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Yayın Stochastic surface mesh reconstruction(International Society for Photogrammetry and Remote Sensing, 2018-05-30) Özendi, Mustafa; Akça, Mehmet Devrim; Topan, HüseyinA generic and practical methodology is presented for 3D surface mesh reconstruction from the terrestrial laser scanner (TLS) derived point clouds. It has two main steps. The first step deals with developing an anisotropic point error model, which is capable of computing the theoretical precisions of 3D coordinates of each individual point in the point cloud. The magnitude and direction of the errors are represented in the form of error ellipsoids. The following second step is focused on the stochastic surface mesh reconstruction. It exploits the previously determined error ellipsoids by computing a point-wise quality measure, which takes into account the semi-diagonal axis length of the error ellipsoid. The points only with the least errors are used in the surface triangulation. The remaining ones are automatically discarded.Yayın Cost-effective fault diagnosis of a multi-component dynamic system under corrective maintenance(Elsevier Ltd, 2021-04) Özgür Ünlüakın, Demet; Türkali, Busenur; Aksezer, Sezgin ÇağlarMaintenance planning and execution are challenging tasks for every system with complex structure. Interdependent nature of the components that builds up the system may have significant effect on system integrity. While preventive maintenance actions can be carried out in a more planned fashion, corrective actions are more time sensitive as they directly affect the availability of the system. This study proposes a cost-effective dynamic Bayesian network modeling scheme to be used in the planning of corrective maintenance actions on systems having hidden components which have stochastic and structural dependencies. In such context, the regenerative air heater system which is a key element of a power plant is taken into consideration. The proposed maintenance framework offers several methods, each aiming to balance the cost with the probability effect using a normalization procedure. The methodologies are extensively simulated for sensitivity analysis under various downtime cost values. Fault effect methods with worst state probability efficiency measures give the least total cost for all downtime cost values and their distinction becomes significant as this value increases. Further statistical analysis concludes that considerable gains on maintenance costs can be achieved by the proposed approach.Yayın Maintenance policy analysis of the regenerative air heater system using factored POMDPs(Elsevier Ltd, 2022-03) Kıvanç, İpek; Özgür Ünlüakın, Demet; Bilgiç, TanerMaintenance optimization of multi-component systems is a difficult problem. Partially Observable Markov Decision Processes (POMDPs) are powerful tools for such problems under uncertainty in stochastic environments. In this study, the main POMDP solution approaches and solvers are surveyed. Then, based on experimental models with different complexities in the size of the system space, selected POMDP solvers using different representation patterns for modeling and different procedures for updating the value function while solving are compared. Furthermore, to show that factored representations are advantageous in modeling and solving the maintenance problem of multi-component systems where there exist also stochastic dependencies among the components, the maintenance problem of the one-line regenerative air heater system available in thermal power plants is modeled and solved with factored POMDPs. In-depth sensitivity analyses are performed on the obtained policy. The results show that factored POMDPs enable compact modeling, efficient policy generation and practical policy analysis for the tackled problem. Furthermore, they also motivate the use of factored POMDPs in the generation and analysis of maintenance policies for similar multi-component systems.Yayın Co-registration of 3d point clouds by using an errors-in-variables model(Copernicus Gesellschaft MBH, 2012-08-25) Aydar, Umut; Altan, Mehmet Orhan; Akyılmaz, Orhan; Akça, Mehmet DevrimCo-registration of point clouds of partially scanned objects is the first step of the 3D modeling workflow. The aim of co-registration is to merge the overlapping point clouds by estimating the spatial transformation parameters. In the literature, one of the most popular methods is the ICP (Iterative Closest Point) algorithm and its variants. There exist the 3D least squares (LS) matching methods as well. In most of the co-registration methods, the stochastic properties of the search surfaces are usually omitted. This omission is expected to be minor and does not disturb the solution vector significantly. However, the a posteriori covariance matrix will be affected by the neglected uncertainty of the function values. This causes deterioration in the realistic precision estimates. In order to overcome this limitation, we propose a new method where the stochastic properties of both (template and search) surfaces are considered under an errors-in-variables (EIV) model. The experiments have been carried out using a close range laser scanning data set and the results of the conventional and EIV types of the ICP matching methods have been compared.Yayın Modeling repair demand in existence of a nonstationary installed base(Elsevier B.V., 2023-09) Hekimoğlu, Mustafa; Karlı, DenizLife cycles of products consist of 3 phases, namely growth, maturity, and decline phases. Modeling repair demand is particularly difficult in the growth and decline stages due to nonstationarity. In this study, we suggest respective stochastic models that capture the dynamics of repair demand in these two phases. We apply our theory to two different operations management problems. First, using the moments of spare parts demand, we suggest an algorithm that selects a parametric distribution from the hypergeometric family (Ord, 1967) for each period in time. We utilize the algorithm in a single echelon inventory control problem. Second, we focus on investment decisions of Original Equipment Manufacturers (OEMs) to extend economic lifetimes of products with technology upgrades. Our results indicate that the second moment is sufficient for growing customer bases, whereas using the third moment doubles the approximation quality of theoretical distributions for a declining customer base. From a cost minimization perspective, using higher moments of demand leads to savings up to 13.6% compared to the single-moment approach. Also, we characterize the optimal investment policy for lifetime extension decisions from risk-neutral and risk-averse perspectives. We find that there exists a critical level of investment cost and installed base size for profitability of lifetime extension for OEMs. From a managerial point of view, we find that a risk-neutral decision maker finds the lifetime extension problem profitable. In contrast, even a slight risk aversion can make the lifetime extension decision economically undesirable.Yayın Modeling and simulation support to the defense planning process(Sage Publications Inc, 2017-04-01) Çayırcı, Erdal; Özçakır, LütfüDefense planning is a crucial part of the defense process. It identifies the capabilities required for the future defense environment, analyzes the capability shortfalls, prioritizes them, and provides the fundamental inputs for their development. Modeling and simulation may significantly contribute to the success of defense planning. However, neither the theory nor the tools are mature enough to fulfill the defense planning requirements. Various types of simulation tools, such as static, dynamic, deterministic, stochastic, closed, discrete, continuous, and symbiotic, in multiple levels of resolution and fidelity are needed to support the different stages and phases. The verification and validation of the models and the analysis of the input and output data are critical. Yet another challenge is that the uncertainties related to the contemporary defense scenarios are mostly not in aleatory but in the epistemic domain. In this paper, we briefly present a new computer-assisted defense planning process. Then, we introduce the service-oriented cloud approach for the modeling and simulation support to the process.Yayın BinBRO: Binary Battle Royale Optimizer algorithm(Elsevier Ltd, 2022-02-04) (Rahkar Farshi), Taymaz Akan; Agahian, Saeid; Dehkharghani, RahimStochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. Optimization algorithms benefit from stochastic methods; however, they do not guarantee to obtain the optimal solution. Many optimization algorithms have been proposed for solving problems with continuous nature; nevertheless, they are unable to solve discrete or binary problems. Adaptation and use of continuous optimization algorithms for solving discrete problems have gained growing popularity in recent decades. In this paper, the binary version of a recently proposed optimization algorithm, Battle Royale Optimization, which we named BinBRO, has been proposed. The proposed algorithm has been applied to two benchmark datasets: the uncapacitated facility location problem, and the maximum-cut graph problem, and has been compared with 6 other binary optimization algorithms, namely, Particle Swarm Optimization, different versions of Genetic Algorithm, and different versions of Artificial Bee Colony algorithm. The BinBRO-based algorithms could rank first among those algorithms when applying on all benchmark datasets of both problems, UFLP and Max-Cut.Yayın Maintenance policy simulation for a factored partially observable system(The Society for Modeling and Simulation International, 2019-07) Özgür Ünlüakın, Demet; Kıvanç, İpekTaking maintenance decisions is one of the well-known stochastic sequential decision problems under uncertainty. Partially Observable Markov Decision Processes (POMDPs) are powerful tools for such problems. Nevertheless, POMDPs are rarely used for tackling maintenance problems of multi-component systems because their state spaces grow exponentially with the increasing number of components. Factored representations have been proposed for POMDPs taking advantage of the factored structure already available in the nature of the problem. Our aim in this study is to show how to formulate a factored POMDP model for the maintenance problem of a multi-component dynamic system and how to simulate and evaluate the obtained policy before implementing it in real life. The sensitivity of the methodology is analyzed under several cost values, and the methodology is compared to other predefined policies. The results show that the policies generated via the POMDP solver perform better than the predefined policies.Yayın Evaluation of proactive maintenance policies on a stochastically dependent hidden multi-component system using DBNs(Elsevier Ltd, 2021-07) Özgür Ünlüakın, Demet; Türkali, BusenurIn complex systems with stochastically dependent components which are not observed directly, determining an effective maintenance policy is a difficult task. In this paper, a dynamic Bayesian network based maintenance decision framework is proposed to evaluate proactive maintenance policies for such systems. Two preventive and one predictive maintenance strategies from a cost perspective are designed for multi-component dependable systems which aim to reduce maintenance cost while increasing system reliability at the same time. Tabu procedure is employed to avoid repetitive similar actions. The performances of the policies are compared with a reactive maintenance strategy and also with each other using different strategy parameters on a real life system confronted in thermal power plants for six different scenarios. The scenarios are designed considering different structures of system dependability and reactive cost. The results show that the threshold based maintenance which is the predictive strategy gives the minimum cost and maintenance number in almost all scenarios.Yayın Leveraging renewable energy for Türkiye's future hydrogen supply chain(Elsevier Ltd, 2025-09-09) Türkali Özbek, Busenur; Erdoğan, Ahmet; Güler, Mehmet GürayAs energy and climate crises necessitate a shift to sustainable resources, hydrogen - with its zero-emission potential-is expected to play a key role in the energy transition. Designing an effective hydrogen supply chain (HSC) is essential to realizing this potential. This study introduces a multi-period, multi-objective stochastic optimization model for Türkiye's transportation-sector HSC. It addresses gaps in existing research by integrating dynamic renewable energy availability, lifecycle-based CO2 emissions, and regional green hydrogen prioritization. The ε-constraint method is used to balance economic and environmental objectives. Results show that Türkiye can significantly reduce emissions by gradually transitioning from fossil-based production and by optimizing facility locations based on regional solar, wind, and hydrogen sulfide potential. Centralized production reduces costs but increases transport risk and emissions, while localized production improves resilience yet may increase fossil fuel reliance in resource-limited regions. These findings offer strategic guidance for aligning hydrogen planning with Türkiye's climate commitments.
