Pinar Kirci

Being a cornerstone of Industry 4.0, Asset Administration Shell (AAS) enables seamless integration and interaction among the physical and digital worlds. There are multiple different tools and technologies available for implementing AAS. The purpose of this study is to support the tool and technology selection decision of AAS modelers and implementers. For that purpose, we conducted a literature survey and identified four active tools, and in the study, we included all of them: AASX server, Eclipse BaSyx, FA³ST service, and NOVAAS. Using a comprehensive criteria list, we conducted a thorough comparison of the selected technologies. The comparison was made in two steps: first for initial learning exercises and second for a real case study where digital twins belong to real assets in a facility belonging to the petrochemical industry. Among the evaluated tools, Eclipse BaSyx demonstrated superior performance compared to the other three tools investigated in this study. Future research will focus on incorporating machine learning (ML) and deep learning (DL) models associated with the assets, leveraging datasets generated by the sensors installed on the system.

This survey provides a comprehensive review of underwater cable detection and tracking literature, identifying key problem types and highlighting unique underwater challenges. It emphasizes the critical role of underwater cable detection in global communications and energy infrastructures, addressing complexities like low visibility and variable sea conditions. The analysis compares the efficacy of various models, particularly deep learning approaches like CNNs and Transformers, in adapting to underwater imagery challenges. A new roadmap for efficient cable detection and tracking systems is proposed, focusing on multimodal data integration and nonoptical detection methods. Importantly, the study includes performance evaluations of state‐of‐the‐art models on custom underwater datasets, offering practical insights. The survey’s findings are validated through an implementation of an underwater object‐tracking model incorporating effective algorithms from the literature.

Bearings are vital components in rotating machinery. Undetected bearing faults may result not only in financial loss, but also in the loss of lives. Hence, there exists an abundance of studies working on the early detection of bearing faults. The rising use of deep learning in recent years increased the number of imaging types/neural network architectures used for bearing fault classification, making it challenging to choose the most suitable 2-D imaging method and neural network. This study aims to address this challenge, by sharing the results of the training of eighteen imaging methods with four different networks using the same vibration data and training metrics. To further strengthen the results, the validation dataset size was taken as five times the training dataset size. The best results obtained is 99.89% accuracy by using Scattergram Filter Bank 1 as the image input, and ResNet-50 as the network for training. Prior to our work, Scattergram images have never been used for bearing fault classification. Ten out of 72 methods used in this work resulted in accuracies higher than 99.5%.

The presented DepApp is an application that provides information about the intensity, time and whereabouts of a recent earthquake. In addition, the presented application is a mobile application that contains emergency phone numbers and can provide general information about an earthquake which people can benefit from before and after the earthquake. With the presented application, people will be able to easily access information about earthquakes. Moreover, safe areas in which to shelter after an earthquake are offered to the user. Information about an earthquake kit is given to user in the application. The earthquake kit should be prepared before the earthquake. The application also includes emergency sounds that can be used in the event of an earthquake. A survey system is designed to determine the intensity of the earthquake as it is felt by the users. After the earthquake, the application presents the intensity, time, and location of the earthquake to the user. Thus, with this application, users will be more knowledgeable and prepared for an earthquake.

Studies carried out in different parts of the world and in different climatic conditions have made it clear that it is very important to use smart technologies in solving the problems experienced in the field of agriculture globally and locally. Another important aim of the studies is to ensure that agricultural products are grown in smart greenhouse environments outside of arable lands. For this reason, growing agricultural products in greenhouses controlled by smart systems by creating suitable soil and climatic conditions and facilitating people’s access to these products has been an important research and application subject. In this paper, the topics of “Smart Agriculture” and “Smart Greenhouse” were worked on. Therefore, a prototype of a smart greenhouse was constructed. Then, it was programmed according to the decided climatic conditions. Consequently, the main aim of this study was to improve the project according to the collected data by the sensors. One of the most important aims of our study was to question the possibility of growing different plants in the same greenhouse. In this context, in our study, a flower and three different vegetables with close growing conditions were grown in the same greenhouse in the same environment. These plants were grown individually both in the smart greenhouse prototype and in the pots outdoors in a natural environment. The differences between the two environments and the differences in the development of the plants were examined and the necessary results were obtained based on the findings. Based on the results obtained, it has been discussed what can be done if the plants grown in the greenhouse, of which we have created a small-scale prototype, are grown in a large-scale smart greenhouse. According to the results obtained, the smart greenhouse made a positive difference in the development of begonia, tomato and pepper. Although, the cucumbers grew more in the pots. In the study, it was observed that the plants were healthier in the smart greenhouse. The cucumbers in the greenhouse grew to 132 mm, the peppers to 61 mm and the tomatoes to 70 mm. The cucumbers in the pot grew to 163 mm, the peppers to 37 mm and the tomatoes to 60 mm. This shows that the yield was positively affected in the smart greenhouse. According to the collected results, the smart greenhouse system saved approximately 16.5% of water compared to the pot. The fact that the system can work both manually and autonomously provides a great convenience for the person controlling the greenhouse.