• Journal of Korean Society for Atmospheric Environment
• /
• v.33 no.6
• /
• pp.616-625
• /
• 2017

Measurements using five real-time particle samplers were compared to measurements using three NRM (National Reference Method system) filter-based samplers(Gravimetric method) at Incheon, Korea, between May and August, 2014. The purpose of this study was to suggest the quality assurance/quality control (QA/QC) method of each instrument for use in a real-time continuous particle sampler to measure the mass of airborne particles with an aerodynamic diameter less than $2.5{\mu}m$ ($PM_{2.5}$). Five real-time particle samplers of BAM1020, FH62C_14, TEOM, PM-711 and SPM-613 were evaluated by comparing its measured 23 hr average $PM_{2.5}$ concentrations with those measured with NRM filter-based samplers simultaneously. The parameters(e.g. Inlet heating condition, Slope factor, Film response, Intercept, Background, Span value) of the real-time samplers were optimized respectively by conducting test performance evaluation during 7 days in field sampling. For example, inlet heating temperature of TEOM sampler controls $35{\sim}40^{\circ}C$ to minimize the fluctuation of the real-time measurement data and background value of BAM1020 is the key factor affecting the accuracy of $PM_{2.5}$ mass concentration. We classified the $PM_{2.5}$ concentration according to relative humidity (80%) to identify water absorbed in aerosols by measuring the ${\beta}$-ray samplers(BAM1020, FH62C_14) and TEOM. ${\beta}$-ray samplers were not strongly affected by relative humidity that the difference of the average $PM_{2.5}$ concentration was about 5%. On the other hand, The TEOM sampler overestimated $PM_{2.5}$ mass concentration about 15% at low relative humidity (<80%).

• The Journal of the Korea Contents Association
• /
• v.10 no.6
• /
• pp.307-311
• /
• 2010

The aim of this study is to investigate PAE, as the result of the test of kVp accuracy, according to detector measurement method. Based on the indicated value of 70kVp, each distance between a focus and a kVp meter was 100cm, 80cm and 60cm and the angle of X-ray tube was set on $5^{\circ},\;10^{\circ},\;15^{\circ},\;20^{\circ},\;25^{\circ},\;30^{\circ}$. Each indicated value, 60kVp, 70kVp, 80kVp, 90kVp and 100 kVp, was used compare Small focus with Large focus. As a result, PAE on the side of cathode was higher than it on the side of anode in the case of 100cm and PAE on the side of anode was higher in the case of 80cm and 60cm. The coefficient rate was stable both the side of cathode and anode in the case of 100cm and it was fluctuated in the case of 80cm and 60cm. PAE in the case of Small focus was higher than Large focus and it was disproportionate to an indicated value. Error rate was in inverse proportion to the indicated value.

• Journal of Biomedical Engineering Research
• /
• v.25 no.5
• /
• pp.335-342
• /
• 2004

This paper describes a trans-admittance scanner for breast cancer detection. A FPGA-based sinusoidal waveform generator produces a constant voltage. The voltage is applied between a hand-held electrode and a scan probe placed on the breast. The scan probe contains an 8x8 array of electrodes that are kept at the ground potential. Multi-channel precision digital ammeters using the phase-sensitive demodulation technique were developed to measure the exit current from each electrode in the array. Different regions of the breast are scanned by moving the probe on the breast. We could get trans-admittance images of resistor and saline phantoms with an anomaly inside. The images provided the information on the depth and location of the anomaly. In future studies, we need to improve the accuracy through a better calibration method. We plan to test the scanner's ability to detect a cancer lesion inside the human breast.

• Journal of KIISE:Information Networking
• /
• v.36 no.6
• /
• pp.514-527
• /
• 2009

Wireless sensor networks (WSNs) can be used in various applications for military or environmental purpose. Recently, there are lots of on-going researches for detecting and tracking the spread of continuous objects or phenomena such as poisonous gas, wildfires, earthquakes, and so on. Some previous work has proposed techniques to detect edge nodes of such a continuous object based on the information of all the 1-hop neighbor nodes. In those techniques, however, a number of nodes are redundantly selected as edge nodes, and thus, the boundary of the continuous object cannot be presented accurately. In this paper, we propose a new edge detection method in which edge nodes of the continuous object are detected based on the information of the neighbor nodes obtained via the Localized Delaunay Triangulation so that a minimum number of nodes are selected as edge nodes. We also define the sensor behavior rule for tracking continuous objects energy-efficiently. Our simulation results show that the proposed edge detection method provides enhanced performance compared with previous 1-hop neighbor node based methods. On the average, the accuracy is improved by 29.95% while the number of edge nodes, the amount of communication messages and energy consumption are reduced by 54.43%, 79.36% and 72.34%, respectively. Moreover, the number of edge nodes decreases by 48.38% on the average in our field test with MICAz motes.

• Journal of Distribution Science
• /
• v.12 no.11
• /
• pp.77-83
• /
• 2014

Purpose - The National Agricultural Cooperative Federation of Korea and National Fisheries Cooperative Federation of Korea have prosecuted both financial and retail businesses. As cooperatives are public institutions and receive government support, their sound management is required by the Financial Supervisory Service in Korea. This is mainly managed by CAEL, which is changed by CAMEL. However, NFFC's business section, managing the finance and retail businesses, is unified and evaluated; the CAEL model has an insufficient classification to evaluate the retail industry. First, there is discrimination power as regards CAEL. Although the retail business sector union can receive a higher rating on a CAEL model, defaults have often been reported. Therefore, a default prediction model is needed to support a CAEL model. As we have the default prediction model using a subdivision of indexes and statistical methods, it can be useful to have a prevention function through the estimation of the retail sector's default probability. Second, separating the difference between the finance and retail business sectors is necessary. Their businesses have different characteristics. Based on various management indexes that have been systematically managed by the National Fisheries Cooperative Federation of Korea, our model predicts retail default, and is better than the CAEL model in its failure prediction because it has various discriminative financial ratios reflecting the retail industry situation. Research design, data, and methodology - The model to predict retail default was presented using logistic analysis. To develop the predictive model, we use the retail financial statements of the NFCF. We consider 93 unions each year from 2006 to 2012 to select confident management indexes. We also adapted the statistical power analysis that is a t-test, logit analysis, AR (accuracy ratio), and AUROC (Area Under Receiver Operating Characteristic) analysis. Finally, through the multivariate logistic model, we show that it is excellent in its discrimination power and higher in its hit ratio for default prediction. We also evaluate its usefulness. Results - The statistical power analysis using the AR (AUROC) method on the short term model shows that the logistic model has excellent discrimination power, with 84.6%. Further, it is higher in its hit ratio for failure (prediction) of total model, at 94%, indicating that it is temporally stable and useful for evaluating the management status of retail institutions. Conclusions - This model is useful for evaluating the management status of retail union institutions. First, subdividing CAEL evaluation is required. The existing CAEL evaluation is underdeveloped, and discrimination power falls. Second, efforts to develop a varied and rational management index are continuously required. An index reflecting retail industry characteristics needs to be developed. However, extending this study will need the following. First, it will require a complementary default model reflecting size differences. Second, in the case of small and medium retail, it will need non-financial information. Therefore, it will be a hybrid default model reflecting financial and non-financial information.

• Journal of KIISE:Software and Applications
• /
• v.30 no.3_4
• /
• pp.278-284
• /
• 2003

Speech act is the speaker's intentions indicated through utterances. It is important for understanding natural language dialogues and generating responses. This paper proposes the method of two stage that increases the performance of the korean speech act decision. The first stage is to select features from the part of speech results in sentence and from the context that uses previous speech acts. We use x$^2$ statistics(CHI) for selecting features that have showed high performance in text categorization. The second stage is to determine speech act with selected features and Neural Network. The proposed method shows the possibility of automatic speech act decision using only POS results, makes good performance by using the higher informative features and speed up by decreasing the number of features. We tested the system using our proposed method in Korean dialogue corpus transcribed from recording in real fields, and this corpus consists of 10,285 utterances and 17 speech acts. We trained it with 8,349 utterances and have test it with 1,936 utterances, obtained the correct speech act for 1,709 utterances(88.3%). This result is about 8% higher accuracy than without selecting features.

• Journal of Intelligence and Information Systems
• /
• v.17 no.3
• /
• pp.147-168
• /
• 2011

As personalized recommendation of products and services is rapidly growing in importance, a number of studies provided fundamental knowledge and techniques for developing recommendation systems. Among them, the CF technique has been most widely used and has proven to be useful in many practices. However, current collaborative filtering (CF) technique has still considerable rooms for improving the effectiveness of recommendation systems: 1) a similarity function most systems use to find so-called like-minded people is not well defined in that similarity is computed from a single perspective of similarity concept; and 2) temporal information that contains the changing preference of customers needs to be taken into account when making recommendations. We hypothesize that integration of multiple aspects of similarity and utilization of temporal information will improve the accuracy of recommendations. The objective of this paper is to test the hypothesis through a series of experiments using MovieLens data. The experimental results show that the proposed recommendation system highly outperforms the conventional CF-based systems, confirming our hypothesis.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.45-66
• /
• 2018

Crowdfunding has become more popular than angel funding for fundraising by venture companies. Identification of success factors may be useful for fundraisers and investors to make decisions related to crowdfunding projects and predict a priori whether they will be successful or not. Recent studies have suggested several numeric factors, such as project goals and the number of associated SNS, studying how these affect the success of crowdfunding campaigns. However, prediction of the success of crowdfunding campaigns via non-numeric and unstructured data is not yet possible, especially through analysis of structural characteristics of documents introducing projects in need of funding. Analysis of these documents is promising because they are open and inexpensive to obtain. We propose a novel method to predict the success of a crowdfunding project based on the introductory text. To test the performance of the proposed method, in our study, texts related to 1,980 actual crowdfunding projects were collected and empirically analyzed. From the text data set, the following details about the projects were collected: category, number of replies, funding goal, fundraising method, reward, number of SNS followers, number of images and videos, and miscellaneous numeric data. These factors were identified as significant input features to be used in classification algorithms. The results suggest that the proposed method outperforms other recently proposed, non-text-based methods in terms of accuracy, F-score, and elapsed time.

• Korean Chemical Engineering Research
• /
• v.58 no.4
• /
• pp.588-595
• /
• 2020

Prediction and control of nitrogen oxides (NO_x) emission is of great interest in industry due to stricter environmental regulations. Herein, we propose an artificial intelligence (AI)-based framework for prediction of NO_x emission. The framework includes pre-processing of data for training of neural networks and evaluation of the AI-based models. In this work, Long-Short-Term Memory (LSTM), one of the recurrent neural networks, was adopted to reflect the time series characteristics of NO_x emissions. A decision tree was used to determine a time window of LSTM prior to training of the network. The neural network was trained with operational data from a heating furnace. The optimal model was obtained by optimizing hyper-parameters. The LSTM model provided a reliable prediction of NO_x emission for both training and test data, showing an accuracy of 93% or more. The application of the proposed AI-based framework will provide new opportunities for predicting the emission of various air pollutants with time series characteristics.

• YAKHAK HOEJI
• /
• v.59 no.3
• /
• pp.98-106
• /
• 2015

Analytical method for related substances can be categorized into two methods depending on the necessity of reference standard (RS). The analytical method of related substances with RS is fast and accurate, but it's very expensive and technically difficult to synthesize RS due to their complicated structure. Another method is using relative retention time (RRT) and relative response factor (RRF) which are already validated with RS. Validation of this method is not easy and time consuming, but once it has been developed, it can save cost and time. In this study, we developed the analytical method for related substances of fenofibrate using RRT and RRF. We validated the method by evaluating specificity, linearity, accuracy and precision according to the "Manual for Guideline Application for Validation of Analytical Procedures" of MFDS. Also, we calculated RRT and RRF between fenofibrate and fenofibrate related substances. The results of this study showed high specificity for fenofibrate and fenofibrate related substances. Correlation coefficient(r) of all substances were more than 0.99, and the recovery of fenofibrate, fenofibrate related substance I, II and III were 99.44%, 100.84%, 99.14% and 101.58%, respectively. Precision of fenofibrate and its related substances were ranged between RSD 0.29% and 0.93%. Quantification limits of fenofibrate, fenofibrate related substance I, II and III were determined to be $0.03{\mu}g/ml$, $0.05{\mu}g/ml$, $0.04{\mu}g/ml$ and $0.02{\mu}g/ml$, respectively by confirming signal to noise ratio of each chromatogram. The RRT for fenofibrate related substance I, II and III were determined to be 0.35, 0.41 and 1.34, respectively. Also, the RRF for fenofibrate related substance I, II and III were determined to be 1.28, 0.98 and 0.79, respectively. The developed method was applied to determine contents for fenofibrate related substances in commercial fenofibrate (active pharmaceutical ingredient). As a result, developed analytical methods of related substances will be used for revising the monograph of fenofibrate in Korean Pharmacopoeia revision and contribute quality control of drugs by improving cost and time consuming problem of RS.