Current Issue : October - December Volume : 2018 Issue Number : 4 Articles : 5 Articles
Research on stealthiness has become an important topic in the field of data integrity (DI) attacks. To construct stealthy DI attacks,\na common assumption in most related studies is that attackers have prior model knowledge of physical systems. In this paper, such\nassumption is relaxed and a covert agent is proposed based on the least squares support vector regression (LSSVR). By estimating\na plant model from control and sensory data, the LSSVR-based covert agent can closely imitate the behavior of the physical plant.\nThen, the covert agent is used to construct a covert loop, which can keep the controller�s input and output both stealthy over a finite\ntime window. Experiments have been carried out to show the effectiveness of the proposed method....
For timing and synchronization system, digital phase-locked loop (DPLL) and Kalman filter all have been widely used as the clock\ntracking and clock correction schemes for the similar structure and properties. This paper compares the two schemes used for\nultrawideband (UWB) location system. The improved Kalman filter is more immune to interference....
This paper introduces a novel double-differential vector phase-locked loop (DD-VPLL)\nfor Global Navigation Satellite Systems (GNSS) that leverages carrier phase position solutions as\nwell as base station measurements in the estimation of rover tracking loop parameters. The use\nof double differencing alleviates the need for estimating receiver clock dynamics and atmospheric\ndelays; therefore, the navigation filter consists of the baseline dynamic states only. It is shown that\nusing vector processing for carrier phase tracking leads to a significant enhancement in the receiver\nsensitivity compared to using the conventional scalar-based tracking loop (STL) and vector frequency\nlocked loop (VFLL). The sensitivity improvement of 8 to 10 dB compared to STL, and 7 to 8 dB\ncompared to VFLL, is obtained based on the test cases reported in the paper. Also, an increased\nprobability of ambiguity resolution in the proposed method results in better availability for real time\nkinematic (RTK) applications....
In order to reduce the pollution caused by fuel vehicles to the environment,\nelectric vehicles are becoming the means of transportation. The replacement\nof fuel vehicles by electric vehicles is a future trend. Based on practical requirements,\na 120 kW direct current charger has been designed. Taking the\nMK60DN512 as the core controller, a direct current charger control system is\ndesigned and implemented. The overall solution of the direct current charger\ncontrol system is designed. According to the functional requirements of the\ndirect current charger, a system hardware platform is built based on embedded\ntechnology. The hardware mainly consists of MK60DN512 microcontroller,\nstart/reset circuit, JTAG download/debugging circuit, clock circuit, minimum\nsystem power supply, output voltage sampling and signal conversion\ncircuit, output current sampling and signal conversion circuit, AC relay control\ncircuit and temperature detection circuit....
Aiming at the problem of network congestion caused by the large number of data\ntransmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward\nan algorithm based on standard particle swarmââ?¬â??neural PID congestion control (PNPID). Firstly, PID\ncontrol theory was applied to the queue management of wireless sensor nodes. Then, the self-learning\nand self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the\nproportion, integral and differential parameters of the PID controller. Finally, the standard particle\nswarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and\ndifferential parameters and neuron learning rates were used for online optimization. This paper\ndescribes experiments and simulations which show that the PNPID algorithm effectively stabilized\nqueue length near the expected value. At the same time, network performance, such as throughput\nand packet loss rate, was greatly improved, which alleviated network congestion and improved\nnetwork QoS....
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