In this study, an enhancement process obtained by applying the heat conduction equation of solid and stagnant fluids on colour images is proposed. After the colour channel stretching, the RGB colour image was converted to the HSI model. The heat conduction equation was applied for each pixel on the I channel of the HSI colour model. The elements of the feature matrix called heat conduction matrix (HCM) can have negative, positive or zero values. A pixel with a small negative HCM value indicates that I needs level enhancement for a good image, whereas a small positive HCM value means that the I level value will be reduced and aligned with its neighbours. High positive or negative values are defined as the edges of the objects and the I level values of such pixels are not changed to protect the edges. In addition, whether HCM is negative or positive, the balanced increment and decrement path at a level I ensures that the mean brightness value performs natural protection. Finally, an enhanced image is obtained by transitioning from the HSI to the RGB colour model. Experimental results show that this method can enhance colour image details better than other methods.

Vibration and noise are undesirable effects in daily life and energy-consuming systems. In this study, the effects of different refrigerants on noise and vibration in a sealed reciprocating compressor are discussed. The study compared the noise and vibration performances of refrigerants with lower ozone depletion potential (ODP) values compared to R22, which has a high ODP value. The study was carried out experimentally in two stages. Firstly, tests were conducted for the coefficient of performance (COP) of different refrigerants. Secondly, vibration and noise data were obtained experimentally for different refrigerants. The results obtained from the experiments showed that both the COP value and the compressor vibration and noise have different values for R22 refrigerant and other alternative refrigerants, but values close to R22 are obtained. It was observed that the compressor noise values and vibration values vary depending on the type of used refrigerant. Average vibration values were determined as 0.604 m/s2 in R22, 0.603 m/s2 in R438A, 0.593 m/s2 in R417A, 0.622 m/s2 in R422D and 0.637 m/s2 in R422A. When the noise values are examined, it was measured as 61.327 dB for R22, 62.913 dB for R438A, 62.187 dB for R417A, 63.715 dB for R422D and 64.913 dB for R422A. R417A, which has a 99% similar noise value to R22, was determined as an alternative refrigerant. COP values were found as 4.07 in R22, 3.98 in R438A, 3.63 in R417A, 3.37 in R422D, and 3.18 in R422A. R438A showing 95% similarity for COP can be used as an alternative to R22. Generally, it was observed that the noise and vibration values are very close to each other for all refrigerants examined.

Mobile ad hoc network (MANET) is a wireless, mobile node network in which the nodes move randomly and operate without centralized management. In MANETs, the network structure increases the energy consumption of the nodes, which shortens the network lifetime and affects packet transmission. The process of clustering in MANETs (Mobile Ad-hoc Network) can be achieved through the division of the network into virtual groups, known as clusters. The Cluster Head (CH) of each cluster is in charge of data transmission within the cluster. In this study, a two-stage Hybrid Adaptive Clustering Algorithm for Dynamics MANETs (HACADM) is proposed to improve the network performance in MANETs. In the first stage, based on the Weighted Clustering Algorithm (WCA) for selecting optimal CHs, criteria such as node degree, neighborhood distance, power of battery and mobility are optimized using the Gravity Search Algorithm (GSA). In the second phase, the clustering is executed by identifying the member nodes and their roles of the selected CHs using the Enhanced Density Based Spatial Clustering of Applications with Noise (Enhanced-DBSCAN) algorithm, which is one of the unsupervised learning methods. Moreover, this approach serves to reduce the load on the CHs and enhance the stability of the cluster by selecting gateway nodes for inter-cluster communication. This study represents a significant step towards optimizing energy efficiency and extending network lifetime by enhancing the adaptability of clustering processes in MANETs under dynamic network conditions. The proposed HACADM method has the potential to enhance the performance of MANETs by ensuring a more balanced load distribution compared to existing clustering approaches. The HACADM method was compared with the EE-WCA, E-MAVMMF, TSDR and MORS-ASO methods using critical performance metrics such as remaining energy, end-to-end delay, packet delivery ratio and throughput. For example, experimental results on remaining energy show that the average energy consumption improvements of HACADM compared with EE-WCA, E-MAVMMF, TSDR and MORS-ASO are 46.38%, 18.35%, 13.08% and 8.33% respectively. Other Performance evaluation results also show that HACADM significantly contributes to the effective management of MANETs, extends the network lifetime and maintains high performance under dynamic network conditions.

Tr en Bu çalışmada, soğutma sistemlerinde yaygın olarak kullanılan R22 soğutucu akışkanına alternatif ve ozon tabakasına dost R417A, R438A, R422A ve R422D soğutucu akışkanlarının performansları termodinamiğin birinci ve ikinci yasasına göre incelenmiştir. Buhar sıkıştırmalı çevrimin tasarımı için Chemours Refrigerant Expert 1.0 programı kullanılmıştır. Yapılan analizlerde yoğuşma sıcaklığı sabit (50 ºC) tutulurken, evaporasyon sıcaklıkları EUROVENT şartlarına göre (0 °C, -8 °C, -25 °C, -31 °C) belirlenmiştir. Farklı evaporasyon sıcaklıklarına göre hesaplanan parametreler, gerekli kompresör gücü, performans katsayısı (COP) ve gerekli soğutucu akışkan kütle akış oranıdır. Sonuçlar, R438A ve R417A soğutucu akışkanlarının COP değerlerinin R22’ye çok yakın olduğunu göstermiştir. R22 soğutucu akışkanına göre COP değerleri, R438A’da %5, R471A’da %6, R422A’da %15 ve R422D’de %10 düşük olmuştur. Bu değerler R422D için %13 ve R422A için ise %17 olarak hesaplanmıştır. Analiz edilen sistemlere ait en yüksek ekserji verimi, -25 °C evaporasyon sıcaklığında R438A için %31.74, R417A için %31 ve R422A için %27.46 ve R422D için ise %29.24 olarak hesaplanmıştır. Çalışmanın sonunda, R22 soğutucu akışkanına alternatif olarak geliştirilen R417A, R438A, R422A ve R422D akışkanlarından, R438A akışkanının daha yüksek COP değerlerine sahip olduğu belirlenmiştir. Ayrıca kondenser ve kompresör yükleri incelendiğinde en iyi sonuçları sırası ile R438A, R417A, R422D ve R422A akışkanlarının verdiği görülmüştür. Genel olarak incelen 4 alternatif akışkandan R438A ve R417A’nın, COP, ekserji verimliliği, ekserji yıkımları açısından R22 için daha iyi bir alternatif olacağı belirlenmiştir In this study, the performances of the R417A, R438A, R422A and R422D refrigerants which are alternative to the commonly used and ozone layer-friendly R22 refrigerant are examined according to the first and second law of thermodynamics. Chemours Refrigerant Expert 1.0 and Genetron Properties 1.4 were used for the design of the vapor compression cycle. While the condensing temperature was kept constant during the analyses, the evaporation temperatures were determined according to the EUROVENT conditions (0 °C, -8 °C, -25 °C, -31 °C). The parameters calculated according to different evaporation temperatures are the required compressor power, performance coefficient (COP), and the required refrigerant mass flow rate. The results showed that the COP values of the R438A and R417A refrigerants were very close to that of R22. The COP values were 5%, 6%, 15% and 10% lower in R438A, in R471A, in R422A, and in R422D, respectively compared to the R22 refrigerant. The COP values were calculated as 13% for R422D and 17% for R422A. The highest exergy efficiency of the analyzed systems was calculated as 31.74% for R438A, 31% for R417A, 27.46% for R422A, and 29.24% for R422D at -25°C evaporation temperature. The results of our study revealed that among the R417A, R438A, R422A and R422D refrigerants developed as an alternative to R22 refrigerant, the R438A refrigerant had comparatively higher COP values. Also, when the condenser and compressor loads were examined, it was found that the R438A, R417A, R422D and R422A refrigerants yielded the best results, respectively. Among the four alternative refrigerants examined, R438A and R417A were found to be better alternatives to R22 in terms of COP values, exergy efficiency, and exergy destruction.

Yetersiz veya düzgün olmayan ışık altında alınan renkli görüntü uygulamalarında, düşük parlaklık, zayıf kontrast ve ışıkta ani değişiklik gibi istenmeyen durumlar meydana gelmektedir. Düşük ışıklı renkli görüntülerini iyileştirilmesi için yaygın kullanılan Retineks teorisi, genel olarak yerel görüntü türevlerini analiz ederek, aydınlatma ve yansıtma bileşenlerine ayırmak için geliştirilmiştir. Bu çalışma, Retineks tabanlı algoritmalara dayanan son teknoloji görüntü geliştirme algoritmalarının karşılaştırmalı bir analizini sunmaktadır. Bunun için günümüze kadar düşük ışıklı renkli görüntülerin iyileştirilmesinde kullanılan ve çok tercih edilen on adet Retineks esaslı yöntem alınmıştır. Ayrıca beş adet yaygın kullanılan karşılaştırma ölçüm metrikleri de incelenmiş ve performans karşılaştırması olarak kullanılmıştır. Karşılaştırma sonuçları görsel ve sayısal olarak verilmiştir. Bu karşılaştırmalı analiz çalışması Retineks esaslı görüntü iyileştirme alanında yeni verimli algoritmalar geliştirmek için araştırmacılara yardımcı olmayı amaçlamaktadır.

With the increase in industrialization and the number of people and buildings, the need for refrigeration systems has also increased. Maintenance of these systems, malfunctions and their late detection cause time and cost problems. Therefore, in this study, a machine learning application is recommended to diagnose the refrigerant undercharge and refrigerant overcharge faults that may occur in the refrigeration system by using infrared images. Firstly, infrared images obtained from normal charge, undercharge and overcharge situations in the refrigeration system, are passed through the two-dimensional discrete wavelet transform (2D-DWT) and the images are decomposed. Then, statistical texture features from the original input images are obtained by separating infrared images. The dimensionality of the extracted features is reduced using the principal component analysis (PCA) and the ReliefF (RF) algorithm. Finally, these selected features are applied to the K nearest neighbor (k-NN), naive Bayes algorithm (NBA), decision tree (DT), and cascade forward neural network (CFNN) classifiers. It has been found that RF-based feature selection is useful in obtaining the optimal feature vector. The classification results revealed that CFNN outperforms k-NN, NBA, and DT. Compared to traditional electrical measurements and fault detection methods, it has been shown that the recommended system is feasible due to its features such as ease of use, remote measurement, and self-adaptive recognition.