• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.114-122
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• 2016

EIS (Electro interstitial scan) is a non-invasive and simple method to find the physio-pathological information inferred by electric current response with respect to low direct current applied between remote sites of the body. Although a few EIS-based devices for diagnosing diabetes were commercialized, they were not successful in offering clinical validity nor in confirming diagnostic principle. In this study, we measured the voltage responses of diabetic patients and normal subjects with a commercialized EIS device to test the usefulness of EIS in screening diabetes. For this purpose, voltage was measured between pairs of electrodes contacted at both palm, both soles of the feet and left and right forehead above both eyes. After feature extraction of voltage signals, the AUC (area under the curve) between the two groups was calculated and we found that seven variables were appropriately shown above 60% of accuracy. In addition, we applied the k-NN (k-nearest neighbors) method and found that the accuracy of classification between the two groups reached the accuracy of 76.2%. This result implies that the voltage response analysis based on EIS has potential as a diabetics screening method.

• 전자공학회논문지 IE
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• v.47 no.2
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• pp.73-81
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• 2010

The main purpose of this study was to examine the effects of meta-cognition, learning flow and problem solving ability of the college engineering students on academic achievement. For this purpose, a total of 396 college engineering freshmen of the six different departments was chosen to conduct a survey. A hypothetical model was proposed, which was composed of meta-cognition, problem solving ability and learning flow as the prediction variables, and academic achievement as the outcome variables. The results of this study through multiple regression analysis showed that meta-cognition, learning flow and problem solving ability significantly influenced on the college engineering studnets' academic achievement. In addition, learning flow was used as a significant mediated variable in the relationships among meta-cognition, problem solving ability and academic achievement. Based on these study results, the above variables investigated in this study should be considered in the design and development of the college engineering courses that enable students to facilitate their problem-solving attitude and improve academic achievement.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.5 s.305
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• pp.27-34
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• 2005

Ventricular fibrillation(VF) is generally caused by chaotic behavior of electrical propagation in heart and may result in sudden cardiac death. In this study, we proposed a ventricular fibrillation detection algorithm based on support vector machine classifier, which could offer benefits to reduce the teaming costs as well as good classification performance. Before the extraction of input features, raw ECG signal was applied to preprocessing procedures, as like wavelet transform based bandpass filtering, R peak detection and segment assignment for feature extraction. We selected input features which of some are related to the rhythm information and of others are related to wavelet coefficients that could describe the morphology of ventricular fibrillation well. Parameters for SVM classifier, C and ${\alpha}$, were chosen as 10 and 1 respectively by trial and error experiments. Each average performance for normal sinus rhythm ventricular tachycardia and VF, was 98.39%, 96.92% and 99.88%. And, when the VF detection performance of SVM classifier was compared to that of multi-layer perceptron and fuzzy inference methods, it showed similar or higher values. Consequently, we could find that the proposed input features and SVM classifier would one of the most useful algorithm for VF detection.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.7
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• pp.36-45
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• 1999

In order to control the flow of traffics in ATM networks and optimize the usage of network resources, an efficient control mechanism is necessary to cope with congestion and prevent the degradation of network performance caused by congestion. In this paper, Buffered Leaky Bucket which applies the same control scheme to a variety of traffics requiring the different QoS(Quality of Service) and Neural Networks lead to the effective buffer utilization and QoS enhancement in aspects of cell loss rate and mean transfer delay. And the cell scheduling algorithms such as DWRR and DWEDF for multiplexing the incoming traffics are enhanced to get the better fair delay. The network congestion information from cell scheduler is used to control the predicted traffic loss rate of Neural Leaky Bucket, and token generation rate and buffer threshold are changed by the predicted values. The prediction of traffic loss rate by neural networks can enhance efficiency in controlling the cell loss rate and cell transfer delay of next incoming cells and also be applied for other traffic controlling schemes. Computer simulation results performed for random cell generation and traffic prediction show that QoSs of the various kinds of traffcis are increased.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.3
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• pp.285-296
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• 2014

This study was to develop a introduction program contents for first year nursing students and to verify the effectiveness of the program contents. The effects of introduction program contents for first year nursing students is to identify a career attitude maturity and self-efficacy. The study used a One Group Pre-post Test Design and the surveys were carried out form 5, March to 26, March, 2012. The participants were 129 students of nursing in K university. The data were obtained via questionnaires survey before and after taking the course. The collected data were analyzed by paired t-test using SPSS 15. As a result in analyzing the effect of introduction program contents on career attitude maturity and self-efficacy for first year nursing students, introduction program contents was appeared to influence the self-efficacy. The scores of self-efficacy(t=2.36, p=.002) showed statistically significant improvement after the education. Also there was significant increase in career attitude maturity(t=2.92, p=.004) after the education. The results of this study were the positive effects of introduction program contents on first nursing students' career attitude maturity and self-efficacy. These findings suggest that introduction program contents as a subject would be an efficient way for self-efficacy and career attitude maturity of first year nursing students. and I recommend that various professor teaching method and nursing education contents development with utilizing ICT.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.859-864
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• 2017

IoT is a new emerging technology to lead the $4^{th}$ industry renovation and has been widely used in industry and home to increase the quality of human being. In this paper, IoT based face detection and recognition system for a smart elevator is developed. Haar cascade classifier is used in a face detection system and a proposed PCA algorithm written in Python in the face recognition system is implemented to reduce the execution time and calculates the eigenfaces. SVM or Euclidean metric is used to recognize the faces detected in the face detection system. The proposed system runs on RaspberryPi 3. 200 sample images in ORL face database are used for training and 200 samples for testing. The simulation results show that the recognition rate is over 93% for PP+EU and over 96% for PP+SVM. The execution times of the proposed PCA and the conventional PCA are 0.11sec and 1.1sec respectively, so the proposed PCA is much faster than the conventional one. The proposed system can be suitable for an elevator monitoring system, real time home security system, etc.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.2
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• pp.21-28
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• 2010

This paper reports an experimental result on optimizing dissimilarity-based classification(DBC) by simultaneously using a dynamic time warping(DTW) and a multiple fusion strategy(MFS). DBC is a way of defining classifiers among classes; they are not based on the feature measurements of individual samples, but rather on a suitable dissimilarity measure among the samples. In DTW, the dissimilarity is measured in two steps: first, we adjust the object samples by finding the best warping path with a correlation coefficient-based DTW technique. We then compute the dissimilarity distance between the adjusted objects with conventional measures. In MFS, fusion strategies are repeatedly used in generating dissimilarity matrices as well as in designing classifiers: we first combine the dissimilarity matrices obtained with the DTW technique to a new matrix. After training some base classifiers in the new matrix, we again combine the results of the base classifiers. Our experimental results for well-known benchmark databases demonstrate that the proposed mechanism achieves further improved results in terms of classification accuracy compared with the previous approaches. From this consideration, the method could also be applied to other high-dimensional tasks, such as multimedia information retrieval.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.131-143
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• 2014

We propose a no-reference perceptual fog density and visibility prediction model in a single foggy scene based on natural scene statistics (NSS) and perceptual "fog aware" statistical features. Unlike previous studies, the proposed model predicts fog density without multiple foggy images, without salient objects in a scene including lane markings or traffic signs, without supplementary geographical information using an onboard camera, and without training on human-rated judgments. The proposed fog density and visibility predictor makes use of only measurable deviations from statistical regularities observed in natural foggy and fog-free images. Perceptual "fog aware" statistical features are derived from a corpus of natural foggy and fog-free images by using a spatial NSS model and observed fog characteristics including low contrast, faint color, and shifted luminance. The proposed model not only predicts perceptual fog density for the entire image but also provides local fog density for each patch size. To evaluate the performance of the proposed model against human judgments regarding fog visibility, we executed a human subjective study using a variety of 100 foggy images. Results show that the predicted fog density of the model correlates well with human judgments. The proposed model is a new fog density assessment work based on human visual perceptions. We hope that the proposed model will provide fertile ground for future research not only to enhance the visibility of foggy scenes but also to accurately evaluate the performance of defog algorithms.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.10
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• pp.1009-1016
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• 2016

In this study, an educational program was developed. The program can perform the claim for examination of medical expense, which is one of NCS Competence Unit Elements for hospital administration. Considering various coding to complex compute and process, VB.Net was employed for this development. For database, ACCESS Database was used because it is easy to learn and use. The learning effects by the developed program are expected to be as follows. First, the composition of medical expense can be understood by analyzing Medical history and then selecting insurance code according to the Standard of Medical Care Code. Second, unit cost per score can be learned according to hospital class. Third, selection of Column (medical materials) and Column II(medical practice) can classify items of additional ratio. Fourth, because patient's payment rate on hospitalization and meal expense and use of special equipment are differently applied, user can know patient's payment rate by type and can calculate it. Fifth, additional amount is the amount calculated by additional ratio of Column II(medical practice), and user can learn additional ratio according by insurance type and hospital class. Sixth, user can learn self-pay rate by hospital class and understand the process that self-pay amount and claim amount are calculated according by self-pay rate.

• The Journal of Society for e-Business Studies
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• v.25 no.1
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• pp.13-33
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• 2020

This study proposes a Korean sentimental analysis algorithm that utilizes a letter-unit embedding and convolutional neural networks. Sentimental analysis is a natural language processing technique for subjective data analysis, such as a person's attitude, opinion, and propensity, as shown in the text. Recently, Korean sentimental analysis research has been steadily increased. However, it has failed to use a general-purpose sentimental dictionary and has built-up and used its own sentimental dictionary in each field. The problem with this phenomenon is that it does not conform to the characteristics of Korean. In this study, we have developed a model for analyzing emotions by producing syllable vectors based on the onset, peak, and coda, excluding morphology analysis during the emotional analysis procedure. As a result, we were able to minimize the problem of word learning and the problem of unregistered words, and the accuracy of the model was 88%. The model is less influenced by the unstructured nature of the input data and allows for polarized classification according to the context of the text. We hope that through this developed model will be easier for non-experts who wish to perform Korean sentimental analysis.