Reliable anomaly detection under data contamination remains a major challenge in Prognostics and Health Management, especially when degradation processes are gradual and clean training data are unavailable. This paper introduces a weighted iterative refinement framework with autoencoders for contaminated anomaly detection (WIRACAD) to address this problem. The method, which is based on reconstruction residuals, re-weights training samples across iterations in order to progressively reduce the influence of suspected anomalies. This continuous refinement improves the robustness of health indicator learning from contaminated time series. The proposed approach is evaluated on two public benchmarks: the NASA C-MAPSS dataset and the IMS Bearing dataset. Results show consistent improvements in key metrics related to degradation monitoring. In particular, the overal fit score and the monotonicty are improved when compared to baseline autoencoder training and recent refinement-based methods. These findings suggest that iterative sample weighting can enhance unsupervised anomaly detection with autoencoders in settings where data contamination is assumed.

Military manpower planning aims to match the required and available staff.Statutory and competence logics are two linked aspects of the military manpower management.Military manpower management involves the long term planning with strategic goals, and also the short term human resources management with operational goals.These two aspects are interdependent; therefore this article proposes a technique to combine both logics in the same integrated model.A combined model allows the simultaneous optimization for both logics.In this article we illustrate a model based on flow network.We present integer programming and goal programming to find optimal solutions.