• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.207-216
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• 2013

The aim of this study is to design a classifier for sleep stage analysis and select important feature set which shows sleep stage well based on physiological signals during sleep. Sleep has a significant effect on the quality of human life. When people undergo lack of sleep or sleep-related disease, they are likely to reduced concentration and cognitive impairment affects, etc. Therefore, there are a lot of research to analyze sleep stage. In this study, after acquisition physiological signals during sleep, we do pre-processing such as filtering for extracting features. The features are used input for the new combination algorithm using genetic algorithm(GA) and neural networks(NN). The algorithm selects features which have high weights to classify sleep stage. As the result of this study, accuracy of the algorithm is up to 90.26% with electroencephalography(EEG) signal and electrocardiography(ECG) signal, and selecting features are alpha and delta frequency band power of EEG signal and standard deviation of all normal RR intervals(SDNN) of ECG signal. We checked the selected features are well shown that they have important information to classify sleep stage as doing repeating the algorithm. This research could use for not only diagnose disease related to sleep but also make a guideline of sleep stage analysis.

• Progress in Medical Physics
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• v.16 no.1
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• pp.32-38
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• 2005

Impaired processing of facial information is one of the broad ranges of cognitive deficits seen in patients with schizophrenia. The purpose of this study was to elucidate the differences in brain activities involved in the process of facial working memory between schizophrenic patients and healthy comparison subjects. Ten patients with schizophrenia were recruited along with matched healthy volunteers as a comparison group. Functional magnetic resonance imaging (fMRI) was used to assess cortical activities during the performance of a 1-back working memory paradigm using images of neutral faces as mnemonic content. The patient group performed the tasks with reduced accuracy. Group analysis revealed that left fusiform gyrus, right superior frontal gyrus, bilateral middle frontal gyri/insula, left middle temporal gyrus, precuneus and vermis of cerebellum and showed decreased cortical activities in the patient group. On the other hand, an increased level of activation in lateral prefrontal cortex and parietal lobule was observed from the patient group, all in the right hemisphere. A decreased level of activity in the left fusiform gyrus among the patient group implicates inefficient processing of facial information. An increased level of activation in prefrontal and parietal neural networks from the patient group confirms earlier findings on the impaired working memory of patients with schizophrenia.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.4
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• pp.137-144
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• 2020

Delirium is among the most common mental disorders encountered in patients with a temporary cognitive impairment such as consciousness disorder, attention disorder, and poor speech, particularly among those who are older. Delirium is distressing for patients and families, can interfere with the management of symptoms such as pain, and is associated with increased elderly mortality. The purpose of this paper is to generate useful clinical knowledge that can be used to distinguish the outcomes of patients with delirium in long-term care facilities. For this purpose, we extracted the clinical classification knowledge associated with delirium using a local covering rule acquisition approach with the rough lower approximation region. The clinical applicability of the proposed method was verified using data collected from a prospective cohort study. From the results of this study, we found six useful clinical pieces of evidence that the duration of delirium could more than 12 days. Also, we confirmed eight factors such as BMI, Charlson Comorbidity Index, hospitalization path, nutrition deficiency, infection, sleep disturbance, bed scores, and diaper use are important in distinguishing the outcomes of delirium patients. The classification performance of the proposed method was verified by comparison with three benchmarking models, ANN, SVM with RBF kernel, and Random Forest, using a statistical five-fold cross-validation method. The proposed method showed an improved average performance of 0.6% and 2.7% in both accuracy and AUC criteria when compared with the SVM model with the highest classification performance of the three models respectively.

• Science of Emotion and Sensibility
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• v.11 no.1
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• pp.39-56
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• 2008

Previous studies of music, user interface, and human-computer interaction have used sounds which include musical structure rather than real music. This study investigated whether real music affects objective and perceived usability. Silence, sound, and music conditions were compared in experiment 1 (kimchi refrigerator) and 2 (remote controller for air conditioner). Participants' performances of reaction time and accuracy, and the degree of subjective satisfaction were analyzed. The results showed that main effects on task performances were not different significantly; however, perceived usability of music condition was better than sound condition, which was better than silence condition. It means that musical user interface improves perceived usability while not interfering task performance. This study provides a basis of emotional and aesthetic effects of music in home appliances design, and can be applied to studies for the blind. More specific guideline for the musical user interface can be drafted if further studies consider more various tasks, context, musical structure and types for the appliances.

• Korean Journal of Acupuncture
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• v.19 no.2
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• pp.63-78
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• 2002

Kupunggibodan(KU), Gamisamul-tang(GA) and Whangryunhaedok-tang(WH) are clinically the most popular prescriptions as an herbal medicine in the treatment of ischemia. In order to compare and evaluate their protective effects on the ischema-induced cognitive deficits by middle cerebral artery occlusion (MCAO), we examined its ability to improve ischemia-induced cell loss and impairements of learning and memory in the Morris water maze and eight-arm radial arm maze. Focal cerebral ischemia produced a marked cell loss, decrease in acetylcholinesterase(AchE) reactivity in the hippocampus, and learning and memory deficits in two behavioral tasks. Pretreatment with WH (100 mg/kg, p.o.) produced a substantial increase in acquisition in the Morris water maze. Pretreatment with KU increased the perfomance of the resention test in the Morris water maze. WH, KU and GA caused a significant improvement in choice accuracy in radial arm maze test. WH was superior to KU and GA in perfomance of the radial arm maze test. Consistent with behavioral data, staining with cresyl violet showed that pretreatments with WH, but not KU and GA significantly recovered the ischemia-induced cell loss in the hippcampal CA1 area. In addition, pretreatments with WH and KU recovered the ischemia-induced reduction of AchE reactivity in the hippocampal CA1 area. These results demonstrated that KU, GA and WH have protective effects against ischimea-induced learning and memory impairments and that the efficacy was the order of WH>KU>GA in tratment of ischemia induced memory deficits. The present studies provide an evidence of KU, GA and WH as putative treatment of vascular dementia. Supported by a fund from the Ministry of Health and Welfare(HMP-00-OO-04-0004), and the Brain Korea 21 Project from Korean Ministry of Education, Korea.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.12
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• pp.549-560
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• 2015

There is a wide variety of data quality attributes such as the ones proposed by the ISO/IEC organization and also by many other domestic and international institutions. However, it takes considerable time and costs to apply those criteria and guidelines to real environment. Therefore, it needs to define data quality evaluation attributes which are easily applicable and are not influenced by organizational environment limitations. The purpose of this paper is to derive data quality attributes and order of their priorities based on customer requirements for managing the process systematically and evaluating the data quantitatively. This study identifies the customer cognitive constructs of data quality attributes using the RGT(Repertory Grid Technique) based on a Korean quality standard model (DQC-M). Also the correlation analysis on the identified constructs is conducted, and the evaluation attributes is prioritized and ranked using the AHP. As the results of this paper, the consistent system, the accurate data, the efficient environment, the flexible management, and the continuous improvement are derived at the first level of the data quality evaluation attributes. Also, Control Compliance(13%), Regulatory Compliance(10%), Requirement Completeness(9.6%), Accuracy(8.4%), and Traceability(6.8%) are ranked on the top 5 of the 19 attributes in the second level.

• Korean Journal of Health Psychology
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• v.16 no.3
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• pp.557-575
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• 2011

This study tried to examine the characteristics of attention deficits in patients with Obstructive Sleep Apenea(OSA) with different age levels, and to examine which indices of polysomnograms might be related to the indices of attention deficits in OSAs. Two age-level groups and a normal control group were subjected to two computerized attention tests, including a continuous performance test(CPT) and a change blindness task(CBT). In addition, the three groups were subjected to a Polysomnography to extract several sub-indicators of polysomnogram, and an Epworth Sleepiness Scale which measures subjective sleepiness. As results, the OSAs showed significantly more omission and commission errors in CPT, and they showed lower accuracy in CBT compared to the normal group. The results of a correlational analysis showed that attention deficits in OSA are significantly correlated with arterial oxygen saturation among sub-indicators of polysomnograms. In conclusion, OSAs seems to be less attentive, having difficulties in response inhibition, and having deficiencies in noticing important environmental changes. Age seems to make these deficiencies even worse. Especially, the relationship between attention deficiency and hypoxia which could cause irreversible cerebrum damage has an implication in cognitive impairment prevention through early treatment.

• Korean Journal of Culture and Social Issue
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• v.22 no.1
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• pp.41-64
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• 2016

In two experiments, we compared envy with admiration in attentional and motivational benefits. In addition, we tested whether individual differences in working memory capacity (WMC) have consequences for emotion regulation. In Study 1, following WMC tasks, the participants were primed either with envy or with admiration through a recall task, in which they had to recall their own experiences about envy or admiration. The participants in the envy condition considered it more undeserved that another person had an advantage over them, than in the admiration condition. Additionally, in the envy condition, WMC was related to happiness, and anxiety was related to the motivation to study more. In contrast, there were no significant relationships between WMC, emotion and study hours in the admiration condition. Study 2 (N=43) found greater memory for the envy scenario in the envy condition than in the admiration and in the control condition. Additionally, there were significant relationships between WMC, anxiety and recall accuracy in the envy condition. However, these relations were not found in the admiration and in the control condition. Findings implicate that envy may play an important role in memory systems and that WMC is related to emotion regulation abilities.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.