Investigasi pengaruh Step Training pada Skema Same-Padding untuk Metode Faster R-CNN dalam Teknologi Augmented Reality

Augmented Reality (AR) is a technology with the concept of combining real-world dimensions with virtual world dimensions that are displayed in realtime. In the AR environment, interaction techniques used can vary. Marker-based AR is one type of AR that allows virtual objects to be displayed in the real world by using markers as pointers. In the use of marker-based AR required object detection method used for tracking markers. In this study, a system that can detect objects in the form of fingertips will be designed. In designing the system the Faster Region-based Convolutional Neural Network (Faster R-CNN) method is used. R-CNN Faster is an object detection method which is a combination of the Fast R-CNN method and the Region Proposal Network (RPN). The results of the detection parameters will be used for tracking, namely the coordinates x, y, width, and length. This research uses the Faster R-CNN method because it has a faster computing speed compared to the previous method, namely Particle Filter. The Faster R-CNN method uses ResNet architecture as the core of CNN. The system configuration to be tested is the 25K, 50K and 75K step training with the same-padding scheme. The testing process is taken from a video consisting of 10800 training data and 3600 test data. The best system configuration based on parameter priority for AR technology is obtained in the 50K step training.Keyword: augmented reality, convolutional neural network, faster region-based convolutional neural network, region proposal network, ResNet.Abstrak Augmented Reality (AR) adalah teknologi dengan konsep menggabungkan dimensi dunia nyata dengan dimensi dunia virtual yang ditampilkan secara real-time. Dalam lingkungan AR, teknik interaksi yang digunakan dapat bermacam – macam. Marker-based AR merupakan salah satu jenis AR yang memungkinkan objek virtual ditampilkan ke dalam dunia nyata dengan digunakannya marker sebagai pointer-nya. Dalam penggunaan AR berbasis marker diperlukan metode deteksi objek yang digunakan untuk tracking marker. Dalam penelitian ini akan dirancang sebuah sistem yang dapat mendeteksi objek berupa ujung jari. Dalam perancangan sistem tersebut digunakan metode Faster Region-Based Convolutional Nueral Network (Faster R-CNN). Faster R-CNN merupakan salah satu metode deteksi objek yang merupakan gabungan dari metode Fast R-CNN dan Region Proposal Network (RPN). Hasil dari parameter deteksi akan digunakan untuk tracking, yaitu koordinat x, y, width, dan length. Penelitian ini menggunakan metode Faster R-CNN karena memiliki kecepatan komputasi yang lebih cepat dibandingkan dengan metode sebelumnya yaitu Particle Filter. Metode Faster R-CNN mengunakan arsitektur ResNet sebagai inti dari CNN. Konfigurasi sistem yang akan diuji adalah step training 25K, 50K dan 75K dengan skema same-padding. Proses pengujian diambil dari video yang terdiri dari 10800 data latih dan 3600 data uji. Konfigurasi sistem terbaik berdasarkan prioritas parameter untuk teknologi AR didapatkan pada step training 50K.Keyword: augmented reality, convolutional neural network, faster region-based convolutional neural network, region proposal network, ResNet.

A controller deployment scheme in 5G-IoT network based on SDN

Due to the wide spread of 5G networks, network users have higher requirements for communication delays. Software-defined network is a new paradigm of decoupling the control logic from packet forwarding devices. We can reduce communication latency in the network by optimizing the location of the controller and improve the communication performance of the network. In this paper, the controller deployment problem of multi-network area is studied in order to reduce the average communication latency. In order to solve this problem, we proposed an optimized DPC algorithm. Specifically, on the basis of DPC algorithm we quoted the idea of triangular stability from the BeeDPC algorithm, and introduced SC measurement indicators, the degree of separation between sub-network regions and the degree of aggregation within the sub-network region, so as to divide the network region more reasonably. At the same time, we have break out of the constraints in the DPC algorithm and introduced closeness centrality to get a more reasonable placement scheme and reduced the limitations of the DPC algorithm. Simulation results have shown that the optimized DPC algorithm can effectively reduce the average delay between the control layer and the data layer, improve the network performance, and enhance network stability and reliability.

New directions of cooperation interlinks development of the network structures within the macroregion

The relevance of a research of network structures communications is obvious for industrial development providing. In the article the author defines the definitions answering to relevant realities of new industrialization and also carries out the analysis of current state of cooperation communications in the territory of Ural economic region, formulating conclusions about their practical importance. New activity of the regional governments is supposed to be the association of efforts to promote the products to foreign markets. The concept of the network region is for the first time formulated.