• Journal of Korea Multimedia Society
• /
• v.11 no.12
• /
• pp.1730-1738
• /
• 2008

Quality of Service (QoS) Guarantees grant ways for service providers to establish service differentiation among subscribers. On the other hand, service subscribers are also assured the level of service they paid for. In addition, the efficient level of service quality can be selected according to the subscribers' needs thus ensuring efficient use of available bandwidth. While network utility optimization techniques assure certain QoS metrics, a number of situations exist where some QoS goals are not met. The optimality of the network parameters is not mandatory to guarantee specified QoS levels. This paper proposes a joint data rate and power control scheme that guarantees service contract QoS level to a subscriber using Goal Programming. In using goal programming, this paper focuses on finding the range of feasible solutions as opposed to solving for the optimal. In addition, in case no feasible solution is found, an acceptable compromised solution is solved.

• Journal of Korea Multimedia Society
• /
• v.12 no.12
• /
• pp.1769-1777
• /
• 2009

With the advent of the ubiquitous technology age, the progress of network technology has enabled a robust sensor communication, not just in cities, but also in poor surroundings such as deserts, polar regions, or underwater environments. In this paper, we propose a Multiple Acknowledgement (MA) technique to replace the conventional Automatic Repeat request (ARQ) technique. The MA mechanism is to send an Ack to many receivers simultaneously. The CH (master, coordinator) of the unit cluster broadcasts a Beacon frame where Ack information of the previously transmitted data is included. This technique can reduce the number of transmissions and overhead significantly. The proposed technique is a scheme improving the efficiency of an underwater sensor network where the uplink data transmission is the mainstream. The Performance of the ARQ, Block Ack, Pervasive Block Ack and the proposed method were compared with one another and analyzed. The proposed method showed significant performance improvement as compared with the ARQ, BA, and PBA in its channel efficiency.

• Journal of Korea Multimedia Society
• /
• v.24 no.10
• /
• pp.1391-1402
• /
• 2021

This paper focuses use context-sensitive spelling error correction using generative adversarial network. Generative adversarial network[1] are attracting attention as they solve data generation problems that have been a challenge in the field of deep learning. In this paper, sentences are generated using word embedding information and reflected in word distribution representation. We experiment with DCGAN[2] used for the stability of learning in the existing image processing and D2GAN[3] with double discriminator. In this paper, we experimented with how the composition of generative adversarial networks and the change of learning corpus influence the context-sensitive spelling error correction In the experiment, we correction the generated word embedding information and compare the performance with the actual word embedding information.

• Journal of Korea Multimedia Society
• /
• v.24 no.10
• /
• pp.1358-1368
• /
• 2021

A research on agricultural automation is a main issues to overcome the shortage of labor in Korea. A sweet persimmon farmers need much time and labors for classifying profitable sweet persimmon and ill profitable products. In this paper, we propose a mixed two-step synthetic neural network model for efficiently classifying sweet persimmon images. In this model, we suggested a surface direction classification model and a quality screening model which constructed from image data sets. Also we studied Class Activation Mapping(CAM) for visualization to easily inspect the quality of the classified products. The proposed mixed two-step model showed high performance compared to the simple binary classification model and the multi-class classification model, and it was possible to easily identify the weak parts of the classification in a dataset.

• Journal of Korea Multimedia Society
• /
• v.22 no.8
• /
• pp.887-896
• /
• 2019

In this paper, we discuss an optimization approach for time-synchronizations in networked simulators. This method is a sub-technology that is required to combine heterogeneous simulators into a single simulation. In previous time-synchronization studies, they had built a network system among networked simulators. The network system collects network packets and adds time-stamps to the networked packets based on the time that occurs in events of simulation objects in the individual simulators. Then, it sorts them in chronological order. Finally, the network system applies time-synchronization to each simulator participating in interworking sequentially. However, the previous approaches have a limitation in that other participating simulators should wait for while processing an event in a simulator in a time stamp order. In this paper, we attempt to solve the problem by optimizing time-synchronizations in networked simulation environments. In order to prove the practicality of our approach, we have conducted an experiment. Finally, we discuss the contributions of this paper.

• Journal of Korea Multimedia Society
• /
• v.22 no.4
• /
• pp.455-462
• /
• 2019

This paper proposes a method of utilizing convolutional neural network to classify the images of Bender Gestalt Test (BGT), which is a tool to understand and analyze a person's characteristic. The proposed network is composed of 29 layers including 18 convolutional layers and 2 fully connected layers, where the network is to be trained with augmented images. To verify the proposed method, 10 fold validation was adopted. In results, the proposed method classified the images into 9 classes with the mean f1 score of 97.05%, which is 13.71%p higher than a previous method. The analysis of the results shows the classification accuracy of the proposed method is stable over all the patterns as the worst f1 score among all the patterns was 92.11%.

• Journal of Korea Multimedia Society
• /
• v.24 no.4
• /
• pp.519-527
• /
• 2021

Alzheimer's disease is one of the challenges to tackle in the coming aging era and is attempting to diagnose and predict through various biomarkers. While the application of various deep learning-based technologies as powerful imaging technologies has recently expanded across the medical industry, empirical design is not easy because there are various deep earning neural networks architecture and categorical hyperparameters that rely on problems and data to solve. In this paper, we show the possibility of optimizing a deep learning neural network structure and hyperparameters for Alzheimer's disease classification in amyloid brain images in a representative deep earning neural networks architecture using genetic algorithms. It was observed that the optimal deep learning neural network structure and hyperparameter were chosen as the values of the experiment were converging.

• Journal of Korea Multimedia Society
• /
• v.24 no.5
• /
• pp.701-708
• /
• 2021

As one of the most widely used technology in the world right now, Face recognition has already received widespread attention by all the researcher and institutes. It has been used in many fields such as safety protection, surveillance system, crime control and even in our ordinary life such as home security and so on. This technology with today's technology has advantages such as high connectivity and real time transformation. But we still need to improve its recognition rate, reaction time and also reduce impact of different environmental status to the whole system. So in this paper we proposed a face recognition system model with improved CNN which combining the characteristics of flat network and residual network, integrated learning, simplify network structure and enhance portability and also improve the recognition accuracy. We also used AR and ORL database to do the experiment and result shows higher recognition rate, efficiency and robustness for different image conditions.

• Journal of Korea Multimedia Society
• /
• v.25 no.1
• /
• pp.52-60
• /
• 2022

Few-shot learning is a sub-area of machine learning problems, which aims to classify target images that only contain a few labeled samples for training. As a representative few-shot learning method, the Prototypical network has been received much attention due to its simplicity and promising results. However, the Prototypical network uses the sample mean of samples from the same class as the prototypes of that class, which easily results in learning uncharacteristic features in the low-data scenery. In this study, we propose to use local descriptors (i.e., patches along the channel within feature maps) from the same class to explicitly obtain more representative prototypes for Prototypical Network so that significant intra-class feature information can be maintained and thus improving the classification performance on few-shot learning tasks. Experimental results on various benchmark datasets including mini-ImageNet, CUB-200-2011, and tiered-ImageNet show that the proposed method can learn more discriminative intra-class features by the local descriptors and obtain more generic prototype representations under the few-shot setting.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.247-256
• /
• 2022

Visual tracking of selected target objects is fundamental challenging problems in computer vision. Object tracking localize the region of target object with bounding box in the video. We propose a Siam-FPN based custom fully CNN to solve visual tracking problems by regressing the target area in an end-to-end manner. A method of preserving the feature information flow using a feature map connection structure was applied. In this way, information is preserved and emphasized across the network. To regress object region and to classify object, the region proposal network was connected with the Siamese network. The performance of the tracking algorithm was evaluated using the OTB-100 dataset. Success Plot and Precision Plot were used as evaluation matrix. As a result of the experiment, 0.621 in Success Plot and 0.838 in Precision Plot were achieved.