Cascade PID controller optimization using bison algorithm

Meta-heuristic algorithms are reliable tools for modern optimization. Yet their amount is so immense that it is hard to pick just one to solve a specific problem. Therefore many researchers hold on known, approved algorithms. But is it always beneficial? In this paper, we use the meta-heuristics for the design of cascade PID controllers and compare the performance of the newly developed Bison Algorithm with well-known algorithms like the Differential Evolution, the Genetics Algorithm, the Particle Swarm Optimization, and the Cuckoo Search. Also, in the proposed approach, the controller parameters were encoded to increase the chance of reducing the controller structure, and thus facilitate the automatic selection of its configuration. The simulations were performed for three different control problems and checked whether the use of cascade structures could bring significant benefits in comparison to the use of classic PID controllers. © 2020, Springer Nature Switzerland AG

Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and $K$-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan

Phenolic plant extract enrichment of enzymatically mineralized hydrogels

Hydrogel mineralization with calcium phosphate (CaP) and antibacterial activity are desirable for applications in bone regeneration. Mineralization with CaP can be induced using the enzyme alkaline phosphatase (ALP), responsible for CaP formation in bone tissue. Incorporation of polyphenols, plant-derived bactericidal molecules, was hypothesized to provide antibacterial activity and enhance ALP-induced mineralization. Three phenolic rich plant extracts from: (i) green tea, rich in epigallocatechin gallate (EGCG) (herafter referred to as EGCG-rich extract); (ii) pine bark and (iii) rosemary were added to gellan gum (GG) hydrogels and subsequently mineralized using ALP. The phenolic composition of the three extracts used were analyzed by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MSn). EGCG-rich extract showed the highest content of phenolic compounds and promoted the highest CaP formation as corroborated by dry mass percentage meassurements and ICP-OES de-termination of mass of elemental Ca and P. All three extracts alone exhibited antibacterial activity in the following order EGCG-rich > PI > RO, respectively. However, extract-loaded and mineralized GG hydro-gels did not exhibit appreciable antibacterial activity by diffusion test. In conclusion, only the EGCG-rich extract promotes ALP-mediated mineralization