• Journal of information and communication convergence engineering
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• v.15 no.1
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• pp.53-61
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• 2017

The preferred feature selection methods for text classification are filter-based. In a common filter-based feature selection scheme, unique scores are assigned to features; then, these features are sorted according to their scores. The last step is to add the top-N features to the feature set. In this paper, we propose an improved global feature selection scheme wherein its last step is modified to obtain a more representative feature set. The proposed method aims to improve the classification performance of global feature selection methods by creating a feature set representing all classes almost equally. For this purpose, a local feature selection method is used in the proposed method to label features according to their discriminative power on classes; these labels are used while producing the feature sets. Experimental results obtained using the well-known 20 Newsgroups and Reuters-21578 datasets with the k-nearest neighbor algorithm and a support vector machine indicate that the proposed method improves the classification performance in terms of a widely known metric ($F_1$).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3771-3788
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• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.19-25
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• 2016

This paper is to recognize and play tab chords from guitar musical sheets. The musical chord area of an input image is segmented by changing the image in saturation and applying the Grabcut algorithm. Based on a template matching, our approach detects tab starting sections on a segmented musical area. The virtual block method is introduced to search blanks over chord lines and extract tab fret segments, which doesn't cause the computation loss to remove tab lines. In the experimental tests, the prototype based classification outperforms Bayesian method and the nearest neighbor rule with the whole set of training data and its performance is similar to that of the support vector machine. The experimental result shows that the prediction rate is about 99.0% and the number of selected prototypes is below 3.0%.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.420-422
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• 2004

The non-ideal characteristics of wireless communication are found in sensor network. And sensor network must also address new raised issues. The efficient ad-hoc routing algorithm is considered the nice solution for new raised sensor network problems. To design this efficient ad-hoc routing algorithm, we study and evaluate new components in routing algorithm. Namely, new components are Link estimator, Neighbor table and Parent selection. We have tested this related experiment using the TIP-30C. TIP-30C is sensor network node that is designed by KETI(Korea Electronic Technology Institute).

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.809-818
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• 2009

Collaborative filtering approach predicts the preference of active user about specific items transacted on the e-commerce by using others' preference information. To improve the prediction accuracy through collaborative filtering approach, it must be needed to gain enough preference information of users' for predicting preference. But, a bit much information of users' preference might wrongly affect on prediction accuracy, and also too small information of users' preference might make bad effect on the prediction accuracy. This research suggests the method, which decides suitable numbers of neighbor users for applying collaborative filtering algorithm, improved by existing k nearest neighbors selection methods. The result of this research provides useful methods for improving the prediction accuracy and also refines exploratory data analysis approach for deciding appropriate numbers of nearest neighbors.

• Journal of Information Processing Systems
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• v.3 no.2
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• pp.73-81
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• 2007

In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naive Bayesian classification algorithms. The architecture of our system is simple and modularized in that each sub-module of the system could be changed or improved efficiently. Moreover, it provides various feature selection mechanisms to be applied to optimize the general-purpose classifiers for a specific domain. As for the enhanced classification performance, our system provides conditional probability boosting (CPB) mechanism which could be used in various domains. In the mood classification domain, our optimized framework using the CPB algorithm showed 1% of improvement in precision and 2% in recall compared with the baseline.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.419-420
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• 2006

In the new video coding standard H.264/AVC, motion estimation (ME) is allowed to search multiple reference frames for improve the rate-distortion performance. The complexity of multi-frame motion estimation increases linearly with the number of used reference frame. However, the distortion gain given by each reference frame varies with the video sequence, and it is not efficient to search through all the candidate frames. In this paper, we propose a fast mult-frame selection method using all zero coefficient block (AZCB) prediction and sum of difference (SAD) of neighbor block. Simulation results show that the speed of the proposed algorithm is up to two times faster than exhaustive search of multiple reference frames with similar quality and bit-rate.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.81-88
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• 2017

Solar-powered wireless sensor nodes can use extra energy to obtain additional data to increase the precision. However, if the amount of data sensed is increased indiscriminately, the overhead of relay nodes may increase, and their energy may be exhausted. In this paper, we introduce a sensing and compression rate selection scheme to increase the amount of data obtained while preventing energy exhaustion. In this scheme, the neighbor nodes of the sink node determine the limit of data to be transmitted according to the allocated energy and their descendant nodes, and the other nodes select a compression algorithm appropriate to the allocated energy and the limitation of data to be transmitted. A simulation result verifies that the proposed scheme gathers more data with a lower number of blackout nodes than other schemes. We also found that it adapts better to changes in node density and the amount of energy harvested.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.121-131
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• 2014

Traffic speed is an important measure in transportation. It can be employed for various purposes, including traffic congestion detection, travel time estimation, and road design. Consequently, accurate speed prediction is essential in the development of intelligent transportation systems. In this paper, we present an analysis and speed prediction of a certain road section in Busan, South Korea. In previous works, only historical data of the target link are used for prediction. Here, we extract features from real traffic data by considering the neighboring links. After obtaining the candidate features, linear regression, model tree, and k-nearest neighbor (k-NN) are employed for both feature selection and speed prediction. The experiment results show that k-NN outperforms model tree and linear regression for the given dataset. Compared to the other predictors, k-NN significantly reduces the error measures that we use, including mean absolute percentage error (MAPE) and root mean square error (RMSE).

• Journal of the Korea Society for Simulation
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• v.27 no.2
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• pp.71-81
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• 2018

Forklifts in a shipyard lift and transport heavy objects. Tasks occur dynamically and the rate of the task occurrence changes over time. Especially, the rate of the task occurrence is high immediately after morning and afternoon business hours. The weight of objects varies according to task characteristic, and a forklift also has the workable or allowable weight limit. In this study, we propose an online forklift dispatching algorithm based on nearest-neighbor dispatching rule using minimal cost assignment approach in order to attain the efficient operations. The proposed algorithm considers various types of forklift and multiple jobs at the same time to determine the dispatch plan. We generate dummy forklifts and dummy tasks to handle unbalance in the numbers of forklifts and tasks by taking their capacity limits and weights. In addition, a method of systematic forklift selection is also devised considering the condition of the forklift. The performance indicator is the total travel distance and the average task waiting time. We validate our approach against the priority rule-based method of the previous study by discrete-event simulation.