• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 추계학술대회 논문집
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• pp.329-334
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• 2002

High-speed machining is one of the most effective technology to improve productivity. Because of the high speed and high feed rate, high-speed machining can give great advantages for the machining of dies and molds. This paper describes on the improvement of machining accuracy in high-speed machining. Depth of cut, feed rate, spindle revolution and cutting force are control factors. The effect of the control factors on machining accuracy is discussed for the results of surface roughness and machining error in Z-direction for the high speed machining of STD11.

• Korean Journal of Acupuncture
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• 제24권3호
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• pp.47-54
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• 2007

A Case Report of Communication disorder associated with Post-stroke Objectives : The Objective Is the Report of Improvement in Communication Disorder Associated with Post-Stroke. Methods : Acupuncture And Medicinal Treatments Were Performed from 17th March 2006 until 8th April 2006. The Medicines Bangpungtongsung-san and Chungpesagan-tang Were Used to Improve Patient Bowel Movement. The Sa-Am Acupuncture Method Was Used to Improve Patient Communication Disorders. Every 5 Days Articulation Accuracy, Vowel Accuracy, Alternation and Speed of Reading Sentences Were Evaluated. Results : After 23 Days of Treatment There was Improvement of Articulation Accuracy, Alternation and Speed of Reading Sentences.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.147-154
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• 2019

In this paper, we conducted a study that utilizes deep learning to calculate appropriate physical exercise information when basic human factors such as sex, age, height, and weight of users come in. To apply deep learning, a method was applied to calculate the amount of fat needed to calculate the amount of one repetition maximum by utilizing the structure of the basic Deep Neural Network. By applying Accuracy improvement methods such as Relu, Weight initialization, and Dropout to existing deep learning structures, we have improved Accuracy to derive a lean body weight that is closer to actual results. In addition, the results were derived by applying a formula for calculating the one repetition maximum load on upper and lower body movements for use in actual physical exercise. If studies continue, such as the way they are applied in this paper, they will be able to suggest effective physical exercise options for different conditions as well as conditions for users.

• Journal of Positioning, Navigation, and Timing
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• 제10권2호
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• pp.83-89
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• 2021

The extended Kalman filter (EKF) is widely used for global navigation satellite system (GNSS) applications. It is difficult to obtain precise positions with an EKF one-way (forward or backward) filter. In this paper, we propose an EKF smoother to improve the positioning accuracy by integrating forward and backward filters. For the EKF smoother experiment, we performed PPP using GNSS data received at the DAEJ reference station for a month. The effectiveness of the proposed approach is validated with multi-GNSS kinematic PPP experiments. The EKF smoother showed 35%, 6%, and 22% improvement in east, north, and up directions, respectively. In addition, accurate tropospheric zenith total delay (ZTD) values were calculated by a smoother. Therefore, the results from EKF smoother demonstrate that better accuracy of position can be achieved.

• Journal of information and communication convergence engineering
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• 제21권3호
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• pp.198-207
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• 2023

Responding to changes in artificial intelligence models and the data environment is crucial for increasing data-learning accuracy and inference stability of industrial applications. A learning model that is overfitted to specific training data leads to poor learning performance and a deterioration in flexibility. Therefore, an early stopping technique is used to stop learning at an appropriate time. However, this technique does not consider the homogeneity and independence of the data collected by heterogeneous nodes in a differential network environment, thus resulting in low learning accuracy and degradation of system performance. In this study, the generalization performance of neural networks is maximized, whereas the effect of the homogeneity of datasets is minimized by achieving an accuracy of 99.7%. This corresponds to a decrease in delay time by a factor of 2.33 and improvement in performance by a factor of 2.5 compared with the conventional method.

• 대한토목학회논문집
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• 제11권4호
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• pp.151-162
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• 1991

본 연구에서는 자료형태 및 전처리수준이 다른 위성영상자료를 이용하여 전처리수준에 따른 외부표정요소의 최적다항식형태와 위치결정정확도를 파악하고, 지상기준점의 획득방법 및 획득방법에 따른 3차원 위치결정정확도를 분석함으로서 SPOT 위성영상을 이용한 3차원 위치결정이론 및 프로그램을 개발하고 위치결정의 정확도를 향상시키는데 목적이 있다. 본 연구수행 결과 각 전처리수준에 대한 외부표정요소의 최적다항식형태(수준 1B; 15변수, 수준 1AP, 1A; 12변수)를 결정할 수 있었으며, 전처리수준에 따른 위치결정 정확도분석에서는 수준 1AP 위성사진의 정확도가 가장 좋았으나, 수치영상인 수준 1A도 유사한 정확도를 나타냄을 알 수 있었다. 또한, 지상기준점의 획득방법이 다른 수준 1A 수치영상의 3차원 위치결정 정확도를 분석한 결과, 지상기준점의 정확도가 양호한 경우 부가매개변수의 도입만으로 정확도 향상률이 큰 반면 지상기준점의 정확도가 저하되는 경우 부가매개변수의 도입만으로 정확도가 크게 향상되지 않아 조정체계에 과대오차소거이론이 포함된 동시조정이론을 적용함이 바람직함을 알 수 있었다.

• 한국측량학회지
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• 제36권5호
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• pp.355-369
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• 2018

LiDAR (Light Detection And Ranging) strip adjustment is process to improve geo-referencing of the ALS (Airborne Laser Scanner) strips that leads to seamless LiDAR data. Multiple strips are required to collect data over the large areas, thus the strips are overlapped in order to ensure data continuity. The LSA (LiDAR Strip Adjustment) consists of identifying corresponding features and minimizing discrepancies in the overlapping strips. The corresponding features are utilized as control features to estimate transformation parameters. This paper applied SURF (Speeded Up Robust Feature) to identify corresponding features. To improve determination of the corresponding feature, false matching points were removed by applying three schemes: (1) minimizing distance of the SURF feature vectors, (2) selecting reliable matching feature with high cross-correlation, and (3) reflecting geometric characteristics of the matching pattern. In the strip adjustment procedure, corresponding points having large residuals were removed iteratively that could achieve improvement of accuracy of the LSA eventually. Only a few iterations were required to reach reasonably high accuracy. The experiments with simulated and real data show that the proposed method is practical and effective to airborne LSA. At least 80 % accuracy improvement was achieved in terms of RMSE (Root Mean Square Error) after applying the proposed schemes.

• 대한음성학회지:말소리
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• 제58호
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• pp.67-81
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• 2006

For a large corpus of time-aligned data, HMM based approaches are most widely used for automatic segmentation, providing a consistent and accurate phone labeling scheme. There are two methods for training in HMM. Flat starting method has a property that human interference is minimized but it has low accuracy. Bootstrap method has a high accuracy, but it has a defect that manual segmentation is required In this paper, a new algorithm is proposed to minimize manual work and to improve the performance of automatic segmentation. At first phase, voiced, unvoiced and silence classification is performed for each speech data frame. At second phase, the phoneme sequence is aligned dynamically to the voiced/unvoiced/silence sequence according to the acoustic phonetic rules. Finally, using these segmented speech data as a bootstrap, phoneme model parameters based on HMM are trained. For the performance test, hand labeled ETRI speech DB was used. The experiment results showed that our algorithm achieved 10% improvement of segmentation accuracy within 20 ms tolerable error range. Especially for the unvoiced consonants, it showed 30% improvement.

• 한국인공지능학회지
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• 제11권3호
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• 한국철도학회:학술대회논문집
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• 한국철도학회 2002년도 추계학술대회 논문집(I)
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• pp.547-552
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• 2002

The research sees the catenary dropper clamp using a test equipment which is using actual sample in the field. This test helps the quality improvement of material. For raising the reliability of data, classified it by type and tests especially in the multiple sample but it was under testing in the sample of decimal at circumstance. From the accuracy side of research data it was insufficient because the dropper clamp was in small quantity but it contributes in quality of material improvement. The quality of material data which it gets with spectroscopic was not accurate so hereafter it needs to follow wet analysis and precise analysis to join in the test which is detailed comes to accomplish with the data accuracy.