Person: KURTULMUŞ, FERHAT
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KURTULMUŞ
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FERHAT
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Publication Developing wind-concentrator systems for the use of wind turbines in areas with low wind-speed potentials(Wiley-V C H Verlag Gmbh, 2015-12-01) Vardar, Ali; Eker, Bülent; Kurtulmuş, Ferhat; Taşkın, Onur; VARDAR, ALİ; KURTULMUŞ, FERHAT; TAŞKIN, ONUR; Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü/Tarımsal Enerji Sistemleri.; Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü/Tarım Makina Sistemleri.; 0000-0001-6349-9687; 0000-0002-5741-8841; AAH-5008-2021; AAH-5018-2021; R-8053-2016The ability to supply energy in rural areas and in agricultural plants with renewable energy technologies, especially wind energy, is advantageous in terms of a sustainable environment and the increasing cost of energy. Today, wind turbines are used actively in many areas, some of which are for commercial purposes. Small-scale wind turbines that produce electricity directly have the necessary characteristics for use in agricultural plants. In this study, wind-concentrator systems for small-scale wind turbines that can be used in agricultural electrification applications have been designed for geographical areas where the wind-speed potential is low. Three different concentrator systems have been designed to make use of low wind-speed potentials and obtain high power values with relatively small rotor diameters. The three different designs have been produced as prototypes, and power values of 324-503 Wm(-2) (at 5 ms(-1) wind speed) can be obtained by concentrating the wind. The efficiency, power, energy production capacity, and economic elements of the models were determined, and the possible results for agricultural plants have been assessed. According to these assessments, the efficiency values are 71 and 90% for wind speed and 410 and 600% for wind power. The energy production capacities are a maximum of 6462, 5193, and 8226 kWhm(-2) per year for the conical wind-concentrator system, the wind-concentrator system with a panel, and the wind-concentrator system without a panel, respectively. If the energy production cost per unit of these systems is considered, these systems are not economical. Therefore, these systems must be produced on a large scale to become economical, and their size must be enlarged to reduce the cost. Consequently, the potential power values per unit area and the potential energy values per unit produced by the wind-concentrator systems will contribute to the production of more energy than that achieved by current wind turbines.Publication Deep learning for proximal soil sensor development towards smart irrigation(Pergamon-elsevier Science Ltd, 2022-03-23) Kurtulmus, Ezgi; ARSLAN, BİLGE; KURTULMUŞ, EZGİ; Arslan, Bilge; KURTULMUŞ, FERHAT; Kurtulmus, Ferhat; Bursa Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü.; R-8053-2016Excessive agricultural water consumption threatens safe access of billions of people to potable water. Smart irrigation systems offer more efficient water use in irrigated agriculture. Determining irrigation requirements of various soil texture classes in production fields requires more sophisticated sensing technologies such as deep learning. This study has proposed a proximal sensing system by means of using a color camera towards smart irrigation based on computer vision and deep learning to identify water requirements of three soil texture classes under different illumination conditions. An imaging station was composed to reduce the workload in obtaining training images required for training deep convolutional neural network models. Five deep learning architectures were employed to identify texture-water classes: AlexNet, GoogleNet, ResNet, VGG16, and SqueezeNet. Those models were experimented with and investigated to determine the best models in terms of detection performance and speed. By using cross-validation rules, approximately 12,214 images were studied individually for the purpose of training and testing. The AlexNet model outperformed the other deep learning models with an F1 score of 0.9973 in identifying twelve soil texture-water classes. GoogleNet and ResNet displayed the fastest detection speeds with an average processing time of 16.92 ms. The findings obtained from this study have indicated that deep learning bears a great potential in determining irrigation requirements of production fields under varying conditionsPublication Determination of pipe diameters for pressurized irrigation systems using linear programming and artificial neural networks(Ankara Üniversitesi, 2023-01-01) Kurtulmuş, Ezgi; Kurtulmuş, Ferhat; Kuşcu, Hayrettin; Arslan, Bilge; Demir, Ali Osman; KURTULMUŞ, EZGİ; KURTULMUŞ, FERHAT; KUŞÇU, HAYRETTİN; ARSLAN, BİLGE; DEMİR, ALİ OSMAN; Bursa Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü.; 0000-0001-9600-7685; AAH-4682-2021; AAH-2936-2021; R-8053-2016; JOP-8553-2023; JLX-2232-2023Pressurized irrigation systems are widespread among other alternatives in Mediterranean countries. Since the initial investment costs of pressurized irrigation systems are quite high, it is crucial to determine design parameters such as pipe diameter. Most of the current optimization techniques for pipe diameter selection are based on linear, non-linear, and dynamic programming models. The ultimate aim of these techniques is to produce solutions to problems with less cost and computation time. In this study, a novel approach for determining pipe diameter was proposed using Artificial Neural Networks (ANN) as an alternative to existing models. For this purpose, three pressurized irrigation systems were investigated. Different ANN architectures were created and tested using hydrant level parameters of the irrigation systems, such as irrigated area per hydrant, hydrant discharge, pipe length, and hydrant elevation. Different training algorithms, transfer functions, and hidden neuron numbers were tried to determine the best ANN model for each irrigation system. Using multilayer feed-forward ANN architecture, the highest coefficients of determination were found to be 0.97, 0.93, and 0.83 for irrigation systems investigated. It was concluded that pipe diameters could be determined by using artificial neural networks in the planning of pressurized irrigation systems.