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    Tytuł:
    Zastosowanie algorytmów rojowych do optymalizacji parametrów w modelach układów regulacji
    Application of swarm intelligence algorithms to optimization of control system models
    Autorzy:
    Tomera, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/269153.pdf
    Data publikacji:
    2015
    Wydawca:
    Politechnika Gdańska. Wydział Elektrotechniki i Automatyki
    Tematy:
    algorytmy rojowe
    optymalizacja parametrów
    algorytm mrówkowy
    algorytm sztucznej kolonii pszczół
    algorytm optymalizacji rojem cząstek
    swarm intelligence
    swarm based optimization
    ant colony optimization
    Artificial Bee Colony
    particle swarm optimization (PSO)
    Opis:
    W pracy przedstawione zostały algorytmy rojowe, takie jak: algorytm mrówkowy, zmodyfikowany algorytm mrówkowy, algorytm sztucznej kolonii pszczół oraz algorytm optymalizacji rojem cząstek. Dla tych algorytmów przygotowane zostało oprogramowanie w Matlabie, pozwalające na optymalizację parametrów poszukiwanych modeli matematycznych, wyznaczanych na podstawie przeprowadzonych testów identyfikacyjnych lub na optymalizację parametrów regulatorów zastosowanych w modelach matematycznych układów sterowania.
    The paper presents the swarm intelligence algorithms, such as: ant colony algorithm (ACO), the modified ant colony algorithm (MACO), the artificial bee colony algorithm (ABC) and the particle swarm optimization algorithm (PSO). Ant colony optimization (ACO) based upon the observation of the behavior of ant colonies looking for food in the surrounding anthill. Feeding ants it is based on finding the shortest path transitions between a food source and the anthill. In the process of foraging ants on their paths crossing from the nest to a food source and back, they leave a pheromone trail. The work presents also the modified ant colony algorithm (MACO). This algorithm is based on searching the solution space surrounded by the best solution obtained in the previous iteration. If you find a local minimum, the proposed algorithm uses pheromone to find a new solution space, while retaining the position information current local minimum. The artificial bee colony algorithm is one of the well-known swarm intelligence algorithms. In the past decade there has been created several different algorithms based on the observation of the behavior of cooperative bees. Among them, the most frequently analyzed and used is bee algorithm proposed in 2005 by Dervis Karaboga and was be used in the proposed paper. The particle swarm optimization algorithm (PSO) is based on adjusting the change speed of the moving particles to a speed of particles movement in the neighborhood. Particle optimization algorithm is one of the computational techniques derived on the basis of swarm behavior such as flocks of birds and schools of fish, which is the basis for the functioning of the exchange of information to enable them to cooperate. It was noticed that the animals in the herd tend to maintain the optimum distance from their neighbors, by appropriate adjustment of their speed. This method allows the synchronous and collision-free motion, often accompanied by sudden changes of direction and due to the rearrangement of the optimal formation. For these algorithms has been prepared the software in Matlab, allowing to optimization of the mathematical models designated on the basis of the carried out identification tests and control parameters used in the mathematical model of the control system.
    Źródło:
    Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2015, 46; 97-102
    1425-5766
    2353-1290
    Pojawia się w:
    Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Semantic segmentation and PSO based method for segmenting liver and lesion from CT images
    Autorzy:
    Nayantara, Vaidehi P.
    Surekha, Kamath
    Manjunath, K.N.
    Rajagopal, Kadavigere
    Powiązania:
    https://bibliotekanauki.pl/articles/2146955.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    liver lesion segmentation
    computed tomography
    semantic segmentation
    SegNet
    particle swarm optimization-based clustering
    Hounsfield Unit
    Opis:
    The liver is a vital organ of the human body and hepatic cancer is one of the major causes of cancer deaths. Early and rapid diagnosis can reduce the mortality rate. It can be achieved through computerized cancer diagnosis and surgery planning systems. Segmentation plays a major role in these systems. This work evaluated the efficacy of the SegNet model in liver and particle swarm optimization-based clustering technique in liver lesion segmentation. Over 2400 CT images were used for training the deep learning network and ten CT datasets for validating the algorithm. The segmentation results were satisfactory. The values for Dice Coefficient and volumetric overlap error achieved were 0.940 ± 0.022 and 0.112 ± 0.038, respectively for liver and the results for lesion delineation were 0.4629 ± 0.287 and 0.6986 ± 0.203, respectively. The proposed method is effective for liver segmentation. However, lesion segmentation needs to be further improved for better accuracy.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 3; 635--640
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Increased Performance of a Hybrid Optimizer for Simulation Based Controller Parameterization
    Autorzy:
    Neugebauer, R.
    Hipp, K.
    Hellmich, A.
    Schlegel, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/384707.pdf
    Data publikacji:
    2012
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    controller parameterization
    simulation based optimization
    particle swarm optimization (PSO)
    Nelder-Mead
    Opis:
    The controller parameterization is often carried out by applying basic empirical formulas within an integrated automatic design. Hence, the determined settings are often insufficiently verified by the resulting system behavior. In this paper an approach for the controller parameterization by using methods of simulation based optimization is presented. This enables the user to define specific restrictions e.g. the complementary sensitivity function (CSF) to influence the dynamic behavior of the control loop. Furthermore it is possible to choose alternative optimization criteria. A main influence factor for practical offline as well as controller internal optimization methods is the execution time, which can be reduced by applying a hybrid optimization strategy. Thus, the paper presents a performance comparison between the straight global Particle-Swarm-Optimization (PSO) algorithm and the combination of the global PSO with the local optimization algorithm of Nelder-Mead (NM) to a hybrid optimizer (HO) based on examples.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2012, 6, 1; 42-45
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Behavior based co-ordination of a troop of vehicles targeted to different goals in an unknown environment
    Autorzy:
    Sanyal, S.
    Barai, R. K.
    Chakrabarti, R.
    Chattopadhyay, P. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/384928.pdf
    Data publikacji:
    2012
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    behavior-based collision avoidance
    randomized obstacles
    multi-vehicle coordination
    paricle swarm optimization
    Opis:
    The issue of coordinated operation of multi-vehicle for a variety of tasks is getting increasing attention day by day and standing as a major research field due to their increased capacity and flexibility they can offer as a team. This paper presents a novel algorithm for multi-vehicle navigation, based on exhaustive search to avoid a set of randomly generated obstacles, predict the approximate position of other vehicles and thus keeping a safe distance to avoid collision and to maintain a formation amongst them while targeted towards the assigned goals. The proposed algorithm uses two optimizing functions in deriving drive commands, direction and turning, for a troop of vehicles. This particular algorithm is similar to the artificial potential field (APF) method which is widely used for autonomous mobile robot path planning due to its simplicity and mathematical elegance. In this work we have taken a behavior based reactive scheme together with artificially generated perturbation as the vehicles are running in a real time environment. Simulations have been carried out for a group of four vehicles, paired in two groups, approaching two different targets avoiding eight randomly generated obstacles, and keeping proper coordination between the members of intra and inter groups. The effectiveness of the proposed approach has been shown by some simulation results.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2012, 6, 4; 20-26
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Maintenance policy of degradation components based on the two-phase Wiener process
    Autorzy:
    Song, Minqiao
    Zhang, Yingzhi
    Yang, Fang
    Wang, Xiaofeng
    Guo, Guiming
    Powiązania:
    https://bibliotekanauki.pl/articles/28328273.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    condition-based maintenance
    two-phase inspection
    two-phase Wiener process
    Particle Swarm Optimization
    sensitivity analysis
    Opis:
    This paper proposes a condition-based maintenance policy for the two-phase Wiener degradation process components. The main contribution of this article is to provide the time distribution of degradation failures for the two-phase Wiener process degradationcomponent, as well as the modeling and solving methods for two-phase maintenance. The two-phase maintenance policy includes two-phase inspection and preventive replacement maintenance operations. The established optimization maintenance policy model aims to minimize long-term operation costs. The specific cost calculation equation and the solution method of the maintenance model are given. The feasibility of the maintenance policy model is verified using the two-phase degradation data of the Liquid Coupling Devices. The Particle swarm optimization algorithm can stably solve the described problem, and the results show that the two-phase maintenance policy can be more economical and improve components availability. After that, we also analyzed the impact of the cost parameters on the maintenance policy through sensitivity analysis.
    Źródło:
    Eksploatacja i Niezawodność; 2023, 25, 4; art. no. 172537
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Wild Image Retrieval with HAAR Features and Hybrid DBSCAN Clustering For 3D Cultural Artefact Landmarks Reconstruction
    Autorzy:
    Pitchandi, Perumal
    Powiązania:
    https://bibliotekanauki.pl/articles/2201730.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    outliers removal
    culturalartefact objects
    3D reconstruction
    particle swarm optimization
    PSO
    spatial clustering
    density based spatial clustering
    noise clustering algorithm
    Opis:
    In this digital age large amounts of information, images and videos can be found in the web repositories which accumulate this information. These repositories include personal, historic, cultural, and business event images. Image mining is a limited field in research where most techniques look at processing images instead of mining. Very limited tools are found for mining these images, specifically 3D (Three Dimensional) images. Open source image datasets are not structured making it difficult for query based retrievals. Techniques extracting visual features from these datasets result in low precision values as images lack proper descriptions or numerous samples exist for the same image or images are in 3D. This work proposes an extraction scheme for retrieving cultural artefact based on voxel descriptors. Image anomalies are eliminated with a new clustering technique and the 3D images are used for reconstructing cultural artefact objects. Corresponding cultural 3D images are grouped for a 3D reconstruction engine’s optimized performance. Spatial clustering techniques based on density like PVDBSCAN (Particle Varied Density Based Spatial Clustering of Applications with Noise) eliminate image outliers. Hence, PVDBSCAN is selected in this work for its capability to handle a variety of outliers. Clustering based on Information theory is also used in this work to identify cultural object’s image views which are then reconstructed using 3D motions. The proposed scheme is benchmarked with DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to prove the proposed scheme’s efficiency. Evaluation on a dataset of about 31,000 cultural heritage images being retrieved from internet collections with many outliers indicate the robustness and cost effectiveness of the proposed method towards a reliable and just-in-time 3D reconstruction than existing state-of-the-art techniques.
    Źródło:
    Advances in Science and Technology. Research Journal; 2022, 16, 3; 269--281
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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