• Journal of Periodontal and Implant Science
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• v.26 no.1
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• pp.161-175
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• 1996

There were many reports that elevations in the levels of active and latent collagenase in gingival crevicular fluid(GCF) have been correlated positively with periodontal disease activity. To provide a simple diagnostic approach for testing GCF collagenolytic activity, the detection limit of enzyme activity was compared using radiofibril assay(Sodek et.al.1981) and spectrophotometric collagenolytic assay(Nethery et al. 1986). The detection limits of both assay for standard bacterial enzyme were similar and the radiofibril assay showed a little (1/2) lower detection limit for tad pole collagenase. To evaluate the relationship between periodontal tissue destruction and the collagenolytic activity, GCF was collected, and latent and active enzyme activities were measured by a spectrophotometric collagenolytic assay. Twelve subjects showing progressive lesions were selected according to the presence of immediate tissue destruction, frequent abscess formation, and increasing need for tooth extraction, and the absence of underlying systemic disease and previous antibiotic medication history within 6 months. Comparisons were made between sites with either: 1) inflammation with a previous history of progressive loss of periodontal tissue and bone support(2l progressive sites): 2) previous history of bone loss and periodontal destruction but now clinically stable(12 comparably stable sites); or 3) no loss of periodontal tissue and bone support(11 control sites including 5 gingivitis sites and 6 healthy sites). Active collagenase activity was the highest in the progressive sites and decreased in the order of the gingivitis sites, the stable sites, and the healthy sites. The total enzyme activity was $2{\sim}3$ fold higher in the progressive sites and the gingivitis sites, compared to the stable and the healthy sites. The ratio of active to total collagenolytic activity was twice in the progressive sites. Analysis of active collagenase level(5mU) and the ratio of active to total collagenolytic activity(0.8) as a diagnositic test indicates that these measurements have the sensitivity of 0.81 and 0.86, the specificity of 0.70 and 0.65, and the overall agreement of 0.75 and 0.73, respectively. Thus, this method has significant merits as a diagnostic tool to determine wherher the site is in a state of remission or progression.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1083-1094
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• 2021

In order to monitor nuclear activity in inaccessible areas, it is necessary to establish a methodology to analyze changesin nuclear activity-related objects using high-resolution satellite images. However, traditional object detection and change detection techniques using satellite images have difficulties in applying detection results to various fields because effects of seasons and weather at the time of image acquisition. Therefore, in this paper, an object of interest was detected in a satellite image using a deep learning model, and object changes in the satellite image were analyzed based on object detection results. An initial training of the deep learning model was performed using an open dataset for object detection, and additional training dataset for the region of interest were generated and applied to transfer learning. After detecting objects by multitemporal and multisensory satellite images, we tried to detect changes in objects in the images by using them. In the experiments, it was confirmed that the object detection results of various satellite images can be directly used for change detection for nuclear activity-related monitoring in inaccessible areas.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.492-500
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• 2014

Activity monitoring has been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior. In this research, we propose a data-driven activity monitoring method to measure relative sales performance which is not sensitive to special event which frequently occur in marketing area. Moreover, the proposed method can automatically updates the monitoring threshold that accommodates a drastically changing business environment. The results from simulation and practical case study from sales of electronic devices demonstrate the usefulness and applicability of the proposed activity monitoring method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.286-293
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• 2008

The design and implementation of a personal navigation system including activity monitoring function is given in this paper. The system consists of a 3 dimensional MEMS accelerometer, digital compasses and ZigBee communication. An accelerometer and digital compasses are used to compute the position and activity. The obtained position and activity information is transmitted to a fixed beacon via ZigBee. At the same time, activity information is stored in the personal navigation system to a batch analysis program. The step detection algorithm which is robust to attaching location is proposed. Also two digital compass error compensation algorithms are proposed to find more precise headings. The experiments with a real system show that the activities of users and continuous locations less than 1.5m errors are obtained after 80m walking.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.95-103
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• 2019

According to the Statistics Korea in 2017, the 10 leading causes of death contain a cardiac disorder disease, self-injury. In terms of these diseases, urgent assistance is highly required when people do not move for certain period of time. We propose an unusual event detection algorithm to identify abnormal user behaviors using dust, vision and activity sensors in their houses. Vision sensors can detect personalized activity behaviors within the CCTV range in the house in their lives. The pattern algorithm using the dust sensors classifies user movements or dust-generated daily behaviors in indoor areas. The accelerometer sensor in the smartphone is suitable to identify activity behaviors of the mobile users. We evaluated the proposed pattern algorithms and the fusion method in the scenarios.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.109-118
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• 2020

Recently, people are spending a lot of time inside their homes because of various diseases. It is difficult to ask others for help in the case of a single-person household that is injured in the house or infected with a disease and needs help from others. In this study, an algorithm is proposed to detect emergency event, which are situations in which single-person households need help from others, such as injuries or disease infections, in their homes. It proposes vision pattern detection algorithms using home CCTVs, audio pattern detection algorithms using artificial intelligence speakers, activity pattern detection algorithms using acceleration sensors in smartphones, and dust pattern detection algorithms using air purifiers. However, if it is difficult to use due to security issues of home CCTVs, it proposes a fusion method combining audio, activity and dust pattern sensors. Each algorithm collected data through YouTube and experiments to measure accuracy.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.249-255
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• 2016

Since the elderly voices include a lot of noise caused by physiological changes in respiration, phonation, and resonance, the performance of the convergence health-care equipments such as speech recognition, synthesis, analysis program done by elderly voice is deteriorated. Therefore it is necessary to develop researches to operate health-care instruments with elderly voices. In this study, a voice activity detection using a symmetric higher-order differential energy function (SHODEO) was developed and was compared with auto-correlation function(ACF) and the average magnitude difference function(AMDF). It was confirmed to have a better performance than other methods in the voice interval detection. The voice activity detection will be applied to a voice interface for the elderly to improve the accessibility of the smart devices.

• ETRI Journal
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• v.33 no.1
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• pp.99-109
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• 2011

This paper concerns a robust real-time voice activity detection (VAD) approach which is easy to understand and implement. The proposed approach employs several short-term speech/nonspeech discriminating features in a voting paradigm to achieve a reliable performance in different environments. This paper mainly focuses on the performance improvement of a recently proposed approach which uses spectral peak valley difference (SPVD) as a feature for silence detection. The main issue of this paper is to apply a set of features with SPVD to improve the VAD robustness. The proposed approach uses a weighted voting scheme in order to take the discriminative power of the employed feature set into account. The experiments show that the proposed approach is more robust than the baseline approach from different points of view, including channel distortion and threshold selection. The proposed approach is also compared with some other VAD techniques for better confirmation of its achievements. Using the proposed weighted voting approach, the average VAD performance is increased to 89.29% for 5 different noise types and 8 SNR levels. The resulting performance is 13.79% higher than the approach based only on SPVD and even 2.25% higher than the not-weighted voting scheme.

• Review of KIISC
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• v.5 no.2
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• pp.32-48
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• 1995

Our paper describes an Intrusion Detection Parallel System(IDPS) which detects an anomaly activity corresponding to the actions that interaction between near detection events. IDES uses parallel inductive approaches regarding the problem of real-time anomaly behavior detection on rule-based system. This approach uses sequential rule that describes user's behavior and characteristics dependent on time. and that audits user's activities by using rule base as data base to store user's behavior pattern. When user's activity deviates significantly from expected behavior described in rule base. anomaly behaviors are recorded. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the parallel inductive system.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.87-93
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• 2019

In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.