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Öğe A Blockchain-Oriented Task Scheduling and Allocation System for ROS Enabled Mobile Robots(Ieee-Inst Electrical Electronics Engineers Inc, 2025) Sen, Mehmed Oguz; Okumus, Fatih; Fatih Kocamaz, AdnanIn multi-mobile robot applications, the operational processes such as the management of robots, task assignment, monitoring of assigned tasks, communication and coordination between robots, and data storage are executed through a centralized server system. Therefore, most critical decisions are made on this centralized server rather than by the robots themselves. However, working in a centralized system has numerous disadvantages such as the obligation to maintain a server, routing all communication through a central unit, susceptibility to connectivity issues that render the system inoperable, and increased bandwidth requirements as the number of robots increases. Moreover, any communication issues between the server computers and any of the robots in centralized systems affect the entire system's operation. To address these limitations, a blockchain-powered distributed communication system for inter-robot communication has been developed in this study. A task allocation application between robots has been implemented on this developed distributed communication system. In the application, Hyperledger Fabric (HLF) has been utilized as the blockchain platform due to its advantages. Each robot is a peer in the blockchain network in the proposed system. A cost function which is computed in all robots has been introduced to reduce the communication load in the blockchain network during task distribution and to enable optimal task allocation among robots. With the proposed system, robots compute and choose the most suitable tasks using the cost function, hence transactions on the blockchain network are kept at optimal level. After reaching consensus of peers on task allocations via HLF, task data are transmitted to robots by Robot Operating System (ROS) integration. With the proposed system, a dynamic and distributed architecture has been introduced and implemented where mobile robots can communicate with each other over a blockchain network without the need for a centralized server. In experimental studies conducted on real robots, the proposed system demonstrated optimal task allocation across multiple phases, effectively adapting to various task requirements in different scenarios. For instance, in one scenario, the system effectively allocated a total of 9 tasks, distributed across two phases: 3 tasks in the first phase and 6 tasks in the second phase. This study presents an innovative contribution to the literature on communication of robots and task allocation. Also, this study has a high potential to be adapted to industrial applications including robotic instruments.Öğe A Study on Using the Golden Ratio in Unique Key Generation(Institute of Electrical and Electronics Engineers Inc., 2024) Topal, Ahmet; Okumus, FatihWith rapidly advancing technology and widespread internet, transfer security of the data has become more prominent. Encryption methods, one of the data protection tools, consist of more than one layer within themselves. One of these layers is the generation of unique keys. There are challenges in preventing key duplication and reducing predictability in unique key generation. Existing methods have difficulties in this regard and are constantly being sought for better ones. This study examines the use of the golden ratio by developing a new approach to produce unique keys. This method, which is based on Steinhaus's mathematical experiments on the golden ratio and the theories he created, has successfully generated unique keys in the tests performed. The proposed method addresses an important problem in the field of cryptographic security and provides a solid foundation for future research in this field. © 2024 IEEE.Öğe Algorithmic Silver Trading via Fine-Tuned CNN-Based Image Classification and Relative Strength Index-Guided Price Direction Prediction(Mdpi, 2025) Altuntas, Yahya; Okumus, Fatih; Kocamaz, Adnan FatihPredicting short-term buy and sell signals in financial markets remains a significant challenge for algorithmic trading. This difficulty stems from the data's inherent volatility and noise, which often leads to spurious signals and poor trading performance. This paper presents a novel algorithmic trading model for silver that combines fine-tuned Convolutional Neural Networks (CNNs) with a decision filter based on the Relative Strength Index (RSI). The technique allows for the prediction of buy and sell points by turning time series data into chart images. Daily silver price per ounce data were turned into chart images using technical analysis indicators. Four pre-trained CNNs, namely AlexNet, VGG16, GoogLeNet, and ResNet-50, were fine-tuned using the generated image dataset to find the best architecture based on classification and financial performance. The models were evaluated using walk-forward validation with an expanding window. This validation method made the tests more realistic and the performance evaluation more robust under different market conditions. Fine-tuned VGG16 with the RSI filter had the best cost-adjusted profitability, with a cumulative return of 115.03% over five years. This was nearly double the 61.62% return of a buy-and-hold strategy. This outperformance is especially impressive because the evaluation period was mostly upward, which makes it harder to beat passive benchmarks. Adding the RSI filter also helped models make more disciplined decisions. This reduced transactions with low confidence. In general, the results show that pre-trained CNNs fine-tuned on visual representations, when supplemented with domain-specific heuristics, can provide strong and cost-effective solutions for algorithmic trading, even when realistic cost assumptions are used.Öğe Cloud Based Indoor Navigation for ROS-enabled Automated Guided Vehicles(Ieee, 2019) Okumus, Fatih; Kocamaz, Adnan FatihIn Cyber-Physical Systems, logistical activities with automatic guided vehicles (AGV) are indispensable for Industry 4.0 integration. In order to navigate the AGVs to be used in logistics, difficulties such as localization of AGV, mapping the environment, mobile-immobile obstacle avoidance, and optimum task allocation must be overcome. All these operations can be realized with an architecture that provides communication infrastructure and management mechanism in multiple robots. In this publication, cloud-based, ROS-enabled communication and navigation methods for multiple AGVs are proposed. The proposed method was tested and applied successfully in a laboratory environment.Öğe A Cloudware Architecture for Collaboration of Multiple AGVs in Indoor Logistics: Case Study in Fabric Manufacturing Enterprises(Mdpi, 2020) Okumus, Fatih; Donmez, Emrah; Kocamaz, Adnan FatihIn Industry 4.0 compatible workshops, the demand for Automated Guided Vehicles (AGVs) used in indoor logistics systems has increased remarkably. In these indoor logistics systems, it may be necessary to execute multiple transport tasks simultaneously using multiple AGVs. However, some challenges require special solutions for AGVs to be used in industrial autonomous transportation. These challenges can be addressed under four main headings: positioning, optimum path planning, collision avoidance and optimum task allocation. The solutions produced for these challenges may require special studies that vary depending on the type of tasks and the working environment in which AGVs are used. This study focuses on the problem of automated indoor logistics carried out in the simultaneous production of textile finishing enterprises. In the study, a centralized cloud system that enables multiple AGVs to work in collaboration has been developed. The finishing enterprise of a denim manufacturing factory was handled as a case study and modelling of mapping-planning processes was carried out using the developed cloud system. In the cloud system, RestFul APIs, for mapping the environment, and WebSocket methods, to track the locations of AGVs, have been developed. A collaboration module in harmony with the working model has been developed for AGVs to be used for fabric transportation. The collaboration module consists of task definition, battery management-optimization, selection of the most suitable batch trolleys (provides mobility of fabrics for the finishing mills), optimum task distribution and collision avoidance stages. In the collaboration module, all the finishing processes until the product arrives the delivery point are defined as tasks. A task allocation algorithm has been developed for the optimum performance of these tasks. The multi-fitness function that optimizes the total path of the AGVs, the elapsed time and the energy spent while performing the tasks have been determined. An assignment matrix based on K nearest neighbor (k-NN) and permutation possibilities was created for the optimal task allocation, and the most appropriate row was selected according to the optimal path totals of each row in the matrix. The D* Lite algorithm has been used to calculate the optimum path between AGVs and goals by avoiding static obstacles. By developing simulation software, the problem model was adapted and the operation of the cloud system was tested. Simulation results showed that the developed cloud system was successfully implemented. Although the developed cloud system has been applied as a case study in fabric finishing workshops with a complex structure, it can be used in different sectors as its logistic processes are similar.Öğe Comparing Path Planning Algorithms for Multiple Mobile Robots(Ieee, 2018) Okumus, Fatih; Kocamaz, Adnan FatihThe use of mobile robots in industrial applications is increasing day by day. As a result of this increase, efficiency in the use of mobile robots has also become important. In particular, path planning is a significant area of research to improve efficiency in mobile robot navigation. In path planning, it is aimed to find optimum and obstacle free paths to the starting and ending points to fulfill multiple tasks. In this paper, A *, D * and PSO algorithms, frequently used to optimize the path multiple robots reach targets in an environment with obstacles, have been compared. In addition, a mobile robot simulation software has been developed to measure the performance of algorithms. At the end of the study, the performance of the algorithms was showed by measuring the path lengths and the process time of algorithms.Öğe Comparison of Extractive and Abstractive Approaches in Automatic Text Summarization: An Evaluation on BBC-News and PubMed Datasets(Institute of Electrical and Electronics Engineers Inc., 2024) Yunus, Said; Hark, Cengiz; Okumus, FatihThis study focuses on the effectiveness of advanced and up-to-date text summarization techniques in the field of automatic text summarization. Among the extractive summarization systems examined for their performance are TextRank, LexRank, KL-Summ, and LSA, while the abstractive summarization systems include Pegasus, BART, T5, and LED. The capabilities of these state-of-the-art models have been evaluated on the BBC-News and PubMed datasets. Several evaluation metrics such as SacreBLEU, METEOR, BERTScore, and ROUGE were employed. Summaries of 50,100, and 150 words were generated for the BBC-News and PubMed datasets. The findings of the study provide a comprehensive evaluation of the performance of different summarization techniques across various summary lengths on the BBC-News and PubMed datasets. It was reported that the TextRank approach achieved notably successful results in text summarization. Among the abstractive methods investigated, LED demonstrated a strong ability to generate contextually accurate summaries. This study is considered to make significant contributions to the literature. © 2024 IEEE.Öğe Detection and Counting of Oncorhynchus Mykiss Spermatozoa(Ieee, 2015) Okumus, Fatih; Kocamaz, Adnan Fatih; Ozgur, Mustafa ErkanToday in the world, a large portion of the trout species grown in the fisheries constitutes Oncorhynchus Mykiss. In order to increase the efficiency of Oncorhynchus Mykiss's production, it is expected to be in fair value of its sperm quality parameters for fertilize. Investigation by examination under a microscope with the conventional method of quality parameters is the waste of time for researchers. In this work, we present a system that can detect the cell concentration, which is an important quality parameter for the fish spermatozoa, with Otsu Threshold and Connected Component Labeling methods.Öğe Enhancing extractive multi-documents summarization with a novel dominating set model for semantic relationship detection(Elsevier - Division Reed Elsevier India Pvt Ltd, 2025) Yunus, Said; Hark, Cengiz; Okumus, FatihIn this paper, the Dominant Set-Based Extractive Text summarizing (DSETS) framework is proposed, which gives a new approach to automatic text summarizing. Utilizing the Minimum Dominant Set technique, the proposed framework creates summaries based on a word-level graphical representation that minimizes information loss while maintaining significant semantics. DSETS aims to inspire an alternative perspective on the computational text summarization method. The proposed framework distributes the processing load and reduces time complexity with the segmentation it applies, thus providing more scalable performance on large datasets. Additionally, empirical runtime and memory evaluations revealed that the proposed segmentation strategy reduced processing time by up to 24 % and offered comparable memory usage to lighter baseline methods, demonstrating its practicality in resource-constrained environments. After comparing the effectiveness of the DSETS framework with a series of text summarization techniques, it was determined that it offers significantly improved text summarization performance. Experiments were conducted using four different datasets (BBC News, XSum, CNN/Daily Mail and MultiNews) and summaries of varying word lengths were generated. The proposed framework achieved the highest ROUGE (1, 2, L, W) scores on most of the summary configurations generated on different datasets and various word counts. In particular, ROUGE-W F-scores improved by up to 15.8 %, while ROUGE-1 and ROUGE-L showed significant increases of 3 % to 8 % across various summary lengths. The evaluation results suggest that the DSETS framework was able to outperform many state-of-the-art summarization methods, with improvements observed between 1.3 % and 15.8 % depending on the metric and dataset. To better understand which parts of the system contributed most to this success, an ablation study was carried out. The findings from this analysis indicated that the segmentation mechanism and the semantic filtering process played a key role-particularly in enhancing recall-based performance. Taken together, these results indicate that DSETS is not only a strong and reliable framework for extractive summarization, especially in single-topic documents, but also a promising option for building lightweight and interpretable summarization systems in future applications.Öğe Exploring the Feasibility of a Multifunctional Software Platform for Cloud Robotics(Ieee, 2018) Okumus, Fatih; Kocamaz, Adnan FatihStrengthening the infrastructure of the Internet has made global data access easier and so cloud computing is at the forefront. Cloud technologies are used in the many work area, because they provide big data storage capacity, low computational costs and scalable data management. Cloud robotics, a special form of cloud computing, manages mobile robots with the advantages mentioned above, as well as providing a telepathic agreement mechanism among the robots. This work presents the development of a software platform for managing cloud-based operations such as simultaneous localization and mapping (SLAM), path planning and optimum task distribution for mobile robots. Demonstration software has also been implemented in the study, which provides a simulation environment that allows testing of the developed system. At the end of the study, the cloud software was tested with the demonstration application and the test results were shown.Öğe The in vitro toxicity analysis of titanium dioxide (TiO2) nanoparticles on kinematics and biochemical quality of rainbow trout sperm cells(Elsevier Science Bv, 2018) Ozgur, Mustafa Erkan; Balcioglu, Sevgi; Ulu, Ahmet; Ozcan, Imren; Okumus, Fatih; Koytepe, Suleyman; Ates, BurhanIn recent years, titanium dioxide (TiO2) nanoparticles (NPs) as metal oxide nanoparticles are widely used in industry, agriculture, personal care products, cosmetics, sun protection and toothpaste, electronics, foodstuffs and food packaging. This use of nano-TiO2 has been associated with environmental toxicity concerns. Therefore, the aim of this study was to evaluate the in vitro effect of different doses of TiO2 NPs (similar to 30-40 nm) (0.01, 0.1, 0.5, 1, 10 and 50 mg/L) at 4oC for 3 h on the sperm cell kinematics as velocities of Rainbow trout (Oncorhynchus mykiss, Walbaum, 1792) sperm cells. Furthermore, oxidative stress markers (total glutathione (TGSH) and superoxide dismutase (SOD) were assessed in sperm cells after exposure to TiO2 NPs. According to the obtained results, there were statistically significant (P < 0.05) decreasing in the velocities of sperm cells after 10 mg/L TiO2 NPs and an increase the activity of SOD (P < 0.05) and TGSH levels were determined.Öğe Load Flow Optimization of 154 kV Malatya Transmission Line Using Differential Evolution Algorithm(Ieee, 2017) Akdag, Ozan; Karadogan, Ismail; Okumus, Fatih; Yeroglu, Celaleddin; Karci, AliAmplitude, phase angle, active and reactive powers flowing in each busbar of a power system can be seen by performing a load flow analysis. From these data, it is possible to determine the voltage drop, the distribution of the forces, the loading of the equipment and the losses of the related power system. Then, Active power losses can be reduced by making improvements at the points where losses are present in the power system. Power losses can be reduced by reactive power compensation considerably. In this study, Differential Evolution (DE) algorithm is used to determine the values of the capacitor groups to be added to the corresponding busbar to reduce the losses of 154 kV transmission system. The optimum load flow is ensured by optimizing a transmission system with the developed algorithm.Öğe MDSA: A Dynamic and Greedy Approach to Solve the Minimum Dominating Set Problem(Mdpi, 2024) Okumus, Fatih; Karci, SeydaThe graph theory is one of the fundamental structures in computer science used to model various scientific and engineering problems. Many problems within the graph theory are categorized as NP-hard and NP-complete. One such problem is the minimum dominating set (MDS) problem, which seeks to identify the minimum possible subsets in a graph such that every other node in the subset is directly connected to a node in this subset. Due to its inherent complexity, developing an efficient polynomial-time method to address the MDS problem remains a significant challenge in graph theory. This paper introduces a novel algorithm that utilizes a centrality measure known as the Malatya Centrality to effectively address the MDS problem. The proposed algorithm, called the Malatya Dominating Set Algorithm (MDSA), leverages centrality values to identify dominating sets within a graph. It extends the Malatya centrality by incorporating a second-level centrality measure, which enhances the identification of dominating nodes. Through a systematic and algorithmic approach, these centrality values are employed to pinpoint the elements of the dominating set. The MDSA uniquely integrates greedy and dynamic programming strategies. At each step, the algorithm selects the most optimal (or near-optimal) node based on the centrality values (greedy approach) while updating the neighboring nodes' criteria to influence subsequent decisions (dynamic programming). The proposed algorithm demonstrates efficient performance, particularly in large-scale graphs, with time and space requirements scaling proportionally with the size of the graph and its average degree. Experimental results indicate that our algorithm outperforms existing methods, especially in terms of time complexity when applied to large datasets, showcasing its effectiveness in addressing the MDS problem.
