Soft Computing approaches may be effectively used for the aim to improve security of communication systems. Recently, we proposed a technique for fuzzy-chaotic masking signals generation. In this paper we developed this approach further. It is proposed a procedure for receiver and transmitter synchronization by using fuzzy chaotic systems (fuzzy Lorentz systems). The receiver system utilizes a fuzzy controller based on the use of Z-numbers in If-Then rules. The transmitter system is designed in form of a classical fuzzy system. The fuzzy controller is used to synchronize the fuzzy Lorentz system-based receiver and transmitter at a minimal error.

Havanın sıcaklık ve nem parametreleri canlı yaşamı başta olmak üzere tarım, ulaşım gibi birçok alanı etkilemektedir. Bu sebepten dolayı bu parametrelerin gelecekteki değerlerini doğru tahmin etmek önemlidir. Bu çalışmada, Tekirdağ ili Süleymanpaşa ilçesi için oluşturulan model sistem üzerinden ve Meteoroloji İl Müdürlüğünden alınan sıcaklık ve nem veri setleri kullanılarak, derin öğrenme tekniklerinden LSTM algoritmaları ile sıcaklık ve nem tahmini yapılmıştır. Tek sensör üzerinden alınan verilerde gürültü kaynaklı hatalardan dolayı çoklu sensörlerden gelen veriler birleştirilerek veri seti oluşturulmuştur. 2015-2021 yılları arasındaki Tekirdağ Meteoroloji İl Müdürlüğünden alınmış sıcaklık ve nem verileri, oluşturulan model sistem üzerinden alınan 2020 yılına ait sıcaklık ve nem verileri ile sensör füzyonu uygulanarak veri seti elde edilmiştir. Bu veri seti ile 2022 yılına ait sıcaklık ve nem verileri derin öğrenme algoritmaları ile tahmin edilmiştir. Zamana göre sıralı olarak gelen veriler için derin öğrenme algoritmalarından Long Short Term Memories (LSTM) kullanılmıştır. Tahmin edilen veriler yine Tekirdağ Meteoroloji İl Müdürlüğünden alınan 2022 yılına ait gerçek veriler ile karşılaştırılmıştır. Bu tahminde başarı ölçütleri olarak RMSE 1.895, MSE 3.547, R-kare skoru değerinin 0.952 ve MAE 1,614 olarak hesaplanmıştır. Zamana göreli sıralı biçimde gelen verilerde bu algoritmanın kullanılabileceği görülmüştür. Oluşturulan model sistem PLC ve SCADA tabanlıdır.

The defense industry is rapidly growing in our country. The domestic and national designs of heavy duty vehicles are standardized by many application software programs which are used to model and analyze the plant model and control model. This paper focuses on the modeling of the transmission components of heavy duty vehicles used in the defense industry and the different techniques to standardize them. The paper first introduces the component which is used in heavy duty vehicle transmission modeling. Second, this paper focuses on the modeled PID controller to control the plant model. Third, the conventional controller is defined. Fourth, the analysis of two different controllers and the outputs of this heavy duty component are explained. The requirements for future work are also defined in this paper.

Communication technologies play a key role in various fields. Hybrid Soft Computing approaches have significant potential for design and investigation of complex systems. In this paper, we use combination of fuzzy logic and chaos theory to model uncertainty and complexity of a secure communication system. Fuzzy rule base is used to describe dependence of behavior of a chaotic system on its parameters and initial conditions. The rule base is constructed by applying fuzzy clustering to a large data set. The approach is characterized by its relatively low computational complexity due to the use of fuzzy rule base instead of intensive simulations of a fuzzy chaotic system. Complexity of this hybrid fuzzy-chaotic approach assures an increased level of security. Trade-off between complexity and security may be achieved by generation of transmitted information using fuzzy modeling of chaotic behavior. Computer simulations are used to verify feasibility and effectiveness of the proposed approach.

As technology develops all over the word, it is desired to be more efficient and less costly, and automatic control systems are demanded accordingly. The most important duties of the elements that constitute the automatic control system are quality of the established system and being cheap and functional. SCADA and PLC control systems, which are among the significant control systems, are utilized to implement these conditions placed against the elements. SCADA and PLC systems are automatic control systems that are very popular today. Both of them are used in almost every field of industry. This study has been implemented in the Academy automation R&D laboratory and applications have been developed with PLC and SCADA, which are indispensable for automation systems. All systems are connected to SCADA using 3 types of PLC, S7-300, S7-1200, S7-1500 and also MODBUS devices. These four systems transferred to SCADA are monitored on a screen. At the same time, the feature of being able to intervene in the system in case of malfunction, which is one of the important advantages of the SCADA system, has also been used.

In this study, the effect of pollution on monocrystalline and polycrystalline PV panels on short circuit current and open circuit voltage was investigated by external experiments. Experiments were carried out under climate conditions in Hatay province for 210 days from October 2019 to May 2020. In the study, 2 pieces of 45 W power monocrystalline PV panels and 2 pieces of 50 W power polycrystalline PV panels were used. PV panels were placed in two groups as manuelly cleaned and uncleaned on the tile roof. One of the monocrystal and polycrystalline panels was cleaned manually for regular periods, while the others were not cleaned except rain. Short circuit current, open circuit voltage, panel surface temperature and radiation values were measured for each panel at certain times during the day, in the morning, at noon and in the evening. In addition, ambient temperature and humidity values were measured and recorded. Environmental factors have been found to affect the short circuit current of the panels, not significantly affecting the open circuit voltage. It has been practically tested that manually cleaned panels have higher short circuit current values than uncleaned panels. Therefore, it has been calculated that cleaning the panels periodically will significantly increase the electricity production in powerful solar power plants. The data obtained as a result of experiments are modeled with artificial neural networks.

The beginning of life could not be described on Earth, yet. For this reason, natural phenomena have been described with the starting point uncertainty. Natural events could not be described by a single field. In H. Brown’s study [1], the main lesson that emerges is described as the special theory of relativity is incomplete. For that reason, the aim of this study is to describe physical reality in a single field. In the light of this aim, first of all, coordinate systems which are under-established are defined. The reason for the deficiency is explained. The system of unified coordinates, which is based on the correct basis, is explained. Moreover, in this study, the coordinate systems which are defective with respect to each other are described. In addition, in this study, the theory describing the relation of these coordinates with the reference body with a single field is explained and proved by equations. Furthermore, the beginning of life is explained by Lorentz Transformation Equations and Gauss Coordinate Systems. At the end of the description, new interpretations, interpretations supporting previous interpretations of scientists, or equations stating that they are wrong are explained. As a result, in this study, the equivalent of the reference body in the natural system is explained

Electronic circuit analysis is an important research field. One of the key application fields is communication. An important problem in the field of the communication is related to the use of chaotic signals under uncertainty. In this paper we consider a fuzzy logic-based approach for analysis of chaotic electronic circuit. Fuzzy differential equations are used to model chaotic dynamics of an electronic circuit under uncertainty.

Modeling a dead-time system is a common issue in engineering applications. To address this issue, existing research has employed neural networks and fuzzy logic-based intelligent systems. Herein, a dead-time system modeled with the aid of support vector machine regression, which has a good generalization feature, was investigated. The performance of the method proposed herein was examined with different parameters in linear and nonlinear dead-time systems.

In this paper, an Expectation-Maximization (EM) based iterative data detection method for downlink of a multicarrier system is proposed. The proposed method has low computational cost when compared with the other iterative ones since it does not require any matrix inversion if the requirements on users' data are met. The performance of the resulting algorithm is compared with Minimum Mean Squared Error (MMSE) estimator in terms of Symbol Error Rate (SER) for downlink of a Multi Carrier-Code Division Multiple Access (MC-CDMA) system in the presence of frequency selective channels using computer simulations. It is illustrated that the proposed algorithm outperforms the MMSE.

In this study, an active Fault Tolerant Control (FTC) method based on Support Vector Machines (SVM) is presented. The proposed FTC method is not limited to certain faults in the reconfiguration manner and but it also includes a reconfiguration mechanism with direct on-line controller calculation. Here, PID type controller is utilized within the method as a reconfiguration sub-system. The reconfiguration mechanism and the diagnosis unit work independently within the method. Therefore, there is no need for the isolation of faults before tolerating them. In diagnosis and reconfiguration stages of the method, support vector regression machines are used. This FTC technique uses the real-time data generated by the system and it produces the appropriate gains of the controller in an on-line manner. The PID controller coefficients or the gains to be used in the training stage for faulty and non-faulty cases are all obtained by using the Genetic Algorithm optimization approach in an off-line manner. Moreover, it has also been shown that the proposed method can handle multiple and simultaneous occurrences of various types of faults. The performance of the proposed method is tested on a simulation model of two tank level control system for various fault scenarios.

This paper presents a fault detection, diagnosis, and reconfiguration method based on support vector machines. This method is appropriate for certain or predetermined faults and involves a fault detection and diagnosis unit and an online controller selection type reconfiguration mechanism. In this method, when a fault is detected and diagnosed by the fault detection and diagnosis unit, a suitable controller, which has been determined via an optimization algorithm in an off-line fashion, is activated to maintain proper closed-loop performance of the system in an on-line manner. In the detection, diagnosis, and reconfiguration stages of the method, support vector classification and regression machines are used and the performance is tested on a simulation model of a two-tank level control system for various fault scenarios.

In this paper, a novel method for reducing the high-order systems to first-order plus time-delay forms is introduced. For this purpose Support Vector Machines, which became a popular learning algorithm, is employed. Three parameters of the first-order plus time-delay forms are estimated by three parallel support vector regression machines. Satisfactory performance is obtained at the simulations.

In this paper, a fault detection and diagnosis (FDD) technique for nonlinear systems based on support vector machines (SVM) is presented. Support vector regression (SVR) has been used in fault detection process and support vector classification (SVC) has been used in diagnosis process. In fault detection process, the confidence band idea represents the normal operating conditions of the system. The upper and the lower boundaries of the confidence band are modelled by two different SVR machines. A fault is detected when an output signal exceeds the upper or lower bounds of the generated confidence band. A support vector multi-classification method, one-against-all, has been used to classify the occurring fault within the group of expected and predefined faults in technical system. The performance of the proposed FDD method is illustrated on simulation example involving a two-tank water level control system under faulty conditions.