{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TBXWSDPRAMPJYYMJEVARWIRAYD","short_pith_number":"pith:TBXWSDPR","schema_version":"1.0","canonical_sha256":"986f690df1031e9c618925411b2220c0f634c4173b06c7532c20a933ceaa0ad2","source":{"kind":"arxiv","id":"2402.14882","version":1},"attestation_state":"computed","paper":{"title":"Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CE"],"primary_cat":"cs.LG","authors_text":"Jihoon Kim, Namwoo Kang, Sumin Lee","submitted_at":"2024-02-22T03:31:00Z","abstract_excerpt":"Mechanisms are essential components designed to perform specific tasks in various mechanical systems. However, designing a mechanism that satisfies certain kinematic or quasi-static requirements is a challenging task. The kinematic requirements may include the workspace of a mechanism, while the quasi-static requirements of a mechanism may include its torque transmission, which refers to the ability of the mechanism to transfer power and torque effectively. In this paper, we propose a deep learning-based generative model for generating multiple crank-rocker four-bar linkage mechanisms that sat"},"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":"2402.14882","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-22T03:31:00Z","cross_cats_sorted":["cs.AI","cs.CE"],"title_canon_sha256":"90c5cf2501ed73f342a5c096a2d6dc4dea8c320944c2af6593d0775d6ae7ce66","abstract_canon_sha256":"20d2858152dbe38c35f8df7b5663e1fcf3c7bdd344e6e54544f435de45b4c5f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:32:25.538039Z","signature_b64":"v+vDfphn+i2n/29jk1sN+HT4C37a0T/4XE3muC4eXYrqNApwyLjuzYn8FHyRExMLvf4Qyrex6rozI+r64rQyDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"986f690df1031e9c618925411b2220c0f634c4173b06c7532c20a933ceaa0ad2","last_reissued_at":"2026-07-05T10:32:25.537110Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:32:25.537110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CE"],"primary_cat":"cs.LG","authors_text":"Jihoon Kim, Namwoo Kang, Sumin Lee","submitted_at":"2024-02-22T03:31:00Z","abstract_excerpt":"Mechanisms are essential components designed to perform specific tasks in various mechanical systems. However, designing a mechanism that satisfies certain kinematic or quasi-static requirements is a challenging task. The kinematic requirements may include the workspace of a mechanism, while the quasi-static requirements of a mechanism may include its torque transmission, which refers to the ability of the mechanism to transfer power and torque effectively. In this paper, we propose a deep learning-based generative model for generating multiple crank-rocker four-bar linkage mechanisms that sat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.14882","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2402.14882/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2402.14882","created_at":"2026-07-05T10:32:25.537235+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.14882v1","created_at":"2026-07-05T10:32:25.537235+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.14882","created_at":"2026-07-05T10:32:25.537235+00:00"},{"alias_kind":"pith_short_12","alias_value":"TBXWSDPRAMPJ","created_at":"2026-07-05T10:32:25.537235+00:00"},{"alias_kind":"pith_short_16","alias_value":"TBXWSDPRAMPJYYMJ","created_at":"2026-07-05T10:32:25.537235+00:00"},{"alias_kind":"pith_short_8","alias_value":"TBXWSDPR","created_at":"2026-07-05T10:32:25.537235+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.17607","citing_title":"Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoning","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TBXWSDPRAMPJYYMJEVARWIRAYD","json":"https://pith.science/pith/TBXWSDPRAMPJYYMJEVARWIRAYD.json","graph_json":"https://pith.science/api/pith-number/TBXWSDPRAMPJYYMJEVARWIRAYD/graph.json","events_json":"https://pith.science/api/pith-number/TBXWSDPRAMPJYYMJEVARWIRAYD/events.json","paper":"https://pith.science/paper/TBXWSDPR"},"agent_actions":{"view_html":"https://pith.science/pith/TBXWSDPRAMPJYYMJEVARWIRAYD","download_json":"https://pith.science/pith/TBXWSDPRAMPJYYMJEVARWIRAYD.json","view_paper":"https://pith.science/paper/TBXWSDPR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.14882&json=true","fetch_graph":"https://pith.science/api/pith-number/TBXWSDPRAMPJYYMJEVARWIRAYD/graph.json","fetch_events":"https://pith.science/api/pith-number/TBXWSDPRAMPJYYMJEVARWIRAYD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TBXWSDPRAMPJYYMJEVARWIRAYD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TBXWSDPRAMPJYYMJEVARWIRAYD/action/storage_attestation","attest_author":"https://pith.science/pith/TBXWSDPRAMPJYYMJEVARWIRAYD/action/author_attestation","sign_citation":"https://pith.science/pith/TBXWSDPRAMPJYYMJEVARWIRAYD/action/citation_signature","submit_replication":"https://pith.science/pith/TBXWSDPRAMPJYYMJEVARWIRAYD/action/replication_record"}},"created_at":"2026-07-05T10:32:25.537235+00:00","updated_at":"2026-07-05T10:32:25.537235+00:00"}