• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5826-5841
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• 2019

In our previous research, we proposed a tag emotion-based item recommendation scheme. The ternary associations among users, items, and tags are described as a three-order tensor in order to capture the emotions in tags. The candidates for recommendation are created based on the latent semantics derived by a high-order singular value decomposition technique (HOSVD). However, the tensor is very sparse because the number of tagged items is smaller than the amount of all items. The previous research do not consider the previous behaviors of users and items. To mitigate the problems, in this paper, the item-based collaborative filtering scheme is used to build an extended data. We also apply the probabilistic ranking algorithm considering the user and item profiles to improve the recommendation performance. The proposed method is evaluated based on Movielens dataset, and the results show that our approach improves the performance compared to other methods.

• Journal of the Korea Safety Management & Science
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• v.11 no.1
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• pp.205-211
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• 2009

In reality, distribution planning for a supply chain is established using a certain probabilistic distribution estimated by forecasting. However, in general, the demands used for an actual distribution planning are of deterministic value, a single value for each of periods. Because of this reason the final result of a planning has to be a single value for each period. Unfortunately, it is very difficult to estimate a single value due to the inherent uncertainty in the probabilistic distribution of customer demand. The issue addressed in this paper is the selection of single demand value among of the distributed demand estimations for a period to be used in the distribution planning. This paper proposes an efficient demand selection scheme for minimizing total inventory costs while satisfying target service level under the various experimental conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.3
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• pp.188-190
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• 2014

In mobile ad hoc networks(MANETs), each node rebroadcast received route request packets for route discovery. Flooding from large number of nodes induces the broadcast storm problem which causes severe degradation in network performance due to redundant retransmission, collision and contention. This paper presents a node status algorithm based on probabilistic scheme to alleviate the broadcast storm problem for wireless ad hoc networks.

• Journal of the Korean Solar Energy Society
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• v.39 no.4
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• pp.79-91
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• 2019

The capacity credit (CC) is a key metric for mid- to long-term power system capacity planning. The purpose of this study is to estimate the CCs of domestic wind turbines. Based on hourly capacity factor (CF) data during the seven years from 2011 to 2017, the new so-called probabilistic CF scheme is introduced to effectively reflect the variability of CFs on CC estimation. The CCs are then estimated through the CF-based method and the ELCC (Effective Load Carrying Capability) method reflecting the probabilistic CF scheme, and the results are compared. The results show that the CC value 0.019 for domestic wind turbines proposed in the $8^{th}$ Basic Plan for Electricity Supply and Demand corresponds to the CC with a confidence level slightly lower than 95%.

• Journal of Advanced Navigation Technology
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• v.16 no.3
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• pp.471-479
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• 2012

In cellular femto systems, femto Base stations(f-BSs) can be installed unnecessarily and overcrowded in small areas. This will cause an interference problem and it can impact on the capacity, blocking probability, and coverage of femtocells in the shared channel systems. In this paper, we propose a load distribution scheme by using forced handover and probabilistic subchannel scheduling policy to resolve the problem. The proposed scheme operates in distributed manner though communication with neighboring f-BSs, and includes self-detection of overcrowded area and radio resource management based on measurements. We evaluate the performance of the proposed scheme in terms of average cell throughput and average throughput per users.

• Convergence Security Journal
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• v.10 no.3
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• pp.29-41
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• 2010

Survelliance and Reconnaissance Sensor Network(SRSN) which can collect various tactical information within battlefield in real time plays an important role in NCW environment, of sensor to shooter architecture. However, due to the resource-limited characteristics of sensor nodes and the intrinsic attributes of sensor network such as wireless communication, the SRSN may be vulnerable to various attacks compared to traditional networks. Therefore, in this paper, we propose a new group key management scheme to guarantee confidentiality, integrity, availability, and authentication during the operation of the SRSN. Proposed scheme generates and distributes the group key based on the topological characteristic of the SRSN and the probabilistic key sharing. The communication cost for distributing the group key is O(logn).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.1
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• pp.18-26
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• 2014

A microgrid can play a significant role for enlargement of renewable energy sources and emission reduction because it is a network of small, distributed electrical power generators operated as a collective unit. In this paper, an application of optimization method to economical operation of a microgrid is studied. The microgrid to be studied here is composed of distributed generation system(DGS), battery systems and loads. The distributed generation systems include combined heat and power(CHP) and small generators such as diesel generators and the renewable energy generators such as photovoltaic(PV) systems, wind power systems. Both of thermal loads and electrical loads are included here as loads. Also the emissions trading scheme to be applied in near future, the cost of unit start-up and the operational characteristics of battery systems are considered as well as the probabilistic characteristics of the renewable energy generation and load. A mathematical equation for optimal operation of this system is modeled based on the mixed integer programming. It is shown that this optimization methodology can be effectively used for economical operation of a microgrid by the case studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.505-511
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• 2018

Long term capacity expansion planning has to be carried out to satisfy pre-defined system reliability criterion. For purpose of assessing system reliability, probabilistic simulation technique has been widely adopted. However, the way how to approximate generator outage, especially maintenance outage, in probabilistic simulation scheme can significantly influence on reliability assessment. Therefore, in this paper, 3 different maintenance approximation methods are applied to investigate the quantitative impact of maintenance approximation method on long term capacity expansion planning.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.731-739
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• 1998

The probabilistic relaxation(PR) scheme based on the conditional probability and probability space partition has the important property that when its compatibility coefficient matrix (CCM) has uniform components it can classify m-dimensional probabilistic distribution vectors into different classes. When consistency or inconsistency measures have been defined, the properties of PRs are completely determined by the compatibility coefficients among labels of labeled objects and influence weight among labeled objects. In this paper we study the properties of PR in which both compatibility coefficients and influence weights are uniform, and then a learning rule for such PR system is derived. Experiments have been performed to verify the effectiveness of the learning rule.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.978-982
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• 2007

A new neuro-control scheme for active control of structures is proposed. It utilizes lattice pattern of state vector as training data of probabilistic neural network (PNN). Therefore, it is the so-called lattice probabilistic neural network (LPNN). PNN makes control forces by using all the training patterns. Therefore, it takes much time to obtain a control force in application. This inevitably may delay the control action. However, control force of LPNN is calculated by using only the adjacent information of LPNN input. So, the response of LPNN is greatly faster than PNN. The proposed control algorithm is applied for one story building under California and El Centro earthquakes. Also, control results of the LPNN are compared with those of the conventional PNN. The structural responses have been suppressed effectively by the proposed algorithm.