Issue Information
Notice bibliographique
Résumé
Aims and ScopePapers must include an element of optimization or optimal control and estimation theory to be considered by the journal, and all papers will be expected to include signifi cant novel material.The journal only considers papers that mainly use model based control design methods and hence papers featuring fuzzy control will not be included. Optimal Control Applications and Methodsprovides a forum for papers on the full range of optimal control and related control design methods.The aim is to encourage new developments in optimal control theory and design methodologies that may lead to advances in real control applications.Papers must include an element of optimization for dynamic systems, or optimal control and estimation theory, and all papers will be expected to include significant novel material.The journal focuses on papers that use model-based control design methods.Papers are encouraged on the development of computational algorithms for solving optimal control and dynamic optimization problems.The scope includes papers on optimal estimation and filtering methods that have control-related applications.The journal is also a venue for interesting optimal control applications and design studies.Papers on the theory of optimal systems with engineering applications potential will be particularly welcome including papers on areas such as nonlinear, safety-critical, fault-tolerant, and reliable control.The journal particularly wishes to encourage papers on predictive control dealing with theory and also applications.Such papers should be optimization based, and for linear or nonlinear systems, and can cover topics such as economic model predictive control.Other design methods covered by the journal include H 2 and H ∞ design, linear-quadratic optimal control, nonlinear optimal control, stochastic optimal control, periodic optimal control, optimal filtering and fault estimation, optimal adaptive control, multi-criteria and multiple-model optimal control, singular perturbation methods, repetitive control and switching, optimal control of large-scale or distributed systems, time-delay systems, nonlinear programming and optimization methods, dynamic programming, and static and dynamic optimization techniques.Optimization of dynamic systems, optimal estimation, optimal control, and optimization-based analysis of dynamic systems are within the scope of OCAM.A typical paper would normally involve some form of optimization and some form of dynamic system.A paper that applies an optimization-based soft computing technique (e.g., dynamic neural network) could be appropriate if it includes a rigorous theory for the analysis or design of such systems, or is applied to a novel optimal estimation and control problem.However, soft computing papers that involve only minor changes to existing algorithms, or use only static optimization, are not suitable.There is a strong interest in engineering applications including: automotive systems, aerospace and defence, energy systems (including wind turbines, wave, tidal, battery, fuel cell and solar), marine and, electro-mechanical systems, robotics, power generation and distribution systems, chemical and petrochemical processes, biological and biomedical systems, environmental control, water treatment and distribution, manufacturing and electrical and electronic systems, and networks.Applications of interest include a wide range of interdisciplinary, switching, hybrid, and complex systems problems, where multi-agent solutions, intelligent sensors, and optimization play a role.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,009 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,821 | 0,781 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».