• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.430-433
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• 1997

We present the preliminary algorithms for automatic sleep scoring. According to the Rechtschaffen & Kales[3]'s critera, we developed six events detectors and eight parameters which contain the background information of signals, such as EEG, EMG, EOG. With the calculated parameters, we scored each epoch by IF-THEN rules, ANFIS for REM preiods, and finally Neural Network for unobvious epochs. The typical point of this algorithm is that the epoch which had good data sets were calculated in the first stage, and unobvious epochs were postponed until the final stage. After staging the good epochs, we classified unobvious epochs by the dominant stage of previous and posterior epochs.

• Toxicological Research
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• v.18 no.4
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• pp.369-374
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• 2002

The present study was performed to validate an automated image analysis system (Loats Automated Micronucleus Scoring System) for the mouse bone marrow micronucleus assay, comparing with conventional microscopic scoring. Two studies were conducted to provide slides for a comparison of micro-nucleated polychromatic erythrocytes (MNPCEs) values collected manually to those collected by the auto-mated system. Test article A was used as an example of a compound negative for the induction of micronuclei and test article B was wed as a micronucleus-inducing agent to elicit a positive response. Cyclophosphamide was included to provide an positive control in two studies. Bone marrow samples were collected 24 h after administration of test article A and B in male ICR mice. The cells were fixed with absolute methanol and stained with May-Grunwald and Giemsa. The number of MNPCEs was determined by the analysis of 1000 total PCEs per bone marrow sample. In addition to micronucleus scoring, an index of bone marrow toxicity based on PCE ratio (% of PCEs to total erythrocytes) was determined for each sample. The automated and manual scoring was similar when the MNPCEs incidence induced by each test article was less than 10. However manual scoring was able to effectively enumerate micronucleated PCEs in mouse bone marrow when MNPCEs incidence was more than 10, such as cyclophosphamide treatment. Conversely, PCE ratio was superior in computer-assisted image analysis. Taken together, it is suggested that improvement of the automated image analysis may be necessary to render the automatic scoring as sensitive as manual scoring for routine counting of micronuclei, especially because it is superior in objectivity and high throughput scoring.

• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.47-59
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• 2009

The purpose of this paper is to propose an automatic evaluation technique for the prosodic aspect of an English sentence uttered by Korean speakers learning English. The underlying hypothesis is that the consistency of the manual prosody scoring is reflected in an imaginary space of prosody evaluation model constructed out of the three physical properties of the prosody considered in this paper, namely: the fundamental frequency (F0) contour, the intensity contour, and the segmental durations. The evaluation proceeds first by building a prosody evaluation model for the sentence. For the creation of the model, utterances from native speakers of English and Korean learners for the target sentence are manually scored by either native teachers of English or Korean phoneticians in terms of their prosody. Multiple native utterances from the manual scoring are selected as the "model" native utterances against which all the other Korean learners' utterances as well as the model utterances themselves can be semi-automatically evaluated by comparison in terms of the three prosodic aspects [7]. Each learner utterance, when compared to the multiple model native utterances, produces multiple coordinates in a three-dimensional space of prosody evaluation, each axis of which corresponds to the three prosodic aspects. The 3D coordinates from all the comparisons form a prosody evaluation model for the particular sentence and the associated manual scores can display regions of particular scores. The model can then be used as a predictive model against which other Korean utterances of the target sentence can be evaluated. The model from a Korean phonetician appears to support the hypothesis.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.527-534
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• 2016

The emergent information society requires the talent for creative thinking based on problem-solving skills and comprehensive thinking rather than simple memorization. Therefore, the Korean curriculum has also changed into the direction of the creative thinking through increasing short-answer questions that can determine the overall thinking of the students. However, their scoring results are a little bit inconsistency because scoring short-answer questions depends on the subjective scoring of human raters. In order to alleviate this point, an automated scoring system using a machine learning has been used as a scoring tool in overseas. Linguistically, Korean and English is totally different in the structure of the sentences. Thus, the automated scoring system used in English cannot be applied to Korean. In this paper, we introduce an automated scoring system for Korean short-answer questions using predictability and unanimity. We also verify the practicality of the automatic scoring system through the correlation coefficient between the results of the automated scoring system and those of human raters. In the experiment of this paper, the proposed system is evaluated for constructed-response items of Korean language, social studies, and science in the National Assessment of Educational Achievement. The analysis was used Pearson correlation coefficients and Kappa coefficient. Results of the experiment had showed a strong positive correlation with all the correlation coefficients at 0.7 or higher. Thus, the scoring results of the proposed scoring system are similar to those of human raters. Therefore, the automated scoring system should be found to be useful as a scoring tool.

• The KIPS Transactions:PartA
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• v.16A no.2
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• pp.101-112
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• 2009

Existing techniques for defragmentation of the file system need intensive disk operation for some periods at specific time such as disk defragmentation program. In this paper, for solving this problem, we design and implement the automatic and continuous defragmentation free system by distributing the disk operation. We propose the Automatic Layout Scoring(ALS) mechanism for measuring defragmentation degree and suggest the Lazy Copy mechanism that copies the defragmented data at idle time for scattering the disk operation. We search the defragmented file by Automatic Layout Scoring mechanism and then find for empty spaces for that searched file. After lazy copy of searched fils to empty space for preventing that file from being lost, the algorithm solves the defragmentation problem by updating the I-node of that file. We implement these algorithms in Linux and evaluate them for small and defragmented file to get the layout scoring. We outperform the Linux EXT2 file system by $2.4%{\sim}10.4%$ in layout scoring evaluation. And the performance of read and write for various file size is better than the EXT2 by $1%{\sim}8.5%$ for write performance and by $1.2%{\sim}7.5%$ for read performance. We suggest this system for solving the problem of defragmentation automatically without disturbing the I/O task and manual management.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.587-595
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• 2011

Credit scoring is an objective and automatic system to assess the credit risk of each customer. The logistic regression model is one of the popular methods of credit scoring to predict the default probability; however, it may not detect possible nonlinear features of predictors despite the advantages of interpretability and low computation cost. In this paper, we propose to use a generalized partially linear model as an alternative to logistic regression. We also introduce modern ensemble technologies such as bagging, boosting and random forests. We compare these methods via a simulation study and illustrate them through a German credit dataset.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.543-553
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• 2005

Linear logistic regression is one of the most widely used method for credit scoring in credit risk management. This paper deals with credit scoring using splines based on Logistic regression. Linear splines and an automatic basis selection algorithm are adopted. The final model is an example of the generalized additive model. A simulation using a real data set is used to illustrate the performance of the spline method.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1190-1195
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• 1990

A new system for automatic interpretation of the awake electroencephalogram(EEG) was developed in this work. We first clarified all the necessary items for EEG interpretation in accordance with an analysis of visual inspection of the rhythms by a qualified electroencephalographer (EEGer), and then defined each item quantitatively. Concerning the automatic interpretation, we made an effort to find out specific EEG parameters which faithfully represent the procedure of visual interpretation by the qualified EEGer. Those specific EEG parameters were calculated from periodograms of the EEG time series. By using EEG data of 14 subjects, the automatic EEG interpretation system was constructed and compared with the visual interpretation done by the EEGer. The automatic EEG interpretation thus established was proved to be in agreement with the visual interpretation by the EEGer.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.6
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• pp.690-699
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• 2014

Jet engine modulation(JEM) is a technique used to identify the jet engine type from the radar target signature modulated by periodic rotation of the jet engine mounted on the aircraft target. As a new approach of JEM, this paper proposes an automatic algorithm for extracting the jet engine information. First, the rotation period of the jet engine is yielded from auto-correlation of the JEM signal preprocessed by complex empirical mode decomposition(CEMD). Then, the final blade number is estimated by introducing the DM(Divisor-Multiplier) rule and the 'Scoring' concept into JEM spectral analysis. Application results of the simulated and measured JEM signals demonstrated that the proposed algorithm is effective in accurate and automatic extraction of the jet engine information.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.45-56
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• 2015

In this paper, we present a method that can detect context-sensitive word errors and generate correction candidates. Spelling error detection is one of the most widespread research topics, however, the approach proposed in this paper is adjusted for an automated English scoring system. A common strategy in context-sensitive word error detection is using a pre-defined confusion set to generate correction candidates. We automatically generate a confusion set in order to consider the characteristics of sentences written by second-language learners. We define a word error that cannot be detected by a conventional grammar checker because of part-of-speech ambiguity, and propose how to detect the error and generate correction candidates for this kind of error. An experiment is performed on the English writings composed by junior-high school students whose mother tongue is Korean. The f1 value of the proposed method is 70.48%, which shows that our method is promising comparing to the current-state-of-the art.