• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.1-9
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• 2023

This study was conducted for the purpose of improving the usability of the product through the usability evaluation of the voice tag reader to improve the life convenience of the visually impaired. Perceived usability evaluation was conducted for 19 evaluation items based on the evaluation model considering the usability principle and the characteristics of the visually impaired. A total of 50 participants were included for the analysis. As a result of the perceived usability evaluation of the visually impaired, the safety of the voice tag reader, voice and sound quality, and accuracy of voice information were relatively satisfactory. It was found that the reader received a low evaluation in terms of efficiency in use, including the size and weight of the reader, and the convenience of carrying and storing. For the usability improvement, the procedure for using a product needs to be more simplified, and it would be helpful to input and supply tags for commonly used objects in advance.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.167-173
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• 2023

As research has been published to predict future data using regression analysis or artificial intelligence as a method of analyzing economic indicators. In this study, we designed a system that predicts prospective futures prices using artificial intelligence that utilizes topic probability data obtained from past news articles using topic modeling. Topic probability distribution data for each news article were obtained using the Latent Dirichlet Allocation (LDA) method that can extract the topic of a document from past news articles via unsupervised learning. Further, the topic probability distribution data were used as the input for a Long Short-Term Memory (LSTM) network, a derivative of Recurrent Neural Networks (RNN) in artificial intelligence, in order to predict prospective futures prices. The method proposed in this study was able to predict the trend of futures prices. Later, this method will also be able to predict the trend of prices for derivative products like options. However, because statistical errors occurred for certain data; further research is required to improve accuracy.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.73-80
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• 2024

In this paper, we proposes a method to improve the accuracy of predicting the Korea Composite Stock Price Index (KOSPI) by combining topic modeling and Long Short-Term Memory (LSTM) neural networks. In this paper, we use the Latent Dirichlet Allocation (LDA) technique to extract ten major topics related to interest rate increases and decreases from financial news data. The extracted topics, along with historical KOSPI index data, are input into an LSTM model to predict the KOSPI index. The proposed model has the characteristic of predicting the KOSPI index by combining the time series prediction method by inputting the historical KOSPI index into the LSTM model and the topic modeling method by inputting news data. To verify the performance of the proposed model, this paper designs four models (LSTM_K model, LSTM_KNS model, LDA_K model, LDA_KNS model) based on the types of input data for the LSTM and presents the predictive performance of each model. The comparison of prediction performance results shows that the LSTM model (LDA_K model), which uses financial news topic data and historical KOSPI index data as inputs, recorded the lowest RMSE (Root Mean Square Error), demonstrating the best predictive performance.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.17 no.2
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• pp.131-140
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• 2006

Objectives : This study was conducted to investigate the deficits of executive function in children with ADHD, Asperger's Disorder(AD), and teaming disorder (LD), and to identify the differential characteristics of executive function deficits among three groups. Methods : The clinical group consisted of 46 children between the ages of 7 and 15 (16 ADHD, 16 LD, 14 AD). Neuropsychological tests for measuring cognitive function, attention and executive function were individually administered to children, and their performance scores were calculated based on the age norm for each test. Results : There was no significant difference in FSIQ, VIQ, and PIQ among the three groups. However, the AD group tended to show higher scores on the subtests of Information, Vocabulary and Digit Span, and lower score on Comprehension subtest than the ADHD and LD groups, while the LD group tended to show the lowest scores on the Information and Vocabulary subtests. On ADS, the ADHD group showed the highest omission and commission errors. All groups showed poor performances belonging to below 25 percentile ranks on executive function tests when compared to the age norms of normative group. The number of completed category on WCST was the smallest in the ADHD group, while the working memory score was the lowest in the LD group. Conclusion : These results suggest that ADHD, LD, and AD children have executive function deficit in common. However, the specific deficit areas in executive function are different for each group.

• Journal of The Korean Association of Information Education
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• v.9 no.3
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• pp.453-462
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• 2005

Recently, due to technical improvements of storage devices and networks, the amount of data increase rapidly. In addition, it is required to find the knowledge embedded in a data stream as fast as possible. Huge data in a data stream are created continuously and changed fast. Various algorithms for finding frequent itemsets in a data stream are actively proposed. Current researches do not offer appropriate method to find frequent itemsets in which flow of time is reflected but provide only frequent items using total aggregation values. In this paper we proposes a novel algorithm for finding the relative frequent itemsets according to the time in a data stream. We also propose the method to save frequent items and sub-frequent items in order to take limited memory into account and the method to update time variant frequent items. The performance of the proposed method is analyzed through a series of experiments. The proposed method can search both frequent itemsets and relative frequent itemsets only using the action patterns of the students at each time slot. Thus, our method can enhance the effectiveness of learning and make the best plan for individual learning.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.46-54
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• 2006

Recently, the important of a personal identification is increasing according to expansion using each on-line commercial transaction and personal ID-card. Although a personal ID-card embedded RFID(Radio Frequency Identification) tag is gradually increased, the way for a person's identification is deficiency. So we need automatic methods. Because RFID tag is vary small storage capacity of memory, it needs effective feature extraction method to store personal biometrics information. We need new recognition method to compare each feature. In this paper, we studied the face verification system using Hippocampal neuron modeling algorithm which can remodel the hippocampal neuron as a principle of a man's brain in engineering, then it can learn the feature vector of the face images very fast. and construct the optimized feature each image. The system is composed of two parts mainly. One is feature extraction using NMF(Non-negative Matrix Factorization) and LDA(Linear Discriminants Analysis) mixture algorithm and the other is hippocampal neuron modeling and recognition simulation experiments confirm the each recognition rate, that are face changes, pose changes and low-level quality image. The results of experiments, we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to the existing method.

• Journal of Oriental Neuropsychiatry
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• v.19 no.1
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• pp.125-146
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• 2008

Objectives : The purpose of this study is to suggest for the following experimental study of dementia by reviewing recent oriental medicine journals that have been published since 2000. Methods: We have investigated various types of studies in relation to dementia through 90 articles that have been published from 2000 to 2007 in recent oriental medicine journals were registered Korea research foundation. Results and Conclusions : 1. Since 2000, 88 articles in relation to dementia have been published and almost of them are herbal medicine-centered studies. Also they show a tendency to increase every year. The journal of oriental neuropsychiatry carries the highest number of studies in relation to dementia. 2. According to the experimental paper, there are 30 cases of using herb simplexes, 48 cases of herb-combined prescription, and 10 cases of other ways. Especially 7 cases of using herb-combined prescription relation to Sasang constitution are all for the Taeumin. 3. There are 85 cases of Animal and cellular experimental, 60 cases of using pathologic model induced cytotoxic activity, a case of using L-NAME, 3 cases of 192 saporin, 4 cases of ibotenic acid, 10 cases of focal cerebral ischemia, 3 cases of alcohol-administered, and one case of natural degradation. 4. Moms water maze, Radial arm maze Passive avoidance learning model were using for examining learning and memory of model animal 5. We propose that following studies of dementia are to he investigated of the applied method of using siRNA with tranceduced gene, sample preparation by water-soaking, oriental medical diagnosis, standardization of differentiating symptom and herb simplexes, building the database by classified prescriptions, and experiment model which are based on precise examining mechanism with cell line as like mouse H19-7 hippocampus, rat HT22 hippocampus, astrocyte, microglia, using the model of animals at APP, PS1, BACE, CT99/PS1, APOE4, Tau, APP/PSI/Tau

• Journal of the Korean Chemical Society
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• v.66 no.4
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• pp.305-322
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• 2022

In this study, a practice-based teacher education program was developed and applied to improve the TPACK of pre-service chemistry teachers. Also the program effect and obstacles were confirmed by measuring the development of TPACK. The participants of this study were 20 pre-service chemistry teachers of 3rd grade and 2 pre-service chemistry teachers of 4th grade who took chemistry education courses at K University located in Chungcheongbuk Province. The developed teacher education program consisted of four stages: preparation, rehearsal, practice, and reflection. The feedbacks from researchers and colleagues pre-service teachers were provided in preparation, rehearsal, and reflection stages. As a result of the study, the program of this study did not show an educational effect in the "constructive learning activities" of preservice teachers, but it was found to have an educational effect in "problem solving". In other words, in "constructive learning activity", most pre-service teachers were at 0 level before and after the program. The pre-service teachers designed the class to unilaterally provide technology to simply use it as a tool to explain subject content or revise misconceptions, and learners can passively acquire knowledge. However, in the case of "problem solving", the pre-service teachers who were at level 0 before the educational program changed to level 1. Before the program, the pre-service teachers designed classes to solve problems by memory without using technology, but after the program they planned classes that provides opportunities to approach and solve various problems through the technology presented by the teacher. However, there were not many pre-service teachers corresponding to level 2, which constitutes voluntary learning in which learners use technology to solve various problems while selecting and variously manipulating technology. In addition, as obstacles to the TPACK development of pre-service chemistry teachers, there were external factors such as lack of classroom support environment for TPACK implementation, lack of time for education planning, and inadequate technology competency. And there were internal factors such as perspectives of traditional education and negative attitude toward technology. In particular, the proportion of pre-service teachers who preceived inappropriate technical competency as an external obstacles of TPACK development was high. Therefore, it was necessary to develop an education program corresponding to type 2 or type 3 that enables TPACK development through TK for pre-service teachers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Journal of KIISE
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• v.43 no.1
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• pp.87-95
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• 2016

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.