Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1941

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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.
A Resonator Design For Mutual Coupling Reduction Between Microstrip Antennas In Mımo Applications At 28 Ghz
(Institute of Electrical and Electronics Engineers Inc., 2024) Gollu, A.A.; Polat, B.; Semerci, D.; Bilgin, E.
A simple resonator structure is proposed to reduce the mutual coupling between rectangular microstrip patch antennas positioned close to each other for MIMO applications at 28 GHz center frequency. Here, the frequency of 28 GHz is chosen because it is one of middle bands for 5G communication in USA. Two microstrip patch antennas with gaps using a common dielectric substrate and a ground plane are employed as antennas and the patches are closely placed with an edge-to-edge distance of 0.6 mm (approximately λ/18). In order to reduce the mutual coupling between these radiating elements and increase the isolation, a resonator is positioned between them and its parameters are optimized. In the simulations, it is observed that the proposed resonator reduces the coupling by approximately 10 dB. By this result, it can be concluded that the proposed structure may be suitable for tightly packed MIMO systems. © 2024 IEEE.
Çevrimde Imza Doğrulama için Fpga Üzerinde Gerçek Zamanlı Sistem Tasarımı
(2020) Ayhan, Tuba; Orak, Remzi
Bu proje kapsamında, çevrimde imza doğrulama sistemi gerçeklenmiştir. Sistem dokunmatik ekran üzerinden imza (paraf ya da el yazısı bir karakter) alıp, belleğindeki imza öznitelikleri ile karşılaştırarak imzanın iddia edilen kişiye ait olup olmadığını göstermektedir. Orjinal imza resimleri bellekte tutulmadığından sistem imza hırsızlığına karşı bir miktar dayanıklıdır. Sistem dokunmatik ekran, Zynq-7000 geliştirme kartı ve dokunmatik ekran kaleminden oluşur. İmza atıldıktan 0.13 s sonra doğrulama sonucu ekranda verilir. Kullanım rahatlığı açısından atılan imzanın resmi ekranda da gösterilmektedir. Sistemin test ortamında sınıflama başarımı yetenekli taklitçi için %60 dolayında kalsa da sıradan taklitçi için %100?ü bulmaktadır. Proje kapsamında oluşturulup araştırmacılara açılan veri kümesinde tasniflenmiş 500 imza bulunmaktadır. Projenin tüm kaynak kodları github üzerinden açılmıştır. Proje ile ilgili bilgiler, kodlar, veri kümesi ve kısa video da proje sayfası (https://sites.google.com/mef.edu.tr/imza) üzerinde yayındadır.
Citation - Scopus: 1
Foundations of Neuroscience-Based Learning
(Springer International Publishing, 2022) Dorantes-Gonzalez, Dante Jorge
Traditional learning and teaching approaches such as problem-based or project-based learning, among others, do not explicitly consider emotional-enhanced learning, which is a well-known driver of engagement leading to long-term memory retention. On the other hand, existing brain-based learning methods do not provide structured and scientifically-based strategies for the formation of the learner’s emotional experience and engagement. The Neuroscience-based Learning (NBL) technique is a novel neuroeducational approach that explains and applies the implicit neurophysiological mechanisms underlying vivid and highly-arousal emotional experiences leading to long-term memory retention. The NBL is devised from a cybernetics and system approach perspective. It starts from the basis of the neurophysiological learning scheme, describing the relationships among the environment and the learner’s internal mental processes ranging from perceptions, comparison with previous experiences and memories, immediate sensations, reactions, emotions, desires, intentions, higher-order cognitive functions, and controlled actions to the environment. The scheme relates memory systems, non-associative and associative learning mechanisms, implicit and explicit learning subsystems, signaling chemicals, and their neural subsystems, as well as identifying the amygdala as a key sensor triggering and modulating implicit learning. The NBL method exposes the triggers for vivid and highly arousal emotional learning: novelty, unpredictability, sense of low control, threat to the ego, avoidance (aversion-mediated learning), and reward (reward-based learning) and devises the principles of NBL toward more didactic applications. The foundations for implementing NBL in education and recommendations for learning during the online and pandemic situations were proposed. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
İnsan-robot Dokunsal (haptik) Etkileşimi için Makine Öğrenme Tabanlı Admitans Kontrolü
(2021) Başdoğan, Çağatay; Patoğlu, Volkan; Niaz, Pouya Pourakbarian; Aydın, Yusuf; Necipoğlu, Serkan; Şirintuna, Doğanay; Çaldıran, Ozan
Yakın gelecekte, fabrika, ev, hastane gibi farklı ortamlarda, insanlar ve robotların birlikte çalışarak, fiziksel etkileşim gerektiren görevleri ortaklaşa yerine getirebilmeleri beklenmektedir. Fiziksel insan-robot etkileşimi konusundaki önemli araştırma konularından birisi, ortaklar arasında doğal bir iletişimin kurulmasıdır. İnsan-robot etkileşimi konusunda hali hazırda çeşitli sayıda çalışmalar bulunmasına rağmen, ortaklar arasındaki fiziksel etkileşimi, bilhassa dokunsal (haptik) tabanlı iletişimi inceleyen çalışmalar sınırlı sayıdadır ve bu tip sistemlerdeki etkileşim hala doğal insan-insan etkileşimine kıyaslandığında yapay kalmaktadır. Bu projede, insanla beraber ortak görevler yapabilecek işbirlikçi bir robot için kesir dereceli ve uyarlamalı (adaptif) bir admitans kontrolcü geliştirildi. Bilgimiz dahilinde kesir dereceli bir admitans kontrolcü insan-robot fiziksel etkileşimi için daha önce kullanılmamıştır. Kesir dereceli kontrolcülerin en önemli özelliği, tamsayı olmayan türev ve integralin kullanılabilmesidir ki bu da bize birleşik sistemin (insan-robot) dinamiğinin modellenmesinde ve denetlenmesinde, tam sayılı bir kontrolcüye göre, esneklik sağlamıştır. Ayrıca, kesir dereceli bir admitans kontrolcünün makine öğrenmesi algoritmaları vasıtasıyla uyarlanabilir şekilde kullanıldığına dair bir örnek literatürde mevcut değildir. Makine öğrenmesi algoritmaları, bizim görev sırasında insanın niyetini anlamamızı ve buna göre görev performansını optimize edecek şekilde kontrolcü parametrelerini seçmemizi sağladı. Projede geliştirilen yöntemlerin etkinliğini sınamak için laboratuvar ortamında, insan ve robot arasında fiziksel etkileşim gerektiren kontrollü deneyler 12 adet denekle yapıldı. Bu deneylerde, denekler, robot koluna bağlanmış bir matkap aracılığıyla dik ve düz tahta bir yüzey üzerinde delikler açtılar. Makina öğrenmesi teknikleri kullanılarak kullanıcın hangi alt-görevi (textit{Bekleme, Serbest Hareket, ve Temas}) yerine getirdiği gerçek zamanlı olarak tespit edildi ve buna göre kontrolcünün parametreleri uyarlandı. Bu sayede, robotun insan tarafından yönlendirilip delik açılacak noktaya yaklaştırılırken (textit{Serbest Hareket}) insana düşük direnç (şeffaflık), delme sırasında (textit{Temas}) ise oluşacak titreşimleri azaltarak sistemi daha kararlı ve güvenli hale getirecek şekilde yüksek direnç göstermesi sağlandı. Bu deneylerden elde edilen sonuçlar, insan-robot etkileşimi için, uyarlamalı ve kesir dereceli bir kontrolcünün tam sayılı ve sabit parametreli bir kontrolcüye göre, görev performanı açısından, çok daha etkili olduğunu gösterdi. Son olarak, projede geliştirilen sistemin endüstriyel ortamda geçerliliğini sınamak için, endüstriyel ortağımız olan As-Metal şirketinden 3 adet işçi laboratuvarımıza davet edildi ve eğrili (curved) bir tahta yüzeyde delik açma deneyleri yapıldı. İşçilerden yüzey üzerinde 3 farklı noktada ve 3 farklı açıda delik açmaları istendi. İşçiler bu görevi yerine getirirken hem işbirlikçi robotumuzdan hem de bir artırılmış gerçeklik arayüzünden destek aldılar. Deneylerden sonra, işçilerden geliştirilen sistem hakkında fikirlerini iletebilecekleri bir anket doldurmaları istendi. Bu anket ve işçilerle yapılan kişisel görüşmeler vasıtasıyla robotun güvenirliği, kullanım kolaylığı ve görevi gerçekleştirmesindeki katkısı ölçüldü. Bu anketten elde edilen sonuçlar bize geliştirilen bu insan-robot etkileşim sisteminin endüstriyel uygulamlar için uygun, kolay, ve etkili olduğunu gösterdi.
Citation - Scopus: 1
Mechanical Design of a Haptic Hand Exoskeleton for Tele-Exploration of Explosive Devices
(IEEE, 2023) Dorantes-Gonzalez, Dante Jorge
There are tasks such as remote exploration and manipulation of explosive objects where high dexterity, accuracy, and practicality are necessary. The proposed haptic hand exoskeleton design uses displacement sensors in both flexion-deflection as well as abduction-adduction to replicate the operator's main three fingers' motion and teleoperate a slave robotic hand for disassembling and disposal of explosive objects. The novel design, component selection, and computer-aided design of the haptic virtual prototype were developed and tested.
Citation - WoS: 10
Citation - Scopus: 11
Resolving Conflicts During Human-Robot Co-Manipulation
(IEEE Computer Society, 2023) Al-Saadi, Zaid; Hamad, Yahya M.; Aydin, Yusuf; Kucukyilmaz, Ayse; Basdogan, Cagatay
This paper proposes a machine learning ( ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish between harmonious and conflicting human-robot interaction behaviors during object co-manipulation. Kinesthetic information generated through the teamwork is used to describe the interactive quality of collaboration. As such, we demonstrate that features derived from haptic (force/torque) data are sufficient to classify if the human and the robot harmoniously manipulate the object or they face a conflict. A conflict resolution strategy is implemented to get the robotic partner to proactively contribute to the task via online trajectory planning whenever interactive motion patterns are harmonious, and to follow the human lead when a conflict is detected. An admittance controller regulates the physical interaction between the human and the robot during the task. This enables the robot to follow the human passively when there is a conflict. An artificial potential field is used to proactively control the robot motion when partners work in harmony. An experimental study is designed to create scenarios involving harmonious and conflicting interactions during collaborative manipulation of an object, and to create a dataset to train and test the random forest classifier. The results of the study show that ML can successfully detect conflicts and the proposed conflict resolution mechanism reduces human force and effort significantly compared to the case of a passive robot that always follows the human partner and a proactive robot that cannot resolve conflicts.
Citation - WoS: 14
Citation - Scopus: 16
Robot-Assisted Drilling on Curved Surfaces With Haptic Guidance Under Adaptive Admittance Control
(IEEE, 2022) Başdoğan, Çağatay; Niaz, Pouya P.; Aydın, Yusuf; Güler, Berk; Madani, Alireza
Drilling a hole on a curved surface with a desired angle is prone to failure when done manually, due to the difficulties in drill alignment and also inherent instabilities of the task, potentially causing injury and fatigue to the workers. On the other hand, it can be impractical to fully automate such a task in real manufacturing environments because the parts arriving at an assembly line can have various complex shapes where drill point locations are not easily accessible, making automated path planning difficult. In this work, an adaptive admittance controller with 6 degrees of freedom is developed and deployed on a KUKA LBR iiwa 7 cobot such that the operator is able to manipulate a drill mounted on the robot with one hand comfortably and open holes on a curved surface with haptic guidance of the cobot and visual guidance provided through an AR interface. Real-time adaptation of the admittance damping provides more transparency when driving the robot in free space while ensuring stability during drilling. After the user brings the drill sufficiently close to the drill target and roughly aligns to the desired drilling angle, the haptic guidance module fine tunes the alignment first and then constrains the user movement to the drilling axis only, after which the operator simply pushes the drill into the workpiece with minimal effort. Two sets of experiments were conducted to investigate the potential benefits of the haptic guidance module quantitatively (Experiment I) and also the practical value of the proposed pHRI system for real manufacturing settings based on the subjective opinion of the participants (Experiment II). The results of Experiment I, conducted with 3 naive participants, show that the haptic guidance improves task completion time by 26% while decreasing human effort by 16% and muscle activation levels by 27% compared to no haptic guidance condition. The results of Experiment II, conducted with 3 experienced industrial workers, show that the proposed system is perceived to be easy to use, safe, and helpful in carrying out the drilling task.
Citation - Scopus: 1
Solving Xor In Spike Neural Network (SNN) With Component-off-the-shelf
(Institute of Electrical and Electronics Engineers Inc., 2024) Cikikci, S.V.; Orek, E.; Ozgen, A.K.; Yavuz, A.; Ayhan, Tuğba
This paper addresses the solution of the XOR problem with Spiking Neural Networks (SNN) in order to improve energy efficiency and computational performance as Moore's Law approaches its limits. SNN is capable of solving nonlinear problems while saving energy by mimicking the working principles of biological neurons. For this purpose, a SNN consisting of 12 neurons was implemented on a breadboard using the Leaky Integrate and Fire (LIF) model. In the input layer of the network, 50 Hz and 100 Hz signals are processed with frequency sensitive filters. With the help of bandpass and low-pass filters, additive and inverting operational amplifiers, the XOR problem is successfully solved. © 2024 IEEE.
The Tuned Mass Damper as a Subject in Engineering Mechanics Dynamics
(IEEE, 2022) Dorantes-Gonzalez, Dante Jorge
The course of Engineering Mechanics Dynamics is one of the most challenging courses for both mechanical and civil engineering programs, among others. But few universities dare to introduce projects to enhance students' curiosity, interest, and engagement toward engineering by constructing do-it-yourself physical prototypes, making measurements, and calculations to compete for the best performance. The intention of this project is to introduce a complex multiple-degree-of-freedom vibration problem in an easy manner, namely, the topic of a tuned mass damper (TMD) applied to earthquake-like oscillations. This type of projects directly addresses all seven student outcomes recommended by the Accreditation Board of Engineering and Technology (ABET). The project develops critical thinking and inquiry skills by designing and constructing the prototype of a building-like structure and its corresponding TMD; conducting an experiment under certain constraints to test the attenuation after an initial displacement; applying an open-source freeware to plot and measure underdamped oscillations; calculating main vibration parameters; as well as comparing performance results with another teams. Students approach this complex tunning problem by trial-and-error of key TMD parameters, a strategy that sparks fun and gambling to the process and competition for the best performance in attenuation efficiency. Data from direct observation of students' performance, student surveys, reports, presentation videos, office hours, and interviews showed that students enthusiastically responded at all project stages, understood the TMD functioning, and appreciated the value of dynamics in engineering in a more meaningful way than it would be without this type of projects.
Toward a Novel Neuroscience-Based System Approach Integrating Cognitive and Implicit Learning in Education
(Springer Science and Business Media Deutschland GmbH, 2023) Tsvetkova, Nadezhda; Çakar, Tuna; Veledinskaya, Svetlana; Babanskaya, Olesya; Dorantes-Gonzalez, Dante Jorge
Emotional-enhanced learning is a meaningful driver of engagement leading to long-term memory retention in learners, however, traditional approaches such as problem-based learning, and project-based learning, among others, do not consider brain-based learning guidelines concerning learner’s emotional experience design. The Neuroscience-based Learning (NBL) technique is a novel neuro-educational approach that applies the implicit neuro-physiological mechanisms underlying vivid and highly-arousal emotion-al experiences leading to long-term memory retention. The NBL is devised from a cybernetic system point of view, by explaining the novel neuro-physiological learning scheme describing the relation among the environment and the learner’s internal mental processes ranging from perceptions, comparison with previous experiences and memories, immediate sensations and reactions, emotions, desires, intentions, higher order cognitive functions, and controlled actions towards the environment. While explaining biological processes, the scheme also relates the types of memory systems with their non-associative and associative learning mechanisms, and the variables that modulate learning. NBL proposes the triggers for a vivid and highly-arousal emotional learning, which are novelty, unpredictability, sense of low control, threat to ego, avoidance (aversion-mediated learning), and reward (reward-based learning). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Citation - WoS: 1
Citation - Scopus: 3
Turkish Data-To Generation Using Sequence-To Neural Networks
(Assoc Computing Machinery, 2023) Demir, Şeniz
End-to-end data-driven approaches lead to rapid development of language generation and dialogue systems. Despite the need for large amounts of well-organized data, these approaches jointly learn multiple components of the traditional generation pipeline without requiring costly human intervention. End-to-end approaches also enable the use of loosely aligned parallel datasets in system development by relaxing the degree of semantic correspondences between training data representations and text spans. However, their potential in Turkish language generation has not yet been fully exploited. In this work, we apply sequenceto-sequence (Seq2Seq) neural models to Turkish data-to-text generation where the input data given in the form of a meaning representation is verbalized. We explore encoder-decoder architectures with attention mechanism in unidirectional, bidirectional, and stacked recurrent neural network (RNN) models. Our models generate one-sentence biographies and dining venue descriptions using a crowdsourced dataset where all field value pairs that appear in meaning representations are fully captured in reference sentences. To support this work, we also explore the performances of our models on a more challenging dataset, where the content of a meaning representation is too large to fit into a single sentence, and hence content selection and surface realization need to be learned jointly. This dataset is retrieved by coupling introductory sentences of person-related Turkish Wikipedia articles with their contained infobox tables. Our empirical experiments on both datasets demonstrate that Seq2Seq models are capable of generating coherent and fluent biographies and venue descriptions from field value pairs. We argue that the wealth of knowledge residing in our datasets and the insights obtained fromthis study hold the potential to give rise to the development of new end-to-end generation approaches for Turkish and other morphologically rich languages.

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