In this study, design and application of a structural health monitoring (SHM) in composite materials with fiber bragg grating (FGB) sensors are investigated. The designed structure consists of laminated composite material that was produced by vacuum infusion method. The composite specimen was obtained by embedding a FGB sensor between the 12-layer glass layers. Moreover, SHM of the produced composite material was achieved by obtaining the strain data from the sensor embedded in the composite material. Application was carried out by making the composite plate fixed support on one side and applying different loads to the specimen. Strain ratios of these loads are obtained and the results graphics are shown.

Normal distribution has a vital role for the most of statistical methods. Box-Cox power transformation is the most usually applied method when the distribution of data is not normal. In this study, a novel algorithm is proposed assembling different Box-Cox transformation estimates of the well performed six techniques proposed by Asar et al. (2017) through random effect model in meta analysis. These techniques include the use of goodness-of-fit tests for normality; Anderson–Darling, Lilliefors, Cramer-von Mises, Shapiro–Wilk, Jarque–Bera and Shapiro–Francia tests. For the estimation of Box-Cox parameter, we assemble all possible combina-tions (63 combinations) of estimates calculated by these six methods. A Monte-Carlo simulation study is implemented to investigate which combination performs better compared to the rest. The simulation study states that the combination of Shapiro–Wilk, Jarque–Bera and Anderson–Darling tests performs well in most of the simulation scenarios constructed under different transformation parameters and sample sizes. In this study, this combination is proposed as ensemble based Box-Cox transformation via meta analysis. The proposed approach is implemented on white blood count data of leukaemia patients which are not normally distributed. Also, the proposed methodology is provided in AID R package with “boxcoxmeta” function for public use.

ÖzBu çalışmada bir konveyör bant üzerinden akan çikolatalar bir kamera yardımıyla toplanmıştır. Rastgele ve dağınık şekilde gelen çikolatalar konveyör bant üzerine yerleştirilmiş bir kamera ile tespit edilmiştir. Tespit edilen çikolataların merkez koordinatları robot kola gönderilmiştir. Robot kol gelen çikolata koordinatları ile konveyör banta bağlı enkoder bilgisini kullanarak sistemi durdurmadan çikolata toplama ve paketleme işlemini gerçekleştirmiştir. Görüntü işleme aşamasında HSV dönüşümü, kenar tespiti ve moment yöntemleri kullanılarak çikolatalar yüksek doğrulukla tespit edilmiştir. Kamera -robot kol birbirine kalibre edilerek toplama işlemini hatasız yapan bir otomasyon sistemi kurulmuştur. Görüntü işleme donanımı olarak bilgisayar kullanılmıştır. Bilgisayar-robot kol arasındaki haberleşme işlemi Modbus TCP/IP yöntemi ile yüksek hızda gerçekleştirilmiştir.