{"id":"W4393114197","doi":"10.1007/978-981-99-9439-7_23","title":"Design and Hardware Implementation of Fuzzy Logic Controller for Boost Converter for Battery Charging Using dSPACE","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"DSPACE; Battery (electricity); Computer science; Fuzzy logic; Controller (irrigation); Computer hardware; Embedded system; Electrical engineering; Engineering; Physics; Power (physics); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001719898,0.0004774519,0.0006844355,0.0007136258,0.0000333448,0.00004119698,0.0001824755,0.0004926104,0.00001455426],"category_scores_gemma":[0.0001489046,0.0004791071,0.0001324892,0.000151733,0.00003532855,0.000085199,0.00005779503,0.0007037086,0.000001352814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004206024,"about_ca_system_score_gemma":0.0000312395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002715846,"about_ca_topic_score_gemma":0.000002579958,"domain_scores_codex":[0.9982994,0.000005491499,0.0004552841,0.000439124,0.0001802651,0.0006204031],"domain_scores_gemma":[0.9985321,0.001090148,0.00006346319,0.0001863485,0.00007443348,0.00005349493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008957764,0.000005947274,0.000007415939,0.001990923,0.000331499,0.00001567808,0.0001024073,0.8443643,0.06423888,0.006383444,0.00006999183,0.08239996],"study_design_scores_gemma":[0.0008641492,0.0001813932,0.000002596052,0.0003042009,0.00008311115,0.00001603363,0.000003081577,0.9506984,0.02514631,0.02050451,0.001690763,0.0005054328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001120087,0.005156345,0.9921553,0.0001320313,0.0001771698,0.001800289,0.00006540533,0.0003281057,0.00007333167],"genre_scores_gemma":[0.7539495,0.001403113,0.2401305,0.0002329291,0.000697486,0.001248806,0.0001798679,0.001207976,0.0009498415],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7538375,"threshold_uncertainty_score":0.9997661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02343190541602654,"score_gpt":0.2813486901691749,"score_spread":0.2579167847531483,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}