• Title/Summary/Keyword: Output Prediction

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Comparative Analysis of SWAT Generated Streamflow and Stream Water Quality Using Different Spatial Resolution Data (SWAT모형에서 공간 입력자료의 다양한 해상도에 따른 수문-수질 모의결과의 비교분석)

  • Park, Jong-Yoon;Lee, Mi-Seon;Park, Geun-Ae;Kim, Seong-Joon
    • Journal of Korea Water Resources Association
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    • v.41 no.11
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    • pp.1079-1094
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    • 2008
  • This study is to evaluate the impact of varying spatial resolutions on the uncertainty of Soil and Water Assessment Tool (SWAT) predicted streamflow, non-point source (NPS) pollution loads transport in a small agricultural watershed (1.21 $km^2$) for three cases of model input; Case A is the combination of 2 m DEM, QuickBird land use, Case B is the combination of 10 m DEM, 1/25,000 land use, and Case C is the combination of 30 m DEM, Landsat land use, soil data is used 1/25,000 for three cases respectively. The model was calibrated for 2 years (1999-2000) using daily streamflow and monthly water quality records, and verified for another 2 years (2001-2002). The average Nash and Sutcliffe model efficiency was 0.59 for streamflow and RMSE were 2.08, 4.30 and 0.70 tons/yr for sediment, T-N and T-P respectively. The model was run for a small agricultural watershed with three cases of spatial input data. The hydrological results showed that output uncertainty was biggest by spatial resolution of land use. Streamflow increase the watershed average CN value of QucikBird land use was 0.4 and 1.8 higher than those of 1/25,000 and Landsat land use caused increase of streamflow. On the other hand, The NPS loadings from the model prediction showed that the sediment, T-N and T-P of QuickBird land use (Case A) showed 23.7 %, 43.3 % and 48.4 % higher value than 1/25,000 land use (Case B) and 50.6 %, 50.8 % and 56.9 % higher value than Landsat land use (Case C) respectively.

An Intelligent Intrusion Detection Model Based on Support Vector Machines and the Classification Threshold Optimization for Considering the Asymmetric Error Cost (비대칭 오류비용을 고려한 분류기준값 최적화와 SVM에 기반한 지능형 침입탐지모형)

  • Lee, Hyeon-Uk;Ahn, Hyun-Chul
    • Journal of Intelligence and Information Systems
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    • v.17 no.4
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    • pp.157-173
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    • 2011
  • As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

Feasibility of Deep Learning Algorithms for Binary Classification Problems (이진 분류문제에서의 딥러닝 알고리즘의 활용 가능성 평가)

  • Kim, Kitae;Lee, Bomi;Kim, Jong Woo
    • Journal of Intelligence and Information Systems
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    • v.23 no.1
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    • pp.95-108
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    • 2017
  • Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

Study on the Effects of R&D Activities on the Exports of Korean Economy (R&D투자가 한국경제 수출에 미치는 영향 분석)

  • Kim Byung-Woo
    • Journal of Technology Innovation
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    • v.14 no.1
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    • pp.31-66
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    • 2006
  • The country with a relative abundance of human capital conducts relatively more R&D in the steady state than its partner. This country acquires the know-how to produce a relatively wider range of innovative goods. High technology comprises a large share of the national economy in the human-capital rich country and real output growth is faster. This prediction would seem to accord weakly with empirical observation of Korean economy.

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Evaluation of Agro-Climatic Index Using Multi-Model Ensemble Downscaled Climate Prediction of CMIP5 (상세화된 CMIP5 기후변화전망의 다중모델앙상블 접근에 의한 농업기후지수 평가)

  • Chung, Uran;Cho, Jaepil;Lee, Eun-Jeong
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.17 no.2
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    • pp.108-125
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    • 2015
  • The agro-climatic index is one of the ways to assess the climate resources of particular agricultural areas on the prospect of agricultural production; it can be a key indicator of agricultural productivity by providing the basic information required for the implementation of different and various farming techniques and practicalities to estimate the growth and yield of crops from the climate resources such as air temperature, solar radiation, and precipitation. However, the agro-climate index can always be changed since the index is not the absolute. Recently, many studies which consider uncertainty of future climate change have been actively conducted using multi-model ensemble (MME) approach by developing and improving dynamic and statistical downscaling of Global Climate Model (GCM) output. In this study, the agro-climatic index of Korean Peninsula, such as growing degree day based on $5^{\circ}C$, plant period based on $5^{\circ}C$, crop period based on $10^{\circ}C$, and frost free day were calculated for assessment of the spatio-temporal variations and uncertainties of the indices according to climate change; the downscaled historical (1976-2005) and near future (2011-2040) RCP climate sceneries of AR5 were applied to the calculation of the index. The result showed four agro-climatic indices calculated by nine individual GCMs as well as MME agreed with agro-climatic indices which were calculated by the observed data. It was confirmed that MME, as well as each individual GCM emulated well on past climate in the four major Rivers of South Korea (Han, Nakdong, Geum, and Seumjin and Yeoungsan). However, spatial downscaling still needs further improvement since the agro-climatic indices of some individual GCMs showed different variations with the observed indices at the change of spatial distribution of the four Rivers. The four agro-climatic indices of the Korean Peninsula were expected to increase in nine individual GCMs and MME in future climate scenarios. The differences and uncertainties of the agro-climatic indices have not been reduced on the unlimited coupling of multi-model ensembles. Further research is still required although the differences started to improve when combining of three or four individual GCMs in the study. The agro-climatic indices which were derived and evaluated in the study will be the baseline for the assessment of agro-climatic abnormal indices and agro-productivity indices of the next research work.

Data collection strategy for building rainfall-runoff LSTM model predicting daily runoff (강수-일유출량 추정 LSTM 모형의 구축을 위한 자료 수집 방안)

  • Kim, Dongkyun;Kang, Seokkoo
    • Journal of Korea Water Resources Association
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    • v.54 no.10
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    • pp.795-805
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    • 2021
  • In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m3/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.

Label Embedding for Improving Classification Accuracy UsingAutoEncoderwithSkip-Connections (다중 레이블 분류의 정확도 향상을 위한 스킵 연결 오토인코더 기반 레이블 임베딩 방법론)

  • Kim, Museong;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.27 no.3
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    • pp.175-197
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    • 2021
  • Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

A study on the derivation and evaluation of flow duration curve (FDC) using deep learning with a long short-term memory (LSTM) networks and soil water assessment tool (SWAT) (LSTM Networks 딥러닝 기법과 SWAT을 이용한 유량지속곡선 도출 및 평가)

  • Choi, Jung-Ryel;An, Sung-Wook;Choi, Jin-Young;Kim, Byung-Sik
    • Journal of Korea Water Resources Association
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    • v.54 no.spc1
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    • pp.1107-1118
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    • 2021
  • Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

Sentiment Analysis of Movie Review Using Integrated CNN-LSTM Mode (CNN-LSTM 조합모델을 이용한 영화리뷰 감성분석)

  • Park, Ho-yeon;Kim, Kyoung-jae
    • Journal of Intelligence and Information Systems
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    • v.25 no.4
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    • pp.141-154
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    • 2019
  • Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

Sensory Information Processing

  • Yoshimoto, Chiyoshi
    • Journal of Biomedical Engineering Research
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    • v.6 no.2
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    • pp.1-8
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    • 1985
  • The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70$\pm$1.32mmHg/min)compared to CF dialyzers(4.32$\pm$0.55mmHg/min)(p<0.05). However, there was no observable difference in the UFR between the two dialyzers. Neither APD nor UFR showed any significant increase with an increasing number of reuses for up to more than 20reuses. A substantial number of failures observed in APD(larger than 20mmHe/min)on the reused dialyzers(2 out of 40 CP and S out 26 C-DAK) were attributed to the Possible damage on the fibers. The CF 15-11 HFDs which failed APD test did not show changes in the UFR compared to normal dialyzers indicating that APD is a more sensitive test than UFR test to evaluate the integrity of the fibers. 30527 T00401030527 ^x For quantitative measurement of reflected light from a clinical diagnostic strip, a prototype old reflectance photometer was designed. The strip loader and cassette were made to obtain more accurate reflectance parameters. The strip was illuminated at 45˚c through optical fiber and the intensity of reflected light was determined at rectanguLat angle using a photodiode. The kubelka-munk coefficient and reflection optical density were determined ar four different wavelengths(500, 550, 570 and 610nm) for blood glucose strip. For higher concentration than 300mg/41 about glucose, a saturation state of abforbance was observed at 500, 550 and 570nm. The correlation between glucose concentration and parameters was the best at 610nm. 30535 T00401030535 ^x Radiation-induced fibrosarcoma tumors were grown on the flanks of C3H mice. The mice were divided into two groups. One group was injected with Photofrin II, intravenously (2.5mg/kg body weight). The other group received no Photofrin II. Mice from both groups were irradialed for approximately 15 minutes at 100, 300, or 500 mW/cm2 with the argon (488nm/514.5 nm), dye(628nm) and gold vapor (pulsed 628 nm) laser light. A photosensitizer behaved as an added absorber. Under our experimental conditions, the presence of Photolfrin II increased surface temperature by at least 40% and the temperature rise due to 300 mW/cm2 irradiation exceeded values for hyperthermia. Light and temperature distributions with depth were estimated by a computer model. The model demonstrated the influence of wavelength on the thermal process and proved to be a valuable tool to investigate internal temperature rise. 30536 T00401030536 ^x We investigated the structural geometry of thirty-eight Korean femurs. The purpose of this study is to identify major geometrical differences between Korean femurs 3nd others that we believe belong to Caucasians so that we would be able to get insights into the femoral component design that fits Asians including Koreans. We utilized computerized tomography (CT) images of femurs extracted from cadavers. The CT images were transformed into bitmap data by using a film scanner, and then analyzed by using a commercially available software called Image v.1.0 and a Macintosh IIci computer.The resulting data were compared with already published data. The major results show that the geometry of the Korean femurs is significantly different from that of Caucasians: (1) the anteversion angle and the canal flare index are greater by the amount of approximately 8˚ and 0.5, respectively, (2) the shape of the isthmus cross section is more round, and (3) the distance between the teaser trochanter and the proximal border of the isthmus is shelter by about 15 mm. The results suggested that the femoral component suitable for Asians should be different from the currently-used components designed and manufactured mostly by European or American companies. 30537 T00401030537 ^x It is well known that nonlinear propagation characteristics of the wave in the tissue may give very useful information for the medical diagnoisis. In this paper, a new method to detect nonlinear propagation characteristics of the internal vibration in the tissue for the low frequency mechanical vibration by using bispectral analysis is proposed. In the method, low frequency vibration of f0( = 100Hz) is applied on the surface of the object, and the waveform of the internal vibration x (t) is measured from Doppler frequency modulation of silmultaneously transmitted probing ultrasonic waves. Then, the bispectra of the signal x (t) at the frequencies (f0, f0) and (f0, 2f0) are calculated to estimate the nonlinear propagation characteristics as their magnitude ratio, w here since bispectrum is free from the gaussian additive noise we can get the value with high S/N. Basic experimental system is constructed by using 3.0 MHz probing ultrasonic waves and the several experiments are carried out for some phantoms. Results show the superiority of the proposed method to the conventional method using power spectrum and also its usefulness for the tissue characterization. 30541 T00401030541 ^x This paper describes the implementation of a computerized radial pulse diagnosis by aids of a clinical expert. On this base, we composed of the radial pulse diagnosis system in korean traditional medicine. The system composed of a radial pulse wave detection system and a radial pulse diagnosis system. With a detection system, we detected Inyoung and Cheongu radial pulse wave and processed it. Then, we have got the characteristic parameters of radial pulse wave and also quantified that according to the method of Inyoung-Cheongu Comparison Radial Pulse Diagnosis. We defined the jugement standard of radial pulse diagnosis system and then we confirmed the possibility for realization of automatic radial pulse diagnosis in korean traditional medicine. 30545 T00401030545 ^x Microspheres are expected to be applied to biomedical areas such as solid-phase immunoassays, drug delivery systems, immunomagnetic cell separation. To synthesize microspheres for biomedical application, "two stage shot growth method" was developed. The uniformity ratio of synthesized microspheres was always smaller than 1.05. And the surface charge density (or the number of ionizable functional groups) of the microspheres synthesized by "two stage shot growth method" was 6~13 times higher than that of the microspheres synthesized by conventional seeded batch copolymerization. As a previous step for biomedical application, adsorption experiments of bovine albumin on microspheres were carried out under various conditions. The maximum adsorbed amount was obtained in the neighborhood of pH 4.5. Isoelectric point of bovine albumin is pH 5.0, so experimental result shows that it shifted to acid area. The adsorption isotherm was obtained, the plateau region was always reached at 2.Og/L (bulk concentration of bovine albumin).The effect of the kind and the amount of surface functional group was also examined. 30575 T00401030575 ^x A medical image workstation was developed using multimedia technique. The system based on PC-486DX was designed to acquire medical images produced by medical imaging instruments and related audio information, that is, doctors' reporting results. Input information was processed and analyzed, then the results were presented in the form of graph and animation. All the informations of the system were hierarchically related with the image as the apex. Processing and analysis algorithms were implemented so that the diagnostic accuracy could be improved. The diagnosed information can be transferred for patient diagnosis through LAN(local area network). 30592 T00401030592 ^x In the conventional infrared imaging system, complex infrared lens systems are usually used for directing collimated narrow infrared beams into the high speed 2-dimensional optic scanner. In this paper, a simple reflective infrared optic system with a 2-dimensional optic scanner is proposed for the realization of medical infrared thermography system. It has been experimentally proven that the intfrared thermography system composed of the proposed optic system has the temperature resolution of 0.1˚c under the spatial resolution of lmrad, the image matrix size of 256 X 240, and tile imaging time of 4 seconds. 30593 T00401030593 ^x In this paper, MIIS (Medical Image Information System) has been designed and implemented using INGRES RDBMS, which is based on a client/server architecture. The implemented system allows users to register and retrieve patient information, medical images and diagnostic reports. It also provides the function to display these information on workstation windows simultaneously by using the designed menu-driven graphic user interface. The medical image compression/decompression techniques are implemented and integrated into the medical image database system for the efficient data storage and the fast access through the network. 30594 T00401030594 ^x In this paper, computerized BEAM was implemented for the space domain analysis of EEG. Trans-formation from temporal summation to two-dimensional mappings is formed by 4 nearest point inter-polaton method. Methods of representation of BEAM are two. One is dot density method which classify brain electrical potential 9 levels by dot density of gray levels and the other is colour method which classify brain electrical 12 levels by red-green colours. In this BEAM, instantaneous change and average energy distribution over any arbitrary time interval of brain electrical activity could be observed and analyzed easily. In the frequency domain, the distribution of energy spectrum of a special band can easily be distinguished normality and abnormality. 30608 T00401030608 ^x Laboratory information system (LIS) is a key tool to manage laboratory data in clinical pathology. Our department has developed an information system for routine hematology using down-sized computer system. We have used an IBM 486 compatible PC with 16MB main memory, 210 MB hard disk drive, 9 RS-232C port and 24 pin dot printer. The operating system and database management system were SCO UNIX and SCO foxbase, respectively. For program development, we used Xbase language provided by SCO foxbase. The C language was used for interface purpose. To make the system use friendly, pull-down menu was used. The system connected to our hospital information system via application program interface (API), so the information related to patient and request details is automatically transmitted to our computer. Our system interfaced with fwd complete blood count analyzers(Sysmex NE-8000 and Coulter STKS) for unidirectional data tansmission from analyzer to computer. The authors suggests that this system based on down-sized computer could provide a progressive approach to total LIS based on local area network, and the implemented system could serve as a model for other hospital's LIS for routine hematology. 30609 T00401030609 ^x To develop an artificial bone substitute that is gradually degraded and replaced by the regenerated natural bone, the authors designed a composite that is consisted of calcium phosphate and collagen. To use as the structural matrix of the composite, collagen was purified from human umbilical cord. The obtained collagen was treated by pepsin to remove telopeptides, and finally, the immune-free atelocollagen was produced: The cross linked atelocollagen was highly resistant to the collagenase induced collagenolysis. The cross linked collagen demonstrated an improved tensile strength. 30618 T00401030618 ^x This paper is a study on the design of adptive filter for QRS complex detection. We propose a simple adaptive algorithm to increase capability of noise cancelation in QRS complex detection with two stage adaptive filter. At the first stage, background noise is removed and at the next stage, only spectrum of QRS complex components is passed. Two adaptive filters can afford to keep track of the changes of both noise and QRS complex. Each adaptive filter consists of prediction error filter and FIR filter The impulse response of FIR filter uses coefficients of prediction error filter. The detection rates for 105 and 108 of MIT/BIH data base were 99.3% and 97.4% respectively. 30619 T00401030619 ^x To develop an artificial bone substitute that is gradually degraded and replaced by the regenerated natural bone, the authors designed and produced a composite that is consisted of calcium phosphate and collagen. Human umbilical cord origin pepsin treated type I atelocollagen was used as the structural matrix, by which sintered or non-sintered carbonate apatite was encapsulated to form an inorganic-organic composite. With cross linking atelocollagen by UV ray irradiation, the resistance to both compressive and tensile strength was increased. Collagen degradation by the collagenase induced collagenolysis was also decreased. 30620 T00401030620 ^x We have developed a monoleaflet polymer valve as an inexpensive and viable alternative, especially for short-term use in the ventricular assist device or total artificial heart. The frame and leaflet of the polymer valve were made from polyurethane, To evaluate the hemodynamic performance of the polymer valve a comparative study of flow dynamics past a polymer valve and a St. Jude Medical prosthetic valve under physiological pulsatile flow conditions in vitro was made. Comparisons between the valves were made on the transvalvular pressure drop, regurgitation volume and maximum valve opening area. The polymer valve showed smaller regurgitation volume and transvalvular pressure drop compared to the mechanical valve at higher heart rate. The results showed that the functional characteristics of the polymer valve compared favorably with those of the mechanical valve at higher heart rate. 30621 T00401030621 ^x Explosive evaporative removal process of biological tissue by absorption of a CW laser has been simulated by using gelatin and a multimode Nd:YAG laser. Because the point of maximun temperature of laser-irradiated gelatin exists below the surface due to surface cooling, evaporation at the boiling temperature is made explosively from below the surface. The important parameters of this process are the conduction loss to laser power absorption (defined as the conduction-to-laser power parameter, Nk), the convection heat transfer at the surface to conduction loss (defined as Bi), dimensionless extinction coefficient (defined as Br.), and dimensionless irradiation time (defined as Fo). Dependence of Fo on Nk and Bi has been observed by experiment, and the results have been compared with the numerical results obtained by solving a 2-dimensional conduction equation. Fo and explosion depth (from the surface to the point of maximun temperature) are increased when Nk and Bi are increased.To find out the minimum laser power for explosive evaporative removal process, steady state analysis has been also made. The limit of Nk to induce evaporative removal, which is proportional to the inverse of the laser power, has been obtained. 30622 T00401030622 ^x N1 and N2 gross neural action potentials were measured from the round window of the guinea pig cochlea at the onset of the acoustic stimuli. N1-N2 audiograms were made by means of regulating stimulant intensities in order to produce constant N1-N2 potentials as criteria for different input tone pip frequencies. The lowest threshold was measured with an input tone pip I5 dB SPL in intensity and 12 KHz in frequency when the animal was in normal physiological condition. The procedure of experimental measurements is explained in detail. This experimental approach is very useful for the investigation of the Cochlear function. Both noN1inear and active functions of the Cochlea can be monitored by N1-N2 audiograms. 30623 T00401030623 ^x In electrical impedance tomography(EIT), we use boundary current and voltage measurements toprovide the information about the cross-sectional distribution of electrical impedance or resistivity. One of the major problems in EIT has been the inaccessibility of internal voltage or current data in finding the internal impedance values. We propose a new image reconstruction method using internal current density data measured by NMR. We obtained a two-dimensional current density distribution within a phantom by processing the real and imaginary MR images from a 4.77 NMR machine. We implemented a resistivity mage reconstruction algorithm using the finite element method and sensitivity matrix. We presented computer simulation results of the mage reconstruction algorithm and furture direction of the research. 30624 T00401030624 ^x A new method of digital image analysis technique for discrimination of cancer cell was presented in this paper. The object image was the Thyroid eland cells image that was diagnosed as normal and abnormal (two types of abnormal: follicular neoplastic cell, and papillary neoplastic cell), respectively. By using the proposed region segmentation algorithm, the cells were segmented into nucleus. The 16 feature parameters were used to calculate the features of each nucleus. A9 a consequence of using dominant feature parameters method proposed in this paper, discrimination rate of 91.11% was obtained for Thyroid Gland cells. 30625 T00401030625 ^x An electrical stimulator was designed to induce locomotion for paraplegic patients caused by central nervous system injury. Optimal stimulus parameters, which can minimize muscle fatigue and can achieve effective muscle contraction were determined in slow and fast muscles in Sprague-Dawley rats. Stimulus patterns of our stimulator were designed to simulate electromyographic activity monitored during locomotion of normal subjects. Muscle types of the lower extremity were classified according to their mechanical property of contraction, which are slow muscle (msoleus m.) and fast muscle (medial gastrocneminus m., rectus femoris m., vastus lateralis m.). Optimal parameters of electrical stimulation for slow muscles were 20 Hz, 0.2 ms square pulse. For fast muscle, 40 Hz, 0.3 ms square pulse was optimal to produce repeated contraction. Higher stimulus intensity was required when synergistic muscles were stimulated simultaneously than when they were stimulated individually. Electrical stimulation for each muscle was designed to generate bipedal locomotion, so that individual muscles alternate contraction and relaxation to simulate stance and swing phases. Portable electrical stimulator with 16 channels built in microprocessor was constructed and applied to paraplegic patients due to lumbar cord injury. The electrical stimulator restored partially gait function in paraplegic patients. 30626 T00401030626 ^x Two-Dimensional modelling of the Cochlear biomechanics is presented in this paper. The Laplace partial differential equation which represents the fluid mechanics of the Cochlea has been transformed into two-dimensional electrical transmission line. The procedure of this transformation is explained in detail. The comparison between one and two dimensional models is also presented. This electrical modelling of the basilar membrane (BM) is clearly useful for the next approach to the further. Development of active elements which are essential in the producing of the sharp tuning of the BM. This paper shows that two-dimension model is qualitatively better than one-dimensional model both in amplitude and phase responses of the BM displacement. The present model is only for frequency response. However because the model is electrical, the two-dimensional transmission line model can be extended to time response without any difficult. 30627 T00401030627 ^x A method has been proposed for the fully automatic detection of left ventricular endocardial boundary in 2D short axis echocardiogram using geometric model. The procedure has the following three distinct stages. First, the initial center is estimated by the initial center estimation algorithm which is applied to decimated image. Second, the center estimation algorithm is applied to original image and then best-fit elliptic model estimation is processed. Third, best-fit boundary is detected by the cost function which is based on the best-fit elliptic model. The proposed method shows effective result without manual intervention by a human operator. 30628 T00401030628 ^x The intelligent trajectory control method that controls moving direction and average velocity for a prosthetic arm is proposed by pattern recognition and force estimations using EMG signals. Also, we propose the real time trajectory planning method which generates continuous accelleration paths using 3 stage linear filters to minimize the impact to human body induced by arm motions and to reduce the muscle fatigue. We use combination of MLP and fuzzy filter for pattern recognition to estimate the direction of a muscle and Hogan's method for the force estimation. EMG signals are acquired by using a amputation simulator and 2 dimensional joystick motion. The simulation results of proposed prosthetic arm control system using the EMf signals show that the arm is effectively followed the desired trajectory depended on estimated force and direction of muscle movements. 30638 T00401030638 ^x A new neural network architecture for the recognition of patterns from images is proposed, which is partially based on the results of physiological studies. The proposed network is composed of multi-layers and the nerve cells in each layer are connected by spatial filters which approximate receptive fields in optic nerve fields. In the proposed method, patterns recognition for complicated images is carried out using global features as well as local features such as lines and end-points. A new generating method of matched filers representing global features is proposed in this network. 30659 T00401030659 ^x An implementation scheme of the magnetic nerve stimulator using a switching mode power supply is proposed. By using a switching mode power supply rather than a conventional linear power supply for charging high voltage capacitors, the weight and size of the magnetic nerve stimulator can be considerably reduced. Maximum output voltage of the developed magnetic nerve stimulator using the switching mode power supply is 3, 000 volts and switching time is about 100 msec. Experimental results or human nerve stimulations using the developed stimulator are presented. 30768 T00401030768 ^x In this paper, we describe the design methodology and specifications of the developed module-based bedside monitors for patient monitoring. The bedside monitor consists of a main unit and module cases with various parameter modules. The main unit includes a 12.1" TFT color LCD, a main CPU board, and peripherals such as a module controller, Ethernet LAN card, video card, rotate/push button controller, etc. The main unit can connect at maximum three module cases each of which can accommodate up to 7 parameter modules. They include the modules for electrocardiograph, respiration, invasive blood pressure, noninvasive blood pressure, temperature, and SpO2 with Plethysmograph.SpO2 with Plethysmograph.

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