• The KIPS Transactions:PartC
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• v.16C no.2
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• pp.165-182
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• 2009

Mobile IPv6 spends considerable bandwidth considering that its signal volume is proportional to the mobile and also it should be strengthened to support the binding signal volume, the traffic, and effective mobility. So, the study in NEMO(Network Mobility), an extended version of Mobile IPv6, has been conducted. NEMO provides its mobility by putting several mobiles and more than one portable router into one unit called as mobile network. Because nodes access Internet via the portable router at this time, it receives transparency without any additional work and that much reduces binding signal while solving binding storm. By supporting mobility, NEMO is able to have various mobile structures which realize several networks hierarchically and it is necessary to improve its safety and security by authenticating among the upper networks or the lower ones while moving. Also, it is extremely required to begin a study in the device to improve efficiency accompanied with mobility, which is executed by the fast hand-off as well as the safe authentication. For those reasons, this paper not only classifies various NEMO mobile scenarios into 7 ways, but also provides AAA authentication of each scenario, the authentication through the safety authentication and fast handoff authentication using F+HMIPv6 and the way to reduce both signaling volume and packet delays efficiently during the handoff.

• Information Systems Review
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• v.5 no.2
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• pp.241-256
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• 2003

This empirical study is to explain critical success factors for building effective regional IT cluster from the literature reviews which have some limitations, and is to suggest new key factors from the views of Regional Innovation System and Sectoral Systems of Innovation. For building successful cluster, the new key factors not only stress on regional networks, the spill-over of tacit knowledge through learning by interacting, institutions which contain regional custom, norms, established practices, culture, and characteristics from the Regional Innovation System, but also emphasize on heterogeneous agents who are interacting by each others from Sectoral Systems of Innovation. From these factors we suggest some strategies for building effective "Daegu IT Cluster" as following; making characterized IT brands which are selected and concentrated based on regional and IT sectoral characteristics, strengthening learning competence of tacit knowledge built in multiple heterogeneous agents network, establishing strong agent networks which are composed of universities, companies, institutes and government, and sharing the institution of mind-opening culture in order to correspond with environmental changes and link to other industrial clusters. By putting above strategies in force, the compatabilities of Daegu region are reinforced. Tacit knowledges spill over and the regional innovation competence are accumulated. Also IT cluster plays core role of employment in Daegu for long term. Especially, "Daegu IT Cluster" changes city's image from medium and small manufacturing city to new industrial city based on high technologies.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.593-600
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• 2019

The similarity in tones between speakers can lower the performance of speaker verification. To improve the performance of speaker verification systems, we propose a multi-task learning technique using deep neural network to learn speaker information and age information. Multi-task learning can improve generalization performances, because it helps deep neural networks to prevent hidden layers from overfitting into one task. However, we found in experiments that learning of age information does not work well in the process of learning the deep neural network. In order to improve the learning, we propose a method to dynamically change the objective function weights of speaker identification and age estimation in the learning process. Results show the equal error rate based on RSR2015 evaluation data set, 6.91 % for the speaker verification system without using age information, 6.77 % using age information only, and 4.73 % using age information when weight change technique was applied.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.791-801
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• 2021

In this paper, an educational case that can be applied as artificial intelligence literacy education for preservice teachers and incumbent teachers was studied. To this end, a case of educating the operating principle of an artificial neural network that recognizes images is proposed. This training case focuses on the basic principles of artificial neural network operation and implementation, and applies the method of finding parameter optimization solutions required for artificial neural network implementation in a spreadsheet. In this paper, we focused on the artificial neural network of supervised learning method. First, as an artificial neural network principle education case, an artificial neural network education case for recognizing two types of images was proposed. Second, as an artificial neural network extension education case, an artificial neural network education case for recognizing three types of images was proposed. Finally, the results of analyzing artificial neural network training cases and training satisfaction analysis results are presented. Through the proposed training case, it is possible to learn about the operation principle of artificial neural networks, the method of writing training data, the number of parameter calculations executed according to the amount of training data, and parameter optimization. The results of the education satisfaction survey for preservice teachers and incumbent teachers showed a positive response result of over 70% for each survey item, indicating high class application suitability.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.85-100
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• 2023

Corporate ESG performance (environmental, social, and corporate governance) reflecting a company's strategic sustainability has emerged as one of the main factors in today's investment decisions. The traditional ESG performance rating process is largely performed in a qualitative and subjective manner based on the institution-specific criteria, entailing limitations in reliability, predictability, and timeliness when making investment decisions. This study attempted to predict the corporate ESG rating through automated machine learning based on quantitative and disclosed corporate financial information. Using 12 types (21,360 cases) of market-disclosed financial information and 1,780 ESG measures available through the Korea Institute of Corporate Governance and Sustainability during 2019 to 2021, we suggested a deep neural network prediction model. Our model yielded about 86% of accurate classification performance in predicting ESG rating, showing better performance than other comparative models. This study contributed the literature in a way that the model achieved relatively accurate ESG rating predictions through an automated process using quantitative and publicly available corporate financial information. In terms of practical implications, the general investors can benefit from the prediction accuracy and time efficiency of our proposed model with nominal cost. In addition, this study can be expanded by accumulating more Korean and international data and by developing a more robust and complex model in the future.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.10
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• pp.299-308
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• 2023

As the spectrum shortage problem has accelerated by the emergence of various services, New Radio-Unlicensed (NR-U) has appeared, allowing users who communicated in licensed bands to communicate in unlicensed bands. However, NR-U network users reduce the performance of Wi-Fi network users who communicate in the same unlicensed band. In this paper, we aim to simultaneously maximize the fairness and throughput of the unlicensed band, where the NR-U network users and the WiFi network users coexist. First, we propose an optimal power allocation scheme based on Monte Carlo Policy Gradient of reinforcement learning to maximize the sum of rates of NR-U networks utilizing rate-splitting multiple access in unlicensed bands. Then, we propose a channel occupancy time division algorithm based on sequential Raiffa bargaining solution of game theory that can simultaneously maximize system throughput and fairness for the coexistence of NR-U and WiFi networks in the same unlicensed band. Simulation results show that the rate splitting multiple access shows better performance than the conventional multiple access technology by comparing the sum-rate when the result value is finally converged under the same transmission power. In addition, we compare the data transfer amount and fairness of NR-U network users, WiFi network users, and total system, and prove that the channel occupancy time division algorithm based on sequential Raiffa bargaining solution of this paper satisfies throughput and fairness at the same time than other algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.166-171
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• 2023

Efficient energy management is a very important factor in sensor networks with limited resources, and cluster techniques have been studied a lot in this respect. However, a problem may occur in which energy use of the cluster header is concentrated, and when the cluster header is not evenly distributed over the entire area but concentrated in a specific area, the transmission distance of the cluster members may be large or very uneven. The transmission distance can be directly related to the problem of energy consumption. Since the energy of a specific node is quickly exhausted, the lifetime of the sensor network is shortened, and the efficiency of the entire sensor network is reduced. Thus, balanced energy consumption of sensor nodes is a very important research task. In this study, factors for balanced energy consumption by cluster headers and sensor nodes are analyzed, and a balancing distribution clustering method in which cluster headers are balanced distributed throughout the sensor network is proposed. The proposed cluster method uses multi-hop routing to reduce energy consumption of sensor nodes due to long-distance transmission. Existing multi-hop cluster studies sets up a multi-hop cluster path through a two-step process of cluster setup and routing path setup, whereas the proposed method establishes a hierarchical cluster routing path in the process of selecting cluster headers to minimize the overhead of control messages.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.1
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• pp.54-69
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• 2021

In 2020, civilized society's overall lifestyle showed a distinct change from consumable analog media, such as paper, to digital media with the increased penetration of cloud computing, and from wired media to wireless media. Based on these social changes, this work examines whether the use of computer media in the field of landscape architecture is appropriately applied. This study will give directions for new computer media classes in landscape architectural education in the 4th Industrial Revolution era. Landscape architecture is a field that directly proposes the realization of a positive lifestyle and the creation of a living environment and is closely connected with social change. However, there is no clear evidence that landscape architectural education is making any visible change, while the digital infrastructure of the 4th Industrial Revolution, such as Artificial Intelligence (AI), Big Data, autonomous vehicles, cloud networks, and the Internet of Things, is changing the contemporary society in terms of technology, culture, and economy among other aspects. Therefore, it is necessary to review the current state of the use of computer technology and media in landscape architectural education, and also to examine the alternative direction of the curriculum for the new digital era. First, the basis for discussion was made by studying the trends of computational design in modern landscape architecture. Next, the changes and current status of computer media classes in domestic and overseas landscape education were analyzed based on prior research and curriculum. As a result, the number and the types of computer media classes increased significantly between the study in 1994 and the current situation in 2020 in the foreign landscape department, whereas there were no obvious changes in the domestic landscape department. This shows that the domestic landscape education is passively coping with the changes in the digital era. Lastly, based on the discussions, this study examined alternatives to the new curriculum that landscape architecture department should pursue in a new degital world.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1160-1168
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• 2000

This paper presents a real-time integrated traffic management (RITM) scheme that can effectively manage variable bit rate (VBR) and available bit rate (ABR) traffics having unpredictable characteristics in asynchronous transfer mode (ATM) networks. An unique feature of this scheme is that it has a special ATM cell control block which makes it possible to monitor bursty traffics in real-time so that the delay incurred to measure cell arrival rate is minimized. Additionally, the proposed scheme intends to dynamically reassign the leftover network resources to VBR/ABR connections without any deterioration in quality of service (QoS) of the existing connections. The RITM scheme has been verified to reliably monitor incoming traffics and to efficiently manage network resources by computer simulations. The capability of managing the incoming ATM traffics in real-time helps determine an optimal acceptable number of user connections for a given network condition. We can use this value as a threshold to protect the network from being congested and to find out a cost-effective buffer design method.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.6
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• pp.743-752
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• 2009

One of the major problems associated with operation of domestic sewer lines involves hydraulic problems such as insufficient conveyance capacity, exceeding maximum velocity, and deficiency of minimum velocity. It has also been pointed out that influent concentration lower than design concentration of pollutants, which is mainly caused by unidentified inflow and infiltration, degrades the operational efficiency of many sewage treatment plants (STPs). A computer-added analysis method supporting a coupled simulation of sewage quality and quantity is essentially required to evaluate the status of existing STPs and to improve their efficiency by a proper sewer rehabilitation work. In this study, dynamic water quality simulations were conducted using MOUSE TRAP to investigate the principal parameters that governs the changes of BOD, ${NH_4}^+$, and ${PO_4}^{3-}$3- concentrations within the sewer networks based on data acquired through on-site and laboratory measurements. The BOD, ${NH_4}^+$ and ${PO_4}^{3-}$3- concentrations estimated by MOUSE TRAP was lower than theoretical pollution loads because of sedimentation and decomposition in the sewer. The results revealed that sedimentation is a most important factor than other biological reactions in decreasing pollutant load in the sewers of C-city. The sensitivity analysis of parameters pertaining to water quality changes indicated that the effect of the BOD decay rate, the initial DO concentration, the half-saturation coefficient of dissolved BOD, and the initial sediment depth is marginal. However, the influence of settling rate and temperature is relatively high because sedimentation and precipitation, rather than biological degradation, are dominant processes that affect water quality in the study sewer systems.