• Science of Emotion and Sensibility
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• v.23 no.1
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• pp.17-28
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• 2020

Lowering observers' view-height may increase the amount of occlusion across objects in a visual scene and prevent the accurate identification of the objects in the scene. Based on this possibility, memory stimuli in relation to their expected views from different heights were displayed in this study. Thereafter, visual short-term memory (VSTM) performance for the stimuli was measured. In Experiment 1, the memory stimuli were presented on a grid-background drawn according to linear perspectives, which varied across observers' three different view-heights (high, middle, and low). This allowed the participants to remember both the color and position of each memory stimulus. The results revealed that testing participants' VSTM performance for the stimuli under a different memory load of two set-sizes (3 vs. 6) demonstrated an evident drop of performance in the lowest view-height condition. In Experiment 2, the performance for six stimuli with or without the grid-background was tested. A similar pattern of performance drop in the lowest condition as in Experiment 1 was found. These results indicated that different view-heights of an observer can change the amount of occlusion across objects in the visual field, and the sensory interference driven by the occlusion may further influence VSTM performance for those objects.

• Sleep Medicine and Psychophysiology
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• v.11 no.1
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• pp.29-36
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• 2004

Objectives: Sound and light (SL) stimulation has been used as a method to induce some useful mental states in the fields of psychology and psychiatry. It is believed that sound and light entrainment device (SLED) has some specific effects through synchronization of EEG in patients who use it. Theta frequency is believed to stimulate deep relaxation and short term memory processing. This study was conducted to evaluate if 4-10 Hz SL stimulation can induce relaxation and improve short term memory function. Methods: Ten medical students with no medical or psychiatric problems participated in this study. Subjects were randomly divided into two groups. One group was applied with real SLED was applied to one group (R group) and pseudo SLED to the other group (P group). The two groups were exposed to SL stimulation with SLED 15 minutes a day for 5 days, and after two days rest the two groups were switched over. The Korean Wechsler Adult Intelligence Scale (K-WAIS), Academic Motivation Tests (AMT), Test Anxiety Scale (TAS), Korean Auditory Verbal Learning Test (K-AVLT), and digit span were used to evaluate short term memory. Spielberger's State-Trait anxiety inventory and heart rate variability (HRV) test were used to evaluate degree of relaxation. Results: Compared with S group, R group showed a significant improvement in K-AVLT and digit span after a single application of SL stimulation. But 5-day long application did not reveal any differences between the two groups. A significant change in HRV was observed in 5-day long application of SL stimulation after being switched over to other SLED. Conclusion: This pilot study suggests that 4-10 Hz SL stimulation has some positive influences on short term memory and relaxation.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.131-140
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• 2020

The purpose of this study is to analyze the possibility of future droughts by calculating the Standardized Groundwater level Index(SGI) after predicting groundwater level using Long Short Term Memory (LSTM) model. The groundwater level of the Kumho River basin was predicted for the next three years by using the LSTM model, and it was validated through RMSE after learning with observation data except the last three years. The temporal SGI was calculated by using the prediction data and the observation data. The calculated SGI was interpolated within the study area, and the spatial SGI was calculated as the average value for each catchment using the interpolated SGI. The possibility of spatio-temporal drought was analyzed using calculated spatio-temporal SGI. It is confirmed that there is a spatio-temporal difference in the possibility of drought. Through the improvement of deep learning model and diversification of validation method, it is expected to obtain more reliable prediction results and the expansion of study area can be used to respond to drought nationwide, and furthermore it can provide important information for future water resource management.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1077-1093
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• 2020

Sea Surface Temperature (SST) is an important environmental indicator that affects climate coupling systems around the world. In particular, coastal regions suffer from abnormal SST resulting in huge socio-economic damage. This study used Long Short Term Memory (LSTM) and Convolutional Long Short Term Memory (ConvLSTM) to predict SST up to 7 days in the south sea region in South Korea. The results showed that the ConvLSTM model outperformed the LSTM model, resulting in a root mean square error (RMSE) of 0.33℃ and a mean difference of -0.0098℃. Seasonal comparison also showed the superiority of ConvLSTM to LSTM for all seasons. However, in summer, the prediction accuracy for both models with all lead times dramatically decreased, resulting in RMSEs of 0.48℃ and 0.27℃ for LSTM and ConvLSTM, respectively. This study also examined the prediction of abnormally high SST based on three ocean heatwave categories (i.e., warning, caution, and attention) with the lead time from one to seven days for an ocean heatwave case in summer 2017. ConvLSTM was able to successfully predict ocean heatwave five days in advance.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1047-1056
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• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1283-1293
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• 2022

The purpose of this study is to predict residual chlorine in order to maintain stable residual chlorine concentration in sedimentation basin by using artificial intelligence algorithms in water treatment process employing pre-chlorination. Available water quantity and quality data are collected and analyzed statistically to apply into mathematical multiple regression and artificial intelligence models including multi-layer perceptron neural network, random forest, long short term memory (LSTM) algorithms. Water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage data are used as the input parameters to develop prediction models. As results, it is presented that the random forest algorithm shows the most moderate prediction result among four cases, which are long short term memory, multi-layer perceptron, multiple regression including random forest. Especially, it is result that the multiple regression model can not represent the residual chlorine with the input parameters which varies independently with seasonal change, numerical scale and dimension difference between quantity and quality. For this reason, random forest model is more appropriate for predict water qualities than other algorithms, which is classified into decision tree type algorithm. Also, it is expected that real time prediction by artificial intelligence models can play role of the stable operation of residual chlorine in water treatment plant including pre-chlorination process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2513-2530
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• 2021

With the fast development of information and communication technologies, video streaming services and applications are increasing rapidly. However, the network condition is volatile. In order to provide users with better quality of service, it is necessary to develop an accurate and low-complexity model for Quality of Experience (QoE) prediction of time-varying video. Memory effects refer to the psychological influence factor of historical experience, which can be taken into account to improve the accuracy of QoE evaluation. In this paper, we design subjective experiments to explore the impact of Short-Term Memory (STM) on QoE. The experimental results show that the user's real-time QoE is influenced by the duration of previous viewing experience and the expectations generated by STM. Furthermore, we propose analytical models to determine the relationship between intrinsic video quality, expectation and real-time QoE. The proposed models have better performance for real-time QoE prediction when the video is transmitted in a fluctuate network. The models are capable of providing more accurate guidance for improving the quality of video streaming services.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.92-94
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• 1997

By measuring the increase of regional cerebral blood flow (rCBF) during the activation tasks, we can describe the brain regions that participate in certain specific functions. In this study, we composed the functional maps of verbal and nonverbal memory by performing the rCBF positron emission tomography (PET) activation studies and analyzing the differences between control and each activation state. Successive four tasks, which consist of one control state and three different activation tasks, were performed on 6 normal volunteers. All images were spatially normalized on standard atlas and the differences between control and activation states were statistically analyzed. The verbal memory activated predominantly left-sided structures, especially left superior temporal cortex, and the nonverbal short-term memory activated the right frontal cortex. Also, some regions ,where is thought to be related with short-term memory system, such as cingulate gyrus and hippocampus were activated. We conclude that biological validity of the brain regions for verbal and nonverbal memory could be tested using rCBF PET imaging technique and statistical analysis.

• Korean Journal of Cognitive Science
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• v.11 no.3_4
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• pp.33-43
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• 2000

The distinction between short-term store (STS) and long-term store (LTS) has been made in the perspective of information processing. Memory system theorists have argued that memory could be conceived as multiple memory systems beyond the concept of a single LTS. Popular memory system models are Schacter & Tulving (994)'s multiple memory systems and Squire (987)'s the taxonomy of long-term memory. Those m models agree that amnesic patients have intact STS but impaired LTS and have preserved implicit memory. However. there is a debate about the nature of the long-term memory impairment. One model considers amnesic deficit as a selective episodic memory impairment. whereas the other sees the deficits as both episodic and semantic memory impairment. At present, it remains unclear that episodic memory should be distinguished from semantic memory in terms of retrieval operation. The distinction between declarative memory and nondeclarative memory would be the alternative way to reflect explicit memory and implicit memory. The research focused on the function of frontal lobe might give clues to the debate about the nature of LTS.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.220-225
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• 2020

This paper investigates the non-intrusive speech intelligibility estimation method in noise environments when the bottleneck feature of autoencoder is used as an input to a neural network. The bottleneck feature-based method has the problem of severe performance degradation when the noise environment is changed. In order to overcome this problem, we propose a novel non-intrusive speech intelligibility estimation method that adds the noise environment information along with bottleneck feature to the input of long short-term memory (LSTM) neural network whose output is a short-time objective intelligence (STOI) score that is a standard tool for measuring intrusive speech intelligibility with reference speech signals. From the experiments in various noise environments, the proposed method showed improved performance when the noise environment is same. In particular, the performance was significant improved compared to that of the conventional methods in different environments. Therefore, we can conclude that the method proposed in this paper can be successfully used for estimating non-intrusive speech intelligibility in various noise environments.