• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• Survey Research
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• v.3 no.2
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• pp.91-122
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• 2002

The use of internet is expanding globally in a rapid way. The situation is similar in Korea. The growth of internet affects the various aspects of a social system. Such an impact is not an exception in the field of survey data-collection. The internet survey method has gained a recognition as an important survey approach in the U.S and Europe. A number of Korean research organizations have already started implementing the new survey method, too. The time is quite ripe for the internet survey in Korea. However, it is hard to find ever a good literature review on the subject. This study intends to review the results of the past studies about the question of what factors influence the response rate, speed, and quality. The influencing factors incorporated in this review include six elements: personalization, the survey sponsorship, incentives, the questionaire format, prenotification, and follow-ups. Regarding there sis factors, the results of the past studies relating to the internet survey response are reviewed in a systematic way.

• Fire Science and Engineering
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• v.24 no.1
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• pp.72-80
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• 2010

This study suggests ubiquitous railway disaster prevention system that gearing Ubiquitous Sensor Network system information take to the subway fire accident information which emergency response procedure as occurring subway fire accident scenario. Also it is proposed that emergency response system though fire scenario. collected Information was analyzed each system over providing information and it is designed to exchanging information structure though relation system. The ubiquitous railway disaster prevention system basically consists by four unit stages as prevention, preparedness, response and recovery system. Especially, in this system can supply real time accident information to the relevant government offices and public through forecasting and warning system by utilizing recognition of the five senses in case of accident. also, it is build that to make decisions as linking 2-dimension and 3-dimension space information interface of ubiquitous sensor networks and expected scenarios.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.242-250
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• 2016

This study was conducted to investigate whether Stroop-related cognitive task will be affected according to the preceding affective valence factored by matchedness in response time(RT) and whether facial recognition will be indexed by specific event-related potentials(ERPs) signature in normal person as in patients suffering from affective disorder. ERPs primed by subliminal(30ms) facial stimuli were recorded when presented with four pairs of affect(positive or negative) and cognitive task(matched or mismatched) to get ERP effects(N2 and P300) in terms of its amplitude and peak latency variations. Behavioral response analysis based on RTs confirmed that subliminal affective stimuli primed the target processing in all affective condition except for the neutral stimulus. Additional results for the ERPs performed in the negative affect with mismatched condition reached significance of emotional-face specificity named N2 showing more amplitude and delayed peak latency compared to the positive counterpart. Furthermore the condition shows more positive amplitude and earlier peak latency of P300 effect denoting cognitive closure than the corresponding positive affect condition. These results are suggested to reflect that negative affect stimulus in subliminal level is automatically inhibited such that this effect had influence on accelerating detection of the affect and facilitating response allowing adequate reallocation of attentional resources. The functional and cognitive significance with these findings was implied in terms of subliminal effect and affect-related recognition modulating the cognitive tasks.

• Journal of dental hygiene science
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• v.14 no.4
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• pp.502-509
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• 2014

The purpose of this study was to investigate the recognition on the national health insurance of denture, implant among the elderly. This survey was performed on 238 of the elderly aged over 60 years in Daejeon. The research was performed using a self-reported questionnaire and interview method from June to July, 2014. The collected data was analyzed using chi-square test, multiple response frequencies by PASW Statistics ver. 18.0. Recognition of national health insurance denture coverage was 76.9%. Channel of information awareness is higher in the media (61.8%). Awareness of application time (36.4%), medical expense by insurance (43.2%) is generally low. And awareness of denture follow up management is significantly low (18.6%). Time of denture and implant coverage needs were over 60 and 65 years old respectively. The respondents want the national health insurance to help medical expenses over 50%. Period of implant and denture re-production required unlimitedness 32.0% and 47.8%, participation to oral hygiene (dentures) management by dental hygienist was 94.1%. In conclusion, denture and implant coverage was higher awareness, but details were not recognized. Therefore, we should provide more detailed information. To increase the efficiency of national health insurance should be considered to lower the coverage age.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.134-139
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• 2021

Currently, the autonomous vehicle market is commercializing a level 3 autonomous vehicle, but it still requires the attention of the driver. After the level 3 autonomous driving, the most notable aspect of level 4 autonomous vehicles is vehicle stability. This is because, unlike Level 3, autonomous vehicles after level 4 must perform autonomous driving, including the driver's carelessness. Therefore, in this paper, we propose the Emergency-notification and Driver-response Confirmation System(EDCS) for an autonomousvehicle safety that notifies the driver of an emergency situation and recognizes the driver's reaction in a situation where the driver is careless. The EDCS uses the emergency situation delivery module to make the emergency situation to text and transmits it to the driver by voice, and the driver response confirmation module recognizes the driver's reaction to the emergency situation and gives the driver permission Decide whether to pass. As a result of the experiment, the HMM of the emergency delivery module learned speech at 25% faster than RNN and 42.86% faster than LSTM. The Tacotron2 of the driver's response confirmation module converted text to speech about 20ms faster than deep voice and 50ms faster than deep mind. Therefore, the emergency notification and driver response confirmation system can efficiently learn the neural network model and check the driver's response in real time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.79-88
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• 2024

We used actual field data to analyze from a traffic engineering perspective how AI and edge computing technologies affect the reduction of traffic accidents. By providing object information from 20m behind with AI object recognition, the driver secures a response time of about 3.6 seconds, and with edge technology, information is displayed in 0.5 to 0.8 seconds, giving the driver time to respond to intersection situations. In addition, it was analyzed that stopping before entering the intersection is possible when speed is controlled at 11-12km at the 10m point of the intersection approach and 20km/h at the 20m point. As a result, it was shown that traffic accidents can be reduced when the high object recognition rate of AI technology, provision of real-time information by edge technology, and the appropriate speed management at intersection approaches are executed simultaneously.

• The Journal of the Korean dental association
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• v.53 no.10
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• pp.732-742
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• 2015

The objective of this study was to identify bisphosphonate-related osteonecrosis of the jaw (BRONJ) awareness and experience level of patients by medical doctors who prescribes bisphosphonate being used, analyze dental examination referral reality and to utilize its result as basic education data for early diagnosis of BRONJ and its prevention. The study was carried out through a self-administered questionnaire distributed among a sample 192 residents and specialists. They belonged to family medicine, internal medicine and orthopedics of 6 tertiary medical centers located in Seoul. The survey consisted of 22 questions; general characteristics, bisphosphonate therapy, awareness of BRONJ, implementation level of dental examination referral. Among 192 medical doctorss, 78.1% (n=150) showed awareness of BRONJ. Only 8.9% (n=17) had correct response in all 5 BRONJ knowledge questions. Dental examination referral by medical doctors was implemented in below 30% of the total patients. At the time of bisphosphonate administration, specialist of oncology most highly recognized necessity of dental examination referral and it was represented in the order of endocrinology, rheumatology, family medicine, orthopedics specialists. As recognition of medical doctors for BRONJ and implementation level of dental referral were represented to be low, it is considered that enhancement of BRONJ recognition for medical doctors and development of high accessible education program for increasing implementation rate of dental examination referral would be required.

• Science of Emotion and Sensibility
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• v.21 no.3
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• pp.73-82
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• 2018

Attempts have been made to recognize social emotion, including competition-cooperation, while designing interaction in work places. This study aimed to determine the cardiac response associated with classifying competition-cooperation of social emotion. Sixty students from Sangmyung University participated in the study and were asked to play a pattern game to experience the social emotion associated with competition and cooperation. Electrocardiograms were measured during the task and were analyzed to obtain time domain indicators, such as RRI, SDNN, and pNN50, and frequency domain indicators, such as VLF, LF, HF, VLF/HF, LF/HF, lnVLF, lnLF, lnHF, and lnVLF/lnHF. The significance of classifying social emotions was assessed using an independent t-test. The rule-base for the classification was determined using significant parameters of 30 participants and verified from data obtained from another 30 participants. As a result, 91.67% participants were correctly classified. This study proposes a new method of classifying social emotions of competition and cooperation and provides objective data for designing social interaction.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.450-463
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• 2018

Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sounds being inevitably corrupted by random disturbance, the most important part of the diagnosis consists of discovering the hidden elements inside the data that can reveal the faulty patterns. This paper presents a novel feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by the drills. Using the Fourier analysis, the magnitude spectrum of the sounds are extracted, converted into two-dimensional vectors and uniformly normalized in such a way that they can be represented as 8-bit grayscale images. Histogram equalization is then performed over the obtained images in order to adjust their very poor contrast. The obtained contrast enhanced images will be used as the features of our diagnosis system. Finally, principal component analysis is performed over the image features for reducing their dimensions and a nonlinear classifier is adopted to produce the final response. Unlike the conventional features, the results demonstrate that the proposed feature extraction method manages to capture the hidden health patterns of the sound.