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IEEE 802.11BB reference channel models for light communications
No full textIncreasing industrial attention to visible light communications (VLC) technology led the IEEE 802.11 to establish the task group 802.11bb 'Light Communications' (LC) for the development of a VLC standard. As a part of the standard development process, the development of realistic channel models according to possible use cases is of critical importance for physical layer system design. This article presents the reference channel models for the mandatory usage models adopted by IEEE 802.11bb for the evaluation of system proposals. The use cases include industrial, medical, enterprise, and residential scenarios. Channel impulse responses and corresponding frequency responses are obtained for each use case using a ray tracing approach based on realistic specifications for transmitters and receivers, and optical characterization of the environment.TÜBİTA
Experimental demonstration of an FPGA-based outdoor VLC broadcasting system
No full textVisible light communication (VLC) uses light-emitting diodes (LEDs) to transmit data by turning them on and off at very high speeds, too fast for the human eye to perceive. In outdoor environments, LED-based streetlights can be utilized as VLC transmitters. Given the ubiquity of street lights, they are particularly useful for outdoor broadcasting as required in public safety systems. In this paper, we develop an FPGA-based VLC system with on-off keying using a LED-based streetlight. The system is built upon the Eclypse Z7 FPGA platform integrated with opto-electronic transmitter and receiver front-ends. We present error rate performance results up to transmission distances of 8 meters.European Union’s Horizon 2020 ; TÜBİTA
Models for government intervention during a pandemic
No full textWhile intervention policies such as social distancing rules, lockdowns, and curfews may save lives during a pandemic, they impose substantial direct and indirect costs on societies. In this paper, we provide a mathematical model to assist governmental policymakers in managing the lost lives during a pandemic through controlling intervention levels. Our model is non-convex in decision variables, and we develop two heuristics to obtain fast and high-quality solutions. Our results indicate that when anticipated economic consequences are higher, healthcare overcapacity will emerge. When the projected economic costs of the pandemic are large and the illness severity is low, however, a no-intervention strategy may be preferable. As the severity of the infection rises, the cost of intervention climbs accordingly. The death toll also increases with the severity of both the economic consequences of interventions and the infection rate of the disease. Our models suggest earlier mitigation strategies that typically start before the saturation of the healthcare system when disease severity is high
Müşteri şikayetlerini otomatize edebilmek için uygulanabilir yaklaşım
No full textCustomer complaint management is critical and time-consuming process for institutions. For an effective management and increased customer satisfaction, developing an instant and automated reply mechanism is essential. This phenomenon leads the motivation which helps data scientists to work for developing chatbots. For complex chatbots state of the art techniques of Natural Language Processing is essential to catch intend whereas its high costs. On the other hand, basic machine learning algorithms which is enhanced with NLP techniques can be more applicable for limited data resources. Also even with the help of Regular Expression techniques, quick and effective solutions can be achieved. This thesis covers the development process of a primitive chatbot which is developed by using the customer complaints which are in Turkish language.Müşteri şikayeti yönetimi firmalar için kritik ve zaman tüketen bir süreçtir. Etkin bir süreç yönetimi ve müşteri memnuniyetini arttırmak için anlık ve otomatik bir cevap yapısı kurulması kritiktir. Bu fenomen, veri bilimcilerin chatbot'ları geliştirmek için motivasyonlarını sağlar. Gelişmiş chatbot'lar için Doğal Dil İşleme alanındaki en modern teknikler niyeti belirlemede başarı için gerekli olduğu kadar aynı zamanda geliştirmesi de maliyetlidir. Diğer taraftan doğal dil işleme teknikleri üzerine kurulu temel makine öğrenmesi algoritmaları limitli veri kaynakları üzerinde daha uygulanabilir olabilir. Hatta Düzenli İfadeler (Regular Expressions) ile bile hızlı ve efektif çözümler üretilebilmektedir. Bu tez Türkçe dilindeki müşteri şikayetlerinin kullanılarak başlangıç seviyesinde bir chatbot oluşturulmasını kapsamaktadır
Esnek ürün tipleri ve sabit bölme boyutları ile çok bölmeli envanter rotalama problemi
No full textThis thesis introduces a \emph{Multi-Compartment Inventory Routing Problem} (MCIRP). The addressed problem aims to minimize the total traveling costs while ensuring customers are not out of stock with multiple products over the given planning time horizon. The distribution is made with a homogenous fleet of vehicles with flexible product types and fixed compartment sizes, where each compartment can accommodate all product types and has a fixed capacity. The supplier manages customer inventory levels by creating a distribution plan respecting the capacity restrictions of the compartments and customers. We examine the application of this problem with liquid products that can be partially delivered to customers with compartments that have debit meters. To address this complex problem, we propose a mathueristic method that combines Adaptive Large Neighborhood Search (ALNS) with mathematical models. The success of the solution method has been demonstrated by comparing it against a lower bound, that is flow formulation from the literature. Our results show that with the generated comprehensive and large-scale instances, our solution algorithm achieves only 18.13\% worse solutions than the conservative lower bound.Bu tez çalışmasında, Çok Bölmeli Envanter Rotalama Problemi, matematiksel modellerle entegre bir sezgisel çözüm yaklaşımı önerilerek çözümlenmektedir. Ele alınan problem, dağıtım ağının toplam seyahat maliyetlerini en aza indirmeyi ve aynı zamanda müşterilerin verilen planlama zaman ufku boyunca birden fazla ürünle stoksuz kalmamasını sağlamayı amaçlamaktadır. Dağıtım, çok kompartmanlı homojen bir araç filosu ile yapılır ve kompartmanlarda taşınan ürünler üzerinde kısıtlama yoktur, aynı zamanda kompartmanlar sabit bölme hacimlerine sahiptir. Tedarikçi, her bir ürünün envanter ve kapasite bilgilerine dayalı bir dağıtım planı oluşturarak müşterilerin envanter seviyelerini yönetir. Bu problemin uygulamasını, debimetreye sahip bölmeleri olan araçlarla müşterilere kısmen teslim edilebilen sıvı ürünlerle incelemekteyiz. Bu şekildeki uygulamalara üreticilerden farklı kalitedeki zeytinyağlarının toplanmasında, benzin istasyonlarına dağıtılan ürünlerde karşılaşmaktayız. Bu karmaşık problemi çözümleyebilmek için, Uyarlanabilir Büyük Komşuluk Arama ile matematiksel modelleri birleştiren bir yöntem öneriyoruz. Çözüm yönteminin başarısı, literatürde akış formülasyonu olan bir alt sınır ile karşılaştırılarak gösterilmiştir. Sonuçlar, oluşturulan kapsamlı ve büyük ölçekli örneklerle, çözüm algoritmamızın alt sınırdan yalnızca \%18,13 daha kötü çözümler elde ettiğini göstermektedir
A model for cognitively valid lifelong learning
No full textIn continual learning, usually a sequence of tasks are given to a learning agent and the performance of the agent after learning is measured in terms of resistance to catastrophic forgetting, efficacy of knowledge transfer and overall performance on the individual tasks. On the other hand, in multi-task learning, the system is designed to simultaneously acquire knowledge in multiple tasks, often through offline batch learning. A more cognitively valid scenario for lifelong robot learning would be to have a robotic agent to autonomously decide which task to engage and disengage while leveraging many-to-many knowledge transfer ability among tasks during online learning. In this study, we propose a novel lifelong robot learning architecture to fulfill the aforementioned desiderata, and show its validity in an environment where a robot learns the effects of its actions in different task settings. To realize the proposed model, we adopt learning progress measure for task selection, and have the tasks learn by independent neural networks with special structure that allows access to the neural layers of the non-selected tasks. The experiments conducted with a simulated robot arm in an object interaction scenario show that the proposed architecture yields better knowledge transfer and facilitates faster learning compared to baselines of fixed sequence task learning and isolated task learners with no knowledge transfer.New Energy and Industrial Technology Development Organization ; Japan Science and Technology Agency ; Core Research for Evolutional Science and Technolog
Automated testing of systems of systems
No full textThere are various kinds of software applications like mobile and Web applications. These applications have different types of user interfaces and user interaction methods. Hence, test automation tools are either dedicated or configured for a particular kind of application. Test scenarios can be implemented in the form of scripts and test execution can be automated separately for each type of application. However, there are systems of systems that embody multiple types of applications deployed on various platforms. Test scenarios might cross-cut these applications to be controlled collectively in the test script. In this paper, we propose an approach for testing cross-platform systems of systems. We present an application of it on a real system that involves a mobile and a Web application that are supposed to work in coordination. Our approach integrates a set of existing tools to facilitate test automation. It provides testers with a unified interface for developing test scripts that involve both mobile and Web applications. We conduct an industrial case study and show that our tool can reduce the testing effort significantly.Dutch Research Council NWO ; German Ministry of Economics ; Norwegian Population Registry ; Horizon 2020 Framework Programme ; Deutsche Forschungsgemeinschaft ; VINNOVA ; Ministero dell’Istruzione, dell’Università e della Ricerca ; Österreichische Forschungsförderungsgesellschaft ; Norges Forskningsrå
A comparative study for 6D pose estimation of textureless and symmetric objects used in automotive manufacturing industry
No full text6D pose estimation of industrial objects on RGB images has a high potential to accelerate the automation of robotic manipulations in the automotive manufacturing industry. Despite its high potential, this problem has not been adequately addressed in the computer vision community. Main factors leading to under investigation of this problem are industrial objects to be textureless, thin, and symmetrical, which hinder the automatic estimation of their poses from color images. Deep learning models have shown promising results for pose estimation of household objects thanks to availability of large datasets with labels. In contrast to many household objects, there are few datasets for industrial objects with limited representation capacity, which restricts the use of deep models in pose estimation of industrial objects. In this study, we examine the eligibility of deep models on 6D pose estimation of industrial objects used in the automotive manufacturing industry. For this aim, we compare the performance of three deep models, DeepIM, CosyPose, and EPOS. To meet the need for large training dataset of these models, we produce a large synthetic dataset from the CAD data of the industrial objects. We also collect a small real dataset for training and performance evaluation purposes. We find that CosyPose outperforms other methods with a large margin, by showing its potential to solve such a hard problem. We also observe that training models with both synthetic and real images yield the best results.TÜBİTA
Model-independent sliding mode control for grid-connected crossover switches cell inverter with reduced switching frequency
No full textIn this study, a finite control set-sliding mode controller (FCS-SMC) is applied to a grid-connected 9-level Crossover Switches Cell (CSC9) inverter. The proposed control technique is model-independent, as it selects the control actions based on the current state variables' errors rather than the past values. The control algorithm optimizes the control inputs to fulfill the reaching requirements of two designed cost functions that were derived based on the sliding mode (SM) theory. Moreover, a small permissible capacitor voltage error (hysteresis width h) is introduced to add an extra degree of freedom to the system, which allows the reduction of the average switching frequency without compromising the design requirements. The main advantages of the proposed method lie in its simplicity, absence of gain adjustment requirements, and straightforward design process. Simulation results are presented to demonstrate the effectiveness of the proposed SMC in achieving excellent dynamic performance and robustness against disturbances while controlling the capacitor voltage and grid current