• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.95-102
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• 2007

Recommender System based on the Collaborative Filtering has a problem of trust of the prediction accuracy because of its problem of sparsity. If the sparsity of a preference value is large, it causes a problem on a process of a choice of neighbors and also lowers the prediction accuracy. In this article, a change of MAE based on the sparsity is studied, groups are classified by sparsity and then, the significant difference among MAEs of classified groups is analyzed. To improve the accuracy of prediction among groups by the problem of sparsity, We studied the improvement of an accurate prediction for recommending system through reducing sparsity by sorting sparsity items, and replacing the average preference among them that has a lot of respondents with the preference evaluation value.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.214-217
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• 2017

In this paper, we propose a sleep efficiency measurement algorithm based on IR-UWB radar sensor in distance. Among the vital signs which can be measured by the IR-UWB radar sensor such as breathing rate, heartbeat rate, and movement, we analyzed correlation between the movement and the sleep efficiency, and based on the result, we propose a sleep efficiency measurement algorithm. In order to verify the performance of the proposed algorithm, we applied the algorithm to three polysomnography patients in hospitals and obtained the performance of an average absolute error within 3.9%.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.1
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• pp.13-18
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• 2010

In this study the validities of runoff prediction methods are reviewed around ESP (Ensemble Streamflow Prediction) techniques. The improvements of runoff predictions on Yongdam river basin are evaluated by the comparison of different prediction methods including ESP incorporated with qualitative meteorological outlooks provided by meteorological agency as well as the runoff forecasting based on the analysis of the historical rainfall scenarios. As a result it is assessed that runoff predictions with ESP may give rise to more accurate results than the ordinary historical average runoffs. In deed the latter gave the mean of yearly absolute error as to be 60.86 MCM while the errors of the former ones amounted to 44.12 MCM (ESP) and 42.83 MCM (ESP incorporated with qualitative meteorological outlooks) respectively. In addition it is confirmed that ESP incorporated with qualitative meteorological outlooks could improve the accuracy of the results more and more. Especially the degree of improvement of ESP with meteorological outlooks shows rising by 10.8% in flood season and 8% in drought season. Therefore the methods of runoff predictions with ESP can be further used as the basic forecasting information tool for the purpose of the effective watershed management.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.877-879
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• 2011

Bowel sounds (BS) are produced by the movement of the intestinal contents in the lumen of the gastro-intestinal tract during peristalsis and thus, it can be used clinically as useful indicators of bowel motility. We devised an estimation algorithm of bowel motility based on the regression modeling of the jitter and shimmer of BS signals measured by auscultation. Ten healthy males ($23.5\pm2.1$ years) were examined. Consequently, the correlation coefficient, coefficient of determination and standard error between the colon transit times (CTT) measured by a conventional radiograph and the values estimated by our algorithm were 0.98, 0.96 and 2.86, respectively. Also, through k-fold cross validation, the average value of the absolute differences between them was $5.0\pm2.5$ hours. This method could be used as a complementary tool for the non-invasive measurement of bowel motility.

• Earthquakes and Structures
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• v.19 no.6
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• pp.411-425
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• 2020

Unreinforced masonry (URM) walls present low shear strength and are prone to brittle failure when subjected to inplane seismic overloads. This paper discusses the shear strengthening of URM walls with Textile Reinforced Mortar (TRM) jackets. The available literature is thoroughly reviewed and an extended database is developed including available brick, concrete and stone URM walls retrofitted and subjected to shear tests to assess their strength. Further, the experimental results of the database are compared against the available shear strength design models from ACI 549.4R-13, CNR DT 215 2018, CNR DT 200 R1/2013, Eurocode 6 and Eurocode 8 guidelines as well as Triantafillou and Antonopoulos 2000, Triantafillou 1998, Triantafillou 2016. The performance of the available models is investigated and the prediction average absolute error (AAE) is as high as 40%. A new model is proposed that takes into account the additional contribution of the reinforcing mortar layer of the TRM jacket that is usually neglected. Further, the approach identifies the plethora of different block materials, joint mortars and TRM mortars and grids and introduces rational calibration of their variable contributions on the shear strength. The proposed model provides more accurate shear strength predictions than the existing models for all different types of the URM substrates, with a low AAE equal to 22.95%.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.127-127
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• 2021

In order to estimate parameter uncertainty of hydrological models, the consideration of the likelihood functions which provide reliable parameters of model is necessary. In this study, the Bayesian Markov Chain Monte Carlo (MCMC) method with informal likelihood functions is used to analyze the uncertainty of parameters of the SURR model for estimating the hourly streamflow of Gunnam station of Imjin basin, Korea. Three events were used to calibrate and one event was used to validate the posterior distributions of parameters. Moreover, the performance of four informal likelihood functions (Nash-Sutcliffe efficiency, Normalized absolute error, Index of agreement, and Chiew-McMahon efficiency) on uncertainty of parameter is assessed. The indicators used to assess the uncertainty of the streamflow simulation were P-factor (percentage of observed streamflow included in the uncertainty interval) and R-factor (the average width of the uncertainty interval). The results showed that the sensitivities of parameters strongly depend on the likelihood functions and vary for different likelihood functions. The uncertainty bounds illustrated the slight differences from various likelihood functions. This study confirms the importance of the likelihood function selection in the application of Bayesian MCMC to the uncertainty assessment of the SURR model.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.714-717
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• 2008

협업여과 추천기법에는 사용자 기반 협업여과와 아이템 기반 협업여과가 있으며, 절차는 유사도 측정, 이웃 선정, 예측값 생성 단계로 이루어진다. 유사도 측정 단계에는 유클리드 거리(Euclidean Distance), 코사인 유사도(Cosine Similarity), 피어슨 상관계수(Pearson Correlation Coefficient) 방법 등이 있고, 이웃 선정 단계에는 상관 한계치(Correlation-Threshold), 근접 N 이웃(Best-N-Neighbors) 방법 등이 있다. 마지막으로 예측값 생성 단계에는 단순평균(Simple Average), 가중합(Weighted Sum), 조정 가중합(Adjusted Weighted Sum) 등이 있다. 이처럼 협업여과 추천기법에는 다양한 기법들이 사용되고 있다. 따라서 본 논문에서는 사용자 기반 협업여과와 아이템 기반 협업여과 추천기법에 사용되는 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 알아보기 위해 성능 실험 및 비교 분석을 하였다. 실험은 GroupLens의 MovieLens 데이터 셋을 활용하였고 MAE(Mean Absolute Error)값을 이용하여 추천기법을 비교 하였다. 실험을 통해 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 찾을 수 있었고, 사용자 기반 협업여과와 아이템 기반 협업여과의 성능비교를 통해 아이템 기반 협업여과의 성능이 보다 우수했음을 확인 하였다.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.44-55
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• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

• Tribology and Lubricants
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• v.40 no.2
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• pp.67-70
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• 2024

Chemical mechanical planarization (CMP) is an essential process for ensuring high integration when manufacturing semiconductor devices. CMP mainly requires the use of polyurethane-based polishing pads as an ultraprecise process to achieve mechanical material removal and the required chemical reactions. A diamond disk performs pad conditioning to remove processing residues on the pad surface and maintain sufficient surface roughness during CMP. However, the diamond grits attached to the disk cause uneven wear of the pad, leading to the poor uniformity of material removal during CMP. This study investigates the pad wear rate profile according to the swing motion of the conditioner during swing-arm-type CMP conditioning using deep learning. During conditioning, the motion of the swing arm is independently controlled in eight zones of the same pad radius. The experiment includes six swingmotion conditions to obtain actual data on the pad wear rate profile, and deep learning learns the pad wear rate profile obtained in the experiment. The absolute average error rate between the experimental values and learning results is 0.01%. This finding confirms that the experimental results can be well represented by learning. Pad wear rate profile prediction using the learning results reveals good agreement between the predicted and experimental values.

• Journal of Biomedical Engineering Research
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• v.22 no.5
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• pp.459-468
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• 2001

The Average Coordinate Referenee System (ACRS) method is developed to reduce experimental errors in human locomotion analysis. Experimentally measured kinematic data is used to conduct analysis in human modeling, and the model accuracy is directly related to the accuracy of the data. However. the accuracy is questionable due to skin movement. deformation of skeletal structure while in motion and limitations of commercial motion analysis system . In this study. the ACRS method is applied to an optically-tracked segment marker system. although it can be applied to many of the others as well. In the ACRS method, each marker can be treated independently. as the origin of a local coordinate system for its body segment. Errors, inherent in the experimental process. result in different values for the recovered Euler angles at each origin. By employing knowledge of an initial, calibrated segment reference frame, the Euler angles at each marker location can be averaged. minimizing the effect of the skin extension and rotation. Using the developed ACRS methodology the error is reduced when compared to the general Euler angle method commonly applied in motion analysis. If there is no error exist in the experimental gait data. the separation and Penetration distance of the femoraltibial joint using absolute coordinate system is supposed to be zero during one gait cycle. The separation and Penetration distance was ranged up to 18 mm using general Euler angle method and 12 mm using the developed ACRS.