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The number of interventional treatments for hepatic cystic echinococcosis is increasing, but the chemicals or high temperatures used in these methodologies cause biliary complications, thus limiting their clinical applications. This experimental study aimed to apply a novel, non-thermal, non-chemical ablation method termed nanosecond pulsed electric field (nsPEF) for the treatment of human hepatic cystic echinococcosis. The nsPEF treatment parameters against protoscolices from human hepatic cystic echinococcosis were optimized in vitro. The efficacy and mechanism of nsPEF treatment were also investigated. Fresh protoscolices were isolated from human hepatic cystic echinococcosis and were exposed to 300 ns of nsPEF with different field strengths (0, 7, 14, 21, and 29 kV/cm) and pulse numbers (50 and 100 pulses). Then, the viability of the nsPEF-treated protoscolices was evaluated in vitro. Morphological and ultra-structural changes were visualized with H&E staining and scanning electron microscopy. The membrane enzyme activity of alkaline phosphatase (AP) and gamma-glutamyl-transpeptidase (GGT) was measured. nsPEF caused dose-dependent protoscolex death. One-hundred pulses of nsPEF at 21 kV/cm or higher caused a significant increase in the death rate of protoscolices. nsPEF induced significant lethal damage with 50 pulses at 21 or 29 kV/cm and with 100 pulses at 14, 21, or 29 kV/cm, accompanied by morphological destruction and increased levels of AP and GGT membrane enzymes. Thus, nsPEF induced dose-dependent protoscolex mortality and caused destruction of protoscolices and increased membrane enzymes. The mechanism may involve direct damage to the membrane structures of the protoscolices, promoting enzyme exhaustion and disruption of metabolism.

Plasma stealth is a new concept and principle of stealth technology. Firstly, WKB analysis method is used to analyse the attenuation of electromagnetic wave under the action of plasma. Then, a certain UAV inlet was selected to establish a model, and the inner wall was covered with a ring of closed cavity. Plasma was generated by discharge, and the stealth performance of the inlet was numerically simulated for the L-band electromagnetic wave and its stealth effect was analyzed. This method verifies the feasibility of applying plasma stealth technology to the l-band radar wave for inlet stealth.

In view of the difficulty of the traditional reliability evaluation method for multivariate performance parameters, this paper puts forward a kind of reliability assessment method which is based on performance degradation and Dempster-Shafer (DS) evidence theory. Firstly, the degradation data of each performance parameter is modeled, and their degradation models and life distributions are obtained. Secondly, a distance function of Jousselme is introduced to modify the traditional evidence theory to improve the processing ability of the conflicting evidence information. Thirdly, the improved DS evidence theory is used to fuse and make decisions on life data of each key performance parameter, and then the overall reliability index of the product can be obtained. Finally, this method is tested with performance degradation data of fiber-optic gyroscope and the storage life of it is 38,000 hours. The experimental result shows that this method is more reasonable and accurate for reliability assessment of multivariate performance parameters of fiber-optic gyroscope and has certain reference value for maintenance support decision of equipments. It is also applicable to the reliability assessment of other equipments.

Accelerated degradation test is an efficient approach to evaluate the reliability of degradation products. An effective accelerated test must guarantee that the failure mechanisms of product under all accelerated stresses are consistent with that under the normal stresses. Otherwise, the reliability of product under the normal stresses cannot be accurately extrapolated from accelerated degradations data. A method of identifying the consistency of failure mechanism is proposed, in which an inverse Gaussian process is used to model accelerated degradation data. First, the acceleration factor constant principle was applied to deduce the changing rules of model parameters with accelerated stresses. Then, the parameter estimates were evaluated from accelerated degradation data by maximum likelihood estimation method. If the parameter estimates obey the changing rules, the failure mechanisms are considered to be consistent, otherwise not. Next, a hypothesis method based on t statistic was developed to verify whether the parameter estimates obey the changing rules or not. Besides, a validation test was designed to assess the performance of the identification method, and a case study was provided to demonstrate the effectiveness and usefulness of the method.

Metal etching process is considered to be a bottleneck step in semiconductor manufacturing, and many parameters of etchers can be monitored with sensor systems. So, an effective fault detection algorithm for the etching process is particularly important. Recently, some classification methods that only need positive samples such as principal component analysis-based methods and k-nearest neighbor rule based methods have been used for fault detection and achieved some meaningful results. But the requirement that the distribution of the monitored parameters obeys certain structure is hard to satisfy due to product mix. Our method-fault detection via sparse representation is based on the assumption that there exists a similar pattern to the testing sample in the training set, while the fault is exotic to the training set. So, normal samples can be linearly represented well (a low representation error) using few training samples while the fault cannot reach the same error precision, which is independent of the distribution of the samples. Once the representation of the testing sample is determined using sparse representation model, the distance between the original sample and its representation using few high-weighted basis can be used to detect the fault. The illustrative and industry experiments have verified the efficiency of our method.

To solve the correlation problem in evidence theory, this paper proposes an evidence theory model based on Copula function and related reliability assessment methods. First, we establish a performance degradation model based on the Wiener process and calculate the PDF of the lifetime distribution. Secondly, the fuzzy C-means algorithm is used to discretize the probability density function and obtain the marginal basic reliability allocation (BPA). Finally, the fusion of multidimensional evidence variables is carried out using the improved synthesis rules based on Copula function. The example analysis shows that this method solves the evidence-related problems in traditional evidence theory, and it is more efficient than other methods in the literature.

This paper propose a novel fault diagnosis of bearings approach based on sparse representation. Three steps are conducted to classify the fault types. In the dictionary learning step, dictionary is learned using training set with known fault types; in the sparse coding step, testing samples with unknown fault types are represented through spares representation model with sub-dictionaries extracted from the dictionary learned in the first step; in the last step, fault types are evaluated through a Hough voting process. Different from most conventional fault diagnosis methods, rejection mechanism can be easily introduced to guarantee the diagnosis accuracy, which is suitable in areas where high diagnosis accuracy is required such as nuclear and aerospace industries. Finally, an experiment is undertaken to verify the effectiveness of our model.

This paper proposes a novel methodology of rolling bearings remaining useful life (RUL) estimation based on sparse representation theory. By analyzing the inner relationships among monitored data, three particular properties of remaining useful life estimation tasks are concluded as the key prior knowledge: monotonicity and continuity of RUL evaluation show the relationships of monitored data and the evaluated RUL; similarity of adjacent monitored data lays the foundation of data driven RUL evaluation methodologies. These properties are then integrated into dictionary learning and sparse coding of sparse representation progress to build a remaining useful life estimation model. In the dictionary learning step, monotonicity and similarity are integrated through a matrix operator to learn more effective and concise dictionary; in the sparse coding step, new sparse model is proposed involving continuity and similarity. In the last step, the life ratio of training set to testing set is calculated through a Hough voting with sparse codes acquired from sparse coding step. With this ratio, the useful life of testing set can be calculated. So, the RUL of the testing set can be evaluated through useful life subtracting the running duration. Experiments are conducted on the ball bearing data provided by FEMTO-ST Institute and the results show the efficiency of our methodologies.

The research on system simulation credibility is the premise of taking good use of simulation results. First, the previous information is summarized in this paper. At the basis of making clear basic concepts related to simulation credibility, the various factors that affect simulation credibility are analyzed. A mathematical model of simulation credibility is set up, which is divided into the process credibility and the result credibility. Then, a preliminary study on methods of confirming the simulation result is carried out, through an example in military aspect. All these provide a certain theoretical basis and practical way to study simulation credibility in depth.

Aiming at forecasting failure time of the missile more accurately, saving the manpower, money and material resources, a data-fusion forecast model based on artificial neural network was put forward. First, several single forecasting methods including grey model, time series model and so on were used. Then, all of the single forecast results were fused by artificial neural network. The model was calculated by using Matlab and the final result was get. Experimental results showed that the model achieved better forecast results compared with other forecast models.

This paper analyzes how to set up a trajectory simulation of the short-range anti-ship missile, considering curvature of the earth. According to the system working mechanism of some missile and curvature of the earth, a motion simulation model of the whole trajectory is built. A lot of shooting simulation tests are conducted, and the results show that various ranges have different influences on acquisition probability, homing probability and hitting probability of the missile. The validity of this method is verified by the tests.

Operation and support cost occupy large proportion of life-cycle cost. The operation cost prediction is difficult due to the complex helicopter flight conditions. Based on the fatigue damage by flight conditions, an analytical model is presented for the calculation of influence of flight time proportion changes on the operation cost of helicopter components by introduced influence factor. Two influence factors for the cases where only one flight condition is modified at a time and all flight conditions except one are modified can provide qualitative ordering and quantitative assessment of flight condition changes on operation cost. Some results are obtained by the analysis to helicopter main rotors based on flight spectrum. For the helicopter main rotors only two of the forty-eight flight conditions would have any significant effect that a 0.1 time fraction change leads to operation cost change more than 0.01. The results indicate this method can be used practically in operation cost prediction of helicopter components.