this article constructed a fiber optic distributed vibration sensing system based on FPGA (Field Programmable Gate Array). the FPGA chip was proposed to collect, preprocess, demodulate, and transmit vibration signals,...
this article constructed a fiber optic distributed vibration sensing system based on FPGA (Field Programmable Gate Array). the FPGA chip was proposed to collect, preprocess, demodulate, and transmit vibration signals, fully leveraging the parallelcomputing advantages of FPGA, improving the data processing speed and real-time response, and thereby reducing the data processing burden on personal computers. In a set of experiments, FPGA was used to complete data collection. through the network interface, the collected data was uploaded to the host, and then in the PC (personal computer), the data was preprocessed and demodulated, which was the demodulation of the PC. In another set of experiments, FPGA was used to collect, preprocess, and demodulate data, that is, FPGA on chip demodulation. the upper computer demodulation+PZT applied vibration response time was 1.36 seconds, and the FPGA on chip demodulation+PZT applied vibration response time was 0.61 seconds. the data transmission scheme in this article has reflected a high processing speed and real-time response to data.
Monitoring network traffic to identify malicious applications is an active research topic in network security. In the era of big data, withthe increasing number of access devices, networks will become more and more d...
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ISBN:
(数字)9798350376548
ISBN:
(纸本)9798350376555
Monitoring network traffic to identify malicious applications is an active research topic in network security. In the era of big data, withthe increasing number of access devices, networks will become more and more dense and require more rapid response. Traditional anomaly detection methods cannot achieve both detection accuracy and latency. To study a high-performance network traffic anomaly detection method is imperative. In this paper, We proposed a novel anomaly detection framework based on big data analytics for network traffic to enhance the detection of sophisticated cyber threats. the framework is divided into two stages: online and offline. the online stage uses a distributed algorithm utilizing customized network traffic characteristics. In the offline stage, the multi-modal method of accurate classification is adopted, and the identification result is used as an expert system of online algorithm to realize data authentication. By integrating advanced machine learning algorithms and big data parallel anomaly detection techniques, the proposed framework aims to strike a balance between accuracy and efficiency in detecting emerging cyber threats. the framework has been tested on both open datasets and real-world datasets. A large number of experiments have been conducted to validate the feasibility and practicality of the framework.
Integration of renewable energy sources accompanied with decommission of fossil fueled power plants inherently results in lack of power system flexibility. In turn, this reduced flexibility calls for additional balanc...
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ISBN:
(数字)9781728129563
ISBN:
(纸本)9781728129570
Integration of renewable energy sources accompanied with decommission of fossil fueled power plants inherently results in lack of power system flexibility. In turn, this reduced flexibility calls for additional balancing services. In parallel to this, the process of transport sector electrification is in place and the large fleets of electric vehicles (EVs) could prove to be one of the solutions for increased power system flexibility needs. If managed adequately, EVs could be able to provide the missing balancing services. In this paper, a model of EV day-ahead market and frequency containment reserve bidding is defined in order to asses the potential challenges that could arise during such service provision. Special attention is given to the EV battery state of energy, since the batteries are energy-limited resources and specific issues may arise both at individual EV and fleet level.
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