Computer Vision in Wind Turbine Blade Inspections: An Analysis of Resolution Impact on Detection and Classification of Leading-Edge Erosion

Wind turbines, as critical components of the renewable energy industry, present unique maintenance challenges, particularly in remote or challenging locations such as offshore wind farms. These are amplified in the inspection of leading-edge erosion on wind turbine blades, a task still largely reliant on traditional methods. Emerging technologies like computer vision and object detection offer promising avenues for enhancing inspections, potentially reducing operational costs and human-associated risks. However, variability in image resolution, a critical factor for these technologies, remains a largely underexplored aspect in the wind energy context. This study explores the application of machine learning in detecting and categorizing leading edge erosion damage on wind turbine blades. YOLOv7, a state-of-the-art object detection model, is trained with a custom dataset consisting of images displaying various forms of leading edge erosion, representing multiple categories of damage severity. Trained model is tested on images acquired with three different tools, each providing images with a different resolution. The effect of image resolution on the performance of the custom object detection model is examined. The research affirms that the YOLOv7 model performs exceptionally well in identifying the most severe types of LEE damage, usually classified as Category 3, characterized by distinct visual features. However, the model's ability to detect less severe damage, namely Category 1 and 2, which are crucial for early detection and preventive measures, exhibits room for improvement. The findings point to a potential correlation between input image resolution and detection confidence in the context of wind turbine maintenance. These results stress the need for high-resolution images, leading to a discussion on the selection of appropriate imaging hardware and the creation of machine learning-ready datasets. The study thereby emphasizes the importance of industry-wide efforts to compile standardized image datasets and the potential impact of machine learning techniques on the efficiency of visual inspections and maintenance strategies. Future directions are proposed with the ultimate aim of enhancing the application of artificial intelligence in wind energy maintenance and management, enabling more efficient and effective operational procedures, and driving the industry towards a more sustainable future

Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ

Air pollution-induced issues involve public health, environmental, agricultural and socio-economic aspects. Therefore, decision-makers need low-cost, efficient tools with high spatiotemporal representation for monitoring air pollutants around urban areas and sensitive regions. Air pollution forecasting models with different time steps and forecast lengths are used as an alternative and support to traditional air quality monitoring stations (AQMS). In recent decades, given their eligibility to reconcile the relationship between parameters of complex systems, artificial neural networks have acquired the utmost importance in the field of air pollution forecasting. In this study, different machine learning regression methods are used to establish a mathematical relationship between air pollutants and meteorological factors from four AQMS (A-D) located between Çerkezköy and Süleymanpaşa, Tekirdağ. The model input variables included air pollutants and meteorological parameters. All developed models were used with the intent to provide instantaneous prediction of the air pollutant parameter NOx within the AQMS and across different stations. In the GMDH (group method of data handling)-type neural network method (namely the self-organizing deep learning approach), a five hidden layer structure consisting of a maximum of five neurons was preferred and, choice of layers and neurons were made in a way to minimize the error. In all models developed, the data were divided into a training (%80) and a testing set (%20). Based on R2, RMSE, and MAE values of all developed models, GMDH provided superior results regarding the NOx prediction within AQMS (reaching 0.94, 10.95, and 6.65, respectively for station A) and between different AQMS. The GMDH model yielded NOx prediction of station B by using station A input variables (without using NOx data as model input) with R2, RMSE and MAE values 0.80, 10.88, 7.31 respectively. The GMDH model is found suitable for being employed to fill in the gaps of air pollution records within and across-AQMS

Letter from a Brazilian Supporter to Geraldine Ferraro

Letter from a Brazilian supporter to Geraldine Ferraro.https://ir.lawnet.fordham.edu/vice_presidential_campaign_correspondence_1984_international/1257/thumbnail.jp

The contamination status of trace metals in Sinop coast of the Black Sea, Turkey

The concentration of some heavy metals in sediment from the Sinop coasts of the Black Sea were measured to monitor metal pollution in 2013. The distribution of the heavy metals in sediments of the Black Sea shows a variable pattern. The objectives of the present study were to elucidate the distribution of heavy metals such as Cu, Pb, Zn, Ni, Mn, Fe, As, Cd, Cr, Hg and Co in sediment from Sinop coast of the Black Sea. In order to determine the quality of sediment in the Black Sea, pollution levels of the metals were evaluated using the enrichment factor technique (EF). The lowest EF values were between 0-5 in Cu, Fe, Zn, Ni, Cd, Pb, Mn and Co. These values did not have statistically significant differences. As, Cr and Hg concentrations were estimated relatively higher enrichment values than other the metals. The results indicated that contamination of surface sediments in Sinop Coast is dominated by As (10.2-7.4 mg.kg-1), Cr (67-374 mg.kg-1) and Hg (0.07-0.03 mg.kg-1) and to a lesser extent Cu (7.24- 5.09 mg.kg-1), Fe (1.76-1.12%), Zn (19.3-13.8 mg.kg-1), Ni (16.2-12.5 mg.kg-1), Cd (0.06-0.04 mg.kg-1), Pb (7.12-6.32 mg.kg-1), Mn (470-227 mg.kg-1), Co (9.5-5.9 mg.kg-1). Also, the requirement of age determination is of great importance to assess the extent of the anthropogenic contribution in pollution

Effect of different protein levels on, testicular parameters and semen quality in Kivircik ram lambs during pubertal development

The aim of this study was to determine the effects of different protein levels on, testicular parameters and semen quality in Kivircik ram lambs during pubertal development. Two experimental groups were formed. Following weaning, crude protein (CP) were 12% CP in group I (low protein diet) and 18% CP in group II (high protein diet). Measurements of live weight and testicular characteristics were performed in 20 days intervals starting from 115 days up to 195 days of age. There was an increase in semen volume, spermatozoa concentration and the percentage of progressively motile sperm in both groups between 135 and 195 days of age. Group I had significantly higher semen volume on day 175 (P<0.05). Furthermore, spermatozoa concentration were higher in group I than those in group II on days 155 and 175 (P<0.05). Values of live weight, testicular diameter, testicular circumference, testicular length and testicular volume of ram lambs in group II (high protein diet) were higher than those in group I (low protein diet). Testicular length and testicular volume of group II were significantly higher than those of group I on day 195 (P<0.05). Live weight and testicular characteristics of ram lambs fed with high protein diet were affected positively during pubertal development. However, it was observed that feeding with high protein diet had negative effect on semen characteristics by impaired thermoregulation mechanism and spermatogenesis in testicles because of excessive fat accumulation in scrotum

Effect of different protein levels on, testicular parameters and semen quality in Kivircik ram lambs during pubertal development

The aim of this study was to determine the effects of different protein levels on, testicular parameters and semen quality in Kivircik ram lambs during pubertal development. Two experimental groups were formed. Following weaning, crude protein (CP) were 12% CP in group I (low protein diet) and 18% CP in group II (high protein diet). Measurements of live weight and testicular characteristics were performed in 20 days intervals starting from 115 days up to 195 days of age. There was an increase in semen volume, spermatozoa concentration and the percentage of progressively motile sperm in both groups between 135 and 195 days of age. Group I had significantly higher semen volume on day 175 (P<0.05). Furthermore, spermatozoa concentration were higher in group I than those in group II on days 155 and 175 (P<0.05). Values of live weight, testicular diameter, testicular circumference, testicular length and testicular volume of ram lambs in group II (high protein diet) were higher than those in group I (low protein diet). Testicular length and testicular volume of group II were significantly higher than those of group I on day 195 (P<0.05). Live weight and testicular characteristics of ram lambs fed with high protein diet were affected positively during pubertal development. However, it was observed that feeding with high protein diet had negative effect on semen characteristics by impaired thermoregulation mechanism and spermatogenesis in testicles because of excessive fat accumulation in scrotum

Krizin kısa dönemli yansımalarının turizm i̇şletmelerinin kriz yönetimi uygulamalarına etkisi

Bu araştırma; Türkiye’de son dönemde yaşanan terör olayları ve Rusya ile yaşanan siyasi gerilim sonrasında ortaya çıkan krizin kısa dönemli olumsuz yansımalarının turizm işletmelerinin kriz yönetimi uygulamaları üzerindeki etkisini tespit etmek ve bu etkilerin işletmelerin faaliyet yıllarına göre farklılık gösterip göstermediğini belirlemek amacıyla gerçekleştirilmiştir. Araştırma verileri, Travel Turkey Izmir’2016 10. Turizm Fuar ve Kongresi’ne katılan ve turizm sektöründe faaliyet gösteren (otel, seyahat acentesi, havayolu firması ve diğer turizm işletmeleri) işletme temsilci ve yöneticilerinden anket formu aracılığıyla toplanmıştır. Araştırmada, toplamda 219 kullanılabilir anket elde edilmiştir. Araştırma verilerine tanımlayıcı istatistikler, paralel test, açıklayıcı faktör analizi ve MANCOVA (Çoklu Kovaryans Analizi) uygulanmıştır. Araştırma sonucunda, krizin kısa dönemli olumsuz yansımalarının turizm işletmelerinin kriz yönetimi uygulamaları üzerinde oldukça büyük bir etkisi olduğu ancak bu etkilerin işletmelerin faaliyet yıllarına göre farklılık göstermediği tespit edilmiştir

Hospitality crisis management in Turkey: a comparative approach

Political instability and terror events commonly occur in many countries. Since 2016, Turkey experienced a surge of political crises and terrorist activities which led to a marked decline in the country’s tourism revenue, and, consequently, caused economic struggles. The study employs the Importance-Performance Analysis framework to evaluate the use and the importance that Turkish hotel managers assign to different crisis management practices. The analysis is based on a list of crisis management practices that belong to four categories: human resources, marketing, hotel maintenance, and governmental assistance. The results suggest that Turkish managers follow the main categories in their crisis management action and focus on marketing and cost-cutting practices. Comparison with previous studies in India and Israel highlight the common focus marketing and cost-cutting as significant crisis management practices to improve competitive position and manage crisis situations

Measurement of Service Quality at Tax Chambers by SERVQUAL Analysis

The aim of the research is to come out the difference between expected service and perceived service at Tax Chamber that taxpayers get service. SERVQUAL Scale was conducted to 90 people who get service from X Tax Chambers between April-May 2016. As a result of the SERVQUAL analysis, the research reveals that the taxpayers of the X Tax Chambers are not satisfied with the service quality and the most difference between expected service quality and perceived service quality is accessibility dimension. The research indicates no significant differences between expactations for service provided and demographics features. However the research shows significant differences between service perceptions and demographics features