• Journal of Digital Convergence
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• v.12 no.2
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• pp.315-320
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• 2014

Recently, although the requirement of quality of VOD content has been transferred upto the class of HD, conventional management systems characterized by OS dependency are truly limited in quality of video image, stability, and compatibility of network environments. In addition most of the content management systems realize very limited capabilities for the real affairs of content management and distribution services in such an OS dependent environment. In this paper, we propose a new scheme of HD-Class VOD Content Management System to solve these problems. We design and implement the proposed system based on open sources by using H.264 video compression method. The proposed system offers high quality content management method based on opened systems and independent on-line distribution method so that it can be realized as an integrated management scheme for VOD contents. Moreover, our system solves the problems of occasional cutting-down video, small screen, and poor image quality that exist in the conventional wmv-type CMS. According to the result of performance evaluation, our system maintains sufficient performance and tolerence for the case of large scale HD content operations or fabrications. We expect that the proposed integrated DB scheme will especially be effective when the content management applications are changed from Internet Web environments to mobile terminal environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.1C
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• pp.1-10
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• 2002

With the explosive growth of the number of Internet users and Internet-related services, the research on construction of broadband Optical Internet by using WDM technologies is actively is actively progressed. In this paper, we design the offset time decision (OTD) algorithm for supporting the QoS in optical networks based on optical burst switching (OBS), which is the new switching paradigm, and evaluate the performance of it. The proposed algorithm determines a reasonable offset time considering traffic load of network and the number of wavelengths to guarantee the burst loss rate (BLR) of high priority Internet traffic as a QoS parameter. In order to design the proposed OTD algorithm, firstly we make the new burst loss formula, which includes the effect of offset time. The decision of offset time corresponding to the requested BLR, however, should use the reverse formula of proposed one, thus we are not able to use it without any changes. In this paper, we define the heuristic loss formula (HLF) that can be changed into reversed formula by using the proposed formula and proportional equation considering its characteristics. Finally we show the OTD algorithm to decide the offset time by using HLF. We show that the requested BLR is guaranteed under various traffic load with the determinded offset time by using the proposed OTD algorithm.

• The KIPS Transactions:PartC
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• v.9C no.1
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• pp.97-106
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• 2002

In 3GPP2 standard, MIP is used and a PDSN performs the function of FA to support macro mobility. When a MS is roaming from a PDSN area to another, the mobility supported is called macro mobility, while it is called micro mobility when a MS is roaming from a RN area to another in a PDSN area. Since a PDSN performs the function of FA in 3GPP2 standard, it is possible to support mobility but its mechanism is actually for supporting macro mobility, not for micro mobility, thus it is weak in processing fast and seamless handoff to support micro mobility. In this paper, we suggest the seamless handoff algorithm barred on multicast group mechanism to support micro mobility. Depending on the moving direction and velocity of a MS, the suggested algorithm constructs a multicast group of RNs on the forecasted MS's moving path, and maximally delays RNs'joining to a multicast group to increase the network efficiency. Moreover, to resolve the buffer overhead problem of the existent multicast scheme, the algorithm suggests that each RN buffers data only after the forecasted handoff time. To prove deadlock freeness and liveness of the algorithm. we use state transition diagrams, a Petri-net modeling and its reachability tree. Then, we evaluate the performance by simulation.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.18-30
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• 2009

Recently, although the requirement of quality of VOD content has been transferred upto the class of HD, conventional management systems characterized by OS dependency are truly limited in quality of video image, stability, and compatibility of network environments. In addition most of the content management systems realize very limited capabilities for the real affairs of content management and distribution services in such an OS dependent environment. In this paper, we propose a new scheme of HD-Class VOD Content Management System to solve these problems. We design and implement the proposed system based on open sources by using H.264 video compression method. The proposed system offers high quality content management method based on opened systems and independent on-line distribution method so that it can be realized as an integrated management scheme for VOD contents. Moreover, our system solves the problems of occasional cutting-down video, small screen, and poor image quality that exist in the conventional wmv-type CMS. According to the result of performance evaluation, our system maintains sufficient performance and tolerence for the case of large scale HD content operations or fabrications. We expect that the proposed integrated DB scheme will especially be effective when the content management applications are changed from Internet Web environments to mobile terminal environments.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.1-18
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• 2019

Due to the great advances in e-Marketplace and changes in type of items purchased from the online market, it has been dramatically increased the demand of the storage and transportation under the special conditions such as restricted temperature. Especially, the cold chain needs the way to transparently measure and monitor the entire network in realtime because it has a very complicated structure and requires totally different criteria at the every different steps and items. In this research, it has been presented the performance evaluation metrics to make contract using service level agreement (SLA), the way to apply the smart contract based on blockchain, the structure of blocks, service platform and application in order to build cold chain which can prevent the risk factors by measuring and sharing information in realtime using block chain technology. In addition, we have proposed the way to store the measured performance and reputation of each player in the block using smart contract based on SLA. With the presented framework, all players including service providers as well as users can secure the information for making the rational decisions. When the service platform is actually built and operated, it seems possible to secure the information in transparently and realtime. Also, it is possible to prevent the risk factors or prepare the preemptive plans to react on them.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.319-325
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• 2022

The variation of utterance lengths is a representative factor that can degrade the performance of speaker verification systems. To handle this issue, previous studies had attempted to extract speaker features from various branches or to use convolution layers with different receptive fields. Combining the advantages of the previous two approaches for variable-length input, this paper proposes integrated receptive field diversification that extracts speaker features through more diverse receptive field. The proposed method processes the input features by convolutional layers with different receptive fields at multiple time-axis branches, and extracts speaker embedding by dynamically aggregating the processed features according to the lengths of input utterances. The deep neural networks in this study were trained on the VoxCeleb2 dataset and tested on the VoxCeleb1 evaluation dataset that divided into 1 s, 2 s, 5 s, and full-length. Experimental results demonstrated that the proposed method reduces the equal error rate by 19.7 % compared to the baseline.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.3
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• pp.263-276
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• 2023

The purpose of this study was to evaluate the performance of a model using You Only Look Once (YOLO) for object detection of proximal caries in periapical radiographs of children. A total of 2016 periapical radiographs in primary dentition were selected from the M6 database as a learning material group, of which 1143 were labeled as proximal caries by an experienced dentist using an annotation tool. After converting the annotations into a training dataset, YOLO was trained on the dataset using a single convolutional neural network (CNN) model. Accuracy, recall, specificity, precision, negative predictive value (NPV), F1-score, Precision-Recall curve, and AP (area under curve) were calculated for evaluation of the object detection model's performance in the 187 test datasets. The results showed that the CNN-based object detection model performed well in detecting proximal caries, with a diagnostic accuracy of 0.95, a recall of 0.94, a specificity of 0.97, a precision of 0.82, a NPV of 0.96, and an F1-score of 0.81. The AP was 0.83. This model could be a valuable tool for dentists in detecting carious lesions in periapical radiographs.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.91-101
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• 2023

In this study, we estimated the concentration of chlorophyll-a (Chl-a) using hyperspectral water surface reflectance in an inland weir (Baekjae weir) and estuarine reservoir (Namyang Reservoir) for monitoring the occurrence of algae in freshwater in South Korea. The hyperspectral reflectance was measured by aircraft in Baekjae Weir (BJW) from 2016 to 2017, and a drone in Namyang Reservoir (NYR) from 2020 to 2021. The 30 reflectance bands (BJW: 400-530, 620-680, 710-730, 760-790 nm, NYR: 400-430, 655-680, 740-800 nm) that were highly related to Chl-a concentration were selected using permutation importance. Artificial neural network based Chl-a estimation model was developed using the selected reflectance in both water bodies. And the performance of the model was evaluated with the coefficient of determination (R²), the root mean square error (RMSE), and the mean absolute error (MAE). The performance evaluation results of the Chl-a estimation model for each watershed was R²: 0.63, 0.82, RMSE: 9.67, 6.99, and MAE: 11.25, 8.48, respectively. The developed Chl-a model of this study may be used as foundation tool for the optimal management of freshwater algal blooms in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.753-762
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• 2024

Tunnels have limited evacuation areas, and It is difficult for cars coming from behind to recognize the accident situation in front. Since an accident is very likely to lead to a serious secondary accident, a IoT-based smart tunnel accident warning system was studied to prepare for traffic accidents that occur in tunnels. If the measured values from the flame detection sensor, gas detection sensor, and shock detection sensor in the tunnel exceed the standard, it is judged to be an emergency situation and an alert system is designed to operate. The accident information message was designed to be displayed on the LCD and transmitted to drivers inside and outside the tunnel through a Wi-Fi communication network. A performance test system was established and performance evaluation was performed for several accident scenarios. As a result of the test, it was confirmed that the accident alert system can accurately detect accidents based on given reference values, perform alert procedures, and transmit alert messages to smart phones through Wi-Fi wireless communication. And through this, its effectiveness could be confirmed.