Graduate School of Engineering and Science

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    An integrated design approach for a series elastic actuator: Stiffness formulation, fatigue analysis, thermal management
    (IEEE, 2017-12-22) Yıldırım, Mehmet Can; Şendur, Polat; Bilgin, Onur; Gülek, Berk; Yapıcı, Güney Güven; Uğurlu, Regaip Barkan; Mechanical Engineering; YAPICI, Güney Güven; UĞURLU, Regaip Barkan; ŞENDUR, Polat; Yıldırım, Mehmet Can; Bilgin, Onur; Gülek, Berk
    This paper presents an integrated mechanical design approach for the long-Term and repetitive use of series elastic actuators (SEAs). Already, computational models for series elastic actuator design have been developed in order to address the challenging weight and volume targets. However, an integrated design method in which the coupling effects between various interacting requirements that are explored at every stage of the design cycle does not exist. In particular, the interactions between the torsional stiffness, strength, fatigue life and thermal performance are not analyzed in-depth. To this end, we propose a comprehensive design approach in which the aforementioned requirements (FEA, stiffness formulation, fatigue analysis, and thermal management) are integrated in a complementary manner. Computer-Aided analyses and experimental results verified the effectiveness of our design approach. The proposed approach is employed to manufacture our SEA module CoEx-SEA.
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    ArticlePublicationOpen Access
    A predictive multistage postdisaster damage assessment framework for drone routing
    (Wiley, 2024-01) Adsanver, Birce; Göktürk, Elvin Çoban; Koyuncu, Burcu Balçık; Industrial Engineering; GÖKTÜRK, Elvin Çoban; Adsanver, Birce
    This study focuses on postdisaster damage assessment operations supported by a set of drones. We propose a multistage framework, consisting of two phases applied iteratively to rapidly gather damage information within an assessment period. In the initial phase, the problem involves determining areas to be scanned by each drone and the optimal sequence for visiting these selected areas. We have adapted an electric vehicle routing formulation and devised a variable neighborhood descent heuristic for this phase. In the second phase, information collected from the scanned areas is employed to predict the damage status of the unscanned areas. We have introduced a novel, fast, and easily implementable imputation policy for this purpose. To evaluate the performance of our approach in real-life disasters, we develop a case study for the expected 7.5 magnitude earthquake in Istanbul, Turkey. Our numerical study demonstrates a significant improvement in response time and priority-based metrics.
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    Provenance aware run-time verification of things for self-healing Internet of Things applications
    (Wiley, 2019-02-10) Aktas, M. S.; Astekin, Merve; Astekin, Merve
    We propose a run-time verification mechanism of things for self-healing capability in the Internet of Things domain. We discuss the software architecture of the proposed verification mechanism and its prototype implementations. To identify faulty running behavior of things, we utilize a complex event processing technique by applying rule-based pattern detection on the events generated real time. For events, we use a descriptor metadata of the measurements (such as CPU usage, memory usage, and bandwidth usage) taken from Internet of Things devices. To understand the usability and effectiveness of the proposed mechanism, we developed prototype applications using different event processing platforms. We test the prototype implementations for performance and scalability under increasing message rates. The results are promising because the processing overhead of the proposed verification mechanism is negligible.
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    ArticlePublicationOpen Access
    Towards interactive explanation-based nutrition virtual coaching systems
    (Springer, 2024-01) Buzcu, Berk; Tessa, M.; Tchappi, I.; Najjar, A.; Hulstijn, J.; Calvaresi, D.; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Buzcu, Berk
    The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human–machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.