• Molecules and Cells
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• v.38 no.9
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• pp.796-805
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• 2015

Gintonin is a novel ginseng-derived lysophosphatidic acid (LPA) receptor ligand. Oral administration of gintonin ameliorates learning and memory dysfunctions in Alzheimer's disease (AD) animal models. The brain cholinergic system plays a key role in cognitive functions. The brains of AD patients show a reduction in acetylcholine concentration caused by cholinergic system impairments. However, little is known about the role of LPA in the cholinergic system. In this study, we used gintonin to investigate the effect of LPA receptor activation on the cholinergic system in vitro and in vivo using wild-type and AD animal models. Gintonin induced $[Ca^{2+}]_i $ transient in cultured mouse hippocampal neural progenitor cells (NPCs). Gintonin-mediated $[Ca^{2+}]_i $ transients were linked to stimulation of acetylcholine release through LPA receptor activation. Oral administration of gintonin-enriched fraction (25, 50, or 100 mg/kg, 3 weeks) significantly attenuated scopolamine-induced memory impairment. Oral administration of gintonin (25 or 50 mg/kg, 1 2 weeks) also significantly attenuated amyloid-${\beta}$ protein ($A{\beta}$)-induced cholinergic dysfunctions, such as decreased acetylcholine concentration, decreased choline acetyltransferase (ChAT) activity and immunoreactivity, and increased acetylcholine esterase (AChE) activity. In a transgenic AD mouse model, long-term oral administration of gintonin (25 or 50 mg/kg, 3 months) also attenuated AD-related cholinergic impairments. In this study, we showed that activation of G protein-coupled LPA receptors by gintonin is coupled to the regulation of cholinergic functions. Furthermore, this study showed that gintonin could be a novel agent for the restoration of cholinergic system damages due to $A{\beta}$ and could be utilized for AD prevention or therapy.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.342-351
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• 2017

The neural decoding is a procedure that uses spike trains fired by neurons to estimate features of original stimulus. This is a fundamental step for understanding how neurons talk each other and, ultimately, how brains manage information. In this paper, the strategies of neural decoding are classified into three methodologies: rate decoding, temporal decoding, and population decoding, which are explained. Rate decoding is the firstly used and simplest decoding method in which the stimulus is reconstructed from the numbers of the spike at given time (e. g. spike rates). Since spike number is a discrete number, the spike rate itself is often not continuous and quantized, therefore if the stimulus is not static and simple, rate decoding may not provide good estimation for stimulus. Temporal decoding is the decoding method in which stimulus is reconstructed from the timing information when the spike fires. It can be useful even for rapidly changing stimulus, and our sensory system is believed to have temporal rather than rate decoding strategy. Since the use of large numbers of neurons is one of the operating principles of most nervous systems, population decoding has advantages such as reduction of uncertainty due to neuronal variability and the ability to represent a stimulus attributes simultaneously. Here, in this paper, three different decoding methods are introduced, how the information theory can be used in the neural decoding area is also given, and at the last machinelearning based algorithms for neural decoding are introduced.

• The Journal of the Korea Contents Association
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• v.8 no.11
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• pp.412-425
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• 2008

One of the important elements for improving academic achievement of learners in education through e-learning is to support learners to study by finding questions they want with providing various evaluation questions. However, most of question retrieval systems usually depend on keyword search based on only a syntactical analysis and/or a hierarchical browsing system classified by the topics of subjects. In such a system it is not easy to find integrative questions associated with each other. In order to improve this problem, in this paper we proposed a question management and retrieval system which allows users to easily manage questions and also to effectively find questions for study on the Web. Then, we implemented a system that gives to access questions for the domain of C language programming. The system makes it possible to easily search questions related to not only a single theme but also questions integrated by interrelationship between topics and questions. This is done by supporting to be able to retrieve questions according to conceptual interrelationships between questions from user query. Consequently, it is expected that the proposed system will provide learners to understand the basic theories and the concepts of the subjects as well as to improve the ability of comprehensive knowledge utilization and problem-solving.

• Journal of East-West Nursing Research
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• v.5 no.1
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• pp.120-126
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• 2000

This study was intended to grasp the history of nursing education from the beginning to the present in Korea, and grip and look-out current diversified systems of nursing education on basis of February, 2000 through literatural review and investigation by close telephone interviews. The basic nursing educational institutions in the whole country were total 113, namely, 3 years course, 65 junior colleges of nursing, and 4 years course, 48 colleges of nursing. And there were 3 types of continuing nursing educational system: two of three were transferring to another college for gain bachelor's degree in nursing; RN-BSN programs and university of broadcasting, and the other was the system of independent learning and then examination for BSN. Total nursing graduates from junior college of nursing courses and college of nursing courses were 7,564 on February, 1999. In general graduate school, the number of master courses were 21 and Ph.D courses were 13. And the number of special graduate schools were 21, i.e., graduate school of education were 7, graduate school of administration were 2, graduate school of public health were 11 and graduate school of industry was 1. As the perspective on nursing education, we overviewed changing nursing organizational environment, increasing the system of continuing education, making standards in nursing education and systemization of nursing educational accreditation, specialization of nursing, information system in nursing education and education of graduate school. The summary of the above overviewed subjects were as follows; Every nursing educational institution needed to educate by educational criteria and standard and characteristically run BSN and graduate courses. Specialization in nursing has to develop more and more, therefore advanced education and law should be prepared appropriately. According to the age's and social needs, we have to establish counter-plan for fundamental educational environment. We have to sensitive to rapidly changing information in the era of globalization. In the level of university education, each university needs characterization of educational objectives, goals and contents, and has to replace the shortage of the number of professor. And the regulation of thesis and dissertation examinations need to be reinforced. Education in nursing should consists with specialization. Collaboration among universities will bring efficiency in the nursing education.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.1-14
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• 2014

Both researchers and practitioners are showing an increased interested in interactive exhibition services. Interactive exhibition services are designed to directly respond to visitor responses in real time, so as to fully engage visitors' interest and enhance their satisfaction. In order to install an effective interactive exhibition service, it is essential to adopt intelligent technologies that enable accurate estimation of a visitor's emotional state from responses to exhibited stimulus. Studies undertaken so far have attempted to estimate the human emotional state, most of them doing so by gauging either facial expressions or audio responses. However, the most recent research suggests that, a multimodal approach that uses people's multiple responses simultaneously may lead to better estimation. Given this context, we propose a new multimodal emotional state estimation model that uses various responses including facial expressions, gestures, and movements measured by the Microsoft Kinect Sensor. In order to effectively handle a large amount of sensory data, we propose to use stratified sampling-based MRA (multiple regression analysis) as our estimation method. To validate the usefulness of the proposed model, we collected 602,599 responses and emotional state data with 274 variables from 15 people. When we applied our model to the data set, we found that our model estimated the levels of valence and arousal in the 10~15% error range. Since our proposed model is simple and stable, we expect that it will be applied not only in intelligent exhibition services, but also in other areas such as e-learning and personalized advertising.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.155-166
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• 2021

Most object detection algorithms are studied based on RGB images. Because the RGB cameras are capturing images based on light, however, the object detection performance is poor when the light condition is not good, e.g., at night or foggy days. On the other hand, high-quality infrared(IR) images regardless of weather condition and light can be acquired because IR images are captured by an IR sensor that makes images with heat information. In this paper, we performed the object detection algorithm based on the compression ratio in RGB and IR images to show the detection capabilities. We selected RGB and IR images that were taken at night from the Free FLIR Thermal dataset for the ADAS(Advanced Driver Assistance Systems) research. We used the pre-trained object detection network for RGB images and a fine-tuned network that is tuned based on night RGB and IR images. Experimental results show that higher object detection performance can be acquired using IR images than using RGB images in both networks.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• Journal of the Korean Applied Science and Technology
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• v.40 no.3
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• pp.476-483
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• 2023

This study aimed to analyze the effects of athletic career and competition participation frequency on exercise commitment of women university taekwondo athletes. Study subjects were 20 women university taekwondo athletes. Athletic career and competition participation frequency was assessed by 4-points scale and the higher points indicate the higher level of each variables. Exercise commitment was assessed by Exercise Commitment Scale. The assessment consists of a total of 8 questions, 4 of which are action immersion and 4 of cognitive immersion, and is evaluated using a 5-point Likert scale. The higher the score, the higher the level of exercise commitment. As the results, positive relationship showed both correlation and casual relationship analysis between competition participation frequency and exercise commitment. Negative casual relationship (-) showed between athletic career and exercise commitment. These results indicated that the increase of competition participation frequency affects the exercise commitment and the longer of athletic career indicates the decrease the level of exercise commitment. Thus, to improve the exercise commitment of women university taekwondo athletes, the competition participation frequency and athletic career should be considered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.