• Journal of KIISE:Information Networking
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• v.31 no.1
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• pp.37-52
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• 2004

Broadcast has been often used to disseminate the frequently requested data efficiently to a large volume of mobile units over a single or multiple channels. Since the mobile units have limited battery power, the minimization of the access time for the broadcast data is an important problem. There have been many research efforts that focus on the improvement if the broadcast techniques by providing indexes on the broadcast data. In this paper, we studied an efficient index allocation method for the broadcast data over multiple physical channels, which cannot be coalesced into a single high bandwidth channel. Previously proposed index allocation techniques either require the equal size of index and data or have a performance degradation problem when the number of given physical channels is not enough. These two problems will result in the increased average access time for the broadcast data. To cope with these problems, we propose an efficient tree- structured index allocation method for the broadcast data with different access frequencies over multiple physical channels. Our method minimizes the average access time for the broadcast data by broadcasting the hot data and their indexes more often than the less hot data and their indexes. We present an in-0e0th experimental and theoretical analysis of our method by comparing it with other similar techniques. Our performance analysis shows that it significantly decrease the average access time for the broadcast data over existing methods.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.6
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• pp.10-21
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• 2010

In the cluster-tree network which covers wide area network and has many nodes for monitoring purpose traffic is concentrated around the sink. There are long transmit delay and high data loss due to the intensive traffic when IEEE 802.15.4 is adapted to the cluster-tree network. In this paper we propose Dynamic Channel Allocation algorithm which dynamically allocates channels to increase the channel usage and the transmission success rate. To evaluate the performance of DCA, we assumed the monitoring network that consists of a cluster-tree in which sensing data is transmitted to the sink. Analysis uses the traffic data which is generated around the sink. As a result, DCA is superior when much traffic is generated. During the experiment assuming the least amount of traffic, IEEE 802.15.4, has the minimum length of active period and 90% data transmission success rate. However DCA maintains 11.8ms of active period length and results in 98.9% data transmission success rate.

• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.946-952
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• 2014

Mobility prediction is one of hot topics using location history information. It is useful for not only user-level applications such as people finder and recommendation sharing service but also for system-level applications such as hand-off management, resource allocation, and quality of service of wireless services. Most of current prediction techniques often use a set of significant locations without taking into account possible location information changes for prediction. Markov-based, LZ-based and Prediction by Pattern Matching techniques consider interesting locations to enhance the prediction accuracy, but they do not consider interesting location changes. In our paper, we propose an algorithm which integrates the changing or emerging new location information. This approach is based on Active LeZi algorithm, but both of new location and all possible location contexts will be updated in the tree with the fixed depth. Furthermore, the tree will also be updated even when there is no new location detected but the expected route is changed. We find that our algorithm is adaptive to predict next location. We evaluate our proposed system on a part of Dartmouth dataset consisting of 1026 users. An accuracy rate of more than 84% is achieved.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6B
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• pp.1080-1087
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• 1999

High-speed network resources and large storage devices on video servers are necessary to Provide interactive VOD services to subscribers, therefore, building networks for VOD services requires a huge amount of cost. A number of studies on network hierarchy for distributed tree architecture, on optimized server allocation with a given network topology, and on program caching etc, are currently in progress. In this study, given specific network resources, a cost effective network architecture including the optimal number of tree levels and the optimal number of branches at a node are designed by modeling cost functions, and some restrictions on network design are discussed. The results of this study are expected to be applied to network architecture design for interactive VOD services

• Nuclear Engineering and Technology
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• v.45 no.5
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• pp.653-664
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• 2013

The design of computer-based instrumentation and control (I&C) systems is determined by the allocation of I&C functions to I&C systems and components. Due to the characteristics of computer-based technology, component failures can negatively affect several I&C functions, so that the reliability proof of the I&C systems requires the accomplishment of I&C system design analyses throughout the I&C life-cycle. On one hand, this paper proposes the restructuring of the sequential IEC 61513 I&C life-cycle according to the V-model, so as to adequately integrate the concept of verification and validation. On the other hand, based on a metamodel for the modeling of I&C systems, this paper introduces a method for the modeling and analysis of the effects with respect to the superposition of failure combinations and event sequences on the I&C system design, i.e. the temporal change of physical structure is analyzed. In the first step, the method is concerned with the modeling of the I&C systems. In the second step, the method considers the analysis of temporal change of physical structure, which integrates the concepts of the diversity and defense-in-depth analysis, fault tree analysis, event tree analysis, and failure mode and effects analysis.

• Nuclear Engineering and Technology
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• v.40 no.5
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• pp.327-348
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• 2008

A risk-informed regulatory approach implies that risk insights be used as supplement of deterministic information for safety decision-making purposes. In this view, the use of risk assessment techniques is expected to lead to improved safety and a more rational allocation of the limited resources available. On the other hand, it is recognized that uncertainties affect both the deterministic safety analyses and the risk assessments. In order for the risk-informed decision making process to be effective, the adequate representation and treatment of such uncertainties is mandatory. In this paper, the risk-informed regulatory framework is considered under the focus of the uncertainty issue. Traditionally, probability theory has provided the language and mathematics for the representation and treatment of uncertainty. More recently, other mathematical structures have been introduced. In particular, the Dempster-Shafer theory of evidence is here illustrated as a generalized framework encompassing probability theory and possibility theory. The special case of probability theory is only addressed as term of comparison, given that it is a well known subject. On the other hand, the special case of possibility theory is amply illustrated. An example of the combination of probability and possibility for treating the uncertainty in the parameters of an event tree is illustrated.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Forest and Environmental Science
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• v.34 no.1
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• pp.1-11
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• 2018

We studied natural and plantation forest ecosystem of Sarguja in Chhattisgarh, India in order to understand how vegetation biomass, carbon stock and its allocation patterns vary among the sites. For this, stratified random sampling was opted to measure the different layers of vegetation. Wide floral diversity was found in the natural forest site as compared to the teak stand. Overall, 17 tree species found in natural forest comprising 8 families while in the teak stand 6 species were recorded. In understory strata 23 species were recorded (18 herbs and 5 shrubs) in natural forest whereas in teak stand 20 herb species and 3 shrubs were found. Great variation was also seen in the population dynamics of the different vegetation stratum in concerned sites. The sapling, seedling and herb density was found to be highest in natural stand while tree and shrub density was more in teak stand. Results indicated that stand biomass of the natural site was $321.19t\;ha^{-1}$ while in the teak stand it was $276.61t\;ha^{-1}$. The total biomass of tree layer in plantation site was $245.22t\;ha^{-1}$ and natural forest $241.44t\;ha^{-1}$. The sapling, seedling, shrub and forest floor biomass was found highest under natural forest as compared to the teak plantation site. Carbon stock has similar trend as that of biomass accumulation in natural forest and teak stand. Higher biomass accumulation and carbon stock were recorded in the higher girth class gradation of the population structure. Proper efforts are required to manage these diverse ecosystems to obtain higher biomass and sustainable ecological services.

• Journal of Forest and Environmental Science
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• v.33 no.4
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• pp.348-354
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• 2017

Agroforestry has been practiced in arid and semi-arid regions for the purposes of preventing desertification and to increase income for locals. However, the intended effects of such practices have been limited due to strong winds and aridity. This study undertook multi-year monitoring of the productivity of income crops associated with windbreak planting in a semi-arid region of Mongolia, and explored strategies of windbreak planning to enhance the multi-purpose effects of agroforestry practices. The tree crown density of windbreak planting was on average 40% in one year after planting and 65% 2-3 years after, and thereby windspeeds were reduced by about 30% and 54%, respectively. Average windspeed reductions at leeward distances from the windbreak planting were approximately 60% within 3H (H=tree height), 50% at 5H, and 42% at 7-9H, presenting a pattern in which the farther the distance the less the reduction in windspeeds. The windbreak planting increased crop productivity by up to 6.8 times, compared to the productivity absent of windbreaks. Increases in the crown density as stated above resulted in increases of crop productivity by up to 3.6 times. Based on such results, this study proposed a model of windbreak planning as a typical land-use system of border windbreak planting or alternate windbreak planting of combining trees and income crops. The model also included tree planting with a crown density of 60% and allocation of income crops within a leeward distance of 5 times the height of the trees to reduce windspeeds by about 50%. The results from this study are applicable to practicing agroforestry not only at the study site but also in other regions worldwide where strong winds and aridity are problematic.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.363-365
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• 1991

This paper presents a method for feeder reconfiguration in order to operate distribution systems efficiently using heuristic rules. The reconfiguration method presented here not only eliminates various abnormal states but also achieves minimum power loss and optimum load balance of the distribution feeders under normal operating condition transfering loads from one feeder to anoter applying the experiences of the experts. To implement the method effectively, a best-first tree searching strategy based on heuristics is used to evaluate the various load transfer alternatives. The development of a rule-based system aimed at the reduction of the search space is presented as a means of implementing the best-first searching strategy. The results of the computer simulation of the above procedure are as follows; 1) achieving minimum power loss of the distribution feeder adopting the optimum load transfer alternative. 2) Enhencing system reliability and achieving load balance through rational allocation of the feeder loads.