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Modern production has stricter requirements for the reliability of complex systems; thus, it is meaningful to estimate the health of complex systems. A complex system has diverse observation features and complex internal structures, which have been difficult to study with regard to health condition estimation. To describe continuous and gradually changing time-based characteristics of a complex system’s health condition, this study develops a feature selection model based on the information amount and stability. Then, a reliability tree analysis model is designed according to the selected relevant features, the reliability tree is developed using expert knowledge, and the node weight is calculated by the correlation coefficient generated during the feature selection process. Using the simple recurrent unit (SRU), which is a time series machine learning algorithm that achieves a high operating efficiency, the results of the reliability tree analysis are combined to establish a tree-structure SRU (T-SRU) model for complex system health condition estimation. Finally, NASA turbofan engine data are used for verification. Results show that the proposed T-SRU model can more accurately estimate a complex system’s health condition and improve the execution efficiency of the SRU networks by approximately 46%.

To address the dynamic path planning for multiple UAVs using incomplete information, this paper studies real-time conflict detection and intelligent resolution methods. When the UAVs execute the task under the condition of incomplete information, the mission strategy of different UAVs may conflict with each other due to the difference in target, departure place, time and other factors. Based on the multi-agent deep deterministic policy gradient algorithm (MADDPG), we designed new global reward and partial local reward functions for the UAVs’ path planning and named the improved algorithm as a complex memory driver-MADDPG (CMD-MADDPG). Thus, the trained UAVs can effectively and efficiently perform path planning tasks in conditions of incomplete information (each UAV does not know its reward function and so on). Finally, the simulation verifies that the proposed method can realize fast and accurate dynamic path planning for multiple UAVs.

In outdoor environments or environments with space restrictions, it is difficult to transport and use conventional computed tomography (CT) systems. Therefore, there is an urgent need for rapid reconstruction of portable cone-beam CT (CBCT) systems. However, owing to its portability and the characteristics of temporary construction environments, high precision spatial location is difficult to achieve with portable CBCT systems. To overcome these limitations, we propose an iterative self-calibration improvement method with a self-calculated initial value based on the projection relationship and image features. The CT value of an open field image was used as the weight value of the projection data in the subsequent experiments to reduce the nonlinear attenuation of the projection intensity. Subsequently, an initial value was obtained based on the invariance of the rotation axis. Finally, self-calibration was realized iteratively using the reconstructed image. This method overcomes the main problem of the rotation axis invariance calibration algorithm—high similarity between the adjacent positions of symmetrical homogeneous materials. The proposed method not only improves the precision of self-calibration based on the projection relationship, but also reduces the performance cost and solution time of the self-calibration algorithm based on the image features. Thus, it satisfies the precision requirements for self-calibration of portable CBCT systems.

This paper presents a new scheme for the online solution of a networked multi-agent pursuit–evasion game based on an online adaptive dynamic programming method. As a multi-agent in the game can form an Internet of Things (IoT) system, by incorporating the relative distance and the control energy as the performance index, the expression of the policies when the agents reach the Nash equilibrium is obtained and proved by the minmax principle. By constructing a Lyapunov function, the capture conditions of the game are obtained and discussed. In order to enable each agent to obtain the policy for reaching the Nash equilibrium in real time, the online adaptive dynamic programming method is used to solve the game problem. Furthermore, the parameters of the neural network are fitted by value function approximation, which avoids the difficulties of solving the Hamilton-Jacobi–Isaacs equation, and the numerical solution of the Nash equilibrium is obtained. Simulation results depict the feasibility of the proposed method for use on multi-agent pursuit–evasion games.

Fault diagnosis is a common problem during the process of complex system maintenance. The rule-based systems is particular fitted to address the diagnosing problem due to its interpretability. By transferring the measured data into belief rules, the extended belief rule-based systems(EBRBS) combines the advantage of both data driven method and the structure of rule-based systems and is proved to be effective in addressing many real word problems. Nevertheless, counterintuitive rule weight calculating procedures, that may deteriorate the performance of EBRBS, exists in the original method. In this study, we point out the existing problem and proposed a new rule weight calculation method. Afterwards, the proposed method is utilized to address real-world liquid ultrasonic flow meter diagnosis problems. The results shows the effectiveness of the proposed method.

A data-driven cloud test fault diagnosis method is proposed for the current testing system based on cloud computing, which has a low utilization rate of test data and fails to give full play to the operation and storage capacity of cloud computing. Firstly, the initial fuzzy reasoning fault diagnosis method is constructed based on expert knowledge and system parameters. Secondly, GSA is used to optimize the model based on historical data. Finally, the simulation platform is used for experimental verification. The results show that the system can effectively improve the utilization rate of cloud test data and achieve more accurate fault diagnosis.

As a new type of artificial intelligence technology, generative adversarial network (GAN) has good data understanding and generation capabilities, and has a wide range of application prospects in the fields of image and speech. However, due to the lack of prior knowledge, its training process is less robust and prone to occur the pattern ignore. Its development is restricted to a certain extent, and its application scope still needs to be expanded. To solve the above problems, this paper introduces a knowledge confidence multimode GAN (KC-MGAN) algorithm, calculates the confidence of the input data through the reasoning method, and then puts the confidence and the input data into the GAN system to generate new sample data. During the training process, the confidence of the input data is continuously calculated, while the generated data samples are continuously evaluated. The training process will end until the GAN system reaches a stable condition. Finally, this paper takes the generation of UAV flight trajectory data as an example to verify the effectiveness of the proposed method. Some explorations have been made for the application of data generation and GAN’s training mode with the prior knowledge.

With the development of artificial intelligence technology, the intelligent level of equipment is gradually improved, and its internal structure is becoming more and more complicated. This not only facilitates users’ use, but also improves their maintenance and auto-test requirements. Taking flight simulator and other intelligent equipment as examples, this paper introduces the development history and research status of flight simulator and embedded training, and combs the development history and research status of intelligent equipment auto-test technology, which mainly involves test resource matching and fault diagnosis.

The widely applied belief-rule-based(BRB) system has demonstrated its advantages in handling both qualitative and quantitative information. As an extension of BRB system, the extended belief-rule-based(EBRB) system bridges the rule-based methods and data-driven methods by efficiently transforming data into extended belief rules(EBRs). Many works have been done to apply EBRB system in addressing classification problems. However, the problems of making use of all attributes indiscriminately and activating almost all EBRs still affect the accuracy and computational efficiency of EBRB system. In this paper, a structure optimization method for EBRB(SO-EBRB) system, including attribute optimization and rule activation, is proposed to address aforementioned problems. In the attribute optimization, a weighted minimum redundancy maximum relevance(MRMR) method is proposed, where the relevance between attributes and label as well as the redundancy among attributes are used to evaluate attributes. Afterwards, the proposed attribute weight calculation method is utilized to assign attribute weights for the EBRB system. In rule activation, an improved minimum centre distance rule activation(MCDRA) method, which considering the weights of attributes in distance calculation, is used to activate customized EBRs for input query data. 15 benchmark classification data sets are utilized to verify the effectiveness of the proposed SO-EBRB method. The results show that, compared with conventional EBRB system, the SO-EBRB system achieves higher classification accuracy, lower rule activation ratio and less response time. Additionally, comparison between the proposed method and some state-of-art machine learning algorithms demonstrates that the SO-EBRB system achieves prominent performance in addressing classification problems.

Following the development of globalization, various industries not only in their own country, but also in the whole world, the relationship between peers is closer, the scale of technology exchange, experience exchange, research results exchange is gradually expanding, the gap of applied English talents is increasing, and more demands are put forward for the training and teaching of specialized English skilled personnel. Under the vigorous expansion situation of MOOC and SPOC, this paper introduces the concepts and processes of MOOC and SPOC, and discusses the relationship between them and their respective characteristics and advantages. The author finds that it is this post-MOOC era that provides a rich approach and a new platform for ESP teaching experiments in colleges and universities.

The calculation method for the form similarity of English words is carried out. An algorithm where the upper limit of string similarity parameters can be set is used for automatic extraction of words with similar spellings from a specified vocabulary range. The frequently confused words with similar spellings screened can enrich the English lexical knowledge base after duplicate removal and classification. The frequently confused word knowledge base is of application value in the fields of textbook writing, vocabulary training design, dictionary compilation, and real word misspelling correction.

Fault diagnosis can be divided into two main tasks: fault feature extraction and fault data classification. Firstly, aiming at the problem that the fault feature extraction method is not significant, this paper proposes a fault feature extraction method based on fuzzy distance (FD). The initial fuzzy distance calculation method is established by expert knowledge. The key parameters of fuzzy distance are optimised based on DE algorithm combined with historical test data. Secondly, aiming at the problem of low accuracy when classifying fault data, this paper proposes a fault data classification method based on grey target (GT) decision. The fault data classification is realised by calculating the target distance of each fault type with the current input signal set. Finally, the fuzzy distance- grey target (FD-GT) method is verified by an example. The results show that it can achieve more efficient and reliable fault diagnosis.