• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.1-7
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• 2024

This paper presents the design and implementation of a VPN (Virtual Private Network) module to address the need for external access and functional expansion of NVR (Network Video Recorder) systems. NVR systems play a critical role in enhancing security across various industries through real-time monitoring and recording. However, they are vulnerable to security threats, particularly when a secure connection to external networks is required. To resolve this issue, this study applied a VPN module to ensure that NVR systems can communicate securely with external networks. This approach enabled remote access and real-time event notifications. Performance tests confirmed 100% accuracy in event notifications. This research contributes to improving the security and operational efficiency of NVR systems, highlighting the necessity and utility of VPN modules for secure communication with external networks.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.1013-1018
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• 2024

This study developed and evaluated a real-time monitoring system utilizing IoT technology to prevent disasters caused by hazardous gases in industrial settings. The developed system detects harmful gases in real-time and issues prompt alerts, achieving over 98% data accuracy and response times under 3 seconds. The system consists of sensor nodes, a central processing unit, and a user interface, monitoring the work environment and worker status in real-time through a cloud-based remote surveillance and control program. Performance evaluation results show that this system presents a new approach for effectively managing safety in industrial sites. Future developments are expected to include improvements in multi-gas detection capabilities, development of AI-based prediction models, and enhanced security measures, evolving into a more advanced monitoring system.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.7 no.2
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• pp.153-162
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• 2004

Purpose: The Helicobacter pylori stool antigen (HpSA) enzyme immunoassay is a non-invasive test for the diagnosis and monitoring of H. pylori infection. But, there are few validation studies on the HpSA test after eradication in children. The aim of this study was to assess the diagnostic accuracy of HpSA enzyme immunoassay for the detection of H. pylori to confirm eradication in children. Methods: From January 2001 to October 2003, 164 tests were performed in 146 children aged 1 to 17.5 years (mean $9.3{\pm}4.3$ years). H. pylori infection was confirmed by endoscopy-based tests (rapid urease test, histology, and culture). All H. pylori infected children were treated with quadruple regimens (Omeprazole, amoxicillin, metronidazole and bismuth subcitrate for 7 days). Stool specimens were collected from all patients for the HpSA enzyme immunoassay (Primier platinum HpSA). The results of HpSA tests were interpreted as positive for $OD{\geq}0.160$, unresolved for $$0.140{\leq_-}OD$$<0.160, and negative for OD<0.140 at 450 nm on spectrophotometer. Results: 1) One hundred thirty-one HpSA tests were performed before treatment. The result of HpSA enzyme immunoassay showed three false positive cases and one false negative case. The sensitivity, specificity, positive predictive value, and negative predictive value of HpSA enzyme immunoassay before treatment were 96.4%, 97.1%, 90%, and 99%, respectively. 2) Thirty-three HpSA enzyme immunoassay were performed at least 4 weeks after eradication therapy. The results of HpSA enzyme immunoassay showed two false positive cases and one false negative case. The sensitivity, specificity, positive predictive value, and negative predictive value after treatment were 88.9%, 91.7%, 80%, and 95.7%, respectively. Conclusion: Diagnostic accuracy of the HpSA enzyme immunoassay after eradication therapy was as high as that of the HpSA test before eradication therapy. The HpSA enzyme immunoassay was found to be a useful non-invasive method to confirm H. pylori eradication in children.

• Sleep Medicine and Psychophysiology
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• v.10 no.1
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• pp.52-60
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• 2003

Objectives: Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. The lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, utilization of simultaneous EEG and EOG processing and analyses to detect stage 1 sleep automatically were attempted. Methods: Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. A relative power of alpha waves less than 50% or relative power of theta waves more than 23% was regarded as stage 1 sleep. SEM(slow eye movement) was defined as the duration of both-eye movement ranging from 1.5 to 4 seconds, and was also regarded as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results were compared to the manual rating results done by two polysomnography experts. Results: A total of 169 epochs were analyzed. The agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3% and Cohen’s Kappa was 0.586 (p<0.01). A significant portion (32%) of automatically detected stage 1 sleep included SEM. Conclusion: Generally, digitally-scored sleep staging shows accuracy up to 70%. Considering potential difficulty in stage 1 sleep scoring, accuracy of 79.3% in this study seems to be strong enough. Simultaneous analysis of EOG differentiates this study from previous ones which mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnari remains a valid one in this study.

• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.11-19
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• 2004

Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. Lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, we attempted to utilize simultaneous EEC and EOG processing and analyses to detect stage 1 sleep automatically. Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. Either the relative power of alpha waves less than 50％ or the relative power of theta waves more than 23％ was regarded as stage 1 sleep. SEM (slow eye movement) was defined as the duration of both eye movement ranging from 1.5 to 4 seconds and regarded also as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results f ere compared to the manual rating results done by two polysomnography experts. Total of 169 epochs was analyzed. Agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3％ and Cohen's Kappa was 0.586 (p＜0.01). A significant portion (32％) of automatically detected stage 1 sleep included SEM. Generally, digitally-scored sleep s1aging shows the accuracy up to 70％. Considering potential difficulties in stage 1 sleep scoring, the accuracy of 79.3％ in this study seems to be robust enough. Simultaneous analysis of EOG provides differential value to the present study from previous oneswhich mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnariet at. remains to be a valid one in this study.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1827-1836
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• 2022

Disasters that occur unexpectedly are difficult to predict. In addition, the scale and damage are increasing compared to the past. Sometimes one disaster can develop into another disaster. Among the four stages of disaster management, search and rescue are carried out in the response stage when an emergency occurs. Therefore, personnel such as firefighters who are put into the scene are put in at a lot of risk. In this respect, in the initial response process at the disaster site, robots are a technology with high potential to reduce damage to human life and property. In addition, Light Detection And Ranging (LiDAR) can acquire a relatively wide range of 3D information using a laser. Due to its high accuracy and precision, it is a very useful sensor when considering the characteristics of a disaster site. Therefore, in this study, development and experiments were conducted so that the robot could perform real-time monitoring at the disaster site. Multi-sensor module was developed by combining LiDAR, Inertial Measurement Unit (IMU) sensor, and computing board. Then, this module was mounted on the robot, and a customized Simultaneous Localization and Mapping (SLAM) algorithm was developed. A method for stably mounting a multi-sensor module to a robot to maintain optimal accuracy at disaster sites was studied. And to check the performance of the module, SLAM was tested inside the disaster building, and various SLAM algorithms and distance comparisons were performed. As a result, PackSLAM developed in this study showed lower error compared to other algorithms, showing the possibility of application in disaster sites. In the future, in order to further enhance usability at disaster sites, various experiments will be conducted by establishing a rough terrain environment with many obstacles.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• The Journal of the Society of Stroke on Korean Medicine
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• v.7 no.1
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• pp.11-16
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• 2006

Objectives : Transcranial Doppler (TCD) has been reported to be established as useful in detecting spasm after subarachnoid hemorrhage and to be probably useful in diagnosing stenosis or occlusion in intracranial arteries. In the detection of intracranial stenosis using TCD there have been reported some kinds of diagnostic criteria. This study was aimed to evaluate the accordance between TCD and magnetic resonance angiography (MRA) in detection of intracranial stenosis and to find out more accurate criteria for intracranial stenosis using TCD. Methods : Seventy-six stroke patients were evaluated by TCD and MRA. TCD criteria for middle cerebral artery (MCA) stenosis were used by 3 methods; ≥ 80cm/sec of mean velocity(Vm), ≥ 140 cm/sec of systolic velocity(Vs), and both. For stenosis of vertebral(VA) and basilar arteries(BA), the TCD criteria followed by 2 methods; ≥ 70 cm/sec of Vm and ≥ 100 cm/sec of Vs. The stenosis of intracranial artery in MRA followed by the interpretation of specialist in the department of radiology. The sensitivity, specificity, positive predictive value, negative predictive value, diagnostic accuracy and kappa agreement were calculated in each criteria of TCD compared with the result of MRA. Results : The sensitivity, specificity, positive predictive value, negative predictive value, diagnostic accuracy and kappa agreement using ≥ 80cm/sec of Vm for MCA stenosis were 55.6%, 81%, 34.5%, 91.0%, 77.1%, and 0.293, respectively. Using 140 cm/sec of Vs, those were 44.4%, 92.0%, 50.5%, 90.2%, 84.7%, 0.380, and using both criteria those were 44.4%, 95.0%, 61.5%, 90.5%, 87.3%, 0.445, respectively. Those using ≥ 70 cm/sec of Vm for VA and BA stenosis were 71.4%, 93.7%, 26.3%, 99.0%, 93.0%, 0.186 and using ≥ 100 cm/sec of Vs those were 71.4%, 97.3%, 45.5%, 99.1%, 96.5%, 0.539, respectively. Conclusion : These results suggested that for the diagnosis of MCA stenosis using TCD we should use the criteria of both ≥ 80cm/sec of Vm and 140 cm/sec of Vs, and for the VA and BA stenosis we adapt the criteria of ≥ 70 cm/sec of Vm.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.949-965
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• 2023

In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.