• KIPS Transactions on Software and Data Engineering
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• v.6 no.12
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• pp.565-572
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• 2017

Traditional method for time series analysis, autoregressive integrated moving average (ARIMA) allows to mine significant patterns from the past observations using autocorrelation and to forecast future sequences. However, Korean baseball games do not have regular intervals to analyze relationship among the past attendance observations. To address this issue, we propose artificial neural network (ANN) based attendance prediction model using various measures including performance, team characteristics and social influences. We optimized ANNs using grid search to construct optimal model for regression problem. The evaluation shows that the optimal and ensemble model outperform the baseline model, linear regression model.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.61-66
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• 2003

Mobile IP has been designed only to maintain communication as they move form place to place, so it doesn't guarantee Quality of Service(QoS). QoS in mobile IP is important to provide multimedia and real-time applications services in mobile, and it is closely related to handoff delay. Therefore, handoff delay problem is actively studied to guarantee QoS as a main issue in mobile IP research area Next generation Mobile IPv6 resolve this problem somewhat, triangle problem for first packet and handoff delay still remain. In this paper, we suggest SNMP Information-based routing that adds keyword management method to Information-based routing in active network in order to resolve such a problem, and then suggest QoS controlled handoff based on SNMP Information-Based routing. After modeling of suggested method and existing handoff method simulations we carried out with NS-2 for performance evaluation. The results of simulations show the some improvement on handoff delay, and therefore on QoS improvement.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.3
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• pp.613-621
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• 2016

Since the error control problem is a critical and sensitive issue in the one-way network, we can adopt a forward error correction code method or data retransmission method based on the response of reception result. In this paper, we propose error control method and continuous data transmission protocol in the one-way network which has unidirectional data transmission channel and special channel to receive only the response of reception result. Furthermore we present data encryption and key update mechanism which is based on the pre-shared key distribution scheme and suggest some ASDU(Application Service Data Unit) formats to implement it in the one-way network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.65-69
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• 2001

Network Management become more and more important issue in the network environment in which many applications such as Mail, teleconferencing, WWW and database software are operated. It can be possible for The Bridge and flouter forwarding data to select next hop device which results in routing incorrect path from the viewpoint of network design. In this paper we address the problem of finding the detour routing path due to incorrect setting on routing devices. We propose the new algorithm for finding detour routing path based on hierarchical network structure analysis using information from SNMP MIB. To prove the correctness of the proposed algorithm we have done simulation with predefined data. Simulation results show that the algorithm finds detour path correctly

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.80-89
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• 2017

The classical approaches to detect cracks are performed by experienced inspection professionals by annotating the crack patterns manually. Because of each inspector's personal subjective experience, it is hard to guarantee objectiveness. To solve this issue, automated crack detection methods have been proposed however the methods are sensitive to image noise. Depending on the quality of image obtained, the image noise affect overall performance. In this paper, we propose crack detection method using a convolutional encoder-decoder network to overcome these weaknesses. Performance of which is significantly improved in terms of the recall, precision rate and F-measure than the previous methods.

• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.146-158
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• 2020

As one of the successful brand activism ad campaigns in recent years, the current study focuses on the Always #LikeAGirl campaign that took on the issue of girls and female empowerment. As a viral video marketing campaign with YouTube as their main vehicle for campaign dissemination, this study examined how Always brand activism campaigns spread on YouTube by conducting a network analysis of YouTube video networks generated by the #LikeAGirl campaign spanning across five campaign periods. Quantifiable data (i.e., views, comments, likes, dislikes, user-generated videos) and structural network patterns show that the Always #LikeAGirl campaign was successful by both standards. Although the follow-up campaign periods were not as successful as the initial campaign, the substantial amount of views, comments, likes, and user-generated content showed that the consecutive campaigns still had impact. As shown through the network patterns, the main campaign ads were central in the diffusion of the campaign during the earlier periods but that role was passed onto the user-generated contents in the later periods. Implications of the findings and future social network analysis studies in brand advertising and brand activism campaigns are further discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1442-1448
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• 2003

Network Management become more and more important issue in the network environment in which many applications such as Mail, teleconferencing, WWW and database software are operated. It can be possible for The Bridge and Router forwarding data to select next hop device which results in routing incorrect path from the viewpoint of network design. In this paper we address the problem of finding the detour routing path due to incorrect setting on routing devices. We propose the new algorithm for finding detour muting path based on hierarchical network structure analysis using information from SNMP MIB. To prove the correctness of the unposed algorithm we have done simulation with predefined data. Simulation results show that the algorithm finds detour path correctly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1275-1292
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• 2021

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.6
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• pp.89-97
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• 2022

Ultrasonic metal welding has been widely used for joining lithium-ion battery tabs. Weld quality monitoring has been an important issue in lithium-ion battery manufacturing. This study focuses on the weld quality monitoring in ultrasonic metal welding with the longitudinal-torsional vibration mode horn developed newly. As the quality of ultrasonic welding depends on welding parameters like pressure, time, and amplitude, the suitable values of these parameters were selected for experimentation. The welds were tested via tensile testing machine and weld strengths were investigated. The dataset collected for performance test was used to train the multi-layer perceptron neural network. The three layer neural network was used for the study and the optimum number of neurons in the first and second hidden layers were selected based on performances of each models. The best models were selected for the horn and then tested to see their performances on an unseen dataset. The neural network models for the longitudinal-torsional mode horn attained test accuracy of 90%. This result implies that proposed models has potential for the weld quality monitoring.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.