microgrid can be guaranteed by the proposed maximum power point controller MPPT. The three energy sources are connected to the load through DC/DC converters, one for each. This type of topology ensures protection for each energy source as well as optimum stability at the load

Word level Bangla Sign Language Dataset for Continuous BSL Recognition

An robust sign language recognition system can greatly alleviate communication barriers, particularly for people who struggle with verbal communication. This is crucial for human growth and progress as it enables the expression of thoughts, feelings, and ideas. However, sign recognition is a complex task that faces numerous challenges such as same gesture patterns for multiple signs, lighting, clothing, carrying conditions, and the presence of large poses, as well as illumination discrepancies across different views. Additionally, the absence of an extensive Bangla sign language video dataset makes it even more challenging to operate recognition systems, particularly when utilizing deep learning techniques. In order to address this issue, firstly, we created a large-scale dataset called the MVBSL-W50, which comprises 50 isolated words across 13 categories. Secondly, we developed an attention-based Bi-GRU model that captures the temporal dynamics of pose information for individuals communicating through sign language. The proposed model utilizes human pose information, which has shown to be successful in analyzing sign language patterns. By focusing solely on movement information and disregarding body appearance and environmental factors, the model is simplified and can achieve a speedier performance. The accuracy of the model is reported to be 85.64%

Hybrid GRU-LSTM Recurrent Neural Network-Based Model for Real Estate Price Prediction

Real estate prices are an important reflection of the economy and their prices are great interest to both buyers and sellers. Hundreds of houses are sold every day and the buyer asks himself what is the reasonable price that this house deserves. In this paper, a new regression model is proposed for the accurate prediction of house prices. This model is based on the hybrid recurrent neural network where the Gated Recurrent Unit (GRU) is fused with the Long Short-Term Memory (LSTM) and applied to a particular dataset that characterizes houses in Boston. Massachusetts dataset from Scikit-learn is used in this research to train and evaluate this regression model using the data on Boston housing. Several experiments were conducted on the proposed algorithm and evaluated with the commonly used metrics. The results of these experiments showed that the proposed model has better performance when the networks are used in the fusion process than when they act individually. It also has better accuracy and lower Root Mean Square Error when compared to several states of art methodologies.</p