DSRC – NEW PRINCIPLES AND IMPLEMENTATIONS IN ITS ROMANIA

Cities around the world are suffering from severe traffic congestion resulting in economic losses via delayed time, fuel consumption, traffic accidents, air pollution and traffic noise. An efficient wireless technology used in the road-to-vehicle radio communication can help reducing this negative effects and improve traffic parameters. Active DSRC is one of the most reliable road-to-vehicle communication methods available in the market today, since it has wide communication area and high-speed communication and is the most suitable for non-stop toll collection system, especially for multi-lane free-flow toll collection system, where vehicles can pass the toll gantry without reducing speed. The Active DSRC also supports other ITS applications related to road-to-vehicle communication applications that need high speed communication, and can improve public transportation, help to reduce air pollution, NOx and CO2, and road noise via a decline in traffic.ITS

DSRC – NEW PRINCIPLES AND IMPLEMENTATIONS IN ITS ROMANIA

Cities around the world are suffering from severe traffic congestion resulting in economic losses via delayed time, fuel consumption, traffic accidents, air pollution and traffic noise. An efficient wireless technology used in the road-to-vehicle radio communication can help reducing this negative effects and improve traffic parameters. Active DSRC is one of the most reliable road-to-vehicle communication methods available in the market today, since it has wide communication area and high-speed communication and is the most suitable for non-stop toll collection system, especially for multi-lane free-flow toll collection system, where vehicles can pass the toll gantry without reducing speed. The Active DSRC also supports other ITS applications related to road-to-vehicle communication applications that need high speed communication, and can improve public transportation, help to reduce air pollution, NOx and CO2, and road noise via a decline in traffic.ITS

Optimization of the Fuel Cell Renewable Hybrid Power System Using the Control Mode of the Required Load Power on the DC Bus

In this paper, a systematic analysis of seven control topologies is performed, based on three possible control variables of the power generated by the Fuel Cell (FC) system: the reference input of the controller for the FC boost converter, and the two reference inputs used by the air regulator and the fuel regulator. The FC system will generate power based on the Required-Power-Following (RPF) control mode in order to ensure the load demand, operating as the main energy source in an FC hybrid power system. The FC system will operate as a backup energy source in an FC renewable Hybrid Power System (by ensuring the lack of power on the DC bus, which is given by the load power minus the renewable power). Thus, power requested from the batteries’ stack will be almost zero during operation of the FC hybrid power system based on RPF-control mode. If the FC hybrid power system operates with a variable load demand, then the lack or excess of power on the DC bus will be dynamically ensured by the hybrid battery/ultracapacitor energy storage system for a safe transition of the FC system under the RPF-control mode. The RPF-control mode will ensure a fair comparison of the seven control topologies based on the same optimization function to improve the fuel savings. The main objective of this paper is to compare the fuel economy obtained by using each strategy under different load cycles in order to identify which is the best strategy operating across entire loading or the best switching strategy using two strategies: one strategy for high load and the other on the rest of the load range. Based on the preliminary results, the fuel consumption using these best strategies can be reduced by more than 15%, compared to commercial strategies

Optimization of the Fuel Cell Renewable Hybrid Power System Using the Control Mode of the Required Load Power on the DC Bus

In this paper, a systematic analysis of seven control topologies is performed, based on three possible control variables of the power generated by the Fuel Cell (FC) system: the reference input of the controller for the FC boost converter, and the two reference inputs used by the air regulator and the fuel regulator. The FC system will generate power based on the Required-Power-Following (RPF) control mode in order to ensure the load demand, operating as the main energy source in an FC hybrid power system. The FC system will operate as a backup energy source in an FC renewable Hybrid Power System (by ensuring the lack of power on the DC bus, which is given by the load power minus the renewable power). Thus, power requested from the batteries’ stack will be almost zero during operation of the FC hybrid power system based on RPF-control mode. If the FC hybrid power system operates with a variable load demand, then the lack or excess of power on the DC bus will be dynamically ensured by the hybrid battery/ultracapacitor energy storage system for a safe transition of the FC system under the RPF-control mode. The RPF-control mode will ensure a fair comparison of the seven control topologies based on the same optimization function to improve the fuel savings. The main objective of this paper is to compare the fuel economy obtained by using each strategy under different load cycles in order to identify which is the best strategy operating across entire loading or the best switching strategy using two strategies: one strategy for high load and the other on the rest of the load range. Based on the preliminary results, the fuel consumption using these best strategies can be reduced by more than 15%, compared to commercial strategies.</jats:p

Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals

Evoked and spontaneous K-complexes are thought to be involved in sleep protection, but their role as biomarkers is still under debate. K-complexes have two major functions: first, they suppress cortical arousal in response to stimuli that the sleeping brain evaluates to avoid signaling danger; and second, they help strengthen memory. K-complexes also play an important role in the analysis of sleep quality, in the detection of diseases associated with sleep disorders, and as biomarkers for the detection of Alzheimer’s and Parkinson’s diseases. Detecting K-complexes is relatively difficult, as reliable methods of identifying this complex cannot be found in the literature. In this paper, we propose a new method for the automatic detection of K-complexes combining the method of recursion and reallocation of the Cohen class and the deep neural networks, obtaining a recursive strategy aimed at increasing the percentage of classification and reducing the computation time required to detect K-complexes by applying the proposed methods.</jats:p