Search

Third-party logistics (3PL) services have seen significant growth in recent years as a result of playing a key role in supply chain management. The demand for 3PL service providers has increased as companies offer better service to their customers, lower costs and gain competitive advantage. This paper includes an application that will help determine the most important criteria in the selection and evaluation of 3PL service providers. The aim of the paper is to be able to determine the selection criteria of the 3PL service providers and the relationships between them, from the point of view of companies already using logistics services outsourcing. For this purpose, grey system theory and DEMATEL approach are integrated in order to describe uncertain and complex decisions with definite numerical values and determine the relations and importance levels between the criteria. The findings revealed interrelations between criteria and presented the most important criteria for 3PL provider selection. It is believed that the results of the paper will help the managers to propose a model that can be implemented with the selection criteria of the 3PL service provider., Üçüncü parti lojistik (3PL) hizmetlerinin, tedarik zinciri yönetiminde temel bir rol oynamasının sonucu olarak son yıllarda kayda değer bir büyüme yaşadığı görülmektedir. 3PL hizmet sağlayıcılarına yönelik talep, şirketlerin müşterine daha iyi hizmetleri sunmaları, maliyetleri düşürmeleri ve rekabet üstünlüğü elde etmeleri gibi avantajlar sağladığı için artış göstermektedir. Bu makalede, 3PL hizmet sağlayıcısı seçimi ve değerlendirilmesi sürecindeki en önemli kriterlerin belirlenmesine yardımcı olacak bir uygulamaya yer verilmiştir. Makalenin amacı, lojistik hizmetlerini zaten dış kaynak kullanan firmaların bakış açısıyla 3PL hizmet sağlayıcılarının seçim kriterlerini ve aralarındaki ilişkileri belirleyebilmektir. Bu amaç doğrultusunda belirsiz ve karmaşık kararları kesin sayısal değerler ile betimleyebilmek ve kriterler arasındaki ilişkileri ve önem düzeylerini tespit edebilmek için gri sistem teorisi ile DEMATEL yaklaşımı bütünleşik olarak ele alınmıştır. Bulgular, kriterler arasındaki karşılıklı ilişkileri ortaya koyarak 3PL hizmet sağlayıcısı seçimindeki en önemli kriterleri ortaya koymuştur. Makale sonuçlarının yöneticilere, 3PL hizmet sağlayıcısı seçim kriterlerinin belirlenmesinde uygulanabilecek bir model önerisiyle yardımcı olacağı düşünülmektedir.

Günümüzde teknoloji hızlı gelişmekte ve müşterilerin ürünlerden beklentileri artmaktadır. Bu nedenle müşteriisteklerinin ön plana çıkması ve sürekli değişmesi ürün tasarımını geçmişe göre daha önemli kılmaktadır. Yaygınolarak kullanılan çamaşır makinesini üreten firmalar pazar payına sahip olabilmeleri için müşteri isteklerini gözönünde bulundurmak zorundadır. Müşteri isteklerinden birisi de ergonomik kullanım kolaylığıdır. Bu çalışmadaergonomik bir çamaşır makinesi tasarımında müşteri isteklerine ve firma kapasitesine göre teknik özellikler üzerindeiyileştirme yapılması amaçlanmıştır. Bu çalışmada kalite fonksiyon yayılımı yöntemi ve bulanık çok amaçlı doğrusalprogramlama yaklaşımı ergonomik çamaşır makinesi tasarımı için önerilmiştir. Kalite fonksiyon yayılımı müşteriistekleri ve teknik özellikler arasındaki ilişkiyi belirleyebilmektedir. Bu sonuçlara göre ergonomik bir çamaşırmakinesi tasarımı için hangi teknik özelliğin daha önemli olduğu belirlenmiştir. Ancak bu sonuç sadece müşteriisteklerine göre belirlenmiştir. Firma kapasitesi ihmal edildiği için firmanın zaman ve maliyet açısından problemlerlekarşılaşması ve pazar rekabetinde geri kalması söz konusudur. Firma ergonomik bir çamaşır makinesi tasarlarkenmüşteri isteklerinin maksimize edilmesi, maliyetin düşürülmesi, zamanın minimize edilmesi gibi farklı amaçlarasahiptir. Bu çalışma bu problemi ortadan kaldırmak için müşteri istekleri ve firma kapasitesini birlikte inceleyenZimmermann ve hibrid yaklaşım olmak üzere iki bulanık çok amaçlı doğrusal programlama yaklaşımını önermiş veelde edilen sonuçları karşılaştırmıştır., Nowadays technology develops rapidly and the customer requirements (CRs) on product increase. Therefore, theCRs change continuously and are more important than past so that product design is more important than past. Firms,which manufacture the washing machines used commonly, must consider the CRs to have the market share. One ofthe CRs is ergonomic ease of use. This paper aims to improve on technical characteristics in an ergonomic washingmachine design according to the CRs and the capacity of the firm. This paper proposes an integrated approach, whichconsists of quality function deployment (QFD) method and fuzzy multi-objective linear programming (FMOLP)method, for an ergonomic washing machine design. QFD method defines the relation between the CRs and technicalcharacteristics. This result presents the importance degrees of technical characteristics for an ergonomic washingmachine design but this result is defined according to only the CRs. Firm can meet with some problems in terms oftime and cost due to neglecting of the capacity of the firm thus the firm can fall behind in market competitive. The firm considers different objectives such as maximizing the CRs, minimizing the cost and minimizing the time for anergonomic washing machine design. This paper proposes two FMOLP methods, which consist of Zimmermann andhybrid approaches, to overcome this drawback so that the obtained results are compared.

Bitcoin, merkezi otoriteye ihtiyaç duymayan, dağıtık ve dijital bir para birimi olarak, son yıllarda finans dünyasındaki popüler konuların başında gelmektedir. İlk dönemde Bitcoin’in finansal özelliklerine duyulan ilgi, zaman içerisinde Bitcoin’e hayat veren blok zinciri teknolojisine doğru kaymaya başlamıştır. Hemen her gün yeni bir uygulama alanını duymaya başladığımız blok zinciri teknolojisi alanındaki akademik araştırmalar sınırlı sayıdadır. Ülkemizde de gerek uygulama gerekse akademik anlamda yeterli sayıda çalışma olmadığını söyleyebiliriz. Bu inceleme, blok zinciri teknolojisini sistematik olarak gözden geçirerek, kullanım alanlarını, açık noktalarını ve gelecek beklentilerini tartışmaktadır., In recent years, as being a distributed and digital currency, and not being in need of a centralized authority, Bitcoin has become the foremost among the most popular subjects of the financial world. The initial interest in Bitcoin’s financial features change course into blockchain technology, which essentially vitalizes the Bitcoin. While we have started to hear about the new implementations of blockchain technology almost every day, worldwide academic research is still limited. Unfortunately, implementation efforts and academic studies on blockchain in our country is also scarce. This paper systematically investigates the blockchain technology to discuss its usage areas, open issues, and future expectations.

Date of Conference: 2-5 May 2018 In this paper, we study online nonlinear data regression and propose a highly efficient long short term memory (LSTM) network based architecture. Here, we also introduce on-line training algorithms to learn the parameters of the introduced architecture. We first propose an LSTM based architecture for data regression. To diminish the complexity of this architecture, we use an energy efficient operator (ef-operator) instead of the multiplication operation. We then factorize the matrices of the LSTM network to reduce the total number of parameters to be learned. In order to train the parameters of this structure, we introduce online learning methods based on the exponentiated gradient (EG) and stochastic gradient descent (SGD) algorithms. Experimental results demonstrate considerable performance and efficiency improvements provided by the introduced architecture.

Date of Conference: 2-5 May 2018 In this paper, we investigate online parameter learning for Long Short Term Memory (LSTM) architectures in distributed networks. Here, we first introduce an LSTM based structure for regression. Then, we provide the equations of this structure in a state space form for each node in our network. Using this form, we then learn the parameters via our Distributed Particle Filtering based (DPF) training method. Our training method asymptotically converges to the optimal parameter set provided that we satisfy certain trivial requirements. While achieving this performance, our training method only causes a computational load that is similar to the efficient first order gradient based training methods. Through real life experiments, we show substantial performance gains compared to the conventional methods.

In this study, a method is proposed to determine the parameters of Proportional Integral Derivative Acceleration (PIDA) controller, which are used in the control of higher degree systems, via Particle Swarm Optimization (PSO) algorithm. Classic controller methods may sometimes be insufficient, especially in the control of higher degree systems. In PIDA controller design, heuristic optimization algorithms, which is one of the recommended methods for these systems, provides successful results. The performance of the PIDA controller obtained by the proposed method in the paper is compared with the gradient search and genetic algorithms in the literature. The unit step response and the frequency response of the controller show the effectiveness of the method.

Pedestrian Detection (PD) is one the most studied application areas of object detection. Although an important effort is already given to develop advanced classifiers for self-driving cars (SDC) especially in recent years, studies about onboard railway systems remain very limited. Although the similarity of railway and highway scenarios suggests that systems developed for SDC-PD can be used for rail vehicles, it is also necessary to examine the low level of generalization ability and the high degree of dependence of the PD test performance on the training set. In this paper, the feasibility of PD systems, whose effectiveness on SDC is proved, was examined on railway systems. For this purpose, SDC-PD systems, created with different training benchmarks such as Caltech and INRIA, have been applied to railway system scenes with various difficulties and the results have been examined. The findings show that the direct implementation of classifiers for SDC does not provide sufficient success to come up with specific challenges to railway systems.

In traditional networking architecture, for each problem a specific protocol is dedicated to network devices and these devices are configured manually. Since they are hardware-based, the implementation of new ideas and technologies to these devices is very difficult. However, today's increasing use of smartphones and other mobile data devices has increased the data traffic, and with the addition of service diversity and the needs of users, the existing network structure has become much more complex. Software Defined Networks (SDN) is a new network paradigm that allows a programmable central controller mechanism which separates the control plane from the data plane. With the advantage of a centralized control mechanism, SDN ensures that network structures can respond more quickly to current demands, be more flexible and reprogrammable. In addition, all these processes are implemented at a lower cost compared to traditional networking architecture. To evaluate the performance of SDN architectures, testbeds are used by researchers. In this paper, a small-scale and cost-effective SDN testbed is designed and implemented. The testing environment is realized using Raspberry Pi 3, Floodlight and Open vSwitch. Controller performance has been observed with the MACtracker application.

Nowadays, it becomes very important to know the level ofinstitutionalization and as a result what improvements they can make fororganizations. Though there are many conceptual studies of institutionalizationin the literature, there is no study based on numerical methods that canprovide a foresight about institutionalization. In this paper, a new model hasbeen proposed by determining concepts that are effective on institutionalizationfrom literature and expert opinions. Firstly, the relationships between theconcepts are taken from the experts linguistically. Linguistic expressions areconverted to numerical values using the center of gravity method (COG) used in fuzzylogic applications. Then, three different scenarios were investigated by usingthe Fuzzy Cognitive Maps (FCMs) algorithm and the future states of the conceptswere determined and interpreted. In the first scenario, an organization withpoorly managed organizational concepts was considered. The institutionalizationtendency in this organization has reached to 0,027 value which is theestimation calculated by FCM algorithm in the future. The second scenario andthe third scenario represents a midlevel and good organization respectively.Institutionalization tendency values were 0.97 for the second and thirdscenarios. However, when the number of iterations representing the time periodis examined, it is seen that the organization thought in the third scenario hasreached this value before 9 iterations. This is because the organization in thethird scenario is well managed in the current situation. With the developedmodel, the most effective concepts on institutionalization were alsoidentified. It has been determined that the most important concepts affectinginstitutionalization are process management, information management andstrategic management. Compared to the literature, the results seem to beconsistent., Kurumsallaşma, organizasyondakiyöneticilere ve çalışanlara bağlı olmadan, tüm süreçlerin şeffaf ve sistematikolarak yürütülmesini ifade eder.Kurumsallaşmanın mükemmeliyeti organizasyonun misyon, vizyon ve stratejikhedeflerine paralel yönde seyreden ticari faaliyetlerle sağlanabilir.Kurumsallaşmanın organizasyon içerisinde benimsenememesi organizasyonların uzunsüreli yaşam döngüsünü engellemektedir. Bu nedenle, organizasyonlarınkurumsallaşma seviyelerini takip edebileceği ve bu takip sonucunda hangiiyileştirmeleri yapabilecekleri konusu çok önemli hale gelmektedir. Literatürdekurumsallaşmanın kavramsal olarak incelendiği birçok çalışma olmasına rağmenileriye yönelik bir öngörü elde edilebilen sayısal yöntemlere dayalı birçalışmaya rastlanmamıştır. Bu çalışmada, kurumsallaşma üzerinde etkili olankonseptler literatür ve uzman görüşleriyle belirlenerek yeni bir modelönerilmiştir. Öncelikle uzmanlardan konseptler arasındaki ilişkiler dilselolarak alınmıştır. Dilsel ifadeler, bulanık mantık uygulamalarında kullanılanağırlık merkezi yöntemiyle sayısal değerlere dönüştürülmüştür. Daha sonra,Bulanık Bilişsel Haritalar(BBH) algoritması kullanılarak 3 farklı senaryoincelenmiş ve konseptlerin gelecekteki durumları tespit edilip, yorumlanmıştır.Geliştirilen model ile aynı zamanda kurumsallaşma üzerindeki en etkilikonseptler ve geleceğe yönelik öngörüler de belirlenmiştir.

Mobile devices begin to spread increasingly recently to offer a lot of services which personal computers offer. This condition has led to increase in the number of security threats in mobile devices and services. In this paper, it has been made a research on mobile malware and malware detection techniques. Within the scope of the study, a a permission based detection system based on the machine learning methods for Android malware was developed. Developed system is analyzed by using Random Forest, Support Vector Machine and Artificial Neural Networks algorithms.

Buçalışmada farklı alanlarda yapılan bilgisayarda benzetim modeli çalışmalarıincelenmiştir. Yapılan çalışmaların ortak özelliği, gerçek ortamda gözlenmesive incelenmesi zor olan karmaşık sistemlerden oluşmaktadır ve bu sistemlerianaliz edebilmek için benzetim modeline ihtiyaç duyulmaktadır. Modellenensistemin hedefi gerçek sistemi yansıtmasıdır. Bunu bir çok kriter belirlediğigibi en önemli kriterlerden biri, geniş parametre uzayına sahip bu sistemlerinistenen davranışı yansıtabilecek doğru parametrelerin kullanımını sağlamaktır.Bunun için, parametre ayarlama işlemlerini otomatik ve sistematik bir şekildegerçekleştirebilecek bir takım yöntemlere ihtiyaç vardır. Bu çalışmamızda modellenen sistemlerlebirlikte ortaya çıkan parametre ayarlama problemini çözmeye yönelik yapılançalışmaların çözüm önerileri ve kullanmış oldukları yöntemler kısacaaçıklanarak sunulmuştur., In this paper, the simulation model studies on computer madein different fields are examined. The common feature of the work carried out isthat the work is related to complex systems which are difficult to observe andanalyze in the real environment thus, simulation models are needed to analyzethese systems. The goal of the modeled system is to reflect the real system. Itsets many criteria. One of the most significant is to use the correctparameters which can reflect the desired behavior of these systems with largeparameter space. In this study proposed solution for the modeling systems'problems associated with the parametere tuning and methods used by modellingsystems to solve these problems are briefly explained.

25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEY WOS: 000413813100213 Superpixel based methods have recently shown success in scene segmentation and labeling. In scene labeling, a superpixel algorithm is used first to segment the image into visually consistent small regions; then several feature descriptors are computed and classification is performed for each superpixel. In this paper, Kernel Codebook Encoding (KCB) of superpixel features is proposed. In KCB feature vectors are mapped to multiple codewords in a soft manner, instead of the usual hard quantization. The weights assigned to the codewords are determined by a kernel distance function. KCB method is used for encoding of SIFT features in SuperParsing image parsing algorithm. The developed approach is tested on the SIFT Flow dataset consisting of 2,688 images and 33 classes, and achieves 2.7% increase in parsing accuracy over SuperParsing. Turk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Univ

Thanks to the new technologies of remote sensors, remote sensor data which increases in size exponentially and transforms to big data with increasing spatial and spectral resolutions causes great difficulties in storage, transportation and processing. It has become a necessity to implement parallel approaches instead of traditional methods, which are inadequate in critical applications when real/near real-time analysis of relevant large data is needed. In this paper, high performance computing approaches such as Multi-core, FPGA, GPU, Cluster and Cloud Computing are investigated for real / near real-time large data analysis in remote sensing applications.

Artificial Bee Colony algorithm, inspired by the foraging behavior of real honey bees, is one of the most important swarm intelligence based optimization algorithms. Like other population based evolutionary computation techniques, Artificial Bee Colony algorithm is suitable for parallelization on distributed architectures. In this paper, we presented a new emigrant creation strategy that is being distributed between subcolonies running simultaneously on the independent compute nodes. The running times and objective function values obtained by the parallelized Artificial Bee Colony algorithm with the proposed model on different number of compute nodes are compared with the sequential counterpart of the algorithm and it is seen that convergence performance of the parallelized Artificial Bee Colony algorithm is significantly improved with the proposed emigrant creation strategy.

The effect of the unmodeled dynamics and unknown disturbances prevent the accurate control of the real-time systems. The designed controllers must undertake the effect of these undesired uncertainties. In this paper, adaptive uncertainty modeling based model predictive controller is proposed for the control of uncertain linear systems. The uncertainty modeling structure uses an artificial neural network with adaptive learning rate for fast approximation. The stability of the proposed adaptive uncertainty modeling based model predictive control (UMPC) is shown using Lyapunov candidate function. Conventional MPC and proposed UMPC are applied to the control of a real-time DC/DC buck power converter. The conventional MPC cannot accurately control the DC/DC converter due to the unknown parameters and unmodeled dynamics. However, the proposed UMPC controller can accurately control the system with modeling the uncertainties in controller dynamics. The proposed controller is promising to control uncertain systems in future applications.

24th Signal Processing and Communication Application Conference (SIU) (2016 : Zonguldak, Turkey ), In this paper, we presented a novel shape recognition framework based on medial axis graph. After extracting medial axis graph of a shape, we constructed a multi-dimensional distribution by calculating distances of each branch node to all other branch nodes in the graph. Then similarity rates between these distributions is found by using a transportation-based distance function. Finally, we compared our results with results of similar works conducted in the past., Bu bildiride, orta eksen çizgeleri (medial axis graph) kullanılarak şekil tanıma alanında yeni bir yaklaşım sunulmaktadır. İskeletleri çıkartılan şekillerin çizgelerinde, tüm kritik noktaların diger kritik noktalara olan en kısa yol uzaklığı hesaplanarak her bir şekil için çok boyutlu bir dagılım oluşturulmuştur. Önerilen şekil tanıma platformunda bu dagılımlar üzerinde taşıma tabanlı bir uzaklık fonksiyonu kullanılarak şekiller arasındaki benzerlik oranı bulunmuştur. Son olarak alınan sonuçlar daha önceden yapılmış benzer çalışmaların sonuçlarıyla karşılaştırılarak önerilen yaklaşımın başarımı gösterilmiştir.

24th Signal Processing and Communication Application Conference (SIU) (2016 : Zonguldak, TURKEY), Critical Infrastructure Security had always been an issue. With the technological advancements, there has been a continuous inflow of novel methods for critical infrastructure security. In this paper, design, implementation, and field tests of a novel critical infrastructure security system based on wireless sensor networks is presented.

Numerous agricultural applications require very accurate guidance of farm vehicles. Current works have established that RTK GPS was a very suitable sensor in order to meet the expected precision: several control laws have been designed for vehicles equipped with such a sensor, and satisfactory results have been achieved as long as vehicles do not slide. Nevertheless, in actual working conditions (sloping fields, entering into curves on a wet land, etc.), sliding inevitably occurs. In this paper, we design a nonlinear adaptive control law in order to preserve guidance precision in presence of sliding: realtime sliding estimation is used to correct vehicle evolution. Field experiments, demonstrating the capabilities of that control scheme are reported and discussed.