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Cyberspace mimic defense (CMD) is an active defense theory that has emerged in recent years. By dynamically constructing and scheduling multiple executors, the CMD can not only effectively defend against security threats caused by unknown cyberspace weaknesses, but also improve the present situation of information asymmetry between attackers and defenders in cyberspace. However, as one of the key technologies of the CMD, the scheduling strategy algorithm still needs to be improved in real-time security, reliability, and universality, which has restricted the development and large-scale deployment of the CMD. To solve this problem, we propose an optimal seed scheduling strategy algorithm (OSSSA) in this paper. After using continuous-time Markov processes to mathematically analyze the model of the mimic defense system in cyberspace, we introduce the key factors and their evaluation methods that affecting the CMD defense performance in the OSSSA, then propose a reliable working mechanism of the OSSSA. Furthermore, we present an evaluation method that can effectively evaluate the defense performance of the scheduling algorithms. Experimental results from software simulations and real experiments show that the OSSSA has better real-time defense performance than the current mainstream scheduling strategy algorithms, and can adapt to different practical application scenarios, which is helpful to the further development of the CMD.

In the big data era, machine learning is expected to be a promising solution for intrusion detection. However, most existing machine learning based Intrusion Detection Systems (IDSs) are not satisfactory in performing the multiclass intrusion detection task in the case when traffic data are imbalanced. In this paper, a multi-layer intrusion detection system named KC-IDS is proposed, which aims to improve the detection performance of multi-class classification task. The motivation of KC-IDS is to predict different kinds of traffic in different layers and combine the advantages of k-Nearest Neighbors (kNN) and Categorical Boosting (CatBoost) to alleviate the effects of imbalanced data. Experimental results on KDD99 dataset verified that KC-IDS can achieve better detection performance compared with some existing machine learning methods.

Aiming at the complex structure of cross-linked polyethylene(XLPE) power cable joints and the characteristics of partial discharge(PD) easily caused by insulation defects, the paper designs and develops an improved built-in coupling sensor based on capacitive coupling effect. Firstly, by constructing the equivalent circuit model of the sensor, the transfer function of the sensor is calculated, and the factors affecting the detection performance of the sensor are comprehensively analyzed. Secondly, based on the analysis of the detection performance of the sensor, the design scheme of the effective detection frequency band of the sensor is determined, and the structural size of the sensor is determined, including the width of the ring electrode and the thickness of the insulating cushion of the sensor. Finally, the key performance test experiments are carried out on the developed sensor. The test results show that the effective detection frequency band of the sensor is $1 \sim 300$ MHz, and the effective detection sensitivity is 5pC, which can effectively detect the internal PD signal of the cable joint.

Gearboxes are important parts of mechanical transmission systems, so that it is very pivotal for avoiding future catastrophic failures to detect faults in gearboxes at the early stage. Various vibration analysis techniques have been proposed and successfully applied to detect faults in gearboxes. The outputs of these techniques are used to identify the signal changes caused by defects. Because of modulations, the low-energy sidebands associated with partial gear teeth failures have also been proved to be notable. However, modulation sidebands are usually very weak and they would be easily submerged in inherent signal produced by mesh vibration. This paper presents an enhanced program based on frequency band analysis and minimum entropy deconvolution (MED) to identify the difference between healthy gear and gear with faulty tooth. With this program, the measured signal is first filtered with the MED filter. Then, spectrum analysis technique is applied to the enhanced-signal to find the modulation sidebands around mesh frequency. The enhanced program is validated using the test data from a two-stage wind turbine gearbox. The results show that the MED enhanced program is an effective tool to detect the pitting fault of gear teeth.

Owing to outstanding properties of two-dimensional electron gas (2DEG) and GaN-base material, such as high electron mobility and high critical electric field, AlGaN/GaN high electron mobility transistors (HEMTs) have been considered as the next-generation power semiconductor devices with high power conversion efficiency, high switching frequency and high-temperature operation capability. For power switching applications, enhancement mode (e-mode, or say normally-off) transistors are desired for fail-safe operation and silicon-compatible gate drive circuit. However, e-mode operation is difficult for AlGaN/GaN-based HEMTs because of the natural existence of 2DEG. To achieve e-mode operation, several approaches have been reported, including gate recess structure, fluorine plasma ion implantation, p-type gate structure and selective channel regrowth, etc. This paper reports normally-off AlGaN/GaN HEMTs with a recessed MOS-gate. After mesa isolation and Source and drain metal, the gate recess process used inductively coupled plasma (ICP). In order to avoid severe etching damage and obtain high drain current density, etching power was carefully optimized. The etching rate is slow for accurate etching depth control to leverage the current density and threshold voltage. After that, an O2 plasma treatment was applied using a plasma asher to oxidize the damaged semiconductor surface. The oxide layer was then removed in HCl: DI-water (1: 3). The next step is an atomic layer deposition (ALD) of Al 2 O 3 as the gate dielectric to increase the breakdown voltage. Follow after that, gate metal and pad metal. By the process, our team made three kinds of HEMTs with different recessed gate depths. The first one, which has been report before, exhibits a high threshold voltage of +4.6V, a specific on-resistance of 4mΩ-cm⁁2 and a drain current density of 108 mA/mm. This paper will show the others fabricated in the later researches. The device transfer curves show that this normally-off recessed MOS-gate AlGaN/GaN HEMTs exhibit threshold voltage of +0.9V and +2.1V, respectively. Additionally, the specific on-resistance and saturation drain current density of the device with 0.9V threshold voltage are 2.26mΩ-cm⁁2 and 326mA/mm, while 2.1V-Threshold Voltage-device are 3.03mΩcm⁁2 and 173mA/mm. Both of the two HEMTs have a breakdown voltage over 400V.

Aspect-Oriented Programming (AOP) aims to address the scattering and tangling of cross-cutting concerns in a system. Many aspect mining techniques are proposed based on the concepts that concerns crosscut other modules of a system. Most of these researches targeted on a single version during the development of a software system. However, it is also possible that the difference between versions during the development of a software system may also be a source of aspects. In this paper, we propose to discover aspect candidates through the analysis of a series of versions of a software system. A supporting tool, AMTV (Aspect-Mining Tool among Versions), is also developed based on the fan-in metric to identify aspect candidates across multiple versions of a software system. An experiment of applying the proposed approach is conducted to a software system JHot Draw, and the results show that eight kinds of aspect candidates may appear during the development of a software system. At last, several advices are given to programmers as a reference for dealing with aspects while developing a software system using aspect-oriented development paradigm.

With the development of power systems and the extensive application of nonlinear elements in the electricity system, the problem of ferroresonance do harm to the network and electricity facilities has become more prominent. Wavelet analysis has good time-frequency localization characteristic, so it is suitable for fault analysis of power system. This paper is base on in the neutral point unearthed system, which is excited by single phase grounding fault, take analysis of the value of neutral point with the Daubechies wavelet analysis. Simulation results show that with wavelet transforms the occurrence of Ferro resonance time can be detected and the frequency resonance can also be determined.

The paper has made an empirical study of the relationship between the prices of soybeans, soybean meal and corns in DCE and the stock price of New Hope Group, the representative of listed agricultural companies in feed industry, by such econometric methods as ADF unit root test, extended E-G test and Granger causality test, etc., and presented relevant conclusion as well as suggestions. The study shows that there is a long-term balanced co-integration and two-way Granger casual relation between the forward prices of dominant futures like soybeans and corns in DCE and the stock price of New Hope Group while between the price of soybean meal futures and the stock price there is only a co-integration and one-way Granger causality with stock price affecting the price of soybean meal futures, from which it can be easily concluded that the price discovery ability of soybeans and corns is better than that of soybean meal.