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Iot kullanarak hibrit enerji sistemleri için akıllı bir izleme ağı
This thesis addresses the design, development, and implementation of a smart monitoring network for hybrid renewable energy systems using Internet of Things (IoT) technologies. The research aims to improve the efficiency, reliability, and performance of renewable energy integration through real-time monitoring and control. This thesis examines the design and implementation of a hybrid renewable energy system which integrates wind turbine and photovoltaic (PV) technologies to satisfy the growing demand for green energy. The approach is multidisciplinary, involving engineering, renewable energy systems, and information technology to design, simulate, and analyze performance of the hybrid system. Background, motivation for renewables integration and objectives of the study are presented in the introduction. To lay the ground for the next the research projects, the equally overview of the In order to build the foundation for the next research projects, a comprehensive overview of the literature is provided, including previous research on smart monitoring networks, hybrid energy systems, and Internet of Things-based energy management solutions. To prepare ground for the forthcoming research, a survey of the literature also incorporates reviewed works of smart monitoring networks, hybrid energy systems and IoT-based energy management solutions. Parameter extraction, Code Generation in MATLAB, and performance estimation Issue are widely described in the chapters of modeling and simulation of PV and WT systems. The hardware development chapter shows the design, and integration of sensors, converters, and control system constituents to effectively construct a network for the real-time monitoring of the hybrid energy system. The system's performance is thoroughly examined in the results chapters, which make use of visualizations from cloud platforms, smartphone application interfaces, and real-time monitoring data. The chapters offer insights into operational dynamics, system efficiency, and patterns of energy generation through in-depth analysis and visualization. In the practical implementation phase, hardware components are deployed to collect real-time data from the hybrid energy system. IoT-based communication protocols enable remote monitoring and control, facilitating optimization of energy generation, storage, and utilization. The research demonstrates the scalability and reliability of the monitoring network in improving the performance of hybrid renewable energy systems. In summary, by providing an extensive foundation to the design, implementation, and assessment of hybrid energy systems, this thesis adds to the rapidly expanding subject of renewable energy. The goal of the research is to promote an environmentally friendly and more resilient energy future by expediting the implementation of sustainable energy solutions by bridging the theory-practice divide.
Geliştirilmiş dikkat ve genişletilmişkonvolüsyon tabanlı topluluk model tabanlıağ saldırı tespit sistemi, geliştirilmiş cheetah optimizörü kullanarak düşmanca kaçınma saldırılarına karşı
A reliable defense system against network threats over a long time is called the Intrusion Detection System (IDS). Defenders are notified by the IDS when suspicious or malevolent activities are identified on the network. In the last ten years, machine learning has helped the IDS to become more accurate, more capable of analysis, and more adept at finding new or modified forms of known intrusions. Deep learning, an advanced version of the machine learning technique, is essential to the field of network security. Additionally, a deep learningbased Network Intrusion Detection System (NIDS) performs better than conventional IDS techniques. Recent studies, however, demonstrate that when faced with attackers in realtime, the deep learning-based IDS becomes somewhat inaccurate. There is no analysis done on how attack models will affect NIDS as well. In order to defend against adversarial evasion attacks, an enhanced deep learning-based NIDS model is designed here. The required data is first collected from commonly available websites. The best feature extraction is carried out on the collected data. Here, the Improved Cheetah Optimizer (ICO). Then, an Attention and Dilated-based Ensemble Network (ADEN) is implemented to detect the intrusions from the optimally extracted features. The Deep Temporal Convolutional Neural Network (DTCN), Long Short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models are assembled together to deploy the suggested ADCEN. In the end, the ADEN detects the viii intrusions and generates the respective outputs using the fuzzy ranking approach. To demonstrate well the recommended deep learning-based NIDS defends against adversarial evasion assaults, experiments are conducted against conventional models.
Blockchaın teknolojisiyle e-devlet hizmetlerinde organizasyonel birlikte çalışabilirliğin otomatik yapılması
The research is concentrated on the development of a smart system for the preservation of government records in smart E-Government benchmark repository. In literature, many existing preservation techniques and models have been discussed and presented with their detailed comparison as preservation rate of digital data growth has increased. Many western countries have already upgraded their paper-based systems to the smart system for preservation of government records including Turkey. In methodology, we used python language for the implementation of smart system application with E-Government benchmark to preserve archived paper-based records, it likewise delivers a roadmap for the innovative arithmetical environment which is trained using the automation based blockchain.. The blockchain model proposes construction that knows how to be utilized to relate household tasks and commitments in the interior of a confined structure. Major archives (i.e. open access, closed, restricted, proprietary) are creating certain assistance for safeguarding numerical objects and files in the E-Government benchmark dataset. Records accession, normalization, and transformation have also been performed on the records during the process of preservation to clean the records and format conversion. As a result, we achieved good preservation of most of the records from 2015 to 2025, the goal is to preserve most of the records it belongs to digital format. The distribution of records preservation for major archives in terms of paper-based and digitally preserved records from the year 2015 to 2025. The preservation of government records was recorded as very low in 2015 as much of the record archival was paper-based in all major repositories of the government. However, year by year the smart system tends to preserve the records in digital format from the old paper-based format, and by 2025 large number of records are being converted to the digital format with a whopping accuracy of 98.68% for all the records where the system was trained on 80% of data and tested on rest of the 20% of data. Having such a smart system can be very helpful for preserving the records of the government, and useful for understanding how records are being preserved and their functionality.
Bulut için blok zincir tabanlı entegre ve kaynak sınıflandırması için yeni bir çerçeve sağlayın
Researchers in the manufacturing sector have recently shown an increased interest in the topic of cloud creation. Cloud generation is a customer-centric production strategy based on cloud computing, with the primary goal of providing access based on comprehensive service demand. The existing cloud architecture, on the other hand, has issues with a centralized network structure and operations. Basically, a centralized network offers insufficient flexibility, effectiveness, reliability, and security. In this study, the development of a decentralized network architecture for the cloud generation is made possible by blockchain technology. With Blockchain acting as a peer-to-peer network, a centralized peer-to-peer network might be created. The healthcare industry grew and became a significant actor in the provision of services. In recent years, patients have found remote observation services to be more beneficial. Healthcare practitioners create observation systems that diagnose and prescribe therapy in hospitals or clinics when patients are not actually present. Sensors are implanted in/on the bodies of patients. Wireless data interchange is possible across healthcare systems. Using characteristics from the blockchain, healthcare systems could be enhanced (e.g. distributed ledger, decentralized storage, and authentication). In this way, smart contracts automate the authentication and distribution of information, as well as participant criteria. When the number of users or other components increases, blockchains and smart contracts should maintain quality service. Scalability, increased security, and optimal performance are all technical challenges of smart contracts based on blockchains
Veri madenciliği tekniği kullanarak sistem sızma tespitlerinin (IDS) sınıflandırılması
Currently, people are living in a world without borders, which means that nothing is beyond reach. The significant growth in technology has led to new threats in the era of computing. These risks are increasing and we should be dealing with them in a more efficient manner. Therefore, it has become necessary for researchers to focus on protecting networks and to work on the production of software for this purpose, namely 'an intrusion detection system' (IDs) .IDS can reveal various types of attacks and analyze events that arise in networks and computer systems to identify any protection problem. However, an IDS generates a considerable number of alerts each day most of which may be false alarms. Therefore, researchers have attempted to find ways to solve the problem of false alerts. One of these methods is data mining algorithms, which is a process of mining knowledge from huge datasets. Data mining may be suitable for dealing with this large number of alerts. This research presents a methodology involving an improved data mining technique to classify alarms as being a real attack or a false attack. This technique is used in designing the proposed classification system. An application has been designed using C# to test the dataset. The classification system is tested by conducting three experiments on the second, fourth, and fifth week of the DARPA 1999 dataset which extracted from a simulation of a military management network. Each experiment produces high accuracy to classify the alerts in order to facilitate the process of analyzing alerts to help security analysts to distinguish between true and false alerts. The first experiment is conducted on the second week with percentage of false alert (PFA) and percentage of true alerts (PTA) equaling 95%, and 5%, respectively. The second test was conducted on the fourth week and the PFA and PTA equaled 94.19% and 5.81%, respectively. The third experiment was conducted in the fifth week with the PFA and PTA equaling 93.768% and 6.232%, respectively. The proposed system achieved the best results when compared with the literature findings that had used the same dataset.
Hasta sağlığı veri tabanı kullanarak veri madenciliğine dayalı kalp hastalığı tahmini
Data mining (DM) is the process of finding or extracting knowledge from information on a huge piece data. DM uses intelligent methods to find patterns in the process of knowledge discovery (KD) in a database. The appearance field of DM promises to give a new technique and good tools. Also, DM can help the person to understand, solve big amounts of data remains on complex and unsolved problem. The wide functions in DM practice includes: classification, clustering, regression rule generation, sequence analysis and discovering association. The classification is one of the most important techniques of DM. As well as, many problems in various fields such as science, business, industry and medicine can be solved by using these approaches. Neural Networks (NN) have appeared as a good tool for classification. The study of Heart Diseases (HD) database is testing by using NN approach. HD diagnosis is not easy work which demands to a lot of experience and acquaintance. The common way for predicting HD is a doctor's checkup or different medical examination like ECG, Heart MRI Stress Test and etc. Nowadays, 'Artificial Neural Network' (ANN) has been commonly used to the technique for dissolving many problem clinical diagnoses. An ANN is the 'simulation of the human brain', it is a supervised learning. This research aims to optimize or reduce the number of biomedical test which asked from patients. Correspondingly to do a classification approach using NN technique and a Feature Subset Selection (FSS) algorithm. FSS is a pre-processing phase used to reduce number of attribute and remove irrelevant data. HD values are used and originally 13 attributes are involved to classify the HD. To reduce or optimize the number of attributes, different evaluators and search methods are determined. In this research the two studies are conducted on the STALOG data set. The first study used three algorithms: Naïve Bayes (NB) got on accuracy equal to 85.182. While J84 obtained on 91.4815 and NN algorithm got on 99.6296. However, in the second study the NB obtained on 85.925 and J84 got on 91.4815. Finally, NN obtained on 99.2593. Moreover, accuracy of ANN algorithm in the two studies got on the best result when compared with the results of the other algorithms.
Optik iletişim sistemlerinde frekans artışı ile yarı iletken lazerin spektral kayma etkisine iletilen verilerin sabit distorsiyonu
Bu çalışmada, yarı iletken bir lazer (lazer diyot (LD) olarak da adlandırılır), ışık yayan diyot (LED)ve Polimerler Işık Yayan Diyot (PLED), Yarıiletkenlerin Spektral Kayma Etkisine bağlı olarak Aktarılan Verilerin Distorsiyonunun Sabitliği İncelenmiştir. Optik İletişim sistemlerinin merkezi dalga boyunda ve genişliğinde frekansın artmasıyla lazer, Veri iletim bant genişliğini artırmak için kullanılan iletişim sisteminde frekansın artmasının çok önemli olduğu yerlerde, Işık kaynaklarında sıcaklık artışına neden olan frekansın artmasıyla etkilenir. İletim sistemini taklit etmek ve dar bir ışık darbesi üretmek için bir salınım devresi tasarladık. Farklı frekanslar (10, 100, 10000, 100000 ve 500000) Hz, küçük darbe genişliği (100, 500 ve 1000) ns için kullanılmıştır. Deneysel test, tüm optik kaynakların ışın demetindeki bir spektrum dağılımından ve görsel bir kırmızı kaymasından muzdarip olduğunu göstermektedir. Bu LD'de daha fazla ve LED'de daha azdır. Spektrum genişlemesi, farklı ortamlardaki ışık aktarımı sırasında, kaynakların spektrumundaki doygunluğa bağlı olarak veri kaybına yol açan optik iletişim sistemlerinde bulunan kötü olgulardır. Sonuçlar, LED'in, ,konuya ek olarak, farklı orta ölçekli durumlarda veri aktarımı ve veri kaybı olduğunda aktarım hızındaki farktan kaynaklanan spektrum genişlemesi, kırmızı kayması olan LD ve PLED'den çok kararlı olan veri iletimi için en iyi seçim olduğunu gösteriyor. LED fiber optik iletişim sisteminde en iyisi olurken, serbest uzay optik haberleşme sistemlerinde(FSO) için en iyi seçim LD olan uzak mesafeler için, iletilemeyen düşük bir güce sahip olan LED dahi bir dezavantaja sahiptir.
Laboratuvar lazer sistemi için düşük maliyetli yüksek hızlı veri toplama kurulu
Data acquisition (DAQ) is the devises that can collect and processing of data in order to uses in automated control. The DAQ systems is available as an integral part that used with many systems. These systems are designed to measure specific factors by used a transducer or a sensor (an instrument that can converts the measurable physical quantity to an electrical signal. Then store these measurements and analyzes it then display it. Due to the large application of DAQ and its usage there is a challenge to design low cost DAQ system that will Analog input channels can vary in number from one to several hundred or thousands. Building low cost DAQ system with multi functionality is one of the most challenge that can reduce cost of systems and make it available for everyone. Microcontroller is low cost, convenient and flexible device, it has been developed rapidly and the application of it become widely in recent years, which can be utilized in DAQ system prototype. In this work we have designed a PC based DAQ system that can be used to control laser system and accruing the effect on laser signal when changes the parameters which is display in scope within user interface program in real time. The proposed system composed from three main types sensor, where LDR sensor has been used to detect the laser signal. The processing unit, we have used Arduino UNO a microcontroller to control the operation and gathering information from sensor. The third part is PC, in which, we have designed a DAQ and controls program under LabVIEW software. This program offers a user-friendly graphic user interface (GUI) that allowing the operator to control the DAQ with ability to change some laser parameters such as increase or decrease the laser beam power, convert continues laser to pulses laser with ability to determine the pulses time by controls the switching function. Furthermore, the LDR will record any change in laser signal and send it to microcontroller and then to PC where the program will show the signal. The test result show that the proposed DAQ system operate smoothly and effectivity and can gathering data with accuracy and the result has been closed to traditional oscilloscope system. The system is low in cost as it dependent on relatively low-cost component. The proposed system can serve to add a DAQ and controller for oldest laser devise or fabricated lasers.
Silüet ve optik üçgenleştirme ile yüzey geriçatımı için hacimsel bir tümleştirme tekniği
A Volumetric Fusion Technique for SurfaceReconstruction from Silhouettes andOptical TriangulationAbstractOptical triangulation, an active reconstruction technique, is known to be an accuratemethod but has several shortcomings due to occlusion and laser reflectance properties ofthe object surface that often lead to holes and inaccuracies on the recovered surface.Shape from silhouette, on the other hand, is a passive reconstruction technique that yieldsrobust, hole-free reconstruction of the visual hull of the object. In this thesis, a hybridsurface reconstruction method that fuses geometrical information obtained fromsilhouette images and optical triangulation is proposed. Our motivation is to recover thegeometry from silhouettes on those parts of the surface which the range data fail tocapture. Silhouettes and laser range images of the object are acquired with a calibratedcamera and re-projected onto a fixed 3D world coordinate system where the fusionprocess takes place. A volumetric octree representation is first obtained from thesilhouette images and then carved by range points to amend the missing cavityinformation inherent in silhouette-based techniques. An average isolevel value on eachcorner of each surface cube in the carved octree structure is accumulated using partialsurface triangulations obtained separately from range data and silhouettes. The marchingcubes algorithm is applied for triangulation of the resulting isolevel surface and the finalshape is constructed by fairing the 3D model. The performance of the proposed techniqueis demonstrated on several real objects.Advisor: Yücel Yemez Date:Director: Yaman Arkun Date:
Biyolojik ağlarda kesişen kümelerin görselleştirilmesi ve topolojik özelliklerine göre filtrelenmesi
With the rapid development of computer sciences and data processing technologies, bioinformatics became one of the rising multidisciplinary fields in this century. In this thesis, we aim to introduce a new perspective on bioinformatics data analysis via developing a new visualization software called BioNetVis. Our tool has ability to visualize intersecting sets in the biological networks, especially in the protein-protein interaction networks; and to filter based on graph topological measures. BioNetVis, is developed with the latest versions of state-of-the-art frameworks and programming libraries for processing data as fast as possible with higher efficiency. The main goal of BioNetVis is to facilitate the analysis of intersecting biological datasets on biological networks. The proposed tool aims to serve to the researchers who are working in the field of drug repurposing, personalized medicine, diagnosis and treatment of rare diseases. The project implementation is realized in the following three steps. Firstly, the biological data is mapped to a biological network and back-end development is performed. Secondly, a visualization is created based on the processed data in the back end with latest framework services. Thirdly, the back-end and front-end developments are connected and BioNetVis is made available to the researchers. We design BioNetVis in a modular fashion such that it is applicable to other types of networks and datasets and hence, it could be used in other domains to visualize intersecting sets in networks and filter based on graph topological properties. Lastly, we present a use case scenario to explain the features of BioNetVis. Keywords: Bioinformatics, Data Visualization, Intersecting Data Sets, Drug Repurposing, Personalized Medicine