• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.441-452
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• 2021

In this paper, we propose a method for generating luma signal mapping function to improve the coding efficiency of luma signal mapping methods in LMCS. In this paper, we propose a method to reflect the cognitive and perceptual features by multiplying the attention map of convolutional neural networks on local spatial variance used to reflect local features in the existing LMCS. To evaluate the performance of the proposed method, BD-rate is compared with VTM-12.0 using classes A1, A2, B, C and D of MPEG standard test sequences under AI (All Intra) conditions. As a result of experiments, the proposed method in this paper shows improvement in performance the average of -0.07% for luma components in terms of BD-rate performance compared to VTM-12.0 and encoding/decoding time is almost the same.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.109-123
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• 2024

Research into vision-based end-to-end autonomous driving systems utilizing deep learning and reinforcement learning has been steadily increasing. These systems typically encode continuous and high-dimensional vehicle states, such as location, velocity, orientation, and sensor data, into latent features, which are then decoded into a vehicular control policy. The complexity of urban driving environments necessitates the use of state representation learning through networks like Variational Autoencoders (VAEs) or Convolutional Neural Networks (CNNs). This paper analyzes the impact of different image state encoding methods on reinforcement learning performance in autonomous driving. Experiments were conducted in the CARLA simulator using RGB images and semantically segmented images captured by the vehicle's front camera. These images were encoded using VAE and Vision Transformer (ViT) networks. The study examines how these networks influence the agents' learning outcomes and experimentally demonstrates the role of each state representation technique in enhancing the learning efficiency and decision- making capabilities of autonomous driving systems.

• ETRI Journal
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• v.23 no.4
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• pp.177-189
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• 2001

Multicarrier DS-CDMA is an effective approach to combat fading and various kinds of interference. In this paper, we present an overlapped multicarrier DS-CDMA system, wherein each of the rate 1/M convolutionally-encoded symbols is also repetition coded and transmitted using overlapped multicarriers. However, since the frequency spectrums of successive carriers are allowed to overlap, the transmission bandwidth is more efficiently utilized. The effect of the overlapping percentage between successive carriers of a multicarrier DS-CDMA system on the performance is investigated to determine the overlapping percentage showing the best performance. We suggest two methods for sub-band overlapping variation. One is to allow variation of sub-band overlapping percentage when the total number of subcarriers is fixed. The other is to increase the number of sub-bands (the number of repetitions R) with fixed sub-band bandwidth. Given a total number of subcarriers MR, we show that the BER variation is highly dependent on the roll-off factor ${\beta}$ of a raised-cosine chip wave-shaping filter irrespective of convolutional encoding rate 1/M and repetition coding rate 1/R. We also analyze the possibility of reduction in total multi-user interference by considering the variation of both the roll-off factor ($0<{\beta}{\leq}1$) and the sub-band overlapping factor ($0<{\lambda}{\leq}2$), and show that the proposed system may outperform the multicarrier DS-CDMA system in [3].

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.309-313
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• 2009

In this paper, the network performance of a turbo coded optical code division multiple access (CDMA) system with cross-layer, which is between physical and network layers, concept is analyzed and simulated. We consider physical and MAC layers in a cross-layer concept. An intensity-modulated/direct-detection (IM/DD) optical system employing pulse position modulation (PPM) for satellite broadcasting communications is considered. In order to increase the system performance, turbo codes composed of parallel concatenated convolutional codes (PCCCs) is utilized. The network performance is evaluated in terms of bit error probability (BEP). From the simulation results, it is demonstrated that turbo coding offers considerable coding gain with reasonable encoding and decoding complexity. Also, it is confirmed that the performance of such an optical CDMA network can be substantially improved by increasing the interleaver length and the number of iterations in the decoding process. The results of this paper can be applied to implement the satellite broadcasting communications.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.314-319
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• 2009

In this paper, the network performance of a turbo coded optical code division multiple access (COMA) system with cross-layer, which is between physical and network layers, concept is analyzed and simulated We consider physical and MAC layers in a cross-layer concept. An intensity-modulated/direct-detection (IM/DD) optical system employing pulse position modulation (PPM) is considered In order to increase the system performance, turbo codes composed of parallel concatenated convolutional codes (PCCCs) is utilized. The network performance is evaluated in terms of bit error probability (BEP). From the simulation results, it is demonstrated that turbo coding offers considerable coding gain with reasonable encoding and decoding complexity. Also, it is confirmed that the performance of such an optical COMA network can be substantially improved by increasing the interleaver length and the number of iterations in the decoding process. The results of this paper can be applied to implement the indoor optical wireless LANs.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.5
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• pp.1-10
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• 1998

Satellite communication is getting more important in the coming 21st century because of its wide areas sevice capability, ease of access, and fast channel establishment. As such, satellite communication networks will be the basis of the global communication system in cooperation with the ground ATM networks. In this paper, we consider an efficient transmission methodology of ATM cells over the satellite communication channel. We first analyze possible bottlenecks and performance deterioration factors in the case, and then propose an enhanced cell trasmission mechanism. In order to use satellite channel for ATM cell transmission, the application of complicated channel coding is inevitable. However, the forwared error control such as convolutional encoding brings forth burst errors, which calls for the application of some kind of interleaving mechanism to randomize the burst errors at the receiver. Another aspect which should b econsidered in satellite communication system is the inherent transmission delay, which can be very considered in satellite communication system is te inherent transmission delay, which can be very critical to the delay-sensitive ATM traffic. Therefore, we propose that the processing delay at the block interleaving stage should be controlled propose a variable-size block interleaving mechanism which utilizes the predicted transmission delay for each traffic in the queues of the transmitter. According to the computer simulation, the proposed mechanism could improve the overall performance by drastically reducing the ATM cell drop rate owing to the excessive transmission delay.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.868-876
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• 2021

Deploying deep neural network model training performs remarkable performance in the fields of Object detection and Instance segmentation. To train these models, features are first extracted from the input image using a backbone network. The extracted features can be reused by various tasks. Research has been actively conducted to serve various tasks by using these learned features. In this process, standardization discussions about encoding, decoding, and transmission methods are proceeding actively. In this scenario, it is necessary to analyze the response characteristics of features against various distortions that may occur in the data transmission or data compression process. In this paper, experiment was conducted to inject various distortions into the feature in the object recognition task. And analyze the mAP (mean Average Precision) metric between the predicted value output from the neural network and the target value as the intensity of various distortions was increased. Experiments have shown that features are more robust to distortion than images. And this points out that using the feature as transmission means can prevent the loss of information against the various distortions during data transmission and compression process.

• International Journal of Contents
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• v.17 no.4
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.436-443
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• 2023

With the advancement of robot intelligence, the concept of man and unmanned teaming (MUM-T) has garnered considerable attention in military research. In this paper, we present a transformer-based architecture for predicting the health status of agents, with the help of multi-head attention mechanism to effectively capture the dynamic interaction between friendly and enemy forces. To this end, we first introduce a framework for generating a dataset of battlefield situations. These situations are simulated on a virtual simulator, allowing for a wide range of scenarios without any restrictions on the number of agents, their missions, or their actions. Then, we define the crucial elements for identifying the battlefield, with a specific emphasis on agents' status. The battlefield data is fed into the transformer architecture, with classification headers on top of the transformer encoding layers to categorize health status of agent. We conduct ablation tests to assess the significance of various factors in determining agents' health status in battlefield scenarios. We conduct 3-Fold corss validation and the experimental results demonstrate that our model achieves a prediction accuracy of over 98%. In addition, the performance of our model are compared with that of other models such as convolutional neural network (CNN) and multi layer perceptron (MLP), and the results establish the superiority of our model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.366-381
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• 2020

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.