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Behrad Mousaei Shir-Mohammad

Publications and source records attributed to Behrad Mousaei Shir-Mohammad.

2 recordsLinked to original sources

When Linear RUL Labels Disagree with Vibration Degradation: A Stage-Aware Target and Dual-Scale Predictor Evaluated on XJTU-SY and IMS

Remaining useful life (RUL) studies commonly treat the label as fixed, although clock-linear labels may decline while measured vibration remains nearly stable and then changes rapidly near failure. We separate target design from prediction. A development-only pipeline constructs an oriented vibration health indicator, identifies chronological early, middle, and late stages, and fits a continuous linear-quadratic-exponential degradation-state target. A compact CNN-LSTM and Transformer learn the target from causal feature sequences, and validation-fitted Ordered Weighted Averaging combines their outputs. In a bearing-wise XJTU-SY hold-out, all bearings ending in 5 are excluded from fitted preprocessing, training, early stopping, and fusion. The fused predictor obtains an RMSE of 0.0608, an MAE of 0.0392, and an R-squared value of 0.9617, with the Transformer providing most of the accuracy. Target shape is assessed independently on three documented IMS failed-bearing trajectories. Against the best anchored linear fit to the same vibration-derived reference, the stage-aware curve reduces RMSE by 3.6-18.2% and MAE by 3.1-31.1%; the mean reductions are 10.2% and 15.0%, respectively. Conservative BIC differences of 128.8-368.1 favor the stage-aware representation, whereas moving-block bootstrap intervals cross zero. Thus, stage-dependent targets better describe the evaluated vibration-derived degradation states, but the evidence remains descriptive because only three official IMS runs are available. The study establishes a measurement-oriented target-validity framework, not a universal nonlinear law for physical time-to-failure or robust cross-domain prediction.

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Topology-Aware Hybrid Wi-Fi/BLE Fingerprinting via Evidence-Theoretic Fusion and Persistent Homology

Indoor localization remains challenging in GNSS-denied environments due to multipath, device heterogeneity, and volatile radio conditions. We propose a topology-aware, hybrid Wi-Fi/BLE fingerprinting framework that (i) applies physically consistent RSS normalization (dBm z-scoring or dBm -> linear mW -> z-score), (ii) denoises streams with classical Bayesian filters (KF/UKF/PF), (iii) combines complementary regressors (Random Forest and weighted kNN with a diagonal Mahalanobis metric), (iv) performs evidence-theoretic fusion via Dempster-Shafer theory (DST), and (v) augments each sample with persistent-homology (PH) descriptors. The system outputs both (x, y) estimates and interpretable belief maps, and is engineered for microcontroller-class deployment with per-update cost O(T log M + log M + Mp + S). We evaluate on two heterogeneous datasets, including a new 1,200-sample ESP32 survey, and report ablations, robustness to test-only noise, and significance across 10 stratified splits. Under 10% synthetic RSS noise, the full pipeline attains 3.40 m (Dataset 1) and 2.45 m (Dataset 2) RMSE, improving a strong PF + RF baseline by about 37%. Averaged across splits, it yields 4.993 +/- 0.15 m versus 6.292 +/- 0.13 m (20.6% relative reduction; p < 0.001). In noise-free tests, accuracy tightens to 0.44 m and 0.32 m (up to 56% better). Compared with recent learning-heavy approaches that assume large site-specific datasets and GPU inference, our method delivers competitive accuracy with formal uncertainty quantification and low computational cost suitable for real-time deployment.

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