Biblio

Incipient faults are faults at their initial stages and occur before permanent faults occur. It is very important to detect incipient faults timely and accurately for the safe and stable operation of the power system. At present, most of the detection methods for incipient faults are designed for the detection of a single device’s incipient fault, but a unified detection for multiple devices cannot be achieved. In order to increase the fault detection capability and enable detection expandability, this paper proposes a waveform vector embedding (WVE) method to embed incipient fault waveforms of different devices into waveform vectors. Then, we utilize the waveform vectors and formulate them into a waveform dictionary. To improve the efficiency of embedding the waveform signature into the learning process, we build a loss function that prevents overflow and overfitting of softmax function during when learning power system waveforms. We use the real data collected from an IEEE Power & Energy Society technical report to verify the feasibility of this method. For the result verification, we compare the superiority of this method with Logistic Regression and Support Vector Machine in different scenarios.

For the intra-pulse modulation classification of radar signal, traditional deep learning algorithms have poor recognition performance without numerous training samples. Meanwhile, the receiver may intercept few pulse radar signals in the real scenes of electronic reconnaissance. To solve this problem, a structure which is made up of signal pretreatment by Smooth Pseudo Wigner-Ville (SPWVD) analysis algorithm, convolution neural network (CNN) and relation network (RN) is proposed in this study. The experimental results show that its classification accuracy is 94.24% under 20 samples per class training and the signal-to-noise ratio (SNR) is -4dB. Moreover, it can classify the novel types without further updating the network.

The Radio Frequency Identification (RFID), as one of the key technologies in sensing layer of the Internet of Things (IoT) framework, has increasingly been deployed in a wide variety of application domains. But the reliability of RFID is still a great concern. This article introduces the group management of RFID passwords method, come up with by YUICHI KOBAYASHI and other researchers, which aimed to reduce the risk of privacy disclosure. But for reason that the password and pass key in the method, which are set to protect the ID, doesn't change and the ID is transmitted directly in the unsafe channel, it causes serious vulnerabilities that may be used by resourceful adversary. Thus, we proposed an improved method by using the random number to encrypt the password and switching the password into the temporally valid information. Besides, the protocol encrypts the ID during to avoid the direct transmission situation significantly increases the reliability.