• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.783-788
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• 2020

Surges generated by direct strike or induction of lightning, frequent opening and closing of power system breakers, etc. cause excessive overvoltage, which can interfere with normal operation of transformers, circuit breakers, and power equipment. In order to prevent such phenomena, this study goes further from the method of establishing countermeasures by installing lightning arresters on power equipment or lines in parallel, and furthermore, remotely checking the condition of the lightning arrester and preparing immediate countermeasures when an event occurs. The purpose of the study is to prevent damage in advance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.143-149
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• 2016

Recent accidents caused by toxic chemicals and the social problems caused by frequent. As of 2010, there are more than 100,000 types of deadly toxic chemicals being distributed throughout Korea, and severely intoxicated patients along with an enormous number of patients can be induced at the time of an accident involving deadly toxic chemicals. Internationally, the seriousness of large-scale disasters due to a NBC disaster (nuclear, biologic and chemical disaster) is being highlighted as well. So, we obtain the information of the RFID tag attached to a glass bottle with containing the toxic chemical to transfer the data to the smart device has been studied a system that can monitor the status of the toxic chemical in real time. The proposed system is the information was sent to the main system using a zigbee communication by recognizing the tag vial containing the toxic chemical with the 13.56MHz bandwidths good permeability. User may check the information in real time by utilizing the smart device. However, the error of the system for managing the toxic chemical generates a result that can not be predicted. Failure of the system was detecting the error by using a comparator as this can cause an error. And the detected error proposed a duplex system so that they do not affect the overall system.

• Fire Science and Engineering
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• v.31 no.1
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• pp.58-62
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• 2017

There is a growing need for seismic resistance design. But it is controversial that standards of sway brace device in non-structural elements for buildings like pump waterway is vary widely. Therefore, in this study to get a valid range of sway brace device in seismic resistance design, using load test of sway brace device. As a result, load of safe range from 0 to 18.5 kN and under 29.4 kN, no structural fault of sway brace device. And using internet of things get a data of seismic resistance design from sensor node like accelerometer, GPS, tilt sensor and temperature sensor through steps of sampling and prediction. These results will be acceptable for monitoring system for seismic resistance in non-structural elements.

• Convergence Security Journal
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• v.20 no.2
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• pp.145-153
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• 2020

The aviation safety policy master plan is promoting the development of aviation safety management technology applying the 4th industrial revolution technology with the goal of establishing a flawless aviation safety management system and establishing a future aviation safety infrastructure. The master plan includes the establishment of various aviation safety management systems such as aircraft fault management using AI & Big data and flight training system using VR/AR. Currently, the Air Force is promoting a flight safety management system using new technology under the goal of building smart air force. Therefore, this study intends to apply the 4th Industrial Revolution technology to the aircraft condition check system that finally checks the safety of the aircraft before flight. The Air Force conducts airframe flaw checks and pre-flight aircraft check. In this study, we architect the airframe flaw check system using AI and drones, and the pre-flight aircraft condition check system using the IoT and big data for more precise and detailed check of aircraft condition and flawlessness check.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.12
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• pp.481-490
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• 2016

Bluetooth utilizes a symmetric key that is exchanged at the first pairing to establish a secure channel. There are four authentication modes which enables device authentication, Just work, Passkey Entry, Out of Band, and Numeric Comparison. Up to now, Just work has been considered as the authentication mode that is vulnerable to Man-In-The-Middle (MITM) Attack. In addition, it is possible to intentionally change any authentication mode to Just work mode, in order to succeed in MITM Attack under Just work mode. However, this kind of attacks have just worked under the assumption that users should not notice that authentication mode was changed. In this paper, We analyze the specification of Secure Simple Pairing, LE Legacy Pairing and LE Secure Connection Pairing. When using Passkey Entry mode on each approach, it seems the MITM attack is possible. Also it offers Passkey Entry MITM attack that does not require assumptions about the user's fault, because it isn't change verification process of the authentication mode unlike traditional attacks. We implement the proposed MITM attacks. Also we presents a scenario in which an attack can be exploited and a countermeasure.