Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1926

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17th International Conference on Mechatronics Technology, October 15-18, 2013, Jeju Island, Korea
(Elsevier, 2015) Hwang, Sung Ho; Kim, Joon-wan; Dorantes-Gonzalez, Dante Jorge
In recent years, Mechatronics has gained a lot of interest as more applications have been introduced to industry and society. The need for new mechatronic technologies in the form of advanced production systems, mechatronic devices, control systems, robotics, biomedical applications, MEMS, and measurement systems, among others, is very much required in improving productivity and competitiveness in many industries. Thus, this conference was organized to address the state-of-the-art technology for the benefit of researchers and users, and this time the conference made a special focus on the topic: Sustainable Mechatronics Technology.
Citation - Scopus: 5
18 - Acoustic and Mechanical Properties of Biofibers and Their Composites
(Elsevier, 2022) Koç, Büşra; Genç, Garip; Körük, Hasan
In this study, the acoustic and mechanical properties of many biofibers and their composites are presented. First, the sound absorption coefficients and transmission losses of commonly used natural fibers and their composites are presented, by clearly reporting the thickness of the samples, for three different frequency ranges (<500 Hz: low, 500–2000 Hz: medium, and >2000 Hz: high). In addition, the sound absorption coefficients (for low- and medium-frequency ranges) and noise reduction coefficients of some 40-cm-thick samples are overlaid in order to directly compare their performances. Second, the physical properties, such as the density, diameter, and length of biofibers, and mechanical properties, such as the damping (or loss factor) and Young’s modulus of biofibers and their composites, are presented in detail. For comparison purposes, the acoustic and mechanical properties of some conventional materials, such as carbon and glass fibers, are included in the tables and figures. Finally, the effects of some parameters, such as pretreatment, fiber diameter, fiber/matrix ratio, moisture content, manufacturing and machining parameters/techniques, and measurement conditions/methods, on the acoustic and mechanical properties of natural materials are presented. Furthermore, current applications and potential usage areas of natural fibers are briefly discussed.
Citation - Scopus: 3
19 - Identification of the Elastic and Damping Properties of Jute and Luffa Fiber-Reinforced Biocomposites
(Elsevier, 2022) Genç, Garip; Saygılı, Yusuf; Körük, Hasan; Şanlıtürk, Yusuf Kenan
Although there are many studies in the literature on the static mechanical properties of biomaterials such as tensile strength, the dynamic mechanical properties of biomaterials such as modal loss factors have not been investigated in detail. In this study, the Young’s moduli and damping (or loss factors) of some jute and luffa fiber-reinforced biocomposites are investigated. The effects of fiber/resin ratio and thickness on the mechanical properties of the jute and luffa composites are identified via an experimental approach. For this purpose, acoustic and structural frequency response functions of some homogeneous and hybrid jute and luffa composite plates with different fiber/resin ratios and thicknesses are measured. By analyzing the measured frequency response functions using the circle-fit method, the modal frequencies and loss factors of the homogeneous and hybrid composite plates are determined. By assuming that the homogeneous plates are isotropic, the same plates are modeled using the finite element method, and by comparing the experimental and theoretical natural frequencies, the elastic properties of the homogeneous plates are determined. In addition, the same homogeneous plates are modeled by considering an anisotropic material model, and the associated material properties are determined. By using the identified material properties, the finite element models of the hybrid composite plates are developed, and by comparing their experimental and theoretical natural frequencies, the identified elastic material properties are evaluated and validated.
Citation - WoS: 2
Citation - Scopus: 1
A Benchmark Dataset for Turkish Data-To Generation
(Elsevier, 2022) Demir, Şeniz; Öktem, Seza
In the last decades, data-to-text (D2T) systems that directly learn from data have gained a lot of attention in natural language generation. These systems need data with high quality and large volume, but unfortunately some natural languages suffer from the lack of readily available generation datasets. This article describes our efforts to create a new Turkish dataset (Tr-D2T) that consists of meaning representation and reference sentence pairs without fine-grained word alignments. We utilize Turkish web resources and existing datasets in other languages for producing meaning representations and collect reference sentences by crowdsourcing native speakers. We particularly focus on the generation of single-sentence biographies and dining venue descriptions. In order to motivate future Turkish D2T studies, we present detailed benchmarking results of different sequence-to-sequence neural models trained on this dataset. To the best of our knowledge, this work is the first of its kind that provides preliminary findings and lessons learned from the creation of a new Turkish D2T dataset. Moreover, our work is the first extensive study that presents generation performances of transformer and recurrent neural network models from meaning representations in this morphologically-rich language.
Citation - WoS: 18
Citation - Scopus: 22
A Capacitated Lot Sizing Problem With Stochastic Setup Times and Overtime
(2019) Jabali, Ola; Gendreau, Michel; Jans, Raf; Taş, Duygu
In this paper, we study a Capacitated Lot Sizing Problem with Stochastic Setup Times and Overtime (CLSPSSTO). We describe a mathematical model that considers both regular costs (including production, setup and inventory holding costs) and expected overtime costs (related to the excess usage of capacity). The CLSP-SSTO is formulated as a two-stage stochastic programming problem. A procedure is proposed to exactly compute the expected overtime for a given setup and production plan when the setup times follow a Gamma distribution. A sample average approximation procedure is applied to obtain upper bounds and a statistical lower bound. This is then used to benchmark the performance of two additional heuristics. A first heuristic is based on changing the capacity in the deterministic counterpart, while the second heuristic artificially modifies the setup time. We conduct our computational experiments on well-known problem instances and provide comprehensive analyses to evaluate the performance of each heuristic. (C) 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 1
A Configurational Analysis of the Impact of Entrepreneurial Orientation and Global Mindset on Export Performance of Smes
(SAGE Publications Inc., 2024) Matemane, R.; Mintah, R.; Şahin, F.; Karadağ, H.
Although contemporary literature provides several important insights into the role of attributes of SMEs, there is much less evidence on what configuration of entrepreneurial orientation and global mindset makes this process successful, that is, contributing to the export performance of SMEs. This study uses a fuzzy set qualitative comparative analysis on a sample of 97 SMEs in Ghana to explore the potential complementary role between the entrepreneurial orientation dimensions and global mindset for superior export performance. The results indicate two different yet equifinal configurations of these factors that lead to a high level of export performance of SMEs. One of the configurations shows that proactive and innovative SMEs with managers high on global mindset achieve superior export performance regardless of their willingness to take risks. Another configuration indicates that regardless of the global mindset of managers, SMEs can achieve higher export performance by being proactive, innovative, and willing to take high risks. Several implications for theory and practice are discussed based on the findings. © The Author(s) 2024.
Citation - WoS: 5
Citation - Scopus: 8
A Data-Assisted Reliability Model for Carrier-Assisted Cold Data Storage Systems
(Elsevier, 2020) Arslan, Şuayb Şefik; Göker, Turguy; Peng, James
Cold data storage systems are used to allow long term digital preservation for institutions’ archive. The common functionality among cold and warm/hot data storage is that the data is stored on some physical medium for read-back at a later time. However in cold storage, write and read operations are not necessarily done in the same exact geographical location. Hence, a third party assistance is typically utilized to bring together the medium and the drive. On the other hand, the reliability modeling of such a decomposed system poses few challenges that do not necessarily exist in other warm/hot storage alternatives such as fault detection and absence of the carrier, all totaling up to the data unavailability issues. In this paper, we propose a generalized non-homogenous Markov model that encompasses the aging of the carriers in order to address the requirements of today's cold data storage systems in which the data is encoded and spread across multiple nodes for the long-term data retention. We have derived useful lower/upper bounds on the overall system availability. Furthermore, the collected field data is used to estimate parameters of a Weibull distribution to accurately predict the lifetime of the carriers in an example scale-out setting.
Citation - Scopus: 2
A Decade of Discriminative Language Modeling for Automatic Speech Recognition
(2015) Arısoy, Ebru; Saraçlar, Murat; Dikici, Erinc
This paper summarizes the research on discriminative language modeling focusing on its application to automatic speech recognition (ASR). A discriminative language model (DLM) is typically a linear or log-linear model consisting of a weight vector associated with a feature vector representation of a sentence. This flexible representation can include linguistically and statistically motivated features that incorporate morphological and syntactic information. At test time, DLMs are used to rerank the output of an ASR system, represented as an N-best list or lattice. During training, both negative and positive examples are used with the aim of directly optimizing the error rate. Various machine learning methods, including the structured perceptron, large margin methods and maximum regularized conditional log-likelihood, have been used for estimating the parameters of DLMs. Typically positive examples for DLM training come from the manual transcriptions of acoustic data while the negative examples are obtained by processing the same acoustic data with an ASR system. Recent research generalizes DLM training by either using automatic transcriptions for the positive examples or simulating the negative examples.
Citation - WoS: 1
Citation - Scopus: 1
A Decomposition Algorithm for Single and Multiobjective Integrated Market Selection and Production Planning
(Informs, 2023) van den Heuvel, Wilco; Ağralı, Semra; Taşkın, Z. Caner
We study an integrated market selection and production planning problem. There is a set of markets with deterministic demand, and each market has a certain revenue that is obtained if the market's demand is satisfied throughout a planning horizon. The demand is satisfied with a production scheme that has a lot-sizing structure. The problem is to decide on which markets' demand to satisfy and plan the production simultaneously. We consider both single and multiobjective settings. The single objective problem maximizes the profit, whereas the multiobjective problem includes the maximization of the revenue and the minimization of the production cost objectives. We develop a decomposition-based exact solution algorithm for the single objective setting and show how it can be used in a proposed three-phase algorithm for the multiobjective setting. The master problem chooses a subset of markets, and the subproblem calculates an optimal production plan to satisfy the selected markets' demand. We investigate the subproblem from a cooperative game theory perspective to devise cuts and strengthen them based on lifting. We also propose a set of valid inequalities and preprocessing rules to improve the proposed algorithm. We test the efficacy of our solution method over a suite of problem instances and show that our algorithm substantially decreases solution times for all problem instances.
Citation - Scopus: 4
A Framework for Automatic Generation of Spoken Question-Answering Data
(Association for Computational Linguistics (ACL), 2022) Manav, Y.; Menevşe, M.Ü.; Özgür, A.; Arısoy, Ebru
This paper describes a framework to automatically generate a spoken question answering (QA) dataset. The framework consists of a question generation (QG) module to generate questions automatically from given text documents, a text-to-speech (TTS) module to convert the text documents into spoken form and an automatic speech recognition (ASR) module to transcribe the spoken content. The final dataset contains question-answer pairs for both the reference text and ASR transcriptions as well as the audio files corresponding to each reference text. For QG and ASR systems we used pre-trained multilingual encoder-decoder transformer models and fine-tuned these models using a limited amount of manually generated QA data and TTS-based speech data, respectively. As a proof of concept, we investigated the proposed framework for Turkish and generated the Turkish Question Answering (TurQuAse) dataset using Wikipedia articles. Manual evaluation of the automatically generated question-answer pairs and QA performance evaluation with state-of-the-art models on TurQuAse show that the proposed framework is efficient for automatically generating spoken QA datasets. To the best of our knowledge, TurQuAse is the first publicly available spoken question answering dataset for Turkish. The proposed framework can be easily extended to other languages where a limited amount of QA data is available. © 2022 Association for Computational Linguistics.
Citation - WoS: 3
Citation - Scopus: 2
A Joint Dedupe-Fountain Coded Archival Storage
(2017) Arslan, Şuayb Şefik; Göker, Turguy; Wideman, Rod
An erasure-coded archival file storage system is presented using a chunk-based deduplication mechanism and fountain codes for space/time efficient operation. Unlike traditional archival storage, this proposal considers the deduplication operation together with correction coding in order to provide a reliable storage solution. The building blocks of deduplication and fountain coding processes are judiciously interleaved to present two novel ideas, reducing memory footprint with weaker hashing and dealing with the increased collisions using correction coding, and applying unequal error protection to deduplicated chunks for increased availability. The combination of these two novel ideas made the performance of the proposed system stand out. For example, it is shown to outperform one of the replication-based as well as RAID data protection schemes. The proposed system also addresses some of the fundamental challenges of today's low-cost deduplicated data storage systems such as hash collisions, disk bottleneck and RAM overflow problems, securing savings up to 90% regular RAM use.
Citation - WoS: 1
Citation - Scopus: 2
A Machine Learning Approach To Resolving Conflicts in Physical Human-Robot Interaction
(Association for Computing Machinery, 2025) Ulas Dincer, Enes; Al-Saadi, Zaid; Hamad, Y.M.; Aydın, Yusuf; Kucukyilmaz, A.; Basdogan, C.
As artificial intelligence techniques become more sophisticated, we anticipate that robots collaborating with humans will develop their own intentions, leading to potential conflicts in interaction. This development calls for advanced conflict resolution strategies in physical human-robot interaction (pHRI), a key focus of our research. We use a machine learning (ML) classifier to detect conflicts during co-manipulation tasks to adapt the robot's behavior accordingly using an admittance controller. In our approach, we focus on two groups of interactions, namely "harmonious"and "conflicting,"corresponding respectively to the cases of the human and the robot working in harmony to transport an object when they aim for the same target, and human and robot are in conflict when human changes the manipulation plan, e.g. due to a change in the direction of movement or parking location of the object.Co-manipulation scenarios were designed to investigate the efficacy of the proposed ML approach, involving 20 participants. Task performance achieved by the ML approach was compared against three alternative approaches: (a) a rule-based (RB) Approach, where interaction behaviors were rule-derived from statistical distributions of haptic features; (b) an unyielding robot that is proactive during harmonious interactions but does not resolve conflicts otherwise, and (c) a passive robot which always follows the human partner. This mode of cooperation is known as "hand guidance"in pHRI literature and is frequently used in industrial settings for so-called "teaching"a trajectory to a collaborative robot.The results show that the proposed ML approach is superior to the others in task performance. However, a detailed questionnaire administered after the experiments, which contains several metrics, covering a spectrum of dimensions to measure the subjective opinion of the participants, reveals that the most preferred mode of interaction with the robot is surprisingly passive. This preference indicates a strong inclination toward an interaction mode that gives more control to humans and offers less demanding interaction, even if it is not the most efficient in task performance. Hence, there is a clear trade-off between task performance and the preferred mode of interaction of humans with a robot, and a well-balanced approach is necessary for designing effective pHRI systems in the future. © 2025 Copyright held by the owner/author(s).
Citation - WoS: 1
Citation - Scopus: 3
A Meta-Analytic Review of the Association Between Theory of Mind and Aggression
(Elsevier, 2023) Imuta, Kana; Selçuk, Bilge; Yavuz-Müren, Melis; Turunç, Gamze; Ekerim-Akbulut, Müge
Although the association between theory of mind (ToM) and aggression has been theorized, empirical findings have not revealed a clear link between these constructs. In the current meta-analytic review, we integrated findings from 83 studies (141 effect sizes) involving 41,650 participants from 18 countries to elucidate the association between ToM and aggression in typically developing children, adolescents, and adults. We found a significant negative association between ToM and aggression overall (r = −0.15). Moreover, each type and function of aggression were negatively associated with Theory of Mind (ToM). Bullying—a distinct form of aggression—was not associated with ToM. The strength of the association between overall aggression and ToM varied as a function of methodological variables: First, studies that used self-report questionnaires to measure ToM and aggression yielded the strongest effect sizes, compared to those that used task-based assessments or questionnaires completed by others (parents, teachers, peers). Second, there was a difference in the ToM measurement with the measures examining ToM with non-false belief understanding tasks yielding a stronger mean effect than those that focused exclusively on false-belief understanding. Third, the magnitude of the negative association was found to increase with participants' age, though significant negative associations between ToM and aggression held across the lifespan. These results point to the critical link between ToM and aggressive tendencies and suggest the value in implementing interventions to improve mental state understanding across the age range to foster positive social interactions.
Citation - WoS: 1
A Modified Newton Method Formulation for Microwave Imaging
(IEEE, 2020) Coşğun, Sema; Çayören, Mehmet; Bilgin, Egemen; Doğu, Semih
A new variant of Newton type methods has been developed for quantitative microwave imaging. To deal with the ill-posedness of the inverse problems, standard Newton type methods involve a linearization of the so called data equation using the Fréchet derivative with respect to the contrast function. Here, the formulation is expanded to include the object equation, therefore, the formulation seeks to reduce the errors in both the data and the object equations. While this modification does not remove the need to solve forward problem at each step, it nevertheless significantly improves convergence rate and the performance. To assess the efficiency of the proposed technique, numerical simulations with synthetic and experimental data have been carried out. The results demonstrate that the proposed variant outperforms the standard Newton method, and shows comparable performance to the contrast source inversion (CSI) algorithm with fewer iterations.
A Multiobjective Evolutionary Algorithm Approach for Map Sketch Generation
(2018) Topcu, Şafak; Etaner-Uyar, A. Sima
In this paper, we present a method to generate map sketches for strategy games using a state of the art many-objective evolutionary algorithm, namely NSGAIII. The map sketch generator proposed in this study outputs a three objective Pareto-front in which all the points are fair and strong in different aspects. The generated map sketch can be used by level designers to create real time strategy maps effectively and/or help them see multiple aspects of a game map simultaneously. The algorithm can also be utilised as a benchmark generator to be used in tests for various cases such as shortest path algorithms and strategy game bots. The results reported in this paper are very promising and promote further study.
A New Benchmark Dataset for P300 Erp-Based Bci Applications
(Academic Press Inc Elsevier Science, 2023) Çakar, Tuna; Özkan, Hüseyin; Musellim, Serkan; Arslan, Suayb S.; Yağan, Mehmet; Çakar, Tuna; Alp, Nihan
Because of its non-invasive nature, one of the most commonly used event-related potentials in brain -computer interface (BCI) system designs is the P300 electroencephalogram (EEG) signal. The fact that the P300 response can easily be stimulated and measured is particularly important for participants with severe motor disabilities. In order to train and test P300-based BCI speller systems in more realistic high-speed settings, there is a pressing need for a large and challenging benchmark dataset. Various datasets already exist in the literature but most of them are not publicly available, and they either have a limited number of participants or utilize relatively long stimulus duration (SD) and inter-stimulus intervals (ISI). They are also typically based on a 36 target (6 x 6) character matrix. The use of long ISI, in particular, not only reduces the speed and the information transfer rates (ITRs) but also oversimplifies the P300 detection. This leaves a limited challenge to state-of-the-art machine learning and signal processing algorithms. In fact, near-perfect P300 classification accuracies are reported with the existing datasets. Therefore, one certainly needs a large-scale dataset with challenging settings to fully exploit the recent advancements in algorithm design (machine learning and signal processing) and achieve high-performance speller results. To this end, in this article we introduce a new freely-and publicly-accessible P300 dataset obtained using 32-channel EEG, in the hope that it will lead to new research findings and eventually more efficient BCI designs. The introduced dataset comprises 18 participants performing a 40 -target (5 x 8) cued-spelling task, with reduced SD (66.6 ms) and ISI (33.3 ms) for fast spelling. We have also processed, analyzed, and character-classified the introduced dataset and we presented the accuracy and ITR results as a benchmark. The introduced dataset and the codes of our experiments are publicly accessible at https://data .mendeley.com /datasets /vyczny2r4w.(c) 2023 Elsevier Inc. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 2
A New Learning Community for Educating Future Teachers: Online Baboratory School
(Taylor and Francis Ltd., 2022) Pekkan, Tunç Zelha; Taylan, Didem Rukiye
To provide quality mathematics education for disadvantaged groups of middle school students and continue to offer quality practicum experience to future teachers during the Covid 19 outbreak, we founded the Online Laboratory School. This school was free and open to public school students: 130 middle school students throughout Turkey attended for a 5-week period. There were 25 pre-service teachers actively involved in teaching, under the close supervision of 7 university supervisors. The entire gamut of planning, teaching and reflection sessions for each virtual class were recorded via an e-learning platform. Additionally, survey data was collected from the participating students, parents, pre-service teachers and supervisors. Our findings indicate that we were able to build a unique and virtual learning community. While pre-service teachers and middle school students benefited the most, university supervisors also reported improving their skills on when and how to give feedback. We describe how the school functioned and the range of opportunities it provided to all participants considering situated-learning perspectives and building online-learning communities. We also discuss how this model can be used in the future as a strong asset for teacher education programs and adaptation of fieldwork practices.
Citation - WoS: 1
Citation - Scopus: 1
A Novel Genetic Algorithm-Based Improvement Model for Online Communities and Trust Networks
(IOS Press, 2020) Bekmezci, ilker; Cimen, Egemen Berkic; Ermiş, Murat
Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.
Citation - WoS: 3
Citation - Scopus: 4
A Novel Graph Transformation Strategy for Optimizing Sptrsv on Cpus
(Wiley, 2023) Yılmaz, Buse
Sparse triangular solve (SpTRSV) is an extensively studied computational kernel. An important obstacle in parallel SpTRSV implementations is that in some parts of a sparse matrix the computation is serial. By transforming the dependency graph, it is possible to increase the parallelism of the parts that lack it. In this work, we present a novel graph transformation strategy to increase the parallelism degree of a sparse matrix and compare it to our previous strategy. It is seen that our transformation strategy can provide a speedup as high as 1.42x$$ 1.42x $$.
A Novel Plasma-Facing Ndb6 Particulate Reinforced W1ni Matrix Composite: Powder Metallurgical Fabrication, Microstructural and Mechanical Characterization
(Elsevier Sci Ltd, 2024) Boztemur, Burçak; Öveçoğlu, Mustafa Lutfi; Luo, Laima; Ağaoğulları, Duygu; Xu, Yue; Alkraidi, Ammar
Tungsten (W) is one of best candidate metal for plasma-facing materials (PFM), especially due to its high melting temperature and neutron absorption capability. However, converting W into bulk PFM is hard because of its high melting point. This problem can be solved by adding metallic sintering aids with low melting points. In this study, W matrix with 1 wt% Ni aid was reinforced by adding NdB6 particles (1, 5, and 10 wt%). It can be introduced as a novel potential PFM, thanks to its low volatility and high neutron absorbability. The ceramic and composite powders produced via mechanochemical synthesis and mechanical alloying were examined in terms of composition, particle size, crystallite size, and lattice strain. Samples sintered via pressureless sintering (PS) and spark plasma sintering (SPS) were microstructurally analyzed by using an X-ray diffractometer (XRD), a scanning electron microscope (SEM) attached with an energy dispersive spectroscope (EDS), and mechanically analyzed in terms of microhardness and wear behavior. Based on the results, W2B and WB phases emerged in the SPS'ed W1Ni-5NdB6 and PS'ed./SPS'ed W1Ni-10NdB6 composites. SPS'ed W1Ni-10NdB6 composite had the highest hardness value and the lowest specific wear rate. The SPS'ed W1Ni-5NdB6 composite showed fewer surface damages and higher irradiation resistance as compared with other samples after exposure of He+ irradiation.

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