• Journal of Korean Society for Geospatial Information Science
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• v.20 no.4
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• pp.29-36
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• 2012

In order to create a wind resource map, we need wind data, contour map and roughness map. Moving Filter technique was applied to Udo of Jeju Island to improve the accuracy and efficiency of creating roughness map based on the Land Cover Map of the Ministry of Environment. The Land Cover Map was simplified using moving filtering, and the roughness map was created with this Land Cover Map. The wind resource map was created using this roughness map. Finally, we verified the validity and application of moving filter technique for wind resource map. As a result, the wind map which was created using the roughness map with moving filtering showed bias values which were all negative. It means the wind map is underestimated to values of wind energy and RMSE values were also from 0.0237m/s to 0.0253m/s at 50m height. In other words, estimation of wind resource using image filtering provides reliable results at 80m height typically when the wind turbine is installed. Finally, we found that image filtering technique is very useful tool to make wind resource map.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.178-180
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• 2004

In this work, we propose new method of sigma filtering for efficient filtering and preserving edge regions in DCT Domain. In block-based image compression technique, the image is first divided into non-overlapping $8{\times}8$ blocks. Then, the two-dimensional DCT is computed for each $8{\times}8$ block. Once the DCT coefficients are obtained, they are quantized using a specific quantization table. Quantization of the DCT coefficients is a lossy process, and in this step, noise is added. In this work, we combine IDCT matrix and filter matrix to a new matrix to simplify filtering process to remove noise after IDCT in spatial domain, for each $8{\times}8$ DCT coefficient block, we determine whether this block is edge or homogeneous region. If this block is edge region, we divide this $8{\times}8$ block into four $4{\times}4$ sub-blocks, and do filtering process for sub-blocks which is homogeneous region. By this process, we can remove blocking artifacts efficiently preserving edge regions at the same time.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2197-2204
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• 2015

In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.

• Journal of KIISE
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• v.44 no.6
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• pp.621-627
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• 2017

Collaborative filtering is a technique that effectively recommends unrated items for users. Collaborative filtering is based on the similarity of the items evaluated by users. The existing top-N recommendation methods are based on pair-wise and list-wise preference models. However, these methods do not effectively represent the relative preference of items that are evaluated by users, and can not reflect the importance of each item. In this paper, we propose a new method to represent user's latent preference by combining an existing preference model and the notion of inverse user frequency. The proposed method improves the accuracy of existing methods by up to two times.

• 전자공학회논문지 IE
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• v.49 no.2
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• pp.75-81
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• 2012

In this paper, restoration methods were applied to restore analog medicine images with an aged image added and then blurred by noises. To restore the aged image blurred by the blurring function and added by noises, it was applied to the restoration methods which are inverse filtering and wiener filtering which are linear restoration techniques and Lucy-Richardson's algorithm which is nonlinear restoration technique. Moreover, ROC curve, a subjective evaluation method, was applied to evaluate the image quality of the restoration image. The wiener filtering using the ratio of constants acquired better image than the inverse filtering, but both of them couldn't improve ability to make a diagnosis. The restoration image applied to Lucy-Richardson algorithm was the best performance of the applied techniques and its sensitivity and specitivity were improved by 15[%] as much performance as the original aged image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.453-456
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• 2003

Spatial database systems achieve filtering steps with MBR(Minimum founding Rectangle) for efficient query processing, and then carry out refinement steps for candidate objects. While most operations require fast execution of filtering, it is necessary to increase the filtering rates and reduce the number of refinement steps in the low computing powered devices. The compact representation method is also needed in the mobile devices with low storage capacity. The paper proposes various approximation methods for efficient spatial operations in the multiplatform environments. This paper also designs a compression technique for MBR, which occupies almost 80% of index data in the two dimensional case. We also analyze the advantages and drawbacks of each method in terms of space utilization, filtering efficiency and speed.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.339-345
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• 2022

Outcome-based education (OBE) is a tried-and-true teaching technique based on a set of predetermined goals. Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs) are the components of OBE. At the end of each year, the Program Outcomes are evaluated, and faculty members can submit many recommended measures which dependent on the relationship between the program outcomes and its courses outcomes to improve the quality of program and hence the overall educational program. When a vast number of courses are considered, bad actions may be proposed, resulting in unwanted and incorrect decisions. In this paper, a recommender system, using collaborative filtering and association rules algorithms, is proposed for predicting the best relationship between the program outcomes and its courses in order to improve the attributes of the graduates. First, a parallel algorithm is used for Collaborative Filtering on Data Model, which is designed to increase the efficiency of processing big data. Then, a parallel similar learning outcomes discovery method based on matrix correlation is proposed by mining association rules. As a case study, the proposed recommender system is applied to the Computer Information Systems program, College of Computer Sciences and Information Technology, Al-Baha University, Saudi Arabia for helping Program Quality Administration improving the quality of program outcomes. The obtained results revealed that the suggested recommender system provides more actions for boosting Graduate Attributes quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3050-3063
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• 2023

Recommendation systems provide personalized products or services to online users by mining their past preferences. Collaborative filtering is a popular recommendation technique because it is easy to implement. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. To address these problems, a novel collaborative filtering recommendation algorithm is proposed. The proposed algorithm partitions the users using affinity propagation clustering, and searches for k nearest neighbors in the partition where active user belongs, which can reduce the range of searching and improve real-time performance. When predicting the ratings of active user's unrated items, mean deviation method is used to impute values for neighbors' missing ratings, thus the sparsity can be decreased and the recommendation quality can be ensured. Experiments based on two different datasets show that the proposed algorithm is excellent both in terms of real-time performance and recommendation quality.

• Journal of KIISE
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• v.43 no.8
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• pp.835-840
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• 2016

Monte Carlo ray tracing has been widely used for simulating a diverse set of photo-realistic effects. However, this technique typically produces noise when insufficient numbers of samples are used. As the number of samples allocated per pixel is increased, the rendered images converge. However, this approach of generating sufficient numbers of samples, requires prohibitive rendering time. To solve this problem, image filtering can be applied to rendered images, by filtering the noisy image rendered using low sample counts and acquiring smoothed images, instead of naively generating additional rays. In this paper, we proposed a Stein's Unbiased Risk Estimator (SURE) based $\grave{A}$-Trous wavelet to filter the noise in rendered images in a near-interactive rate. Based on SURE, we can estimate filtering errors associated with $\grave{A}$-Trous wavelet, and identify wavelet coefficients reducing filtering errors. Our approach showed improvement, up to 6:1, over the original $\grave{A}$-Trous filter on various regions in the image, while maintaining a minor computational overhead. We have integrated our propsed filtering method with the recent interactive ray tracing system, Embree, and demonstrated its benefits.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1672-1683
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• 2004

Recommender system is a kind of web intelligence techniques to make a daily information filtering for people. Researchers have developed collaborative recommenders (social recommenders), content-based recommenders, and some hybrid systems. In this paper, we introduce a new hybrid recommender method - ICHM where clustering techniques have been applied to the item-based collaborative filtering framework. It provides a way to integrate the content information into the collaborative filtering, which contributes to not only reducing the sparsity of data set but also solving the cold start problem. Extensive experiments have been conducted on MovieLense data to analyze the characteristics of our technique. The results show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem.