• 한국컴퓨터정보학회논문지
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• 제19권2호
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• pp.39-48
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• 2014

본 논문에서는 아무런 불편함이 없이 사람이 하루 중 가장 많은 시간을 보내는 침대에서 사람 동작의 관찰을 가능하게 하는 비침습식 센서 시스템을 제안한다. 제안된 센서 시스템은 얇고 넓은 필름 형태의 압전센서, 신호처리 보드, 그리고 데이터 수집 프로그램으로 구성된다. 사람 동작에 따라 힘이 가해진 압전 센서는 전압 신호를 생성하게 되고, 이 신호는 제안 시스템에 의해 수집, 전처리, 변환된다. 최종 단계에서 FFT 결과는 k-NN 분류기에 의해 식별된다. 침대에서 10,000개 사람 동작을 식별하는 실험을 수행하였고, 약 89.4%의 정인식률을 달성하였다. 실험 결과는 제안된 시스템이 침대를 사용하는 사람이 정상인인지 중풍환자인지 식별할 능력이 있음을 시사한다. 본 논문의 성과는 침대 사용자의 동작을 지속적으로 관찰 가능하게 한다는 점이다. 이러한 지속적인 관찰은 동작 또는 수면 패턴에서 건강상 이상 징후를 탐지하는데 매우 유용하게 활용될 것이다.

• 한국산업정보학회논문지
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• 제12권3호
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• pp.60-68
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• 2007

본 논문에서는 자동으로 사용자의 얼굴 표정을 인식할 수 있는 시스템을 제안한다. 제안된 시스템은 휴리스틱 정보를 기반으로 설계된 트리 구조를 이용하여 행복, 역겨움, 놀람의 감정과 무표정을 인식한다. 카메라로부터 영상이 들어오면 먼저 얼굴 특징 검출기에서 피부색 모델과 연결성분 분석을 이용하여 얼굴 영역을 획득한다. 그 후에 신경망 기반의 텍스처 분류기를 사용하여 눈 영역과 비 눈 영역으로 구분한 뒤 눈의 중심 영역과 에지 정보를 기반으로 하여 눈, 눈썹, 입 등의 얼굴 특징을 찾는다. 검출된 얼굴 특징들은 얼굴 표정 인식기에 사용되며 얼굴 인식기는 이를 기반으로 한 decision tree를 이용하여 얼굴 감정을 인식한다. 제안된 방법의 성능을 평가하기 위해 MMI JAFFE, VAK DB에서 총 180장의 이미지를 사용하여 테스트하였고 약 93%의 정확도를 보였다.

• 감성과학
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• 제15권3호
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• pp.345-354
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• 2012

본 논문의 목적은 불안과 우울 상태를 평가하는데 널리 사용되고 있는 주관적 설문 평가지(Beck의 우울척도, 상태-특성불안척도)의 지표 값과 심박 변이도의 파라미터 간의 상호-상관관계를 통계적으로 분석하여, 주관적 설문평가지의 지표 값을 대신 할 수 있는 심박 변이도 파라미터를 선정하기 위함이다. 그리고 심박 변이도 측정 시, 휴식 및 업무 상태를 인위적으로 유도하였고 각 유도된 상태에서의 심박 변이도를 이용하여 추출된 생리학적 특징값들과 주관적 설문 평가지의 지표 값의 상관관계를 통계적으로 분석하여 주관적 감성지표를 대체 할 수 있는 객관화된 정량적 지표를 도출하고자 한다. 본 논문의 결과로부터 얻을 수 있는 사실은 심박 변이도의 비 정규화 파라미터가 상태불안척도와 우울척도보다 특성불안척도와 더욱 높은 상관관계를 나타냈다는 점이다. 반면, 업무 상태와 휴식 상태의 비율인 정규화 파라미터 m_RRI(MeanRR interval), SDNN(Standard deviation of all NN intervals), LF(Low-Frequency), LF/HF(LF/HF ratio)는 특성불안척도, 우울척도보다 상태불안척도와 더 높은 상관관계를 나타냈다. 이 중, LF/HF는 상태불안뿐만 아니라, 특성불안을 반영할 수 있는 정량적인 생리학적 파라미터로 나타났다.

• 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.

• International Journal of Computer Science & Network Security
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• 제24권9호
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• pp.169-185
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• 2024

Due to levitate and rapid growth of E-Commerce, most of the organizations are moving towards cashless transaction Unfortunately, the cashless transactions are not only used by legitimate users but also it is used by illegitimate users and which results in trouncing of billions of dollars each year worldwide. Fraud prevention and Fraud Detection are two methods used by the financial institutions to protect against these frauds. Fraud prevention systems (FPSs) are not sufficient enough to provide fully security to the E-Commerce systems. However, with the combined effect of Fraud Detection Systems (FDS) and FPS might protect the frauds. However, there still exist so many issues and challenges that degrade the performances of FDSs, such as overlapping of data, noisy data, misclassification of data, etc. This paper presents a comprehensive survey on financial fraud detection system using such data mining techniques. Over seventy research papers have been reviewed, mainly within the period 2002-2015, were analyzed in this study. The data mining approaches employed in this research includes Neural Network, Logistic Regression, Bayesian Belief Network, Support Vector Machine (SVM), Self Organizing Map(SOM), K-Nearest Neighbor(K-NN), Random Forest and Genetic Algorithm. The algorithms that have achieved high success rate in detecting credit card fraud are Logistic Regression (99.2%), SVM (99.6%) and Random Forests (99.6%). But, the most suitable approach is SOM because it has achieved perfect accuracy of 100%. But the algorithms implemented for financial statement fraud have shown a large difference in accuracy from CDA at 71.4% to a probabilistic neural network with 98.1%. In this paper, we have identified the research gap and specified the performance achieved by different algorithms based on parameters like, accuracy, sensitivity and specificity. Some of the key issues and challenges associated with the FDS have also been identified.