• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1153-1160
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• 2021

This paper proposes a routing algorithm that determines the optimal path using deep reinforcement learning in software-defined networks. The deep reinforcement learning model for learning is based on DQN, the inputs are the current network state, source, and destination nodes, and the output returns a list of routes from source to destination. The routing task is defined as a discrete control problem, and the quality of service parameters for routing consider delay, bandwidth, and loss rate. The routing agent classifies the appropriate service class according to the user's quality of service profile, and converts the service class that can be provided for each link from the current network state collected from the SDN. Based on this converted information, it learns to select a route that satisfies the required service level from the source to the destination. The simulation results indicated that if the proposed algorithm proceeds with a certain episode, the correct path is selected and the learning is successfully performed.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.1-7
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• 2022

As the number of IoT devices increases, power management of the cluster head, which acts as a gateway between the cluster and sink nodes in the IoT network, becomes crucial. Particularly when the cluster head is a mobile wireless terminal, the power consumption of the IoT network must be minimized over its lifetime. In addition, the delay of information transmission in the IoT network is one of the primary metrics for rapid information collecting in the IoT network. In this paper, we propose a low-power buffer management algorithm that takes into account the information transmission delay in an IoT network. By forwarding or skipping received packets utilizing deep Q learning employed in deep reinforcement learning methods, the suggested method is able to reduce power consumption while decreasing transmission delay level. The proposed approach is demonstrated to reduce power consumption and to improve delay relative to the existing buffer management technique used as a comparison in slotted ALOHA protocol.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.231-240
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• 2023

It remains a challenge for robots to learn avoiding obstacles automatically in path planning using deep reinforcement learning (DRL). More and more researchers use DRL to train a robot in a simulated environment and verify the possibility of DRL to achieve automatic obstacle avoidance. Due to the influence factors of different environments robots and sensors, it is rare to realize automatic obstacle avoidance of robots in real scenarios. In order to learn automatic path planning by avoiding obstacles in the actual scene we designed a simple Testbed with the wall and the obstacle and had a camera on the robot. The robot's goal is to get from the start point to the end point without hitting the wall as soon as possible. For the robot to learn to avoid the wall and obstacle we propose to use the double deep Q networks (DDQN) to verify the possibility of DRL in automatic obstacle avoidance. In the experiment the robot used is Jetbot, and it can be applied to some robot task scenarios that require obstacle avoidance in automated path planning.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.87-97
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we proposed to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collected two new video datasets, and proposed a new deep learning architecture named Temporal and Spatial Segment Networks (TSSN) for rainfall depth recognition. Under TSSN, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. Also, the proposed TSSN architecture outperforms other architectures implemented in this paper.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.185-207
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• 2022

Recently, with the development of deep learning technology, researches to apply a deep learning algorithm to analyze unstructured data such as text and images are being actively conducted. Text classification has been studied for a long time in academia and industry, and various attempts are being performed to utilize data characteristics to improve classification performance. In particular, a hierarchical relationship of labels has been utilized for hierarchical classification. However, the top-down approach mainly used for hierarchical classification has a limitation that misclassification at a higher level blocks the opportunity for correct classification at a lower level. Therefore, in this study, we propose a methodology for classifying hierarchical data using the autoencoder-based deeply supervised network that high-level classification does not block the low-level classification while considering the hierarchical relationship of labels. The proposed methodology adds a main classifier that predicts a low-level label to the autoencoder's latent variable and an auxiliary classifier that predicts a high-level label to the hidden layer of the autoencoder. As a result of experiments on 22,512 academic papers to evaluate the performance of the proposed methodology, it was confirmed that the proposed model showed superior classification accuracy and F1-score compared to the traditional supervised autoencoder and DNN model.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.720-731
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• 2016

This study aims to understand characteristics and roles of community media in Seoul to discuss the meanings of the media. Community media is constructed based upon the networks of relations of community participants, whose characteristic relations of participants within the network determines the roles and meanings of the media for the community. Research have shown that community media's unique open, participatory, and horizontal communicative structures enable to realize such objects. For this study, we chose Mapo FM to conduct case study based upon network analysis of 'participant network' of the media. In particular, we tried to measure the density and centrality of the network, which can explain the relational characteristics of the networks of the media. In addition, we also conducted in-depth interviews of participants to better understand the problems and meanings of the community media. The findings suggest that unlike the conceptual and theoretical assumptions, the community media under the study lacks of open, participatory, and horizontal ways of participation in the media.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.227-233
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• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.99-106
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• 2023

Recently, many studies on crop yield prediction using deep learning technology have been conducted. These algorithms have difficulty constructing a linear map between input data sets and crop prediction results. Furthermore, implementation of these algorithms positively depends on the rate of acquired attributes. Deep reinforcement learning can overcome these limitations. This paper analyzes the performance of DQN, Double DQN and Dueling DQN to improve crop yield prediction. The DQN algorithm retains the overestimation problem. Whereas, Double DQN declines the over-estimations and leads to getting better results. The proposed models achieves these by reducing the falsehood and increasing the prediction exactness.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.2
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• pp.69-76
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• 2018

Referring expressions are natural language constructions used to identify particular objects within a scene. In this paper, we propose a new deep neural network model for referring expression comprehension. The proposed model finds out the region of the referred object in the given image by making use of the rich information about the referred object itself, the context object, and the relationship with the context object mentioned in the referring expression. In the proposed model, the object matching score and the relationship matching score are combined to compute the fitness score of each candidate region according to the structure of the referring expression sentence. Therefore, the proposed model consists of four different sub-networks: Language Representation Network(LRN), Object Matching Network (OMN), Relationship Matching Network(RMN), and Weighted Composition Network(WCN). We demonstrate that our model achieves state-of-the-art results for comprehension on three referring expression datasets.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.205-212
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• 2019

Scene graph is a kind of knowledge graph that represents both objects and their relationships found in a image. This paper proposes a 3D scene graph generation model for three-dimensional indoor environments. An 3D scene graph includes not only object types, their positions and attributes, but also three-dimensional spatial relationships between them, An 3D scene graph can be viewed as a prior knowledge base describing the given environment within that the agent will be deployed later. Therefore, 3D scene graphs can be used in many useful applications, such as visual question answering (VQA) and service robots. This proposed 3D scene graph generation model consists of four sub-networks: object detection network (ObjNet), attribute prediction network (AttNet), transfer network (TransNet), relationship prediction network (RelNet). Conducting several experiments with 3D simulated indoor environments provided by AI2-THOR, we confirmed that the proposed model shows high performance.