• PNF and Movement
• /
• v.9 no.1
• /
• pp.55-63
• /
• 2011

Purpose : This study was to investigate the application of Rhythmic Initiation(RI) in the respect of information processing in motor learning. Methods : A computer-aided literature search was performed in PubMed and adapted to the other databases and the others were in published books. The following keywords were used: Rhythmic Initiation, attention, memory, motor accuracy, feedback, motor learning, motor control, PNF, cognition. Results : The characterization of RI is rhythmic motion of limb or body through the desired range, starting with passive motion and progressing to active resisted movement. This study suggested that the relationship between of RI and motor learning through the respect of information processing, memory, attention and motor accuracy. Conclusion : Only Rhythmic Initiation, specifically focused on the effects of information processing approach, suggesting that RI can be positively influeced on sensory-perception, attention, memory, motor accuracy. however, it is unclear whether positive effects in the laboratory and field can be generalized to improve. In addition, sustainability of motor learning with RI remains uncertain.

• Anxiety and mood
• /
• v.18 no.1
• /
• pp.32-37
• /
• 2022

Objective : Impaired working memory has been known to play an important role in the pathophysiology of obsessive-compulsive disorder (OCD) with growing evidence. Delayed matching-to-sample task (DMST) is a working memory task which have an advantage in analyzing several different working memory processes in one task. However, most of the studies have failed to reveal the working memory impairment with the DMST. The aim of this study was to identify whether working memory deficit in OCD can be evaluated with the DMST. Methods : The participants included 20 OCD patients and 20 healthy volunteers. Working memory was evaluated with the DMST with two different working memory loads. Accuracy of response and mean response time were measured. Results : OCD patients showed a significantly longer reaction time and lower accuracy in DMST compared to healthy controls in the task with high working memory loads. Moreover, the difference in accuracy showed interaction with the working memory load. Conclusion : The present results indicate that working memory deficit in patients with OCD can be evaluated with the DMST. The findings also suggest that previous negative behavioral results using the DMST were from low working memory load of the task.

• Journal of information and communication convergence engineering
• /
• v.19 no.1
• /
• pp.22-28
• /
• 2021

Convolutional neural networks (CNNs) are one of the most frequently used artificial intelligence techniques. Among CNN-based applications, small and timing-sensitive applications have emerged, which must be reliable to prevent severe accidents. However, as the small and timing-sensitive systems do not have sufficient system resources, they do not possess proper error protection schemes. In this paper, we propose MATE, which is a low-cost CNN weight error correction technique. Based on the observation that all mantissa bits are not closely related to the accuracy, MATE replaces some mantissa bits in the weight with error correction codes. Therefore, MATE can provide high data protection without requiring additional memory space or modifying the memory architecture. The experimental results demonstrate that MATE retains nearly the same accuracy as the ideal error-free case on erroneous DRAM and has approximately 60% accuracy, even with extremely high bit error rates.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.2
• /
• pp.467-479
• /
• 2022

Working memory (WM), which plays a vital role in daily activities, is a memory system that temporarily stores and processes information when people are engaged in complex cognitive activities. The influence of music on WM has been widely studied. In this work, we conducted a series of n-back memory experiments with different task difficulties and multiple trials on 14 subjects under the condition of no music and Alpha wave leading music. The analysis of behavioral data show that the change of music condition has significant effect on the accuracy and time of memory reaction (p<0.01), both of which are improved after the stimulation of Alpha wave music. Behavioral results also suggest that short-term training has no significant impact on working memory. In the further analysis of electrophysiology (EEG) data recorded in the experiment, auto-regressive (AR) model is employed to extract features, after which an average classification accuracy of 82.9% is achieved with support vector machine (SVM) classifier in distinguishing between before and after WM enhancement. The above findings indicate that Alpha wave leading music can improve WM, and the combination of AR model and SVM classifier is effective in detecting the brain activity changes resulting from music stimulation.

• Journal of Families and Better Life
• /
• v.23 no.1 s.73
• /
• pp.209-222
• /
• 2005

This study was conducted to investigate the effect of interview techniques on memory accuracy and suggestibility of preschool children. Forty-five preschool children participated in a magic show(target event) and 1 week later, all children received suggestive interview in two conditions(language condition & drawing condition). Another 1 week later, all children's recall contents of the magic show was assessed. During suggestive interview, children in drawing condition show more 'acception' response than children in language condition, and children in the question condition show less 'remember' response than children in drawing condition. In second interview children reported more words, and specially ones in language condition report more suggested words than ones in drawing condition. Finally, children's recalls were more accurate on controled informations of the event than on suggestive.

• The Journal of the Korea Contents Association
• /
• v.19 no.9
• /
• pp.101-111
• /
• 2019

This study was to examine association between working memory and social cognitive ability, and the influence of story-use on social cognitive ability. To this end, this study measured working memory(via n-back), and randomly assigned 82 participants into three groups(5th level intentionality, 3rd-level intentionality, and exposition conditions), and then compared the accuracy of perspective taking and emotion recognition(RMET: Reading Minds in the Eyes Test) as social cognitive ability. The results suggested that perspective taking accuracy was significantly associated with working memory capacity, whereas emotion recognition accuracy was not. Contrary to the hypothesis, perspective taking in the 5th-level intentionality story group were significantly lower than those in the 3rd-level intentionality story group. Emotions recognition accuracy was not significantly different among the three groups. Overall, this study produced inconsistent results, which has been discussed in terms of theory and methods.

• Journal of Computing Science and Engineering
• /
• v.11 no.1
• /
• pp.1-8
• /
• 2017

Naive Bayes nearest neighbor (NBNN) is a simple image classifier based on identifying nearest neighbors. NBNN uses original image descriptors (e.g., SIFTs) without vector quantization for preserving the discriminative power of descriptors and has a powerful generalization characteristic. However, it has a distinct disadvantage. Its memory requirement can be prohibitively high while processing a large amount of data. To deal with this problem, we apply a spherical hashing binary code embedding technique, to compactly encode data without significantly losing classification accuracy. We also propose using an inverted index to identify nearest neighbors among binarized image descriptors. To demonstrate the benefits of our method, we apply our method to two existing NBNN techniques with an image dataset. By using 64 bit length, we are able to reduce memory 16 times with higher runtime performance and no significant loss of classification accuracy. This result is achieved by our compact encoding scheme for image descriptors without losing much information from original image descriptors.

• Communications for Statistical Applications and Methods
• /
• v.28 no.5
• /
• pp.521-536
• /
• 2021

As life expectancies increase continuously over the world, the accuracy of forecasting mortality is more and more important to maintain social systems in the aging era. Currently, the most popular model used is the Lee-Carter model but various studies have been conducted to improve this model with one of them being 6-parametric factor model (6-PFM) which is introduced in this paper. To this new model, long short-term memory (LSTM) and regularized LSTM are applied in addition to vector autoregression (VAR), which is a traditional time-series method. Forecasting accuracies of several models, including the LC model, 4-PFM, 5-PFM, and 3 6-PFM's, are compared by using the U.S. and Korea life-tables. The results show that 6-PFM forecasts better than the other models (LC model, 4-PFM, and 5-PFM). Among the three 6-PFMs studied, regularized LSTM performs better than the other two methods for most of the tests.

• International Journal of Computer Science & Network Security
• /
• v.23 no.4
• /
• pp.166-171
• /
• 2023

The Stock price analysis is an increasing concern in a financial time series. The purpose of the study is to analyze the price parameters of date, high, low, and news feed about the stock exchange price. Long short term memory (LSTM) is a cutting-edge technology used for predicting the data based on time series. LSTM performs well in executing large sequence of data. This paper presents the Long Short Term Memory Model has used to analyze the stock price ranges of 10 days and 20 days by exponential moving average. The proposed approach gives better performance using technical indicators of stock price with an accuracy of 82.6% and cross entropy of 71%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.516-518
• /
• 2022

In this paper, we introduce a dependence of number of nodes of hidden-layer in fall detection system using Long Short-Term Memory that can detect falls. Its training is carried out using the parameter theta(θ), which indicates the angle formed by the x, y, and z-axis data for the direction of gravity using a 3-axis acceleration sensor. In its learning, validation is performed and divided into training data and test data in a ratio of 8:2, and training is performed by changing the number of nodes in the hidden layer to increase efficiency. When the number of nodes is 128, the best accuracy is shown with Accuracy = 99.82%, Specificity = 99.58%, and Sensitivity = 100%.