「damping」の検索結果

Locate BotProfile::LookAngleDampingAttacking in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "BotProfile::LookAngleDampingAttacking" (symbol BotProfile_LookAngleDampingAttacking). This is a VIRTUAL function - resolve the BotProfile::LookAngleDampingAttacking vtable via RTTI and identify the slot, then emit the slot. Verify by xrefs from expected call sites. vtable slots commonly differ between platforms - never assume identical indices. Trigger: BotProfile_LookAngleDampingAttacking, BotProfile::LookAngleDampingAttacking

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Expert-level mechanical vibrations covering free and forced vibration, damping, resonance, modal analysis, vibration isolation, and rotating machinery.

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Add restrained mouse-driven orbit and parallax depth to a Three.js hero by damping one pointer target and splitting it across camera translation, look-at, and small object rotations. Use for passive cinematic 3D heroes, pointer-responsive scenes, organic model parallax, and interactive depth where OrbitControls would feel like a product viewer.

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Think and work like an expert MEMS Engineer. Use when a task calls for MEMS Engineer judgment. Reasons from scale-dependent mechanics, squeeze-film damping, and electrostatic pull-in through DRIE Bosch/surface micromachining, CoventorMP/COMSOL, foundry PDKs, LDV/WLI metrology, and AEC-Q103 qual while treating release stiction, DRIE scallop bias, package-stress offset drift, and functional-WLT-vs-reliability gaps as first-class failure modes.

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Design and audit PFC 5.0 asphalt dynamic loading and stress-wave studies with damping, timestep, rate, dispersion, source direction, boundary reflection, histories, and wheel-load handoff checks.

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Principled action selection for autonomous agents using Expected Free Energy (EFE) under the Active Inference framework. Each candidate action is scored as G(n) = w_prag * (-E[p_n] * damping) + w_epist * (-Var[p_n] * novelty(n)), where agent beliefs are maintained as per-node Beta distributions and updated via Bayesian observation. Selection is a softmax over -gamma * G, making exploration an automatic consequence of uncertainty rather than a hardcoded epsilon. Precision weights w_prag and w_epist self-adapt via surprise history, shifting the agent between exploitation and exploration regimes without any manually tuned schedule.

日本語の概要は準備中です。原文の説明を表示しています。