Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Article Citation - WoS: 2Citation - Scopus: 2Order-Preserving Languages for the Supervisory Control of Automated Manufacturing Systems(Ieee-inst Electrical Electronics Engineers inc, 2020) Nooruldeen, Anas; Schmidt, Klaus WernerAutomated manufacturing systems (AMSs) consist of computer-controlled interconnected manufacturing components (MCs) that are used to transport and process different product types. Each product type requires a certain sequence of processing steps in different MCs. Hereby, multiple product types can share processing steps on the same MC and the paths of different products types can overlap. In this paper we consider the modeling of AMSs in the scope of supervisory control for discrete event systems (DES). On the one hand, a suitable AMS model must allow the representation of sequential and concurrent processing steps in MCs. On the other hand, such model must be able to track different product types traveling through the AMS so as to process the products correctly. While previous work is commonly concerned with the first requirement, this paper identifies that the existing literature lacks a general treatment of the second requirement. Accordingly, we first introduce order-preserving (OP) languages that preserve the order of different product types in MCs and we propose a suitable finite state automaton model for OP languages. Then, we show that the composition of OP languages again leads to an OP language. That is, modeling MCs by OP languages, an OP model of a complete AMS that is suitable for supervisory control is obtained. In addition, it is possible to use both OP models and non-OP models for general AMSs, where MCs have different properties. We demonstrate the applicability of the proposed modeling technique by a flexible manufacturing system example.Article Citation - WoS: 10Citation - Scopus: 14A Pairwise Deep Ranking Model for Relative Assessment of Parkinson's Disease Patients From Gait Signals(Ieee-inst Electrical Electronics Engineers inc, 2022) Ogul, Burcin Buket; Ozdemir, SuatContinuous monitoring of the symptoms is crucial to improve the quality of life for patients with Parkinson's Disease (PD). Thus, it is necessary to objectively assess the PD symptoms. Since manual assessment is subjective and prone to misinterpretation, computer-aided methods that use sensory measurements have recently been used to make objective PD assessment. Current methods follow an absolute assessment strategy, where the symptoms are classified into known categories or quantified with exact values. These methods are usually difficult to generalize and considered to be unreliable in practice. In this paper, we formulate the PD assessment problem as a relative assessment of one patient compared to another. For this assessment, we propose a new approach to the comparative analysis of gait signals obtained via foot-worn sensors. We introduce a novel pairwise deep-ranking model that is fed by data from a pair of patients, where the data is obtained from multiple ground reaction force sensors. The proposed model, called Ranking by Siamese Recurrent Network with Attention, takes two multivariate time-series as inputs and produces a probability of the first signal having a higher continuous attribute than the second one. In ten-fold cross-validation, the accuracy of pairwise ranking predictions can reach up to 82% with an AUROC of 0.89. The model outperforms the previous methods for PD monitoring when run in the same experimental setup. To the best of our knowledge, this is the first study that attempts to relatively assess PD patients using a pairwise ranking measure on sensory data. The model can serve as a complementary model to computer-aided prognosis tools by monitoring the progress of the patient during the applied treatment.