Yazar "Kocamaz, Adnan Fatih" seçeneğine göre listele
Listeleniyor 1 - 6 / 6
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A chaotic optimization method based on logistic-sine map for numerical function optimization(Springer, 2020) Fahrettin Burak; Tuncer, Türker; Kocamaz, Adnan FatihMeta-heuristic optimization algorithms have been used to solve mathematically unidentifiable problems. The main purpose of the optimization methods on problem-solving is to choose the best solution in predefined conditions. To increase performance of the optimization methods, chaotic maps for instance Logistic, Singer, Sine, Tent, Chebyshev, Circle have been widely used in the literature. However, hybrid 1D chaotic maps have higher performance than the 1D chaotic maps. The hybrid chaotic maps have not been used in the optimization process. In this article, 1D hybrid chaotic map (logistic-sine map)-based novel swarm optimization method is proposed to achieve higher numerical results than other optimization methods. Logistic-sine map has good statistical result, and this advantage is used directly to calculate global optimum value in this study. The proposed algorithm is a swarm-based optimization algorithm, and the seed value of the logistic-sine map is generated from local best solutions to reach global optimum. In order to test the proposed hybrid chaotic map-based optimization method, widely used numerical benchmark functions are chosen. The proposed chaotic optimization method is also tested on compression spring design problem. Results and comparisons clearly show that the proposed chaotic optimization method is successful.Öğe Cloud Based Indoor Navigation for ROS-enabled Automated Guided Vehicles(IEEE (Institute of Electrical and Electronics Engineers), 2019) Okumuş, 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(Multidisciplinary Digital Publishing Institute (MDPI), 2020) Okumuş, Fatih; Dönmez, 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 Lojistik-Singer Harita Tabanlı Yeni Bir Kaotik Sürü Optimizasyon Yöntemi(IEEE (Institute of Electrical and Electronics Engineers), 2019) Demir, Fahrettin Burak; Tuncer, Türker; Kocamaz, Adnan FatihGünlük yaşamda pek çok problem, sonsuz çözüm uzayına sahip olduğu için klasik matematiksel yöntemler kullanılarak çözülememektedir. Bu nedenle, benzer problemlerin çözümünde, sonsuz çözüm uzayını küçülten ve matematiksel tahmin prensibine dayanan meta-sezgisel optimizasyon yöntemlerinin kullanılması önerilmektedir. Meta-sezgisel optimizasyon yöntemlerinin başarımını artırmak amacıyla sayı üreteci ve parametre belirleyici olarak kaotik haritalar kullanılmaktadır. Bu makalede yeni bir kaotik optimizasyon yöntemi geliştirilmiş ve önerilen optimizasyon yönteminde lojistik ve singer harita kullanılmıştır. Önerilen yöntemin performansını test etmek amacıyla literatürde sıkça kullanılan 6 farklı kıyaslama fonksiyonu ve 3 farklı sürü tabanlı optimizasyon yöntemi kullanılmıştır. Önerilen yöntem bütün fonksiyonlar için daha optimum sonuçlar üretmiştir. Ve bu sayede sürü optimizasyon yöntemlerinin lokal çözümlere takılması önlenmeye çalışılmıştır.Öğe A novel computer assisted sperm analyzer for assessment of spermatozoa motility in fish; BASA-Sperm Aqua(Tüm Bilim İnsanları ve Akademisyenler Derneği, 2019) Özgür, Mustafa Erkan; Okumuş, Fatih; Kocamaz, Adnan FatihThis study was conducted to determine the working principle and operability of the BASA-Sperm Aqua module software of the newly developed computer-assisted sperm analysis system (BASA) for the evaluation of spermatozoa motility in fish. Semen samples of trout (Oncorhynchus mykiss) species were examined for this purpose. Sperm motility parameters such as VSL (?m/s), VCL (?m/s), VAP (?m/s), LIN (%), BCF (Hz), ALH (?m) and MAD (o) were examined. The investigated parameters were compared with data which analyzed in similar computer systems and published in international manuscripts. Finally, the BASA-Sperm Aqua has been found to be a software that performs its functions very quickly and practical and produces accurate and understandable results in the determining sperm quality parameters of fish.Öğe A survival classification method for hepatocellular carcinoma patients with chaotic Darcy optimization method based feature selection(Elsevier, 2020) Demir, Fahrettin Burak; Tuncer, Turker; Kocamaz, Adnan Fatih; Ertam, FatihSurvey is one of the crucial data retrieval methods in the literature. However, surveys often contain missing data and redundant features. Therefore, missing feature completion and feature selection have been widely used for knowledge extraction from surveys. We have a hypothesis to solve these two problems. To implement our hypothesis, a classification method is presented. Our proposed method consists of missing feature completion with a statistical moment (average) and feature selection using a novel swarm optimization method. Firstly, an average based supervised feature completion method is applied to Hepatocellular Carcinoma survey (HCC). The used HCC survey consists of 49 features. To select meaningful features, a chaotic Darcy optimization based feature selection method is presented and this method selects 31 most discriminative features of the completed HCC dataset. 0.9879 accuracy rate was obtained by using the proposed chaotic Darcy optimization-based HCC survival classification method.