Testability plays an important role in improving the readiness and decreasing the life-cycle cost of equipment. Testability demonstration and evaluation is of significance in measuring such testability indexes as fault detection rate (FDR) and fault isolation rate (FIR), which is useful to the producer in mastering the testability level and improving the testability design, and helpful to the consumer in making purchase decisions. Aiming at the problems with a small sample of testability demonstration test data (TDTD) such as low evaluation confidence and inaccurate result, a testability evaluation method is proposed based on the prior information of multiple sources and Bayes theory. Firstly, the types of prior information are analyzed. The maximum entropy method is applied to the prior information with the mean and interval estimate forms on the testability index to obtain the parameters of prior probability density function (PDF), and the empirical Bayesian method is used to get the parameters for the prior information with a success-fail form. Then, a parametrical data consistency check method is used to check the compatibility between all the sources of prior information and TDTD. For the prior information to pass the check, the prior credibility is calculated. A mixed prior distribution is formed based on the prior PDFs and the corresponding credibility. The Bayesian posterior distribution model is acquired with the mixed prior distribution and TDTD, based on which the point and interval estimates are calculated. Finally, examples of a flying control system are used to verify the proposed method. The results show that the proposed method is feasible and effective

Within the field of remote sensing, Unmanned Aerial Vehicle (UAV) infrared object detection plays a pivotal role, especially in complex environments. However, existing methods face challenges such as insufficient accuracy or low computational efficiency, particularly in the detection of small objects. This paper proposes a lightweight and accurate UAV infrared object detection model, YOLO-UIR, for small object detection from a UAV perspective. The model is based on the YOLO architecture and mainly includes the Efficient C2f module, lightweight spatial perception (LSP) module, and bidirectional feature interaction fusion (BFIF) module. The Efficient C2f module significantly enhances feature extraction capabilities by combining local and global features through an Adaptive Dual-Stream Attention Mechanism. Compared with the existing C2f module, the introduction of Partial Convolution reduces the model’s parameter count while maintaining high detection accuracy. The BFIF module further enhances feature fusion effects through cross-level semantic interaction, thereby improving the model’s ability to fuse contextual features. Moreover, the LSP module efficiently combines features from different distances using Large Receptive Field Convolution Layers, significantly enhancing the model’s long-range information capture capability. Additionally, the use of Reparameterized Convolution and Depthwise Separable Convolution ensures the model’s lightweight nature, making it highly suitable for real-time applications. On the DroneVehicle and HIT-UAV datasets, YOLO-UIR achieves superior detection performance compared to existing methods, with an mAP of 71.1% and 90.7%, respectively. The model also demonstrates significant advantages in terms of computational efficiency and parameter count. Ablation experiments verify the effectiveness of each optimization module.

The flight control system plays an important role in adjusting the attitude of manned or auto-pilot aircrafts. To reduce the fault diagnosis time and accelerate the maintenance actions, many flight control systems have adopted the design for testability. Testability demonstration for the flight control system is needed to check the indexes of testability such as fault detection rate and fault isolation rate. Currently, the standards and statistical methods for the testability demonstration planning have the problems such as large sample, long test period and it is not optimal for the flight control systems which are of complex structure and high cost. A testability demonstration planning method based on the sequential probability ratio test method is proposed as it can decrease the sample size with almost the same operation characteristic as the classical method. Firstly, the decision factor and rules of the sequential probability ratio test method and truncated decision rules are introduced. Secondly, the establishment of failure mode set based on the failure rate for the sequential probability ratio test method is illustrated. Finally, the demonstration of fault detection rate for a flight control system is implemented with the given method and steps. Software named testability demonstration and evaluation system which can calculate the decision criteria, plot decision chart, select failure mode and make decisions is used to assist the implementation of the test. The result shows that the fault detection rate passes the test with a credible performance and the actual sample size is remarkably decreased while comparing with the classical method.

Testability is a new critical characteristic of equipment, and testability demonstration test plays an important role in assuring the designed testability levels. As a result of the complex structure and higher cost of the current equipment, a reasonable testability demonstration test plan can both guarantee the desired testability level and effectively decrease the test cost. This article considers the testability demonstration test planning with the binomial distribution. The sequential posterior odds test method that combines Bayesian method and sequential probability ratio test method is used to form a sequential decision process by calculating the Bayes factor and making judgment according to the test result sequentially. With a sequential decision criterion, the test result space is divided into three subspaces, based on which a Bayes factor threshold calculation method is presented according to the restriction parameters and prior probability density function of the testability index. Operation characteristic and average sampling number are compared among the classical method, sequential probability ratio test method and the sequential posterior odds test method, which shows that with nearly the same operation characteristic, sequential posterior odds test method has smaller average sampling number than sequential probability ratio test method and classical method. The impact of prior parameters on the sequential posterior odds test method is analyzed, and the results indicate that the sequential posterior odds test method can perform well with a range of prior parameters. Physical testability demonstration tests are carried out to verify the features of sequential posterior odds test and sequential probability ratio test methods. The result indicates that with the same restriction parameters and test condition, the sample size of sequential posterior odds test is smaller than the sample size of sequential probability ratio test as a result of the introduction of prior information.

Testability demonstration plays an important role in assuring the testability capability, which can decrease the fault diagnosis time and accelerate the maintenance actions. However, testability demonstration test with classical planning method has the problems of large fault sample size, high test cost, and long test period. A new testability demonstration planning method is proposed, which takes the component level data from both virtual and physical demonstration tests as prior information. Owing to the limitations of the testability modeling technology and relevant programming tools, the virtual testability prototype of the system level cannot be established and the virtual testability test data is not totally credible. So a data conversion method based on the information entropy theory is proposed to convert the component level virtual and physical test data into equivalent system test data, in which the data credibility is taken into consideration. The equivalent system test data is then used to get the prior probability density function of the testability indexes with an empirical Bayesian method. Then, a testability demonstration planning method of Bayesian posterior risk criteria is presented. Finally, the fault detection rate demonstration tests of a flight control system and a heating controller are taken as examples to verify the proposed method. The results show that the introduction of prior test data can effectively decrease the sample size and the credibility of the virtual testability test data can affect the test plan.

Accurate modeling of event sequences is valuable in domains like electronic health records, financial risk management, and social networks. Random time intervals in these sequences contain key dynamic information, and temporal point processes (TPPs) are widely used to analyze event triggering mechanisms and probability evolution patterns in asynchronous sequences. Neural TPPs (NTPPs) enhanced by deep learning improve modeling capabilities, but most suffer from limited interpretability due to predefined functional structures. This study proposes KANJDP (Kolmogorov–Arnold Neural Jump-Diffusion Process), a novel event sequence modeling method: it decomposes the intensity function via stochastic differential equations (SDEs), with each component parameterized by learnable spline functions. By analyzing each component’s contribution to event occurrence, KANJDP quantitatively reveals core event generation mechanisms. Experiments on real-world and synthetic datasets show that KANJDP achieves higher prediction accuracy with fewer trainable parameters.