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Pakiet modernizacyjny do 23-milimetrowej armaty przeciwlotniczej ZU-23-2

Bielawski Krzysztof, Chmieliński Mirosław, Lachewicz Cezary, Krupa Arkadiusz, Tamberg Sławomir

Mechanik

2022

R. 95, nr 10

22--26

Zakład Automatyki i Urządzeń Pomiarowych AREX sp. z o.o. z Gdyni działa na rynku od 1989 r., a od kilku lat wchodzi w skład Grupy WB (dalej: AREX) - największej prywatnej grupy kapitałowej polskiego przemysłu obronnego, oferującej zaawansowane rozwiązania dla sił zbrojnych z całego świata. AREX specjalizuje się w projektowaniu i produkcji pakietów modernizacyjnych do 23-milimetrowej armaty ZU-23-2. Celem modernizacji jest przede wszystkim poprawa skuteczności ognia armat poprzez unowocześnienie mechanizmów naprowadzania i przyrządów celowniczych. Jednym z priorytetów AREX jest też realizacja projektów od początku do końca - są to rozwiązania krajowe, z komponentami wytwarzanymi w Polsce, dzięki czemu zapewniona jest pełna kontrola nad nimi. AREX uczestniczy w licznych projektach poświęconych opracowaniu i modernizacji uzbrojenia dla Sił Zbrojnych RP w zakresie systemów sterowania napędami elektrycznymi. Współpracuje przy tym z czołowymi partnerami branżowymi i jest otwarta na udział w innowacyjnych projektach dotyczących opracowania i wdrożenia nowych produktów dla sektora obronnego.

AREX Ltd. the WB Group from Gdynia, has been operating on the market since 1989, and for several years has been a part of the WB Group (hereinafter referred to as AREX Ltd.), which is the largest private capital group of the Polish defense industry, offering advanced solutions for the armed forces from around the world. AREX Ltd. specializes in the design and production of modernization packages for the 23 mm ZU-23-2 cannon. The aim of the modernization is primarily to improve the fire efficiency of the cannons by modernizing the guidance mechanisms and sights. One of the priorities of AREX Ltd. is also the implementation of projects from start to finish. They are domestic solutions, with components manufactured in Poland, thanks to which full control over the solutions created is ensured. AREX Ltd. participates in numerous projects aimed at the development and modernization of weapons for the Polish Armed Forces in the field of electric drive control systems. AREX Ltd. cooperates with leading partners from specialized industry and is open to participation in innovative projects related to the development and implementation of new products for the defense sector.

Knowledge-based clustering as a conceptual and algorithmic environment of biomedical data analysis

Pedrycz W., Gacek A.

Journal of Medical Informatics & Technologies

2004

Vol. 7

KB13--22

While a genuine abundance of biomedical data available nowadays becomes a genuine blessing, it also posses a lot of challenges. The two fundamental and commonly occurring directions in data analysis deal with its supervised or unsupervised pursuits. Our conjecture is that in the area of biomedical data processing and understanding where we encounter a genuine diversity of patterns, problem descriptions and design objectives, this type of dichotomy is neither ideal nor the most productive. In particular, the limitations of such taxonomy become profoundly evident in the context of unsupervised learning. Clustering (being usually regarded as a synonym of unsupervised data analysis) is aimed at determining a structure in a data set by optimizing a given partition criterion. In this sense, a structure emerges (becomes formed) without a direct intervention of the user. While the underlying concept looks appealing, there are numerous sources of domain knowledge that could be effectively incorporated into clustering mechanisms and subsequently help navigate throughout large data spaces. In unsupervised learning, this unified treatment of data and domain knowledge leads to the general concept of what could be coined as knowledge-based clustering. In this study, we discuss the underlying principles of this paradigm and present its various methodological and algorithmic facets. In particular, we elaborate on the main issues of incorporating domain knowledge into the clustering environment such as (a) partial labelling, (b) referential labelling (including proximity and entropy constraints), (c) usage of conditional (navigational) variables, (d) exploitation of external structure. Presented are also concepts of stepwise clustering in which the structure of data is revealed via a series of refinements of existing domain granular information.