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With the development of 5G communication technology and the rise of power semiconductors, the switching frequency of the circuit keeps increasing, which pushes for miniaturization of power modules and related components. Therefore, in this paper, a one-step molding technology was proposed for a DC/DC buck converter power module. We proposed a method of using Soft Magnetic Powder filled Epoxy (SMPE) adhesive as a molding material to encapsulate a power module, which is a DC/DC buck converter power module contains several passive components, 1 power inductor, and a high-efficiency switching regulator with two integrated N-channel MOSFETs. On the basis of Finite Element Method (FEM), models were firstly established with component level moldings and checked with actual module samples for calibration. Based on the calibrated model, inductors without component level molding were then simulated. SMPE with 4~7μm insulated carbonyl ferrous powder were prepared and measured the magnetic relative permeability. Such material was investigated to pot the whole power module as a one-step molding, instead of separate molding for the power inductor and the power module. After that, thermal analysis and inductance were calculated and compared.

The diesel engine is the core power equipment of ships, so keeping it in good working condition is vital for maintaining the overall efficiency of sea transportation. Generally speaking, the principal component analysis (PCA) based fault detection methods are highly depend on the unlabeled information of training data, that is means, the information contained in labeled data is largely ignored. However, the diesel engine working process is always accompanied with small amount of faulty samples, which may seriously affect the performance of the PCA model when the faulty samples are included for modeling. Besides, even though there are enough faulty samples for modelling, labeling these samples is also time-consuming and costly. In this paper, a semi-supervised PCA (SSPCA) is proposed and applied to diesel engine fault diagnosis instead of unsupervised learning, which incorporates both the labeled and unlabeled samples and gains enhanced fault diagnosis performance regarding marine diesel engine. The methodology presented is evaluated in the real-world diesel engine working process, the experimental results indicate good robustness to false alarms and certain practical significance towards the fault diagnosis of engineering object.

Bearings play an important role in mechanical systems, and it is of great significance to monitor the health status of the bearing in real time for ensuring long-term stable operation of mechanical system. This paper proposes a new reconstruction-based fault prognosis method to process the time domain and frequency domain characteristics of vibration signals. Firstly, the principal component analysis (PCA) model is established to detect the fault factors in current measurement data. And then, the fault directions are selected according to its fault correlation and the robustness of the corresponding fault magnitude evolution process. Finally, the vector autocorrelation (VAR) model is used to predict the fault degradation process to achieve complete fault prognosis. The effectiveness of the developed method is validated by PRONOSTIA data.

Web service is a popular solution to integrate components when building a software system, or to allow communication between a system and third-party users, providing a flexible and reusable mechanism to access its functionalities. Various web service based systems are prevailing in health service provision. We propose a framework of construction, searching and protection of typed health resources in terms of data, information and knowledge through a hierarchy consisting of Data Graph, Information Graph and Knowledge Graph. We use cases to illustrate the mechanism of the framework.

The suppression of antenna direct coupled wave and medium surface wave is an very important step in the signal processing of the shallow surface penetrating imaging radar. For shallow buried targets, due to its buried depth is less than the depth resolution, the traditional clutter inhibition method based on the mean cancellation and distance gating is not applicable. In addition, in order to improve the scanning efficiency, array antennas are usually used to realize the simultaneous sampling to multiple spatial position echoes. The discordance of amplitude and phase between the multi-channel of the antenna array also makes the clutter suppression effect of the mean cancellation algorithm not ideal. In this paper, an adaptive suppression method for array antenna based on the maximum entropy gradient is proposed. Experimental results prove that this method can effectively repress the two kinds of clutter and improve the image quality effectively. The processing results using measured data prove this method effective in clutter suppression.

For the 2D radar system, in order to improve the performance of target plots, a new algorithm based on geometric center and amplitudes of measurements was proposed, aimed at that the traditional plots centroid algorithm does not use the amplitude information or over-uses it. The proposed algorithm can reduce the weights of the measurements which are far away from the geometric centre, so as to obtain mere accurate range and azimuth estimations even under the circumstances of strong false measurements. Simulations show that the proposed method works better than the traditional methods.

Radar High Resolution Range Profile (HRRP) represents the radial distribution of target scattering centers on radar line of sight, which infers something about target structure signatures. In this paper, the method of HRRP target recognition based on Gradient Boosting Decision Tree (GBDT) is proposed and the method how to choose model parameters is investigated. The experimental results from measurement data have shown that GBDT can achieve better recognition results and high calculation efficiency than SVM and Naive Bayes classifier.

This article firstly analyses the principles and characteristics generated by CAPP in view of elaboration on the main mode of IP financing security. Secondly, it reveals the patterns and contents of IP financing security based on CAPP from market-oriented security operation, systematic process control and proactive risk management. Finally, according to the feasibility and superiority of CAPP in the IP financing security and the practical situation of IP financing security for SMEs in China, it puts forward the significance and development of IP financing security based on CAPP.