Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 33Citation - Scopus: 41Development of a Personalized Thermal Comfort Driven Controller for Hvac Systems(Elsevier Ltd., 2021) Turhan, Cihan; Simani, Silvio; Gökçen Akkurt, GüldenIncreasing thermal comfort and reducing energy consumption are two main objectives of advanced HVAC control systems. In this study, a thermal comfort driven control (PTC-DC) algorithm was developed to improve HVAC control systems with no need of retrofitting HVAC system components. A case building located in Izmir Institute of Technology Campus-Izmir-Turkey was selected to test the developed system. First, wireless sensors were installed to the building and a mobile application was developed to monitor/collect temperature, relative humidity and thermal comfort data of an occupant. Then, the PTC-DC algorithm was developed to meet the highest occupant thermal comfort as well as saving energy. The prototypes of the controller were tested on the case building from July 3rd, 2017 to November 1st, 2018 and compared with a conventional PID controller. The results showed that the developed control algorithm and conventional controller satisfy neutral thermal comfort for 92 % and 6 % of total measurement days, respectively. From energy consumption point of view, the PTC-DC decreased energy consumption by 13.2 % compared to the conventional controller. Consequently, the PTC-DC differs from other works in the literature that the prototype of PTC-DC can be easily deployed in real environments. Moreover, the PTC-DC is low-cost and user-friendly.Conference Object Citation - Scopus: 1Fuzzy-Syllogistic Systems: a Generic Model for Approximate Reasoning(Springer, 2016) Kumova, Bora İsmailThe well known Aristotelian syllogistic system S consists of 256 moods. We have found earlier that 136 moods are distinct in terms of equal truth ratios that range in tau = [ 0,1]. The truth ratio of a particular mood is calculated by relating the number of true and false syllogistic cases that the mood matches. The introduction of (n -1) fuzzy existential quantifiers, extends the system to fuzzy-syllogistic systems S-n, 1 < n, of which every fuzzy-syllogistic mood can be interpreted as a vague inference with a generic truth ratio, which is determined by its syllogistic structure. Here we introduce two new concepts, the relative truth ratio (r)tau = [ 0,1] that is calculated from the cardinalities of the syllogistic cases of the mood and fuzzy-syllogistic ontology (FSO). We experimentally apply the fuzzy-syllogistic systems S-2 and S-6 as underlying logic of a FSO reasoner (FSR) and discuss sample cases for approximate reasoning.yBook Part Citation - Scopus: 1Symmetric Properties of the Syllogistic System Inherited From the Square of Opposition(Birkhäuser, 2017) Kumova, Bora İsmailThe logical square Omega has a simple symmetric structure that visualises the bivalent relationships of the classical quantifiers A, I, E, O. In philosophy it is perceived as a self-complete possibilistic logic. In linguistics however its modelling capability is insufficient, since intermediate quantifiers like few, half, most, etc cannot be distinguished, which makes the existential quantifier I too generic and the universal quantifier A too specific. Furthermore, the latter is a special case of the former, i.e. A subset of I, making the square a logic with inclusive quantifiers. The inclusive quantifiers I and O can produce redundancies in linguistic systems and are too generic to differentiate any intermediate quantifiers. The redundancy can be resolved by excluding A from I, i.e. I-2=I-A, analogously E from O, i.e. O-2=O-E. Although the philosophical possibility of A subset of I is thus lost in I-2, the symmetric structure of the exclusive square (2)Omega remains preserved. The impact of the exclusion on the traditional syllogistic system S with inclusive existential quantifiers is that most of its symmetric structures are obviously lost in the syllogistic system S-2 with exclusive existential quantifiers too. Symmetry properties of S are found in the distribution of the syllogistic cases that are matched by the moods and their intersections. A syllogistic case is a distinct combination of the seven possible spaces of the Venn diagram for three sets, of which there exist 96 possible cases. Every quantifier can be represented with a fixed set of syllogistic cases and so the moods too. Therefore, the 96 cases open a universe of validity for all moods of the syllogistic system S, as well as all fuzzy-syllogistic systems S-n, with n-1 intermediate quantifiers. As a by-product of the fuzzy syllogistic system and its properties, we suggest in return that the logical square of opposition can be generalised to a fuzzy-logical graph of opposition, for 2<n.Article Citation - WoS: 4Citation - Scopus: 5Generating Ontologies From Relational Data With Fuzzy-Syllogistic Reasoning(Springer Verlag, 2015) Kumova, Bora İsmailExisting standards for crisp description logics facilitate information exchange between systems that reason with crisp ontologies. Applications with probabilistic or possibilistic extensions of ontologies and reasoners promise to capture more information, because they can deal with more uncertainties or vagueness of information. However, since there are no standards for either extension, information exchange between such applications is not generic. Fuzzy-syllogistic reasoning with the fuzzy-syllogistic system4S provides 2048 possible fuzzy inference schema for every possible triple concept relationship of an ontology. Since the inference schema are the result of all possible set-theoretic relationships between three sets with three out of 8 possible fuzzy-quantifiers, the whole set of 2048 possible fuzzy inferences can be used as one generic fuzzy reasoner for quantified ontologies. In that sense, a fuzzy syllogistic reasoner can be employed as a generic reasoner that combines possibilistic inferencing with probabilistic ontologies, thus facilitating knowledge exchange between ontology applications of different domains as well as information fusion over them.Article Citation - WoS: 7Citation - Scopus: 8Performance Indices of Soft Computing Models To Predict the Heat Load of Buildings in Terms of Architectural Indicators(Yıldız Teknik Üniversitesi, 2017) Turhan, Cihan; Kazanasmaz, Zehra Tuğçe; Gökçen Akkurt, GüldenThis study estimates the heat load of buildings in Izmir/Turkey by three soft computing (SC) methods; Artificial Neural Networks (ANNs), Fuzzy Logic (FL) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) and compares their prediction indices. Obtaining knowledge about what the heat load of buildings would be in architectural design stage is necessary to forecast the building performance and take precautions against any possible failure. The best accuracy and prediction power of novel soft computing techniques would assist the practical way of this process. For this purpose, four inputs, namely, wall overall heat transfer coefficient, building area/ volume ratio, total external surface area and total window area/total external surface area ratio were employed in each model of this study. The predicted heat load is evaluated comparatively using simulation outputs. The ANN model estimated the heat load of the case apartments with a rate of 97.7% and the MAPE of 5.06%; while these ratios are 98.6% and 3.56% in Mamdani fuzzy inference systems (FL); 99.0% and 2.43% in ANFIS. When these values were compared, it was found that the ANFIS model has become the best learning technique among the others and can be applicable in building energy performance studies.Article Citation - WoS: 3Citation - Scopus: 3Developing Cation Exchange Capacity and Soil Index Properties Relationships Using a Neuro-Fuzzy Approach(Springer Verlag, 2014) Pulat, Hasan Fırat; Tayfur, Gökmen; Yükselen Aksoy, YelizArtificial intelligence methods are employed to predict cation exchange capacity (CEC) from five different soil index properties, namely specific surface area (SSA), liquid limit, plasticity index, activity (ACT), and clay fraction (CF). Artificial neural networks (ANNs) analyses were first employed to determine the most related index parameters with cation exchange capacity. For this purpose, 40 datasets were employed to train the network and 10 datasets were used to test it. The ANN analyses were conducted with 15 different input vector combinations using same datasets. As a result of this investigation, the ANN analyses revealed that SSA and ACT are the most effective parameters on the CEC. Next, based upon these most effective input parameters, the fuzzy logic (FL) model was developed for the CEC. In the developed FL model, triangular membership functions were employed for both the input (SSA and ACT) variables and the output variable (CEC). A total of nine Mamdani fuzzy rules were deduced from the datasets, used for the training of the ANN model. Minimization (min) inferencing, maximum (max) composition, and centroid defuzzification methods are employed for the constructed FL model. The developed FL model was then tested against the remaining datasets, which were also used for testing the ANN model. The prediction results are satisfactory with a determination coefficient, R2 = 0.94 and mean absolute error, (MAE) = 7.1.Article Citation - WoS: 3Citation - Scopus: 5Classifications for Planimetric Efficiency of Nursing Unit Floors(Middle East Technical University, 2012) Kazanasmaz, Zehra Tuğçe; Tayfur, GökmenBu çalışma, Türkiye’deki 15 Devlet Hastanesine ait ‘Hasta Bakım Üniteleri’ verilerini kullanarak, mimarlık alanında oldukça yeni olan Bulanık Mantık metodu ile verimlilik tahmini yapmakta; bu çerçevede bulanık mantık algoritması geliştirerek, Türkiye’de örnek olarak seçilen kamu hastanelerinin plan (planimetric) tasarım verimliliği için sınıflandırmalar geliştirmeye çalışmaktadır. Hasta bakım ve tedavi ünitelerinin kat planlarından hasta kullanım alanları ve dolaşım alanları elde edilerek bulanık mantık modeli alt kümeleri için üyelik fonksiyonları oluşturulmuştur. ‘Mamdanni’ kural sistemi, kuralların ağırlıklarını hesaplamada ‘min’ fonksiyonu, ve ‘max’ kompozisyonu ve ‘centroid’ metodu da bulanık işlemcisi için kullanılmıştır. Girdi değişkenleri olarak hasta kullanım alanları ve dolaşım alanları modellenmiştir. Girdi değişkenleri ile çıktı değişkeni olan tasarım verimliliği arasındaki ilişkiler bulanık mantık kuralları ile ortaya çıkarılmıştır. Varolan hasta bakım ünitelerini incelemek için, verimlilik çıktı değerleri modelden elde edilmiştir. Genel tasarım normları, tasarım ölçütleri ve önceki çalışmalar ışığında ve de bu model aracılığıyla verimlilik sınıfları oluşturulmuştur. Modelde test edilen 15 hastane kat planından altısının düşük verimli sınıf içinde, dokuzunun ise orta verimli sınıf içinde olduğu görülmüştür. Hiçbiri güncel standartlara ve gereksinimlere uygun değildir. Bu çalışmada elde edilen modelin faydası, verimlilik sınıflarının sınır değerlerini belirleme yeteneğinde olmasıdır. Hastanelerin karşılaştırılarak incelenmesi için oluşturulan verimlilik sınıflandırılması başarı ile sonuçlanmıştır. Hastane tasarımcıları ve yöneticileri, mevcut hastanelerin değerlendirmesini ve karşılaştırmaları yapabilmek için bu çalışmadan geribildirim yoluyla bilgi edinebilir. Sonuç olarak, ilgili binalar hakkında karar verme aşmasında(örneğin binanın iyileştirme ihtiyacının olup olmadığı, yeni mekanlara gerek duyulup duyulmadığı gibi) bu modelden faydalanabilirler.Article Citation - WoS: 9Citation - Scopus: 10Modelling Trip Distribution With Fuzzy and Genetic Fuzzy Systems(Taylor and Francis Ltd., 2013) Kompil, Mert; Çelik, Hüseyin MuratThis paper explores the potential capabilities of fuzzy and genetic fuzzy system approaches in urban trip distribution modelling with some new features. First, a simple fuzzy rule-based system (FRBS) and a novel genetic fuzzy rule-based system [GFRBS: a fuzzy system improved by a knowledge base learning process with genetic algorithms (GAs)] are designed to model intra-city passenger flows for Istanbul. Subsequently, their accuracy, applicability and generalizability characteristics are evaluated against the well-known gravity- and neural network (NN)-based trip distribution models. The overall results show that: traditional doubly constrained gravity models are still simple and efficient; NNs may not show expected performance when they are forced to satisfy trip constraints; simply-designed FRBSs, learning from observations and expertise, are both efficient and interpretable even if the data are large and noisy; and use of GAs in fuzzy rule-based learning considerably increases modelling performance, although it brings additional computation cost.Article Citation - WoS: 175Citation - Scopus: 203Fuzzy Logic Model for the Prediction of Cement Compressive Strength(Elsevier Ltd., 2004) Akkurt, Sedat; Tayfur, Gökmen; Can, SeverA fuzzy logic prediction model for the 28-day compressive strength of cement mortar under standard curing conditions was created. Data collected from a cement plant were used in the model construction and testing. The input variables of alkali, Blaine, SO3, and C3S and the output variable of 28-day cement strength were fuzzified by the use of artificial neural networks (ANNs), and triangular membership functions were employed for the fuzzy subsets. The Mamdani fuzzy rules relating the input variables to the output variable were created by the ANN model and were laid out in the If-Then format. Product (prod) inference operator and the centre of gravity (COG; centroid) defuzzification methods were employed. The prediction of 50 sets of the 28-day cement strength data by the developed fuzzy model was quite satisfactory. The average percentage error levels in the fuzzy model were successfully low (2.69%). The model was compared with the ANN model for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly and more explicit model than the ANNs could be produced within successfully low error margins.Article Citation - WoS: 68Citation - Scopus: 86Fuzzy Logic Algorithm for Runoff-Induced Sediment Transport From Bare Soil Surfaces(Elsevier Ltd., 2003) Tayfur, Gökmen; Özdemir, Serhan; Singh, Vijay P.Utilizing the rainfall intensity, and slope data, a fuzzy logic algorithm was developed to estimate sediment loads from bare soil surfaces. Considering slope and rainfall as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relations among rainfall intensity, slope, and sediment transport were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF-THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The sediment load predicted by the fuzzy model was in satisfactory agreement with the measured sediment load data. Predicting the mean sediment loads from experimental runs, the performance of the fuzzy model was compared with that of the artificial neural networks (ANNs) and the physics-based models. The results of showed revealed that the fuzzy model performed better under very high rainfall intensities over different slopes and over very steep slopes under different rainfall intensities. This is closely related to the selection of the shape and frequency of the fuzzy membership functions in the fuzzy model.