• PNF and Movement
• /
• 제9권1호
• /
• 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.

• Journal of information and communication convergence engineering
• /
• 제22권2호
• /
• pp.139-144
• /
• 2024

In this study, four types of fall detection systems - designed with YOLOPose, principal component analysis (PCA), convolutional neural network (CNN), and long short-term memory (LSTM) architectures - were developed and compared in the detection of everyday falls. The experimental dataset encompassed seven types of activities: walking, lying, jumping, jumping in activities of daily living, falling backward, falling forward, and falling sideways. Keypoints extracted from YOLOPose were entered into the following architectures: RAW-LSTM, PCA-LSTM, RAW-PCA-LSTM, and PCA-CNN-LSTM. For the PCA architectures, the reduced input size stemming from a dimensionality reduction enhanced the operational efficiency in terms of computational time and memory at the cost of decreased accuracy. In contrast, the addition of a CNN resulted in higher complexity and lower accuracy. The RAW-LSTM architecture, which did not include either PCA or CNN, had the least number of parameters, which resulted in the best computational time and memory while also achieving the highest accuracy.

• 대한불안의학회지
• /
• 제18권1호
• /
• 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
• /
• 제19권1호
• /
• 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)
• /
• 제16권2호
• /
• 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.

• 가정과삶의질연구
• /
• 제23권1호
• /
• 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.

• 한국콘텐츠학회논문지
• /
• 제19권9호
• /
• pp.101-111
• /
• 2019

이 연구는 작업기억과 사회인지능력 사이의 관계 및 이야기의 이용이 사회인지 능력에 미치는 영향을 탐구하고자 했다. 이를 위해 연구참여자 82명에 대해 작업기업(n-back)을 측정한 다음, 사회인지 부하로서의 지향성 수준(5차 지향성 대 3차 지향성)을 달리한 이야기 및 설명문 등 3개 집단에 무작위로 배치해 사회인지능력으로서 관점채택과 감정추론 정확성을 비교분석했다. 분석결과 관점채택은 작업기억과 유의한 정비례의 관계가 나타났다. 반면 감정추론은 작업기억과 유의한 관계가 나타나지 않았다. 실험집단별 차이는 연구가설과는 반대로 인지부하가 가장 높은 5차지향성 이야기집단의 관점채택이 인지부하가 낮은 3차지향성 이야기집단의 관점채택보다 유의하게 낮았다. 설명문 집단과는 2종의 이야기 집단 모두 유의한 차이가 없었다. 감정추론은 3개 집단 사이에 모두 유의한 차이가 없었다. 전체적으로 이 연구는 일관되지 않은 결과가 나타났는데, 이에 대한 이론 및 방법론적 의의에 대한 논의를 제시했다.

• Journal of Computing Science and Engineering
• /
• 제11권1호
• /
• 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
• /
• 제28권5호
• /
• 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
• /
• 제23권4호
• /
• 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%.