• Korean Journal of Child Studies
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• v.23 no.5
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• pp.89-104
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• 2002

This investigation compared the eyewitness accounts of 5-year-old children by verbal-only inter views and interviews using anatomically neutral dolls. The verbal interviews consisted of free recall, specific question, and leading questions. While the overall accuracy of the interviews increased with the introduction of dolls as memory aids, the efficacy of the dolls was not uniform across types of interviews. In free recall, the dolls were not effective in eliciting accurate accounts. The use of dolls also did not compromise the memory of children in free recall which recalled greater number of correct details with the memory aids than without, but it should be careful in free recall could be reported exactly correct in spite of little bit amounts. Responses to leading questions showed that the children were affected by suggestive misleading question and were susceptible to incorporation of that information into their memory.

• JSTS:Journal of Semiconductor Technology and Science
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• v.8 no.2
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• pp.150-155
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• 2008

This paper is related to developing new Bit Error Rate (BER) simulator, Sam sung BER simulator (SBERS), in order to evaluate the link compliance and all kinds of effects of link compliance in a real environment. SBERS allows to generate transmit pulse accurately by using the various parameters, and obtain the eye diagram and bathtub curve, which represents the performance of link, by calculating the transmit pulse and the measured frequency response characteristics. SBERS give results as same as real environment after taking account of distribution and value of noise. To verify the accuracy of simulator, we derive the simulated and measured result and compare eye opening. The difference came out to be within 5% error. It is possible to estimate the real environment and design the transmitter and receiver circuit effectively using new BER simulator, SBERS.

• Korean Journal of Cognitive Science
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• v.19 no.3
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• pp.311-330
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• 2008

The effect of spatial attention gradually decreases as a function of the distance between the locus of attention and a target. According to this hypothesis, we tested the spatial-effect profile of visual attention when it operates on perception and memory. Experiment 1 measured accuracy of discriminating the color of a simultaneously masked target after presenting a pre-cue to either at the target location or away from the target (perception-intensive task). Experiment 2 measured accuracy of recognizing the color of several items at and around the pre-cued location (memory-intensive task). In the perception-intensive condition, the accuracy gradually dropped as the distance between the cue and target location increases. However, in the memory-intensive condition, subjects remembered only the item at the cued location. This suggests spatial attention in a memory-intensive process would operate on object-based representations. Experiment 2 showed the object-based effect observed in Experiment 1 can be also present in perception under a special circumstance. The results indicate that spatial attention can operate on object-based representations in a memory-intensive process whereas it flexibly can operate either on location-based or object-based representations in a perception-intensive process.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.124-130
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• 2017

Character encoding is the method used to represent characters or symbols on a computer, and there are many encoding detection software tools. For the widely used encoding detection software"uchardet", the accuracy of encoding detection of unmodified normal text document is 91.39%, but the accuracy of language detection is only 32.09%. Also, if a text document is encrypted by substitution, the accuracy of encoding detection is 3.55% and the accuracy of language detection is 0.06%. Therefore, in this paper, we propose encoding and language detection of text document using the deep learning algorithm called LSTM(Long Short-Term Memory). The results of LSTM are better than encoding detection software"uchardet". The accuracy of encoding detection of normal text document using the LSTM is 99.89% and the accuracy of language detection is 99.92%. Also, if a text document is encrypted by substitution, the accuracy of encoding detection is 99.26%, the accuracy of language detection is 99.77%.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.741-754
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• 2008

Due to the characteristics of a data stream, it is very important to confine the memory usage of a data mining process regardless of the amount of information generated in the data stream. For this purpose, this paper proposes the Prime pattern tree(PPT) for finding frequent itemsets over data streams with using the confined memory space. Unlike a prefix tree, a node of a PPT can maintain the information necessary to estimate the current supports of several itemsets together. The length of items in a prime pattern can be reduced the total number of nodes and controlled by split_delta $S_{\delta}$. The size and the accuracy of the PPT is determined by $S_{\delta}$. The accuracy is better as the value of $S_{\delta}$ is smaller since the value of $S_{\delta}$ is large, many itemsets are estimated their frequencies. So it is important to consider trade-off between the size of a PPT and the accuracy of the mining result. Based on this characteristic, the size and the accuracy of the PPT can be flexibly controlled by merging or splitting nodes in a mining process. For finding all frequent itemsets over the data stream, this paper proposes a PPT to replace the role of a prefix tree in the estDec method which was proposed as a previous work. It is efficient to optimize the memory usage for finding frequent itemsets over a data stream in confined memory space. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.1-9
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• 2017

Recently, flash memory has been in a great demand from embedded system sectors for storage devices. However, program/erase (P/E) cycles per block are limited on flash memory. For the limited number of P/E cycles, many wear leveling techniques are studied. They prolonged the life time of flash memory using information tables. As one of the techniques, block erase table (BET) method using a bit array table was studied for embedded devices. However, it has a disadvantage in that performance of wear leveling is sharply low, when the consumption of memory is reduced. To solve this problem, we propose a novel wear leveling technique using Sampling-based Block Erase Table (SBET). SBET relates one bit of the bit array table to each block by using exclusive OR operation with round robin function. Accordingly, SBET enhances accuracy of cold block information and can prevent to decrease the performance of wear leveling. In our experiment, SBET prolongs life time of flash memory by up to 88%, compared with previous techniques which use a bit array table.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.95-102
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• 2019

This study was to investigate the effect of eye movement and intentional finger movement on cognitive ability. Normal adult subjects were randomly divided into two groups: saccadic eye movement(SEM) and intentional finger movement(IFM). After 2 weeks of intervention, Digit span was used for short-term memory test and N-back was used for working memory test. As a result, the short-term memory of the IFM group increased significantly over time, and the follow-up test showed difference between group. The IFM group's the execution time, the error count and the accuracy rate of n-back item showed significant effects over time. The SEM group's the execution time and the accuracy of n-back item showed significant effects over time. In conclusion, the IFM method, which is a multiple stimulus that can activate the cerebral cortex more extensively than the single stimulus SEM, may be more useful as an intervention method of cognitive function improvement.

• JSTS:Journal of Semiconductor Technology and Science
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• v.9 no.4
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• pp.216-224
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• 2009

The endless scaling down of the semiconductor process made the impact of the power network noise on the performance of the state-of-the-art chip a serious design problem. This paper compares the performances of two popular circuit reduction approaches used to improve the efficiency of power network noise analysis: moment matching-based model order reduction (MOR) and node elimination-based MOR. As the benchmarks, we chose PRIMA and R2Power as the matching-based MOR and the node elimination-based MOR. Experimental results indicate that the accuracy, efficiency, and memory requirement of both methods very strongly depend on the structure of the given circuit, i.e., numbers of the nodes and sources, and the number of moments to preserve for PRIMA. PRIMA has higher accuracy in general, while the error of R2Power is also in the acceptable range. On the other hand, PRIMA has the higher efficiency than R2Power, only when the numbers of nodes and sources are small enough. Otherwise, R2Power clearly outperforms PRIMA in efficiency. In the memory requirement, the memory size of PRIMA increases very quickly as the numbers of nodes, sources, and preserved moments increase.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.238-245
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• 2021

In this study, we carried out case study to predict dissolved oxygen (DO) concentration of Nakdong river estuary with LSTM model. we aimed to figure out a optimal model condition and appropriate predictor for prediction in dissolved oxygen concentration with model parameter and predictor as cases. Model parameter case study results showed that Epoch = 300 and Sequence length = 1 showed higher accuracy than other conditions. In predictor case study, it was highest accuracy where DO and Temperature were used as a predictor, it was caused by high correlation between DO concentration and Temperature. From above results, we figured out an appropriate model condition and predictor for prediction in DO concentration of Nakdong river estuary.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.391-393
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• 2021

In this paper, we introduce a long short-term memory (LSTM)-based fall detection system using TensorFlow that can detect falls occurring in the elderly in daily living. 3-axis accelerometer data are aggregated for fall detection, and then three types of parameter are calculated. 4 types of activity of daily living (ADL) and 3 types of fall situation patterns are classified. The parameterized data applied to LSTM. Learning proceeds until the Loss value becomes 0.5 or less. The results are calculated for each parameter θ, SVM, and GSVM. The best result was GSVM, which showed Sensitivity 98.75%, Specificity 99.68%, and Accuracy 99.28%.