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Update parm_dict.json

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  1. parm_dict.json +115 -0
parm_dict.json ADDED
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+ {
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+ "feature_file_name": "multi_features_data_simu5.pkl",
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+ "dsp": "model config file (Example)",
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+ "datasource": [
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+ {
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+ "type": "vibration",
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+ "field": "esp32_vib.z_acc",
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+ "sample_rate": 1000
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+ },
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+ {
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+ "type": "power",
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+ "field": "esp32_power.ch0",
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+ "sample_rate": 200
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+ },
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+ {
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+ "type": "sound",
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+ "field": "sound.data",
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+ "sample_rate": 16000
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+ }
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+ ],
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+ "FeatureDummyPoweroff": {
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+ "Is_enable": true,
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+ "_Is_enable_dsp": "true: append synthetic power-off feature rows during Feature(wav) and Feature(Direct). false: keep only real data.",
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+ "power_threshold_mv": 20,
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+ "_power_threshold_mv_dsp": "Dummy power RMS values are uniform random between 0 and this raw mV threshold.",
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+ "ratio": 0.1,
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+ "_ratio_dsp": "Dummy rows relative to real feature windows. 0.1 adds 10%.",
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+ "label": 0,
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+ "_label_dsp": "Label value appended only when the feature run already has labels.",
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+ "random_seed": 42,
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+ "_random_seed_dsp": "Seed for repeatable dummy data. Use null for non-repeatable random values."
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+ },
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+ "Is_train_autoencoder": false,
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+ "autoencoder_structure": 4,
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+ "L1": {
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+ "Is_enable": true,
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+ "_Is_enable_dsp": "true: train/use L1 for poweroff/standby/running gate. false: skip L1 and let L2 classify all machine states directly.",
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+ "noise_threshold": 0.01,
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+ "_noise_threshold_dsp": "0603 the number less of cluster than the threshold, consider to be noise",
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+ "choose_model_power": "Mix",
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+ "_choose_model_power_dsp": "options: Mix, DBSCAN",
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+ "choose_model_vib": "Hierarchical",
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+ "_choose_model_vib_dsp": "options, Hierarchical, DBSCAN",
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+ "choose_model_pressure": "Hierarchical",
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+ "_choose_model_pressure_dsp": "options, Hierarchical, DBSCAN. Pressure is optional and trains only when pressure_features exists.",
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+ "DBSCAN_power_params": [
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+ 1.2,
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+ 50,
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+ 0.9
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+ ],
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+ "DBSCAN_params_dsp": "p1= eps, p2= min count in cluster, p3= standby threshold",
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+ "Hierarchical_power_params": [
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+ 0.2,
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+ 1,
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+ 2,
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+ 0.2
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+ ],
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+ "_param1_dsp": "param 1 is factor ,the stable indicattor (less than value)(difference from the reverse previous 2 level)",
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+ "_param2_dsp": "param 2 is min number of cluster",
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+ "_param3_dsp": "param 3 is max number of cluster",
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+ "_param4_dsp": "param 4 is standby threshold (percentage)",
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+ "Is_save_model": true,
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+ "Hierarchical_vib_params": [
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+ 0.2,
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+ 4,
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+ 10,
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+ 0.5
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+ ],
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+ "_Hierarchical_vib_params_dsp": "For vibration, the format is same",
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+ "Hierarchical_pressure_params": [
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+ 0.2,
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+ 4,
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+ 10,
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+ 0.5
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+ ],
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+ "_Hierarchical_pressure_params_dsp": "For PU5401 pressure L1 clustering. Same format as vibration.",
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+ "pressure_l1_feature": "max",
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+ "_pressure_l1_feature_dsp": "Pressure feature used by L1/noise logic. Options: max, min, average, peak_to_peak.",
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+ "L1_models_name": "L1_models.pkl"
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+ },
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+ "L2": {
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+ "strategy": "consensus",
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+ "_strategy_dsp": "options: consensus, feature_fusion, meta_clustering, multi_view_cluster",
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+ "Is_save_model": true,
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+ "consensus_model": "GMM",
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+ "_consensus_model_dsp": "model using on each type of sensor clusting: KMeans, GMM",
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+ "result_type": "prob",
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+ "_result_type_dsp": "The input the consensus matrix is each submodel result: prob (probability), one-hot",
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+ "feature_fusion_pca_factor": 0.95,
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+ "_feature_fusion_pca_factor_dsp": "how much the PCA catch of the variance for feature_fusion, meta_clustering, multi_view_cluster",
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+ "feature_fusion_model": "GMM",
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+ "meta_clustering_model": "GMM",
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+ "number_class": 0,
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+ "_number_class_dsp": "The spicify cluster number of each type of sensor. 0 mean search optimal number base on certain score",
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+ "min_class": 3,
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+ "_min_class_dsp": "minimum cluster number for search optimal option",
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+ "max_class": 7,
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+ "_max_class_dsp": "maximum cluster number (not include) for search optimal option",
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+ "stable_indicator": 0.3,
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+ "_stable_indicator_dsp": "when change less than the value, consider it's stable",
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+ "covariance_type": "diag",
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+ "_covariance_type_dsp": "GMM parameter, effect on model size",
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+ "gmm_reg_covar": 1e-06,
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+ "_gmm_reg_covar_dsp": "Small covariance regularization for GMM. Increase if a nearly constant sensor causes ill-defined covariance.",
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+ "consensus_max_matrix_samples": 10000,
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+ "_consensus_max_matrix_samples_dsp": "Maximum sample count for the full L2 consensus N x N matrix. Larger runs use direct probability clustering.",
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+ "label_sort_preference": [
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+ "vibration",
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+ "pressure",
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+ "power",
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+ "sound"
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+ ],
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+ "_label_sort_preference_dsp": "Preferred active sensor order for sorting L2 labels from lower to higher activity."
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+ }
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+ }