{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:OPDUQJTFPLNSMGYIWZZH6MVFWG","short_pith_number":"pith:OPDUQJTF","schema_version":"1.0","canonical_sha256":"73c74826657adb261b08b6727f32a5b1b33fe3e8370dceaf36189a632f5f8acc","source":{"kind":"arxiv","id":"1812.07737","version":1},"attestation_state":"computed","paper":{"title":"Optimized Feedforward Neural Network Training for Efficient Brillouin Frequency Shift Retrieval in Fiber","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Chongyu Lu, Jialin Jiang, Richeng Zhu, Yongxiang Chen, Yongxin Liang, Zinan Wang","submitted_at":"2018-12-19T02:46:06Z","abstract_excerpt":"Artificial neural networks (ANNs) can be used to replace traditional methods in various fields, making signal processing more efficient and meeting the real-time processing requirements of the Internet of Things (IoT). As a special type of ANN, recently the feedforward neural network (FNN) has been used to replace the time-consuming Lorentzian curve fitting (LCF) method in Brillouin optical time-domain analysis (BOTDA) to retrieve the Brillouin frequency shift (BFS), which could be used as the indicator in temperature/strain sensing, etc. However, FNN needs to be re-trained if the generalizati"},"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":"1812.07737","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-12-19T02:46:06Z","cross_cats_sorted":[],"title_canon_sha256":"deb9d7197dd7861774d229c247139957cb510503e52c2a67c6dac9c51dbe9a7a","abstract_canon_sha256":"68a478c9bd7afeac4ede0d4da1e78176a88f5c2de2241288bcb80cf2f8957227"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:56.174085Z","signature_b64":"FefPn+K66qlJeCoh/SWw68YEAXSLVKNZgPvli+L/oMH+dWwewrQER6+KAtdwhCp/597FneZkwVXZi8gqZ3o+Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73c74826657adb261b08b6727f32a5b1b33fe3e8370dceaf36189a632f5f8acc","last_reissued_at":"2026-05-17T23:57:56.173453Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:56.173453Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimized Feedforward Neural Network Training for Efficient Brillouin Frequency Shift Retrieval in Fiber","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Chongyu Lu, Jialin Jiang, Richeng Zhu, Yongxiang Chen, Yongxin Liang, Zinan Wang","submitted_at":"2018-12-19T02:46:06Z","abstract_excerpt":"Artificial neural networks (ANNs) can be used to replace traditional methods in various fields, making signal processing more efficient and meeting the real-time processing requirements of the Internet of Things (IoT). As a special type of ANN, recently the feedforward neural network (FNN) has been used to replace the time-consuming Lorentzian curve fitting (LCF) method in Brillouin optical time-domain analysis (BOTDA) to retrieve the Brillouin frequency shift (BFS), which could be used as the indicator in temperature/strain sensing, etc. However, FNN needs to be re-trained if the generalizati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07737","kind":"arxiv","version":1},"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":"1812.07737","created_at":"2026-05-17T23:57:56.173536+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.07737v1","created_at":"2026-05-17T23:57:56.173536+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07737","created_at":"2026-05-17T23:57:56.173536+00:00"},{"alias_kind":"pith_short_12","alias_value":"OPDUQJTFPLNS","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"OPDUQJTFPLNSMGYI","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"OPDUQJTF","created_at":"2026-05-18T12:32:43.782077+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/OPDUQJTFPLNSMGYIWZZH6MVFWG","json":"https://pith.science/pith/OPDUQJTFPLNSMGYIWZZH6MVFWG.json","graph_json":"https://pith.science/api/pith-number/OPDUQJTFPLNSMGYIWZZH6MVFWG/graph.json","events_json":"https://pith.science/api/pith-number/OPDUQJTFPLNSMGYIWZZH6MVFWG/events.json","paper":"https://pith.science/paper/OPDUQJTF"},"agent_actions":{"view_html":"https://pith.science/pith/OPDUQJTFPLNSMGYIWZZH6MVFWG","download_json":"https://pith.science/pith/OPDUQJTFPLNSMGYIWZZH6MVFWG.json","view_paper":"https://pith.science/paper/OPDUQJTF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.07737&json=true","fetch_graph":"https://pith.science/api/pith-number/OPDUQJTFPLNSMGYIWZZH6MVFWG/graph.json","fetch_events":"https://pith.science/api/pith-number/OPDUQJTFPLNSMGYIWZZH6MVFWG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OPDUQJTFPLNSMGYIWZZH6MVFWG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OPDUQJTFPLNSMGYIWZZH6MVFWG/action/storage_attestation","attest_author":"https://pith.science/pith/OPDUQJTFPLNSMGYIWZZH6MVFWG/action/author_attestation","sign_citation":"https://pith.science/pith/OPDUQJTFPLNSMGYIWZZH6MVFWG/action/citation_signature","submit_replication":"https://pith.science/pith/OPDUQJTFPLNSMGYIWZZH6MVFWG/action/replication_record"}},"created_at":"2026-05-17T23:57:56.173536+00:00","updated_at":"2026-05-17T23:57:56.173536+00:00"}