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Evolutionary Computation & Swarm Intelligence
| Evolutionary Computation & Swarm Intelligence |
| Autore | Caraffini Fabio |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (286 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
dynamic stream clustering
online clustering metaheuristics optimisation population based algorithms density based clustering k-means centroid concept drift concept evolution imbalanced data screening criteria DE-MPFSC algorithm Markov process entanglement degree data integration PSO robot manipulator analysis kinematic parameters identification approximate matching context-triggered piecewise hashing edit distance fuzzy hashing LZJD multi-thread programming sdhash signatures similarity detection ssdeep maximum k-coverage redundant representation normalization genetic algorithm hybrid algorithms memetic algorithms particle swarm multi-objective deterministic optimization, derivative-free global/local optimization simulation-based design optimization wireless sensor networks routing Swarm Intelligence Particle Swarm Optimization Social Network Optimization compact optimization discrete optimization large-scale optimization one billion variables evolutionary algorithms estimation distribution algorithms algorithmic design metaheuristic optimisation evolutionary computation swarm intelligence memetic computing parameter tuning fitness trend Wilcoxon rank-sum Holm–Bonferroni benchmark suite data sampling feature selection instance weighting nature-inspired algorithms meta-heuristic algorithms |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557283803321 |
Caraffini Fabio
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Novel Industry 4.0 Technologies and Applications
| Novel Industry 4.0 Technologies and Applications |
| Autore | Papakostas Nikolaos |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (270 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
computer-aided tolerance
processing features degradation skin model shape statistical tolerance analysis tolerance allocation collaborative learning engineering graphics PLM 3D modeling engineering education Industry 4.0 index smart intensity of technology manufacturing implementation statistical process control pattern recognition long short-term memory feature learning control chart histogram digital hydraulic technology digital hydraulic components digital hydraulic system shearer virtual reality path planning automatic height-adjusting Unity3D technology Rapidly-exploring Random Tree (RRT) manipulator motion planning obstacle avoidance complex environment exoskeletons planning methods ergonomics time management augmented reality maintenance real-time digital twin decision support system factor analysis KPI quantitative analysis root-cause analysis life cycle knowledge- and technology-intensive industry (KTI) VUCA key enabling technology (KET) Operator 4.0 cyber-physical system (CPS) DfHFinI4.0 PERA 4.0 process planning scheduling design for additive manufacturing multiple criteria SMEs technologies cluster analysis maturity model |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557378703321 |
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Papakostas Nikolaos
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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