• Bulletin of the Korean Chemical Society
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• 제19권6호
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• pp.671-676
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• 1998

The formation and dissociation rates of $Ce^{3+}$ Complexes of the 1,4,7,10-tetraaza-13,16-dioxacyclooctadecane-NN', N",N"'-tetraacetic acid (1), 1,4,7,10-tetraaza-13,16-dioxacyclooctadecane-N,N',N",N"'-tetramethylacetic acid (2), and 1,4,7,10-tetraaza-13,16-dioxacyclooctadecane-N,N',N",N"'-tetrapropionic acid (3) have been measured by the use of stopped-flow spectrophotometry. Observations were made at 25.0±0.1 ℃ and at an ionic strength of 0.10 M $NaClO_4$. The complexation of $Ce^{3+}$ ion with 1 and 2 proceeds through the formation of an intermediate complex $(CeH_3L^{2+})^*$ in which the $Ce^{3+}$ ion is incompletely coordinated. This may then lead to be a final product in the rate-determining step. Between pH 4.76 and 5.76, the diprotonated $(H_2L^{2-})$ from is revealed to be a kinetically active species despite of its low concentration. The stability constants $(logK(CeH_3L^{2+}))$ and specific water-assisted rate constants $(k_{OH})$ of intermediate complexes have been determined from the kinetic data. The dissociation reactions of $Ce^{3+}$ complexes of 1, 2, and 3 were investigated with $Cu^{2+}$, ions as a scavenger in acetate buffer. All complexes exhibit acid-independent and acid-catalyzed contributions. The effect of buffer and $Cu^{2+}$ concentration on the dissociation rate has also been investigated. The ligand effect on the dissociation rate of $Ce^{3+}$ complexes is discussed in terms of the side-pendant arms and the chelate ring sizes of the ligands.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.120-130
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• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.148-157
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• 2021

The effectiveness of recommendation systems depends on the performance of the algorithms with which these systems are designed. The quality of the algorithms themselves depends on the quality of the strategies with which they were designed. These strategies differ from author to author. Thus, designing a good recommendation system means implementing the good strategies. It's in this context that several research works have been proposed on various strategies applied to algorithms to meet the needs of recommendations. Researchers are trying indefinitely to address this objective of seeking the qualities of recommendation algorithms. In this paper, we propose a new algorithm for recommending learning items. Learner performance predictions and collaborative recommendation methods are used as strategies for this algorithm. The proposed performance prediction model is based on convolutional neural networks (CNN). The results of the performance predictions are used by the proposed recommendation algorithm. The results of the predictions obtained show the efficiency of Deep Learning compared to the k-nearest neighbor (k-NN) algorithm. The proposed recommendation algorithm improves the recommendations of the learners' learning items. This algorithm also has the particularity of dissuading learning items in the learner's profile that are deemed inadequate for his or her training.

• 한국운동역학회지
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• 제32권1호
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• pp.1-8
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• 2022

Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).

• Journal of Positioning, Navigation, and Timing
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• 제12권2호
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• pp.91-100
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• 2023

The existing indoor localization method using Wi-Fi fingerprinting has a high collection cost and relatively low accuracy, thus requiring integrated correction of convergence with other technologies. This paper proposes a new method that significantly reduces collection costs compared to existing methods using Wi-Fi fingerprinting. Furthermore, it does not require labeling of data at collection and can estimate pedestrian travel paths even in large indoor spaces. The proposed pedestrian movement path estimation process is as follows. Data collection is accomplished by setting up a feature area near an indoor space intersection, moving through the set feature areas, and then collecting data without labels. The collected data are processed using Kernel Linear Discriminant Analysis (KLDA) and the valley point of the Euclidean distance value between two data is obtained within the feature space of the data. We build learning data by labeling data corresponding to valley points and some nearby data by feature area numbers, and labeling data between valley points and other valley points as path data between each corresponding feature area. Finally, for testing, data are collected randomly through indoor space, KLDA is applied as previous data to build test data, the K-Nearest Neighbor (K-NN) algorithm is applied, and the path of movement of test data is estimated by applying a correction algorithm to estimate only routes that can be reached from the most recently estimated location. The estimation results verified the accuracy by comparing the true paths in indoor space with those estimated by the proposed method and achieved approximately 90.8% and 81.4% accuracy in two experimental spaces, respectively.

• 대한통합의학회지
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• 제12권2호
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• Journal of Power Electronics
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• 제5권2호
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• pp.129-141
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• 2005

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

• Journal of Chest Surgery
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• 제29권3호
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• pp.303-310
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• 1996

1994년 12월부터 1995년 4월까지 서울대학교병원 소아흉부외과에서 개심술을 시행받는 95명의 환아 들을 무작위로 아프로티닌을 쓴 환아군(n=47)과 쓰지않은 환아군(n=48)으로 분류하여 아프로티닌의 효과에 대하여 연구하였다. 아프로티닌은 50,000K/U/kg를 인공심폐기의 충전용액에 단일 투여하였다. 술전, 마취후 5분, 심폐기 관류 5분후, 심폐기 관류 35분후, 재관류 5분후, 재관류 3시간후 및 24시간후에 혈액을 채 취하여 헤모글로빈, 헤마토크릿, BUW, creatinine, 섬유소원, 전해질 농도, 활성 응고시간, 프 로트롬빈 시간, 제3항 트롬빈 등을 측정하였다. 또한 술후 24시간 동안 흥관을 통한 배액 양, 수혈 혈액의 양을 체중으로 나누어 비교하였다. 두 환자군간에 술후 24시간 동안 수혈한 충전 적혈구 양을 제외한 다른 모든 검사에서 통계 학적 의의는 없었으나 아프로티닌의 부작용은 발견되지 않았고 출혈로 인한 재수술도 없었다.

• 정보처리학회논문지B
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• 제16B권3호
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• pp.233-238
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• 2009

자질 수가 적은 소규모 크기 문서들의 자동분류는 좋은 성능을 얻기 어렵다. 그 이유는 문서집단 전체의 자질 수는 크지만 단위 문서 내 자질 수가 상대적으로 너무 적기 때문에 문서간 유사도가 너무 낮아 우수한 분류 알고리즘을 적용해도 좋은 성능을 얻지 못한다. 특히 웹 디렉토리 문서들의 자동분류에서나, 디스크 복구 작업에서 유사도 평가와 자동분류로 연결되지 않은 섹터를 연결하는 작업에서와 같은 소규모 크기 문서의 자동분류에서는 좋은 성능을 얻지 못한다. 따라서 본 논문에서는 소규모 크기 문서의 자동분류에서의 문제점을 해결하기 위해 분류 사전작업으로, 예제기반 자질 필터링 방법 Relief-F알고리즘을 소규모 문서 내 자질 필터링에 적합한 ERelief-F 알고리즘을 제시한다. 또 비교 실험을 위해, 기존의 자질 필터링 방법 중 Odds Ratio와 정보이득, 또 Relief-F 알고리즘을 함께 실험하여 분류결과를 비교하였다. 그 결과, ERelief-F 알고리즘을 사용했을 때의 결과가 정보이득과 Odds Ratio, Relief-F보다 월등히 우수한 성능을 보였고 부적절한 자질도 많이 줄일 수 있었다.

• 전기학회논문지
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• 제59권2호
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.