{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:UBLIORRMSUWKUQ3U3OF7FVRFRE","short_pith_number":"pith:UBLIORRM","schema_version":"1.0","canonical_sha256":"a05687462c952caa4374db8bf2d6258919582d777dff05547217c98404ad03f1","source":{"kind":"arxiv","id":"1604.04789","version":3},"attestation_state":"computed","paper":{"title":"A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.AI","authors_text":"Alireza Sadeghian, Antonello Rizzi, Enrico De Santis","submitted_at":"2016-04-16T19:38:21Z","abstract_excerpt":"Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in commercial and industrial environment. In this paper we present an interesting application of the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model equipped by renewable sources and an energy storage system, taking into account the economical profit in energy trading with the main-grid. In particular, this study focuses on the application of a Hierarchical Genetic"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1604.04789","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-04-16T19:38:21Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"9a9b2b65695dbd7adf824f7541aa44cbf965f5e3c6a9b27c41199f26dba420e1","abstract_canon_sha256":"549b08f71c0fdf662505db46e5b756cfa5e84a7548d83a5fcd80f3dd9e6f2510"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:23.965517Z","signature_b64":"2T7ivF65MdShKrl4F8WFILJW7rKnWxIPV3iXuQAv0IKWIbmpezEITmKljL1Mmilh1sLuOBjvBXELICBAcMYoBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a05687462c952caa4374db8bf2d6258919582d777dff05547217c98404ad03f1","last_reissued_at":"2026-05-18T00:42:23.964917Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:23.964917Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.AI","authors_text":"Alireza Sadeghian, Antonello Rizzi, Enrico De Santis","submitted_at":"2016-04-16T19:38:21Z","abstract_excerpt":"Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in commercial and industrial environment. In this paper we present an interesting application of the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model equipped by renewable sources and an energy storage system, taking into account the economical profit in energy trading with the main-grid. In particular, this study focuses on the application of a Hierarchical Genetic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.04789","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1604.04789","created_at":"2026-05-18T00:42:23.965000+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.04789v3","created_at":"2026-05-18T00:42:23.965000+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.04789","created_at":"2026-05-18T00:42:23.965000+00:00"},{"alias_kind":"pith_short_12","alias_value":"UBLIORRMSUWK","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_16","alias_value":"UBLIORRMSUWKUQ3U","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_8","alias_value":"UBLIORRM","created_at":"2026-05-18T12:30:46.583412+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UBLIORRMSUWKUQ3U3OF7FVRFRE","json":"https://pith.science/pith/UBLIORRMSUWKUQ3U3OF7FVRFRE.json","graph_json":"https://pith.science/api/pith-number/UBLIORRMSUWKUQ3U3OF7FVRFRE/graph.json","events_json":"https://pith.science/api/pith-number/UBLIORRMSUWKUQ3U3OF7FVRFRE/events.json","paper":"https://pith.science/paper/UBLIORRM"},"agent_actions":{"view_html":"https://pith.science/pith/UBLIORRMSUWKUQ3U3OF7FVRFRE","download_json":"https://pith.science/pith/UBLIORRMSUWKUQ3U3OF7FVRFRE.json","view_paper":"https://pith.science/paper/UBLIORRM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.04789&json=true","fetch_graph":"https://pith.science/api/pith-number/UBLIORRMSUWKUQ3U3OF7FVRFRE/graph.json","fetch_events":"https://pith.science/api/pith-number/UBLIORRMSUWKUQ3U3OF7FVRFRE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UBLIORRMSUWKUQ3U3OF7FVRFRE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UBLIORRMSUWKUQ3U3OF7FVRFRE/action/storage_attestation","attest_author":"https://pith.science/pith/UBLIORRMSUWKUQ3U3OF7FVRFRE/action/author_attestation","sign_citation":"https://pith.science/pith/UBLIORRMSUWKUQ3U3OF7FVRFRE/action/citation_signature","submit_replication":"https://pith.science/pith/UBLIORRMSUWKUQ3U3OF7FVRFRE/action/replication_record"}},"created_at":"2026-05-18T00:42:23.965000+00:00","updated_at":"2026-05-18T00:42:23.965000+00:00"}