A Deep Incremental Boltzmann Machine for Modeling Context in Robots

Context is an essential capability for robots that are to be as adaptive as possible in challenging environments. Although there are many context modeling efforts, they assume a fixed structure and number of contexts. In this paper, we propose an incremental deep model that extends Restricted Boltzmann Machines. Our model gets one scene at a time, and gradually extends the contextual model when necessary, either by adding a new context or a new context layer to form a hierarchy. We show on a scene classification benchmark that our method converges to a good estimate of the contexts of the scenes, and performs better or on-par on several tasks compared to other incremental models or non-incremental models.Comment: 6 pages, 5 figures, International Conference on Robotics and Automation (ICRA 2018

Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments

Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We compare our method to the state of the art algorithm in ambiguous environments (e.g., environments that include very similar objects with similar relationships). We show that our method generates referring expressions that people find to be more accurate ($\sim$30% better) and would prefer to use ($\sim$32% more often).Comment: International Conference on Intelligent Robots and Systems (IROS 2019), Demo 1: Finding the described object (https://youtu.be/BE6-F6chW0w), Demo 2: Referring to the pointed object (https://youtu.be/nmmv6JUpy8M), Supplementary Video (https://youtu.be/sFjBa_MHS98

CINet: A Learning Based Approach to Incremental Context Modeling in Robots

There have been several attempts at modeling context in robots. However, either these attempts assume a fixed number of contexts or use a rule-based approach to determine when to increment the number of contexts. In this paper, we pose the task of when to increment as a learning problem, which we solve using a Recurrent Neural Network. We show that the network successfully (with 98\% testing accuracy) learns to predict when to increment, and demonstrate, in a scene modeling problem (where the correct number of contexts is not known), that the robot increments the number of contexts in an expected manner (i.e., the entropy of the system is reduced). We also present how the incremental model can be used for various scene reasoning tasks.Comment: The first two authors have contributed equally, 6 pages, 8 figures, International Conference on Intelligent Robots (IROS 2018

Corporate Social Responsibility and Environmental Sustainability Relationship: the Case of Companies in the Marmara Region of Turkey

DergiPark: 517534trakyasobedCorporate Social Responsibility is to ensure their output during theiractivities without harming to the society, to see all environmental actors as apart of their business existence and to prioritize social utility. The aim ofthis study is to investigate the importance and the place of the CorporateSocial Responsibility principles in the provision of a sustainable environment.It has been understood that no study has been conducted to examine therelationship between Corporate Social Responsibility and EnvironmentalSustainability in the literature. For this purpose, a survey was conducted with128 employees from various companies in the Marmara Region. Surveys wereanalyzed using the Structural Equation Model and the results were interpreted.According to the results of the analysis, a positive and strong relationshiphas emerged between Corporate Social Responsibility and EnvironmentalSustainability. In this case, companies with a sense of corporate socialresponsibility and management style succeed in achieving EnvironmentalSustainability.Kurumsal SosyalSorumluluk, işletmelerin faaliyetleri sırasında çıktılarını, topluma zararvermeden sağlaması, tüm çevresel aktörleri işletme varlığının bir parçasıolarak görmesi ve toplumsal faydayı ön plana almasıdır. Yapılan literatür taramasındaKurumsal Sosyal Sorumluluk ve Çevresel Sürdürülebilirlik arasındaki ilişkiyiincelemek için çalışma gerçekleştirilmediği anlaşılmıştır.  Bu çalışmanın amacı, Sürdürülebilir çevreninsağlanmasında Kurumsal Sosyal Sorumluluk ilkelerinin önemi ve yeriniaraştırmaktır. Bu amaçla İstanbul, Kocaeli ve Bursa illerinde bulunan çeşitliişletmelerden 128 personel ile anket yapılmıştır. Anketler, Yapısal EşitlikModeli kullanılarak analiz edilmiş ve sonuçlar yorumlanmıştır. Analizsonuçlarına göre, Kurumsal Sosyal Sorumluluk ve Çevresel Sürdürülebilirlikarasında pozitif ve güçlü bir ilişki çıkmıştır. Bu durumda Kurumsal SosyalSorumluluk anlayışına ve yönetim biçimine sahip işletmeler ÇevreselSürdürülebilirliği sağlamada başarılı olmaktadır

Patterns and mechanisms of coseismic and postseismic slips of the 2011 M W 7.1 Van (Turkey) earthquake revealed by multi-platform synthetic aperture radar interferometry

On 23rd October 2011, a MW 7.1 reverse slip earthquake occurred in the Bardakçı-Saray thrust fault zone in the Van region, Eastern Turkey. Earlier geodetic studies have found different slip distributions in terms of both magnitude and pattern. In this paper, we present several COSMO-SkyMED (CSK), Envisat ASAR and RADARSAT-2 interferograms spanning different time intervals, showing that significant postseismic signals can be observed in the first three days after the mainshock. Using observations that combine coseismic and postseismic signals is shown to significantly underestimate coseismic slip. We hence employed the CSK pair with the minimum postseismic signals to generate one conventional interferogram and one along-track interferogram for further coseismic modelling. Our best-fit coseismic slip model suggests that: (1) this event is associated with a buried NNW dipping fault with a preferable dip angle of 49° and a maximum slip of 6.5 m at a depth of 12 km; and (2) two unequal asperities can be observed, consistent with previous seismic solutions. Significant oblique aseismic slip with predominant left-lateral slip components above the coseismic rupture zone within the first 3 days after the mainshock is also revealed by a postseismic CSK interferogram, indicating that the greatest principal stress axis might have rotated due to a significant stress drop during the coseismic rupture

Growth of zinc oxide nanostructures on carbon fibers: Production, characterization and photocatalytic properties Çinko oksit nanoyapıların karbon fiber üzerinde büyütülmesi: Üretimi, karakterizasyonu ve fotokatalitik özellikleri

xiv, 88 pages29 cm. 1 CDÖZETSon yıllarda, karbon fiber üzerine büyütülmüş çinko oksit nano yapıları, üstünyapısal, elektriksel ve optik özellikle dolayısı ile yoğun olarak çalışılmaktadır. ZnO, geniş band aralığı (3.37 eV) nedeniyle morötesi ışık altında aktive olabilen bir yarı iletken fotokatalizördür. Ancak, çinko oksidin karbon temelli malzemeler üzerinde büyütülmesi veya yapısına metallerin katkılanması ile yasak band aralığının daraltılması ve görünür bölge ışığı altında aktive edilmesi sağlanabilir. Bu bilgiler ışığında, yapılacak çalışmada, çinko oksit nanotellerin, karbon fiber üzerinde hidrotermal yöntem ile büyütülmesi amaçlanmıştır. Fotokatalizör olarak en etkin yapıyı elde etmek için, hidrotermal yöntem koşulları olan sıcaklık, konsantrasyon ve süre optimize edilmiştir. Hidrothermal sentez parametlerinin, ZnO nanotellerin boyutları üzerine etkileri yanıt yüzey yöntemi ve merkezi kompozit yöntemi ile incelenmiştir. Hidrotermal yöntem parametrelerinin, üretilen yapılar üzerindeki etkilerinin anlaşılabilmesi için, yapısal, morfolojik, optik ve fotokatalitik özellikleri detaylı bir şekilde analiz edildi.ABSTRACTOver the last decades, zinc oxide nanostructures on flexible carbon fibers have been extensively studied due to their superior structural, electrical and optical properties. ZnO is a wide band gap (3.37 eV) metal oxide semiconductor photocatalyst activating under ultraviolet light. However, as ZnO nanowires are grown on carbon based substrates, due to synergistic effect, narrower band gap structures could be obtained and catalysts become active under visible light irradiation. Owing to this motivation, ZnO nanowires were grown successfully on carbon fibers by hydrothermal method. To obtain most effective photocatalyst, the effects of hydrothermal synthesis parameters, temperature, concentration and growth time, on dimensions of zinc oxide nanowire structures on carbon fibers were evaluated via response surface methodology and central composite design. Morphological, structural, photocatalytic properties of fabricated structures were analyzed by scanning electron microscopy, X-ray diffraction, and UV-Visible spectrophotometer

Robotlarda hiyerarşik arttırımlı bağlam modellenmesi.

Context is very crucial for robots to be able to adapt themselves to circumstances and to fulfill their tasks accordingly. There have been many studies on modeling context on robots, however, these studies either do not construct an incremental and hierarchical structure (i.e., use a fixed number of contexts and context layers) or determine the necessity of adding a new context by using rule-based approaches. In this thesis, we propose two different methods to model context. In the first method, we extend the Restricted Boltzmann Machines, a generative associative model, by incrementing the number of contexts and context layers when needed. This model constructs the hierarchical and incremental contextual representations by considering the confidence of the objects and contexts after each new scene encountered. Moreover, this deep incremental model obtains better or on-par results when compared to the incremental or non-incremental models in the literature on different tasks. In the second method, in contrast to our first method and the methods in the literature, determining the necessity of adding a new context is formulated as a learning problem. In order to be able to do that, Latent Dirichlet Allocation (LDA) model is used to generate the data with known number of contexts. The intermediate LDA models with/without the correct number of contexts are then fed to a Recurrent Model, which is trained to predict whether to add a new context or not. Our analysis on artificial and real datasets demonstrate that such a learning-based approach generalizes well, and is a promising approach for solving such incremental problems.M.S. - Master of Scienc

Effectiveness of music therapy and emotional freedom technique on test anxiety in Turkish nursing students: A randomised controlled trial

Introduction: Test anxiety, one of the forms of situational anxiety, is a crucial biopsychological factor negatively affecting the wellbeing and academic performance of students throughout their education. The study aimed to determine the effects of music therapy and EFT (Emotional Freedom Technique) on situational anxiety and vital signs in nursing students before they took an OSCE (Objective Structured Clinical Exam). Methods: This study was conducted with 90 volunteer students. A computer-based random number generator was used to randomly assign the students into three groups (Music, EFT, and control), each group consisted of 30 students. Data was collected using a Student Identification Form, the Situational Anxiety Scale, and the Vital Signs Form. Results: Before the interventions, the mean anxiety scores of the students were similar. After the interventions, however, the mean anxiety scores of those in both experimental groups were significantly lower (p < .05). The difference between the mean vital signs of the groups was not statistically significant, except the pulse rate in the EFT and peripheral capillary oxygen saturation (SpO2) in the music group. Conclusions: According to the results of the study, both music therapy and EFT led to a decrease in the nursing students’ average scores before the OSCE, as measured by the Situational Anxiety Scale

A study on the corrosion behavior of 7072/3004/7072 clad aluminum alloy in different media

Cladding is an easy and economical method to design multifunctional aluminum structures with improved corrosion resistance, mechanical strength, and physical appearance. In this study, the electrochemical properties and corrosion behavior of an aluminum-clad product made of 7072/3004/7072 aluminum alloys were investigated by Tafel extrapolation and impedance spectroscopy. 3004 aluminum sheet was used as core, and it was cladded with 7072 aluminum sheets on both sides. The long-term corrosion behavior of clad samples was examined after being immersed in tap water, rainwater, and 3.5 wt.% NaCl solution for 4 weeks. The results show that because of the potential difference between 7072 and 3004 aluminum alloys, 7072 alloy behaves more anodic and corrodes preferentially (with a slower rate) compared to 3004 aluminum alloy. Cladding 3004 with embossed 7072 alloy improves the corrosion resistance of the alloy. Moreover, the electrolyte comparison demonstrates that the carbonate-forming ions (such as Ca) in tap water and HCO3-, NO3-, and SO(4)(2-)in rainwater decelerate the corrosion rate