In this paper, a novel zero voltage transition (ZVT) boost converter is proposed, and the overall efficiency of the converter is predicted with an artificial neural network (ANN) model. In the proposed converter, the main switch is turned on by ZVT and turned off by zero voltage switching (ZVS). Also, the other semiconductor elements operate by soft switching (SS). Besides, the proposed snubber cell has the bidirectional direct power transfer feature. The theoretical analyzes of the converter are verified by an prototype having 50 V DC input voltage, 100 V DC output voltage, 250 W output power, and 100 kHz switching frequency. The overall efficiency of the converter in hard switching (HS) condition is increased from 87.2% to 95.4% thanks to proposed snubber cell. Moreover, the efficiency of converter at HS operation is estimated with ANN. For this estimation, 110 efficiency values are obtained based on the different switching frequency and the output power values. When the actual efficiency measurements and the estimation results obtained with the ANN model are compared, it is seen that the results overlap and is obtained very close result to the truth by ANN. Thus, owing to the ANN model, the semiconductor power elements will not need to be operated at high frequencies and overheating, and the damaging to the elements will be prevented. Finally, the efficiency curve measurement of the converter takes long time in the experimental study when it takes highly short time as a few minutes in the estimation with ANN.
ÖzFosil kaynaklı yakıtların gün geçtikçe tükenmesi sebebiyle yenilenebilir enerji kaynaklarının önemi de gittikçe artmaktadır. Güneş enerjisi teknolojisi, mevcut yenilenebilir enerji kaynakları arasında en hızlı büyüyen ve en popüler olanlardan biridir. Fotovoltaik (PV) sistemler olarak da bilinen güneş enerjisi sistemleri, güneş ışınımının evrensel olarak kullanılabilirliği ve PV panelini tüketici tarafında kurma esnekliği nedeniyle en çok aranan yenilenebilir enerji kaynağıdır. Ülkemizde son yıllarda popülerliği giderek artan güneş enerjisi sistemleri genelde ticari amaçlı ve çatı tipi denilen evsel uygulamalarda yaygın olarak kullanılmaktadır. Monokristal, perc monokristal, polikristal, ince film ve yarı esnek olmak üzere beş tip güneş paneli bulunmaktadır. Kullanıcıların bu farklı hücre tipine sahip panellerden hangisini kullanacağına karar vermesi aşamasında göz önünde bulundurduğu ve öncelik verdiği parametreler bulunmaktadır. Bu parametreler doğrultusunda kullanıcıların kendilerine en uygun güneş panelini belirlemeleri her zaman gözle ve kısa sürede mümkün olmayabilir. Bu çalışmada çok kriterli karar verme yöntemlerinden TOPSİS ile kullanıcıların kendi belirledikleri önem derecelerine göre maliyet, sıcaklık katsayısı ve enerji verimliliği açısından en uygun güneş paneli seçimlerine yardımcı olmak için bir model gerçekleştirilmiştir. Bu model sayesinde evsel uygulamalarda kullanıcıların kendi tercihlerine en uygun olan güneş panelleri sıralanarak kullanıcılara liste halinde sunulmakta ve böylece en optimum ürüne karar verme işlemi başarıyla ve mümkün olan en kısa sürede sağlanmaktadır.
In this study, an artificial neural network (ANN) model is developed for the purpose of estimating the output current ripple of a power factor correction (PFC) AC/DC interleaved boost converter (IBC) used in battery charger of electrical vehicles (EVs) based on the inductance current ripple, switching frequency and load changes. Besides, the improved ANN model is compared with some different machine learning (ML) techniques like linear regression (LR), random forest (RF). The PFC-IBC is simulated with the PSIM simulation program to estimate the output current ripple. As a result, 336 output current ripple values are obtained based on inductance current ripple, different switching frequency and load changes. Then, the value of output current ripple is estimated by training the input parameters with LR, RF and ANN machine learning techniques (MLTs) for controlling the current harmonics drawn from the grid and for reliable charging of batteries. It is seen that the estimation value obtained with MLTs is quite compatible with the actual value obtained with the simulation. In addition, in the study carried out with the simulation, it takes a period of several days to obtain the estimation results; whereas, the operation of estimation with MLTs can be completed in a short period such as a few minutes. This clearly reveals the advantage of the MLTs. Therefore, this value is estimated through the MLTs with a high accuracy before the design of the charging device in order to maintain at a secure level the output current ripple posing considerable importance in electrical vehicle battery charge. Also, in this estimation process, LR, RF and developed ANN techniques are examined and compared separately in the WEKA program and it is observed that the developed ANN model proposes better results than other techniques.INDEX TERMS Machine learning, artificial neural network, electrical vehicle, battery charging, power factor correction.
ÖzAraştırmada "Programlama Temelleri" dersinde kullanılan sınıf yönetim yazılımının, işbirlikli yöntem ile birlikte kullanılmasının, öğrenci başarısı üzerindeki etkisinin belirlenmesi amaçlanmıştır. Çalışmanın örneklemini Erzincan Üniversitesi Meslek Yüksekokulu Bilgisayar Programcılığı programında öğrenim gören ve Programlama Temelleri dersi alan öğrenciler oluşturmaktadır. Araştırmada öntest -sontest kontrol gruplu seçkisiz deneysel desen kullanılmıştır. Deneysel desene bağlı olarak seçkisiz örnekleme yöntemi ile deney grubu (n=35) ve kontrol grubu (n=35) belirlenmiştir. Deney grubunda yer alan öğrencilerin sordukları sorular sınıf yönetim yazılımı ile tahtaya yansıtılmış ve çözümün grupta yer alan diğer öğrencilerle birlikte işbirlikli öğrenme yöntemine göre bulunması istenmiştir. Kontrol grubunda yer alan öğrencilere ise öğretim elemanı tarafından doğrudan sınıf yönetim yazılımı ile öğrencinin ekranından cevap verilmiştir. Çalışma sonucunda deney grubunda yer alan öğrencilerin Programlama Temelleri dersindeki son test sonuçlarına bakıldığında istatistiksel olarak daha başarılı oldukları görülmüştür.Öğrencilerle yapılan görüşme sonuçlarına göre, öğrencilerin kendi hatalarını gördükleri, düşüncelerini serbest bir şekilde ifade edebildikleri, hatırlama becerilerinin arttığı ve kod yapısını daha iyi anladıkları sonucuna ulaşılmıştır. AbstractThe purpose of the study was to determine the effect of use of classroom management software in "Programming Fundamentals" course with collaborative method on student success. The sample of the study consisted of the students studying at Computer Programming program of Vocational School, Erzincan University and taking the course of Programming Fundamentals. Random experimental design with pretest-posttest control groups was used in the study. Depending on the experimental design, experimental group (n=35) and control group (n=35) were determined by using random sampling method. The questions asked by students in the experimental group were projected to blackboard by using classroom management software and the solution was asked to be found with other students in the group according to collaborative learning method. The students in the control group were answered directly from student's monitor with classroom management software by instructor. It was seen as a result of study that they were statistically more significant when the posttest results of the students in the course of Programming Fundamentals were examined. According the results of interview made with student, it was concluded that the students saw their mistakes, they freely expressed their thoughts, their recall skills increased and they understood the code structure better.
In this day where quality personnel employment is also important for the production of quality products and services, the organizations evaluate the performances by their performance management systems within the organization and try to achieve their targeted business performance by determining appropriate training and development programs. Development activities are spread over a longer period, as they are individual and continuous, and require the implementation of individual development activities. When the studies in the literature are examined, it is observed that standardized development activities are generally applied to the employees in this process and mostly socio-psychological factors are not taken into consideration. In this study, a decision support system, in which the employees' knowledge and skills, psychological status, communication skills, job satisfaction and demographic characteristics can be evaluated, has been developed in order to assist managers in their decision-making during the job placement and development processes of the employees. It will be possible to obtain the maximum efficiency and work performance with minor cost and time by planning the development activities in accordance with the needs and situation of each personnel thanks to the system developed in the study
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