• ETRI Journal
• /
• v.41 no.5
• /
• pp.560-573
• /
• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.

• Journal of Ginseng Research
• /
• v.39 no.3
• /
• pp.250-256
• /
• 2015

Background: It has been known that ginseng can be applied as a potential nutraceutical for memory impairment; however, experiments with animals of old age are few. Methods: To determine the memory enhancing effect of red ginseng, C57BL/6 mice (21 mo old) were given experimental diet pellets containing 0.12% red ginseng extract (approximately 200 mg/kg/d) for 3 mo. Young and old mice (4 mo and 21 mo old, respectively) were used as the control group. The effect of red ginseng, which ameliorated memory impairment in aged mice, was quantified using Y-maze test, novel objective test, and Morris water maze. Red ginseng ameliorated age-related declines in learning and memory in older mice. In addition, red ginseng's effect on the induction of inducible nitric oxide synthase and proinflammatory cytokines was investigated in the hippocampus of aged mice. Results: Red ginseng treatment suppressed the production of age-processed inducible nitric oxide synthase, cyclooxygenase-2, tumor necrosis factor-${\alpha}$, and interleukin-$1{\beta}$ expressions. Moreover, it was observed that red ginseng had an antioxidative effect on aged mice. The suppressed glutathione level in aged mice was restored with red ginseng treatment. The antioxidative-related enzymes Nrf2 and HO-1 were increased with red ginseng treatment. Conclusion: The results revealed that when red ginseng is administered over long periods, age-related decline of learning and memory is ameliorated through anti-inflammatory activity.

• Journal of IKEEE
• /
• v.21 no.2
• /
• pp.136-141
• /
• 2017

In this paper, we propose an algorithm for memory optimization to apply deep learning to PC. The proposed algorithm minimizes the memory and computation processing time by reducing the amount of computation processing and data required in the conventional deep learning structure in a general PC. The algorithm proposed in this paper consists of three steps: a convolution layer configuration process using a random filter with discriminating power, a data reduction process using PCA, and a CNN structure creation using SVM. The learning process is not necessary in the convolution layer construction process using the discriminating random filter, thereby shortening the learning time of the overall deep learning. PCA reduces the amount of memory and computation throughput. The creation of the CNN structure using SVM maximizes the effect of reducing the amount of memory and computational throughput required. In order to evaluate the performance of the proposed algorithm, we experimented with Yale University's Extended Yale B face database. The results show that the algorithm proposed in this paper has a similar performance recognition rate compared with the existing CNN algorithm. And it was confirmed to be excellent. Based on the algorithm proposed in this paper, it is expected that a deep learning algorithm with many data and computation processes can be implemented in a general PC.

• Journal of the Korean Society of Systems Engineering
• /
• v.19 no.2
• /
• pp.18-31
• /
• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• International journal of advanced smart convergence
• /
• v.10 no.2
• /
• pp.73-78
• /
• 2021

Recently, the number of people who visit the hospital due to diabetes is increasing. According to the Korean Diabetes Association, it is statistically indicated that one in seven adults aged 30 years or older in Korea suffers from diabetes, and it is expected to be more if the pre-diabetes, fasting blood sugar disorders, are combined. In the last study, the validity of Triglyceride and Cholesterol associated with diabetes was confirmed and analyzed using Random Forest. Random Forest has a disadvantage that as the amount of data increases, it uses more memory and slows down the speed. Therefore, in this paper, we compared and analyzed Random Forest and XGBoost, focusing on improvement of learning speed and prevention of memory waste, which are mainly dealt with in machine learning. Using XGBoost, the problem of slowing down and wasting memory was solved, and the accuracy of the diabetes recognition factor was further increased.

• Korean Journal of Cognitive Science
• /
• v.13 no.1
• /
• pp.53-59
• /
• 2002

This paper presents a model for diagnosing students'learning misconceptions in the domain of heat and temperature using fuzzy cognitive maps(FCM) and fuzzy associative memory(FAM). In a model for diagnosing learning misconceptions. an FCM can represent with cause and effect between preconceptions and misconceptions that students have about scientific phenomenon. An FAM which represents a neurallike memory for memorizing causal relationships is used to diagnose causes of misconceptions in learning. This study will present a new method for more autonomous and intelligent system than a model to diagnose misconceptions that was being done with classical methods in learning and may contribute as an intelligent tutoring system for learning diagnosis within various educational contexts.

• Knowledge Management Research
• /
• v.6 no.2
• /
• pp.67-90
• /
• 2005

Attention has been focused recently on the concept of shared cognition which encompasses the notion that effective team members hold knowledge that is overlapping and complementary with teammates. This shared cognition is expected to improve team effectiveness. In contrast to the continued efforts in developing theoretical approach of shared cognition, empirical studies are meager. Thus, we conducted an empirical study to investigate the role of shared cognition on team effectiveness. This study classifies shared cognition into two types, team mental model and transactive memory system, by shared meaning. A total of 121 new product development teams in the IT industry were surveyed for the data collection. The results of analysis can be summarized as follows: first, team mental model has a positive influence on team performance, team innovative behavior and team learning effect. And the relation with team mental model and team performance is moderated by the similarity of knowledge structure among the expert. Second, transactive memory system has a positive influence on team performance, team innovative behavior and team learning effect.

• Journal of Pharmacopuncture
• /
• v.4 no.1
• /
• pp.49-53
• /
• 2001

This experiment has investigated the influence of Yamen (Du. 15) point injection on learning and memory dysfunction caused by cerebral ischemia and reprofusion in bilateral cervical general artery combined with bleeding on mouse tail to mimic vascular dementia in human beings. By dividing 40 mice into 4 groups (group1false operation group, group2model group, group3point injection with Cerebrolysin group4point injection with saline.) According to random dividing principles, we observed the influence of Yamen(Du. 15) point injection on the time of swimming the whole course used by model mice which had received treatment for different days in different groups, and the influence of those mice on wrong times they entered blind end. The result showed that point injection with Cerebrolysin and saline could improve learning and memory dysfunction of the mice caused by cerebral ischemia.

• The Journal of Korean Association of Computer Education
• /
• v.14 no.5
• /
• pp.39-46
• /
• 2011

Serious games are designed for special purposes of education, training, treatment as well as game-like fun and entertainment. Recently, domestic and foreign market of serious game are growing rapidly. By dissemination of smartphone, the global market for serious game will be expanded for various purposes and users. In this paper, we design and implement serious game 'QUICK REMEMBER 20' for memory improvement using smartphone. We analyze game users based on socio-demographic characteristics and evaluate the learning effectiveness of this game with statistic method.

• Smart Media Journal
• /
• v.12 no.11
• /
• pp.134-144
• /
• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.