• Journal of the Korea Convergence Society
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• v.4 no.1
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• pp.9-13
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• 2013

In this paper, broadcasting contents management system which is for sharing user-created information in restricted areas is proposed. By the proposed system, the information is compulsively sent to a television set receiving broadcasting contents via various transmit media. So, user should be contacting the information while they are watching broadcasting contents.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.157-162
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• 2014

The international COSPAS-SARSAT program is a satellite-based search and rescue distress alert detection and information distribution system and best known as the system that detects and locates emergency beacons activated by aircraft, ships and so on. However, the current message format of the system is not encrypted so that, if the rescue signal can be intercepted by the unintended receivers, the subsequent rescue activities can be handled in a hostile environment. So, this article concerns how to deal with the rescue signals in a secure way and proposes some adequate encryption methods and the corresponding key management.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2144-2145
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• 2011

We carry out a demonstration project to verify performance of a Home- Smart Cabinet Panel(H-SCP) at the child care facility. It is difficult to prevent an electrical disaster using a existing cabinet panel because electrical events are invisible and unforeseeable. So we construct a integrated information system with a Home-Smart Cabinet Panel(H-SCP) for management of low-voltage customers. The integrated information system with the H-SCP maintain the transmitted data from H-SCP, alert a electrical event to a administrator and show a state of customer health in real time respectively. A manager of electrical safety can prevent electrical disaster to maintain electrical facilities after analysis on the integrated information system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1994.10a
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• pp.138-143
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• 1994

기기의 결함을 진단하는데에 전문자동진단시스템(EADS)을 사용하는 것은 고도의 숙련된 진단요원 없이, 시스템저자와의 질의응답과 같은 일련의 회의를 갖지 않고도 정확하고 또한 믿을만하게 기기상태를 측정 분석할 수 있는 가장 효과적인 방법이다. 전문자동진단시스템(EADS)은 일분에 5개의 기기들을 분석하고 진동전문분석가에 버금가는(94%) 정확성으로 진단결과를 제공한다. 많은 전문진단시스템 중에서 DLI의 ExpertALERT[4]는 가장 정확하고 정교한 진단시스템으로 평가되고 있다. 전문자동진단시스템(EADS)의 시행으로 프렌트의 기기고장으로 인한 조업중단의 회수가 줄어지고 정비비용을 절감하며 불필요한 정기점검식정비(PM)을 없앤다면 관계기술요원들의 진동에 대한 이해와 기술습득으로 한차원 높은 기기 정비를 통해 효율적인 생산성증가, 정비비용감소[5], 안전사고 미연방지등 많은 것을 함께 얻을 수 있다. Expert System 기술의 성공적인 적용이라고 정의할 수 있겠다.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.8
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• pp.990-997
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• 2007

In the last 5 years, collisions of fishing vessels have recorded about 54.6% of the total marine accidents. Specially about 64.0% of these collisions were caused by navigator's negligence of watch keeping during works. The purpose of this paper is to develop vessel detecting system that is able to receive the whistle blast of other vessel and make a warning sound and light when the fishermen can not confirm the approaching another vessel on account of fishing works. It is designed to receive the whistle signal blast by a weather tight microphone. The signal is processed by analog active filter in order to enhance the SNR(Signal to noise ratio). And this microprocessor-based system is programmed to do ADC(Analog to digital converting), FFT analysis, controls of warning sound and light.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.427-429
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• 2004

인터넷상에서 해킹도구들을 구할 수 있게 되고 이러한 정보들이 쉽고 빠르게 전파됨에 따라 쉽게 해킹을 시도할 수 있게 되었고 이로 인해 침입시도의 수가 급증하고 있다. 그 결과 침입탐지시스템(Intrusion Detection System, IDS)에서 발생하는 침입시도정보의 수도 늘어나고 있다. 또한 이렇게 생성되는 많은 침입시도정보들에서 긍정오류(false positive)와 같은 잘못된 침입시도정보들이 큰 문제이다. 침입으로 오인된 정보가 너무 많음으로 인해 네트워크 관리자가 정확하게 판단을 하여 대응하는데 많은 노력이 요구된다. 이러한 노력을 줄여주기 위하여 긍정오류와 반복되는 침입시도정보를 줄여주는 기법이 필요하다. 본 논문에서는 이러한 필터링 시스템을 제안한다. 시스템 정보를 이용하여 위험이 될 수 없는 공격을 제거하여 관리자에게 정확한 정보를 전달하고 동일한 공격들을 제거하여 침입 시도정보의 수를 줄여 주는 방법을 제안한다.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.1-12
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• 2022

The current progression in the Internet of Things (IoT) and Machine Learning (ML) based technologies converted the traditional healthcare system into a smart healthcare system. The incorporation of IoT and ML has changed the way of treating patients and offers lots of opportunities in the healthcare domain. In this view, this research article presents a new IoT and ML-based disease diagnosis model for the diagnosis of different diseases. In the proposed model, vital signs are collected via IoT-based smart medical devices, and the analysis is done by using different data mining techniques for detecting the possibility of risk in people's health status. Recommendations are made based on the results generated by different data mining techniques, for high-risk patients, an emergency alert will be generated to healthcare service providers and family members. Implementation of this model is done on Anaconda Jupyter notebook by using different Python libraries in it. The result states that among all data mining techniques, SVM achieved the highest accuracy of 0.897 on the same dataset for classification of Parkinson's disease.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.355-358
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• 2024

본 논문에서는 기존 재난문자의 원리인 CBS(Cell Broadcast Service)의 한계를 극복하는 알고리즘을 제안한다. 재난 상황에서 CBS는 단방향으로 사람들에게 정보를 전달할 수 있으나 양방향 소통이 불가능하여, 어떤 사람이 어디에서 문제를 겪고 있는지를 파악할 수 없다. 재난 상황 시 효율적으로 정보를 관리하고, 요구조자에 대한 구조를 진행할 수 있다. 본 논문에서 제안하는 방법은 상황 설명과 위치 등을 생략하는 등 재난 피해 신고를 간편화하여 재난 상황으로 인한 피해를 추정하기 힘들다는 문제를 개선할 수 있다. 또한 이전에는 위치와 상황을 구체적으로 알려야 신고가 접수되었지만, 사용자가 위치와 상황을 구체적으로 알리지 않더라도 문자로 구조 요청이 가능해진다. 더 나아가, 재난 상황 시 음성을 추적할 수 있게 하여 직접 구조요청을 하지 못하는 상황에 대한 대처도 가능하다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.222-230
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• 2018

This was a retrospective study that compared the characteristics of patients with decreased mental status (DMS) during their stays in an emergency department (ED) with that of patients with alert mental status (AMS). The participants in this study consisted of 90 patients with DMS who were staying in an ED along with 100 randomly selected patients with AMS. The DMS group contained more instances of cerebrovascular disease, liver disease, diabetes, and hypertension compared to the AMS group (p=0.001, p=0.003, p<0.001, and p=0.002, respectively). With regard to triage, the DMS group had higher severity than the AMS group (p<.001). The final diagnosis for the DMS group was as follows: 64.9% neurological and 63.3% related to the digestive system, which differed from that of the AMS group (p<.001). Fifty-six (62.2%) patients with DMS had their statuses discovered by nurses, while nine patients in the DMS group had received cardiopulmonary resuscitation. These results suggest that ED nurses frequently assess the consciousness of patients who they suspect may have neurologic or digestive diseases and are classified as high severity, even when the patients are alert.

• Korean Journal of Clinical Pharmacy
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• v.32 no.2
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• pp.116-124
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• 2022

Background: High-alert medications (HAMs) are medications that bear a heightened risk of causing significant patient harm if used in error. To facilitate safe use of HAMs, identifying specific HAM lists for clinical setting is necessary. We aimed to develop the national level HAM list for acute care setting. Methods: We used three-step process. First, we compiled the pre-existing lists referring HAMs. Second, we analyzed medication related incidents reported from national patient safety incident report data and adverse events indicating medication errors from the Korea Adverse Event Reporting System (KAERS). We also surveyed the assistant staffs to support patient safety tasks and pharmacist in charge of medication safety in acute care hospital. From findings from analysis and survey results we created additional candidate list of HAMs. Third, we derived the final list for HAMs in acute care settings through expert panel surveys. Results: From pre-existing HAM list, preliminary list consisting of 42 medication class/ingredients was derived. Eight assistant staff to support patient safety tasks and 39 pharmacists in charge of medication safety responded to the survey. Additional 44 medication were listed from national patient safety incident report data, KAERS data and common medications involved in prescribing errors and dispensing errors from survey data. A list of mandatory and optional HAMs consisting of 10 and 6 medication classes, respectively, was developed by consensus of the expert group. Conclusion: We developed national level HAM list for Korean acute care setting from pre-existing lists, analyzing medication error data, survey and expert panel consensus.