Publications

Improving the detection ability of small targets in sea clutter is always a popular subject in the field of radar technology. The traditional sea clutter prediction method only considers the fluid alteration of radar echo, but neglects the spatial correlation. This research proposes a new temporal graph convolutional network (T-GCN) detection technique for tiny sea surface targets. Multiple historical sea clutter sequences are input by recurrence plot (RP). In this case, the graph convolutional neural network extract spatial characteristics from numerous sequences by capturing their topological structure. The time series is input to the gated recursive unit. Prediction result is output using the time characteristics. Experiments show that T-GCN can get spatiotemporal properties from sea clutter model, and the prediction results are better than that of support vector regression model, temporal dynamic model constructed by gated recursive unit and spatial correlation model constructed by graph convolutional network, which increases the visibility of small sea surface targets and offers a fresh concept and method for predicting sea clutter.

By introducing the oil price shock into the new Keynesian Phil-lips Curve(NKPC),this paper investigates the effects the oil price pass-through to inflation in China.According to the theoretical results,the influencing extent of international oil price fluctuation to China's inflation is determined by the dy-namic characters of NKPC and the share of oil input in production in China.The results of empirical analysis show that the oil augment NKPC in China has the classical dynamical characters of NKPC,the short run pass through effects of oil price to various inflations are positive,as well as the long run effects are not statistically significant.

A new QRS complex detection algorithm based on the empirical mode decomposition (EMD) is proposed in this paper. The EMD can first decompose the ECG signal into a series of oscillatory components called intrinsic mode functions (IMFs). Then with the soft- threshold denoising method on the first three IMFs, we construct the detection layer that is suitable for QRS detection. Using the corresponding relationship between the feature points of QRS complex and the modulus maxima of the detection layer, the QRS complex detection is realized. The proposed EMD-based method was validated through experiments on the MIT-BIH arrhythmia database and a QRS detection rate of 99.34% was achieved.

In response to the limitations of manually extracting features from target signals in the background of sea clutter, this paper studies Markov transition fields and convolutional neural networks, and proposes a sea small target detection method based on Markov transition fields, which transforms the detection problem into a binary classification problem. The one-dimensional observation echo is transformed into a two-dimensional image through the Markov transition field, and a sea clutter and weak signal classification model is established to complete the task of detecting weak signals in sea clutter. Taking IPIX measured radar data as the experimental object, a transfer learning model is built to autonomously learn MTF image features, improve the performance of the ResNet model, reduce training costs, compare the effects of different sampling points on detection results, and find that the effect is optimal when the sampling point is 1024. Compared with other image coding methods and classification networks, the proposed method can deeply explore the differences between targets and clutter, and has better detection and classification performance.

Wind speed and direction are critical meteorological elements. Multi-rotor unmanned aerial vehicles UAVs are widely used as a premium payload platform in meteorological monitoring. The meteorological UAV is able to improve the spatial and temporal resolution of the elements collected. However, during wind measurement missions, the installed anemometers are susceptible to interference caused by rotor turbulence. This paper puts forward a wind pressure orthogonal decomposition (WPOD) strategy to overcome this limitation in three ways: the location of the sensors, a new wind measurement method, and supporting equipment. A weak turbulence zone (WTZ) is found around the airframe, where the turbulence strength decays rapidly and is more suitable for installing wind measurement sensors. For the sensors to match the spatial structure of this area, a WPOD wind measurement method is proposed. An anemometer based on this principle was mounted on a quadrotor UAV to build a wind measurement system. Compared with a standard anemometer, this system has satisfactory performance. Analysis of the resulting data indicates that the error of the system is ±0.3 m/s and ±2° under hovering conditions and ±0.7 m/s and ±5° under moving conditions. In summary, WPOD points to a new orientation for wind measurement under a small spatial–temporal scale.

In contrast to most genomic DNA in mitotic cells, the promoter regions of some genes, such as the stress-inducible hsp70i gene that codes for a heat shock protein, remain uncompacted, a phenomenon called bookmarking. Here we show that hsp70i bookmarking is mediated by a transcription factor called HSF2, which binds this promoter in mitotic cells, recruits protein phosphatase 2A, and interacts with the CAP-G subunit of the condensin enzyme to promote efficient dephosphorylation and inactivation of condensin complexes in the vicinity, thereby preventing compaction at this site. Blocking HSF2-mediated bookmarking by HSF2 RNA interference decreases hsp70i induction and survival of stressed cells in the G1 phase, which demonstrates the biological importance of gene bookmarking.

Recently, the diagnosability of {\it stochastic discrete event systems} (SDESs) was investigated in the literature, and, the failure diagnosis considered was {\it centralized}. In this paper, we propose an approach to {\it decentralized} failure diagnosis of SDESs, where the stochastic system uses multiple local diagnosers to detect failures and each local diagnoser possesses its own information. In a way, the centralized failure diagnosis of SDESs can be viewed as a special case of the decentralized failure diagnosis presented in this paper with only one projection. The main contributions are as follows: (1) We formalize the notion of codiagnosability for stochastic automata, which means that a failure can be detected by at least one local stochastic diagnoser within a finite delay. (2) We construct a codiagnoser from a given stochastic automaton with multiple projections, and the codiagnoser associated with the local diagnosers is used to test codiagnosability condition of SDESs. (3) We deal with a number of basic properties of the codiagnoser. In particular, a necessary and sufficient condition for the codiagnosability of SDESs is presented. (4) We give a computing method in detail to check whether codiagnosability is violated. And (5) some examples are described to illustrate the applications of the codiagnosability and its computing method.

To extract weak signal from the chaotic background, in this paper we analyze the theory of state space reconstruction of complicated nonlinear system, and put forward an estimation method utilizing the least-squares support vector machine (LS-SVM) based on a generalized window function. In the algorithm the generalized embedded window is taken as a foundation and the correlation function method is used to determine the embedded dimension and time delay of Lorenz system and so the state space reconstruction is realized and by combining the error forecasting model in which the LS-SVM is used to estimate the errors, the detection of the weak target signal, such as transient and periodic signal, is achieved. It is illustrated in the simulation experiments that the model proposed can detect the weak signals effectively from a chaotic background and reduce the influence of noise on the target signals, which possesses minor forecasting error. Compared with those conventional methods, this method has a remarkable advantage in reducing detection threshold and improving the accuracy of prediction. When the signal-to-noise ratio is -87.41 dB in the chaotic noise background, the new method can reduce the root mean square error nearly two orders of magnitude, reach 0.000036123, while the traditional SVM can only reach 0.049 under the condition of -54.60 dB.

Multi-mode ultrasonic Lamb waves, which are suitable for non-destructive testing of the thin plates, have dispersion characters and accompany with a lot of useful information of the time delay. But there are many difficulties to estimate the time delay parameter. Based on the adaptive chirplet dispersion transform , we put forward a new method of the time delay estimation to make the parameter estimation possible. The results of the simulations and experiments show its effectiveness.

Atmospheric electric field signal (AEFS) features can be characterized by their average value (AV), standard deviation (SD), and entropy value (EV). How to mine and fully utilize AEFS features to ensure reliable and efficient thunderstorm detection has not been considered so far. In this article, based on the stacked autoencoder (SAE) and extreme gradient boosting (XGBoost) model, extracted deep-seated features of AEFS are used to obtain its predicted value (PV). It fuses three regular features plus one PV feature and proposes a thunderstorm moving path (TMP) prediction system with switchable patterns among the applied three AEFS prediction models based on the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). This fully considers that a single model is difficult to meet AEFS predictions with different weather attributes. Specifically, AEF data measured by a self-made AEF apparatus are adopted to determine feature values (FVs). According to FV intervals (FVIs) in sunny and thunderstorm weathers, the proportion of each feature satisfying FVIs is taken as the weighting factors of corresponding feature terms. A switchable pattern function with different switching conditions is formed by combining weightings and feature variables. Optimal AEFS prediction models are fixed under the same switching condition and applied to corresponding patterns. Empirical results confirm that the proposed system effectively predicts TMPs, with an average determination coefficient of 95.58%. This is the first study to design switchable patterns to detect thunderstorms from a new perspective of multiple AEFS feature fusion, which provides promising solutions to the refinement and intelligent prediction of thunderstorms.

The principle of the polarographic oxygen electrode was introduced,and a quick and accurate measuring instrument of dissolved oxygen(DO) was designed based on the low power consumption and high-precision MSP430 microcontroller.On the basis of analyzing the temperature characteristics of DO electrode,this paper designed a software temperature compensation strategy.Then,the function of automatic temperature compensation was implemented.The spot tests indicate that the measuring instrument has the characteristics of fast response,stability and strong noise resistances.It can meet the measuring precision in environmental protection and aquaculture.

We investigate the decentralized diagnosis of stochastic discrete event systems (SDESs) by using multiple local stochastic diagnosers, each possessing its own sensors to deal with different information. We formalize the notions of decentralized diagnosis for SDESs by defining the concept of codiagnosability for stochastic automata, in which any communication among the local stochastic diagnosers or to any coordinators is not involved. These notions are weaker than the corresponding notions of decentralized diagnosis of classical DESs. A stochastic system being codiagnosable means that a fault can be detected by at least one local stochastic diagnoser within a finite delay. We construct a codiagnoser from a given stochastic system with a finite number of projections whose each diagnosis component uses the complete model of the system. We also deal with a number of basic properties of the codiagnoser. In particular, a necessary and sufficient condition of the codiagnosability for SDESs is presented, which generalizes the corresponding results of centralized diagnosis for SDESs. Also, we give a computing method in detail to check the codiagnosability of SDESs. As an application of our results, some examples are described.