Accurate Analysis of the Spatial Pattern of Reflected Light and Surface Orientations Based on Color Illumination

3D Recovery approaches require a variety of clues to obtain shape information. The shape from shading (SFS) method uses shading information in images to estimate depth maps. Although shading contains detailed information, it causes some well-known ambiguities such as convex-concave ambiguity. In this study, a system installation, using red, green, and blue illumination, and an algorithm, processing reflections on the surface, were proposed for the accurate analysis of surface orientations, and ambiguity problems. Surface orientations, erroneously predicted by six different methods, were improved by implementing the proposed system. Consequently, the correct orientation of the surface points was determined by removing the ambiguities in images taken without considering the location of illumination, and all the tested methods provided successful results using the proposed system

A real-time virtual sculpting application with a haptic device

In this paper, a 3D virtual sculpting application is developed for 3D virtual models with removing or adding materials by using Boolean operations. Virtual sculpting simulation reads 3D virtual models in a variety of file formats such as raw and stl consisting of a triangle poligon mesh and voxelizes its outer surface and interiror volüme to generate its volumetric dataset. We used octree and hashing techniques to reduce the memory requirement needed for volumetric dataset. The surface is locally reconstructed using Marching Cubes algorithm known as the most popular isosurface extraction algorithm after removing or adding material to the 3D virtual model. The user interacts with the model by using a haptic device to give the force-feedback like real-life sculpting.Publisher's Versio

A study on application container resource efficiency

Nowadays, the IT service environment develops in a dynamic, rapid, and unpredictable way. Microservices and application containers in this process have a significant impact on new generation IT service models. The fact that they have important capabilities such as modelability, presentability as service, and restructurability, are reasons for preferring them in many areas. Moreover, microservices can meet various needs of IT personnel. As it is known, all server system components, such as CPU, network, hard-drive I/O, affect energy consumption. At this point, microservices also play an important mediator role in resource management. Energy consumption of microservice-based applications is lower than that of the traditional approaches. However, there are still cases of recoverable and unnecessary consumption at some points. Microservices can be monitored and controlled using many methods. Thus, this provides us with opportunities to recover the wasted energy resources considerably. In this article, the effects of container-based microservice architectures on the energy consumption of the system and how to reduce these effects are presented. For this purpose, a methodology, which has 3 approaches (disconnect, pause, stop), and a tracing mechanism are proposed. The results show that this methodology has a considerable effect on energy efficiency

Tourist distribution in Northern Mediterranean Basin countries: 2004–2020

Purpose: The feasibility of measuring the touristic ecosystem in European countries with a Mediterranean coast based on various parameters, including diversity, turnover, and the number of tourists, was investigated in this study. The data from the period between 2004 and 2020 were analyzed. Methodology: A distribution analysis of annual tourist gains was conducted, and the distribution of incoming tourists across the countries was examined based on their area, using Atkinson, Theil, and Hoover inequality indices. Secondary data from the World Bank were utilized by the authors for the 13 countries studied. It was suggested by the authors that the Mediterranean region could be analyzed based on factors such as the length of the coast, the number and type of hotel beds, and the volume of coastal tourism. This study can be expressed as a mixed methodology supported by bibliometric analysis. Findings: An overall improvement in the distribution of tourists was indicated by the results of the analysis, with the exception of a decline in 2016 and 2020, as confirmed by all three indices. The most significant decline in 2020 was shown by the Hoover Index. Originality: This study is a significant contribution to the existing literature, as it is the first to analyze the distribution of tourists considering the Mediterranean Basin coast length and the number of tourists of the illustrated countries, using the Atkinson, Theil, and Hoover inequality indices. The study was deemed original and supported by bibliometric analysis. The results of this study have important managerial implications

Üç eklemli bir robot kolunun yapay sinir ağları ile eklem esaslı yörünge kontrolü

Bu tezin, veri tabanı üzerinden yayınlanma izni bulunmamaktadır.Robot kontrol, yapay sinir ağları, genelleştirilmiş öngörülü kontrol Bu çalışmada yapay sinir ağları (YSA) üç eklemli bir robotik manipülatöre eklem esaslı yörünge kontrolü için uygulanmıştır. Sonuçlar öngörülü kontrol ailesinden olan genelleştirilmiş öngörülü kontrol algoritması sonuçları ile karşılaştırmıştır. Giriş bölümünden sonra, ikinci bölümde robotlar tanıtılmıştır. 3. Bölümde adaptif kontrol algoritmaları ve bu konuda yapılan çalışmalar anlatılmıştır. Yapay sinir ağları eğitme yoluyla öğrenmektedir. Robot kolunu kontrol etmek amacıyla kullanılacak eğitme seti verilerini elde etmek için robot kolu modeli önce genelleştirilmiş öngörülü kontrol algoritması ile simüle edildiğinden 4. Bölümde genelleştirilmiş öngörülü kontrol algoritması, 5. Bölümde yapay sinir ağları ve genel özellikleri tanıtılmıştır. 6. bölümde yapay sinir ağları ile kontrol ve robotik uygulamaları verilmiştir. 7. bölümde robot kinematiği ve dinamiğini modellemede kullanılan matematiksel bağıntılar verilmiştir. Robot kolunun eklem esaslı bir yörünge kontrolü yapılmasından dolayı 8. Bölümde yörünge planlaması anlatılmıştır. 9. Bölümde üç eklemli robot kolunun genelleştirilmiş öngörülü kontrol algoritması ile simülasyonu gerçekleştirilmiş ve sonuçları literatürdeki sonuçlarla uyuştuğu gözlenmiştir. Yapılan simülasyon da robot kolunun çok iyi bir performansla kontrol edildiği gözlenmiştir. Elde edilen sonuç verileri model YSA' yi eğitme ve test setlerini oluşturmak üzere saklanmıştır. Elde edilen bu veriler, robot kolunu kontrol edecek model YSA nın eğitilmesinde ve test edilmesinde kullanılmıştır. Elde edilen sonuçlar ve yapılan çalışmalar ayrıntıları ile verilmiştir. Son aşamada genelleştirilmiş öngörülü kontrol algoritması devreden çıkarılarak model YSA kontrolör robot kolunu kontrol edilmiştir. Elde edilen sonuçlar öngörülü kontrol algoritması İle karşılaştırılmıştır. Sonuçlar öngörülü kontrolör performansına yakın çıkmıştır.A JOINT BASE TRAJECTORY CONTROL WITH ANN FOR A THREE JOINT ROBOT ARM Key words : Robotics control, artifical neural networks, the generalized predictive control In this work artificial neural networks (ANN) are applied to a three-joint robotic manipulator for a joint-based control algorithm. Following the introductory Chapter 1, some basics of robots are given in Chapter 2. In Chapter 3, adaptive control algorithms and works done on this subject are given. Artifical neural networks involve learning by way of being educated.Because the robotic arm model is first simulated by the generalized predictive control algorithm to obtain the educating data sets with the aim of control, in Chapter 4 the generalized predictive control algoritm, and in Chapter 5 ANN's are explained to some extent. In Chapter 6, control with ANN and some ANN applications are studied. In Cahpter 7, some mathematical relations used in modelling of robotic arm kinematics and dynamics are provided. Since the robotic arm is to be joint-based trajectory controlled, Chapter 8 is devoted to trajectorj planning. In Chapter 9 a three- joint robotic arm is simulated by the generalized predictive control algorithm and the results are observed to be well consistent with those in the literature. The obtained data are stored on the computer memory to use them in the education of control model ANN. The results of the education and test stages are provided in detail using graphics and tables. In the last phase of the work, the generalized predictive control algorithm is excluded and the robotic arm is controlled with the developed model ANN. The results are compared to those of the generalized predictive algoritm and good consistence is observed. X

Linguistic properties based on American Sign Language isolated word recognition with artificial neural networks using a sensory glove and motion tracker

Sign language (SL), which is a highly visual-spatial, linguistically complete, and natural language, is the main mode of communication among deaf people. Described in this paper are two different American Sign Language (ASL) word recognition systems developed using artificial neural networks (ANN) to translate the ASL words into English. Feature vectors of signing words taken at five time instants were used in the first system, while histograms of feature vectors of signing words were used in the second system. The systems use a sensory glove, Cyberglove (TM), and a Flock of Birds (R) 3-D motion tracker to extract the gesture features. The finger joint angle data obtained from strain gauges in the sensory glove define the hand shape, and the data from the tracker describe the trajectory of hand movement. In both systems, the data from these devices were processed by two neural networks: a velocity network and a word recognition network. The velocity network uses hand speed to determine the duration of words. Signs are defined by feature vectors such as hand shape, hand location, orientation, movement, bounding box, and distance. The second network was used as a classifier to convert ASL signs into words based on features or histograms of these features. We trained and tested our ANN models with 60 ASL words for a different number of samples. These methods were compared with each other. Our test results show that the accuracy of recognition of these two systems is 92% and 95%, respectively. (c) 2007 Published by Elsevier B.V

LECTURE NOTES IN COMPUTER SCIENCE

In this paper, we present off-line signature recognition and verification system which is based on image processing, moment invariant method and ANN. Two separate sequential neural networks are designed; one for signature recognition, and another for verification (i.e. for detecting forgery). Verification network parameters which are produced individually for every signature are controlled by a recognition network. The System overall performs is enough to signature recognition and verification

LECTURE NOTES IN COMPUTER SCIENCE

Sign language, which is a highly visual-spatial, linguistically complete and natural language, is the main mode of communication among deaf people. In this paper, an American Sign Language (ASL) word recognition system is being developed using artificial neural networks (ANN) to translate the ASL words into English. The system uses a sensory glove Cyberglove (TM) and a Hock of Birds (R) 3D motion tracker to extract the gesture features. The finger joint angle data obtained from strain gauges in the sensory glove define the hand-shape while the data from the tracker describe the trajectory of hand movement. The trajectory of hand is normalized for increase of the signer position flexibility. The data from these devices are processed by two neural networks, a velocity network and a word recognition network. The velocity network uses hand speed to determine the duration of words. To convey the meaning of a sign, signs are defined by feature vectors such as hand shape, hand location, orientation, movement, bounding box, and distance. The second network is used as a classifier to convert ASL signs into words based on features. We trained and tested our ANN model for 60 ASL words for different number of samples. Our test results show that the accuracy of recognition is 92 %

Üç eklemli bir robot kolunun yapay sinir ağları ile eklem esaslı yörünge kontrolü

Bu tezin, veri tabanı üzerinden yayınlanma izni bulunmamaktadır.Robot kontrol, yapay sinir ağları, genelleştirilmiş öngörülü kontrol Bu çalışmada yapay sinir ağları (YSA) üç eklemli bir robotik manipülatöre eklem esaslı yörünge kontrolü için uygulanmıştır. Sonuçlar öngörülü kontrol ailesinden olan genelleştirilmiş öngörülü kontrol algoritması sonuçları ile karşılaştırmıştır. Giriş bölümünden sonra, ikinci bölümde robotlar tanıtılmıştır. 3. Bölümde adaptif kontrol algoritmaları ve bu konuda yapılan çalışmalar anlatılmıştır. Yapay sinir ağları eğitme yoluyla öğrenmektedir. Robot kolunu kontrol etmek amacıyla kullanılacak eğitme seti verilerini elde etmek için robot kolu modeli önce genelleştirilmiş öngörülü kontrol algoritması ile simüle edildiğinden 4. Bölümde genelleştirilmiş öngörülü kontrol algoritması, 5. Bölümde yapay sinir ağları ve genel özellikleri tanıtılmıştır. 6. bölümde yapay sinir ağları ile kontrol ve robotik uygulamaları verilmiştir. 7. bölümde robot kinematiği ve dinamiğini modellemede kullanılan matematiksel bağıntılar verilmiştir. Robot kolunun eklem esaslı bir yörünge kontrolü yapılmasından dolayı 8. Bölümde yörünge planlaması anlatılmıştır. 9. Bölümde üç eklemli robot kolunun genelleştirilmiş öngörülü kontrol algoritması ile simülasyonu gerçekleştirilmiş ve sonuçları literatürdeki sonuçlarla uyuştuğu gözlenmiştir. Yapılan simülasyon da robot kolunun çok iyi bir performansla kontrol edildiği gözlenmiştir. Elde edilen sonuç verileri model YSA' yi eğitme ve test setlerini oluşturmak üzere saklanmıştır. Elde edilen bu veriler, robot kolunu kontrol edecek model YSA nın eğitilmesinde ve test edilmesinde kullanılmıştır. Elde edilen sonuçlar ve yapılan çalışmalar ayrıntıları ile verilmiştir. Son aşamada genelleştirilmiş öngörülü kontrol algoritması devreden çıkarılarak model YSA kontrolör robot kolunu kontrol edilmiştir. Elde edilen sonuçlar öngörülü kontrol algoritması İle karşılaştırılmıştır. Sonuçlar öngörülü kontrolör performansına yakın çıkmıştır.A JOINT BASE TRAJECTORY CONTROL WITH ANN FOR A THREE JOINT ROBOT ARM Key words : Robotics control, artifical neural networks, the generalized predictive control In this work artificial neural networks (ANN) are applied to a three-joint robotic manipulator for a joint-based control algorithm. Following the introductory Chapter 1, some basics of robots are given in Chapter 2. In Chapter 3, adaptive control algorithms and works done on this subject are given. Artifical neural networks involve learning by way of being educated.Because the robotic arm model is first simulated by the generalized predictive control algorithm to obtain the educating data sets with the aim of control, in Chapter 4 the generalized predictive control algoritm, and in Chapter 5 ANN's are explained to some extent. In Chapter 6, control with ANN and some ANN applications are studied. In Cahpter 7, some mathematical relations used in modelling of robotic arm kinematics and dynamics are provided. Since the robotic arm is to be joint-based trajectory controlled, Chapter 8 is devoted to trajectorj planning. In Chapter 9 a three- joint robotic arm is simulated by the generalized predictive control algorithm and the results are observed to be well consistent with those in the literature. The obtained data are stored on the computer memory to use them in the education of control model ANN. The results of the education and test stages are provided in detail using graphics and tables. In the last phase of the work, the generalized predictive control algorithm is excluded and the robotic arm is controlled with the developed model ANN. The results are compared to those of the generalized predictive algoritm and good consistence is observed. X