Interaction among the Criteria Affecting Main Battle Tank Selection: An Analysis with DEMATEL Method

Main battle tanks (MBTs) have always been in the heart of all military campaigns and have enabled armies to fight across the full spectrum of war. Countries need to consider the complex interactions between subsystems of MBTs in the decision phase of a design process or MBT acquisition. In order to define the interaction among the subsystems of ‘system of systems’, which is MBT system for this case, this study aims to determine the criteria and their sub criteria affecting MBT selection problem and to analyse the cause and effect relations among these criteria. The criteria and the complex interaction among them have been determined by consulting a group of experts. Because of multiple complex criteria interactions in MBT selection problem, decision making trial and evaluation laboratory (DEMATEL) method is used as a multiple criteria decision making method. DEMATEL method is applied on the main and the sub criteria separately to understand the cause and effect relations. The results show that Survivability main criterion has the strongest central role among the main criteria for MBT selection, while the followers are firepower, mobility and command and control (C2). It is also shown that, in terms of sub criteria for MBT selection, ballistic protection, a sub criterion of survivability main criterion, has the highest degree of influence over most of the other sub criteria. However, physical dimensions/silhouette, another sub criterion of survivability, is the most affected sub criteria. The top five sub criteria in terms of central role are determined as physical dimensions/silhouette, ballistic protection, power/weight ratio, ground pressure and suspension system

The Artificial Neural Networks Approach To The Modelling On Experimental Processes Of Fuel Cell

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2002Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2002Yakıt pilleri bir reaksiyonun kimyasal enerjisini doğrudan elektrik enerjisine dönüştüren elektrokimyasal cihazlardır.Yakıt pilleri, onları tercih edilen enerji dönüşüm sistemleri yapan birçok özelliğe sahiptir. Bunlardan en önemlileri; oldukça yüksek verime sahip olmaları ve düşük oranda zararlı atık üretmeleridir. Yapay Sinir Ağı (YSA) yaklaşımı, bir sürecin modellenmesinde kullanılan en yaygın metotlardan biridir. YSA, birçok küçük mantık biriminin bir ağda birleşmesi anlamına gelen birleştirici modelin bir uygulamasıdır. Yapay sinir ağının öğrenme kabiliyeti ağın mimarisi ve eğitim için seçilen algoritmik metot ile belirlenir. Bu tezde, ileri sürümlü yapay sinir ağı direk metanol yakıt pilinin bir uygulamasından elde edilen deneysel sonuçlara uygulanmıştır. Geriye yayılım algoritması kullanılarak yapay sinir ağı; tarama hızı, sıcaklık ve yakıt (metanol) konsantrasyonu ile metanolun iletken polimer matrisi üzerine yayılmış Pt mikro parçacıklar üzerindeki elektrooksidasyon reaksiyonundan elde edilen akım yoğunlukları arasındaki ilişkiyi öğrenmek üzere eğitilmiştir. Eğitim tekrarlarından sonra, eğitim süreci iyi bir öğrenme sağlamıştır. Aynı zamanda, ileri sürümlü yapay sinir ağı yaklaşımının kullanımı ölçüm sınırları dışındaki akım yoğunluklarının tahmininde de oldukça iyi bir başarım gerçekleştirmiştir.Fuel cells are electrochemical devices that convert the chemical energy of a reaction directly into electrical energy. Also, fuel cells have many characteristics that make them favorable as energy conversion devices. Two of the most important characteristics are high efficiency and very low environmental intrusion. Artificial Neural Network (ANN) approach is one of the most popular methods in the modeling of a process. An ANN is an example of a connectionist model, in which many small logical units are connected in a network. The learning ability of neural network is determined by its architecture and the algorithmic method chosen for training. In this thesis, a multilayered feed forward neural network study was applied to the experimental results obtained from an application of direct methanol fuel cell. Using the algorithm of back propagation, the artificial neural network was trained to learn a possible correlation between scan rate of potential, temperature and fuel (methanol) concentration and obtained peak current densities for the electrooxidation reaction of methanol on platinum electrodes. After the training epochs, the training process provides a very high learning capability. Also, using of the feed forward neural network procedure realizes a very good performance to estimate the peak current densities at the out of the measurement range.Yüksek LisansM.Sc

Concept mapping sustainable energy management for a holistic approach to energy strategies

One of the important reasons for global warming and climate change is regarded as the improper management of fossil energy consumption. There may be various models or approaches to energy management (EM). However, strategic EM and its applications are still away from achieving their goals. In this regard, the main objectives of the present study are to design, develop and propose a knowledge framework primarily based on an EM concept map for a holistic EM. Aligned with the research purposes, a qualitative four-phase study was conducted, and the research question was formulated as: 'What would be the components and concepts required for the holistic EM?' The study also integrated systematic literature review (SLR) and focus group discussion (FGD) techniques. Initially, a list of the key EM concepts was formed and a preliminary concept map was constructed. Then, the final version of the concept map was achieved through several iterative revisions and FGDs. This concept map may be seen as a complementary and contributory tool for strategy development and decision-making processes in the EM knowledge domain. It is hoped that the study may extend the previous knowledge both by the tools it has utilised and approaches it has adopted for the holistic EM