• Journal of Physiology & Pathology in Korean Medicine
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• v.19 no.1
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• pp.254-261
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• 2005

The effects on memory and learning ability of the Korean herbal medicine, Xingyo-tang(XGT, 神交湯), which consists of Ginseng Radix(人蔘) 4 g, Liriopis Tuber(麥門冬) 40 g, Morindae Officinalis Radix(巴戟天) 40 g, Biotae Semen(柏子仁) 20 g, Dioscoreae Rhizoma(山藥) 40 g, Euryales Semen(?實) 20 g, Scrophulariae Radix(玄蔘) 40 g, Salviae Miltiorrhizae Radix(丹蔘) 12 g, Poria(茯神) 12 g, Cuscutae Semen(免絲子) 40 g, was investigated. The effects of XGT on learning and memory performance were examined in normal or memory impaired mice by using avoidance tests, Pentobarbital -induced sleep test, fear conditioning task, novel object recognition task, and water maze task. Hot water extract from XGT was used for the studies. Learning ability and memory are based on modifications of synaptic strength among neurons that are simultaneously active. Enhanced synaptic coincidence detection leads to better learning and memory. The XGT-treated (30 mg/100 g and 60 mg/100 g, p.o.) mice exhibit superior ability in learning and memorizing when performing various behavioral tasks. XGT did not affect the passive avoidance responses of normal mice in the step through and step down tests, the conditioned and unconditioned avoidance responses of normal mice in the shuttle box, lever press performance tests, and the ambulatory activity of normal mice in normal condition. In contrast, XGT produced ameliorating effects on the memory retrieval impairment induced by ethanol. XGT also improved the memory consolidation disability induced by electric convulsive shock (ECS). XGT extended the sleeping time induced by pentobarbital dose-dependently, suggesting its transquilizing or antianxiety action. These results suggest that XGT has an improving effect on the impaired learning through the effects on memory registration and retrieval.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.174-174
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• 2000

Q-learning, based on discrete state and action space, is a most widely used reinforcement Learning. However, this requires a lot of memory and much time for learning all actions of each state when it is applied to a real mobile robot navigation using continuous state and action space Region-based Q-learning is a reinforcement learning method that estimates action values of real state by using triangular-type action distribution model and relationship with its neighboring state which was defined and learned before. This paper proposes a new Region-based Q-learning which uses a reward assigned only when the agent reached the target, and get out of the Local optimal path with adjustment of random action rate. If this is applied to mobile robot navigation, less memory can be used and robot can move smoothly, and optimal solution can be learned fast. To show the validity of our method, computer simulations are illusrated.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.527-535
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• 2003

A behavior-based control and learning architecture is proposed, where reinforcement learning is applied to learn proper associations between stimulus and response by using two types of memory called as short Term Memory and Long Term Memory. In particular, to solve delayed-reward problem, a knowledge-propagation (KP) method is proposed, where well-designed or well-trained S-R(stimulus-response) associations for low-level sensors are utilized to learn new S-R associations for high-level sensors, in case that those S-R associations require the same objective such as obstacle avoidance. To show the validity of our proposed KP method, comparative experiments are performed for the cases that (ⅰ) only a delayed reward is used, (ⅱ) some of S-R pairs are preprogrammed, (ⅲ) immediate reward is possible, and (ⅳ) the proposed KP method is applied.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.39-43
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• 2017

As the industry grows, the amount of data grows exponentially, and data analysis using these serves as a predictable solution. As data size increases and processing speed increases, it has begun to be applied to new fields by combining artificial intelligence technology as well as simple big data analysis. In this paper, we propose a method to quickly apply a machine learning based algorithm through efficient resource allocation. The proposed algorithm allocates memory for each attribute. Learning Distinct of Attribute and allocating the right memory. In order to compare the performance of the proposed algorithm, we compared it with the existing K-means algorithm. As a result of measuring the execution time, the speed was improved.

• Journal of IKEEE
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• v.12 no.1
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• pp.65-74
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• 2008

One of the popular methods used for pattern classification is the MBR (Memory-Based Reasoning) algorithm. Since it simply computes distances between a test pattern and training patterns or hyperplanes stored in memory, and then assigns the class of the nearest training pattern, it is notorious for memory usage and can't learn additional information from new data. In order to overcome this problem, we propose an incremental learning algorithm (iMPA). iMPA divides the entire pattern space into fixed number partitions, and generates representatives from each partition. Also, due to the fact that it can not learn additional information from new data, we present iMPA which can learn additional information from new data and not require access to the original data, used to train. Proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory using benchmark data sets from UCI Machine Learning Repository.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.409-410
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• 2022

This paper proposes a fusion model based on Long-Short Term Memory networks (LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification. This method will become an important way to optimize the model and improve the performance of the model.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1516-1522
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• 2007

We had proposed the FPA(Fixed Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. The algorithm worked not bad in many area, but it lead to some overhead for memory usage and lengthy computation in the multi classes area. We propose an Repetitive FPA algorithm which repetitively partitioning pattern space in the multi classes area. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.139-147
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• 2009

The memory based reasoning just stores in the memory in the form of the training pattern of the representative pattern. And it classifies through the distance calculation with the test pattern. Because it uses the techniques which stores the training pattern whole in the memory or in which it replaces training patterns with the representative pattern. Due to this, the memory in which it is a lot for the other machine learning techniques is required. And as the moreover stored training pattern increases, the time required for a classification is very much required. In this paper, We propose the EAS(Evaluation And Selection) algorithm in order to minimize memory usage and to improve classification performance. After partitioning the training space, this evaluates each partitioned space as MDL and PM method. The partitioned space in which the evaluation result is most excellent makes into the representative pattern. Remainder partitioned spaces again partitions and repeat the evaluation. We verify the performance of Proposed algorithm using benchmark data sets from UCI Machine Learning Repository.

• Hwankyungkyoyuk
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• v.21 no.4
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• pp.94-110
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• 2008

Recently environmental problem becomes such a big issue all over the world that the necessity and importance of the environmental education in school has been simultaneously emphasized. While diverse methods for the environmental education have been researched, this paper, especially focused on a teaching-learning model called GBS (Goal-based scenario), aims to provide a new learner-centered approach for the environmental education. For this purpose, this paper first briefly presents two theoretical backgrounds of GBS (i.e., constructivism and Schank's dynamic memory theory), which is followed by specific and concrete strategies and methods of how to apply GBS in class for the teacher. GBS(Goal-Based Scenario) is a learner-centered model in which learners are presented with a reality-based scenario (or task or problem) and go through several stages of 'missions' to get to a final solution of the given scenario. GBS, while completely resonant with other constructivist learning models in terms of learner-centered approaches, is distinctive from others, when it supplies more specific, structured guides of learning, called 'missions', to the students throughout the whole learning process. In a words, GBS ought to be recognized as an unique learner-centered model compromising the contradictory concepts of 'learner control' and 'structure and specifics' in learning environments still without any damage of constructivist learning principles.