• 방송공학회논문지
• /
• 제26권4호
• /
• pp.356-367
• /
• 2021

최근 사물인터넷 및 인공지능 기술의 발전에 따라 제조, 스마트시티 등 다양한 분야에서 실시간으로 데이터를 수집하고 분석하여 현실세계 문제에 대한 최적화를 수행하는 연구 및 적용사례가 증가하고 있다. 대표적으로 현실세계를 디지털화한 가상세계와 양방향으로 실시간 동기화를 지원하는 디지털 트윈 기술이 주목받고 있다. 본 논문에서는 디지털 트윈을 정의하고 사물인터넷 국제표준인 oneM2M 기반의 IoT 플랫폼을 활용하여 현실사물과 가상세계의 예측결과를 실시간으로 연결하는 디지털 트윈 플랫폼의 프로토타입을 제안한다. 또한, 제안된 프로토타입을 적용하여 물체의 충돌을 사전에 예측하여 사고를 예방할 수 있는 응용서비스를 구현한다. 응용서비스에서는 사전 정의한 테스트 케이스 수행을 통해 제안한 디지털 트윈 프로토타입이 크레인의 동작을 사전 예측하여 충돌 위험을 감지하고 이를 기반으로 최적 제어를 수행할 수 있으며 실제 환경에 응용 가능함을 보였다.

• 문화기술의 융합
• /
• 제8권3호
• /
• pp.183-189
• /
• 2022

COVID-19는 커피산업에도 영향을 주고 있다. 이에 새로운 소비 트렌드인 언택트 소비가 증가하고 있으며 언택트 소비를 대표하는 온라인 채널과 배달 어플리케이션을 활용한 소비가 일상화되고 있다. 커피산업에서도 최소한의 접촉만으로 주문이 가능한 드라이브스루, 스마트오더 시스템을 갖춘 커피전문점의 이용이 증가하고 있다. 그러나 언택트 서비스의 대부분이 프랜차이즈에서 선점하고 있는 반면, 개인 커피전문점에서는 차별화된 서비스로 고객과 소통하며 직접서비스를 제공하는 매장들이 많다. COVID-19 감염의 장기화와 함께 전염병으로부터 자유로울 수 없는 현시대의 커피전문점에서는 배달서비스에 대한 고민을 하지 않을 수 없다. 이에 본 연구는 커피배달 서비스에 영향을 미치는 요소들을 분석하였다. 연구결과 COVID-19의 영향으로 커피배달 서비스와 함께 정기배달 서비스 또한 증가하였다. 커피를 다양한 방법으로 즐기고자 하는 소비자들의 홈 카페 이용 증가로 정기배달 서비스가 커피배달 서비스에 중심적인 역할을 하게 될 것이다.

• Structural Engineering and Mechanics
• /
• 제82권1호
• /
• pp.107-120
• /
• 2022

The bracing members capable of active control against seismic loads to reduce earthquake damage have been widely utilized in construction projects. Effectively reducing the structural damage caused by earthquake events, bracing systems equipped with retrofitting damper devices, which take advantage of the energy dissipation and impact absorption, have been widely used in practical construction sites. Shape Memory Alloys (SMAs) are a new generation of smart materials with the capability of recovering their predefined shape after experiencing a large strain. This is mainly due to the shape memory effects and the superelasticity of SMA. These properties make SMA an excellent alternative to be used in passive, semi-active, and active control systems in civil engineering applications. In this research, a new system in diagonal braces with slit damper combined with SMA is investigated. The diagonal element under the effect of tensile and compressive force turns to shear force in the slit damper and creates tension in the SMA. Therefore, by creating shear forces in the damper, it leads to yield and increases the energy absorption capacity of the system. The purpose of using SMA, in addition to increasing the stiffness and strength of the system, is to create reversibility for the system. According to the results, the highest capacity is related to the case where the ratio of the width of the middle section to the width of the end section (b₁/b) is 1.0 and the ratio of the height of the middle part to the total height of the damper (h₁/h) is 0.1. This is mainly because in this case, the damper section has the highest cross-section. In contrast, the lowest capacity is related to the case where b₁/b=0.1 and the ratio h₁/h=0.8.

• Smart Structures and Systems
• /
• 제29권1호
• /
• pp.251-266
• /
• 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.

• Smart Structures and Systems
• /
• 제29권1호
• /
• pp.167-179
• /
• 2022

This article develops a long-term condition assessment method for stay cables in cable stayed bridges using the monitored cable tension forces under operational condition. Based on the concept of influence surface, the matched cable tension ratio of two cables located at the same side (either in the upstream side or downstream side) is theoretically proven to be related to the condition of stay cables and independent of the positions of vehicles on the bridge. A sensor grouping scheme is designed to ensure that reliable damage detection result can be obtained even when sensor fault occurs in the neighbor of the damaged cable. Cable forces measured from an in-service cable-stayed bridge in China are used to demonstrate the accuracy and effectiveness of the proposed method. Damage detection results show that the proposed approach is sensitive to the rupture of wire damage in a specific cable and is robust to environmental effects, measurement noise, sensor fault and different traffic patterns. Using the damage sensitive feature in the proposed approach, the metrics such as accuracy, precision, recall and F1 score, which are used to evaluate the performance of damage detection, are 97.97%, 95.08%, 100% and 97.48%, respectively. These results indicate that the proposed approach can reliably detect the damage in stay cables. In addition, the proposed approach is efficient and promising with applications to the field monitoring of cables in cable-stayed bridges.

• Smart Structures and Systems
• /
• 제29권1호
• /
• pp.77-91
• /
• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• 한국해양공학회지
• /
• 제36권3호
• /
• pp.153-160
• /
• 2022

Moored floating platforms have great potential in ocean engineering applications because a mooring system is necessary to keep the platform in station, which is directly related to the operational efficiency and safety of the platform. This paper briefly introduces the technical and operational details of an optical sensor for measuring the tension of mooring lines of a moored platform in waves. In order to check the performance of optical sensors, an experiment with a moored floating platform in waves is carried out in the wave tank at Changwon National University. The experiment is performed in regular waves and irregular waves with a semi-submersible and triangle platform. The performance of the optical sensor is confirmed by comparing the results of the tension of the mooring lines by the optical sensor and tension gauges. The maximum tension of the mooring lines is estimated to investigate the mooring dynamics due to the effect of the wave direction and wavelength in the regular waves. The significant value of the tension of mooring lines in various wave directions is estimated in the case of irregular waves. The results show that the optical sensor is effective in measuring the tension of the mooring lines.

• 한국전자통신학회논문지
• /
• 제18권1호
• /
• pp.63-70
• /
• 2023

고화질 블랙박스의 확산과 '스마트 국민제보', '안전신문고' 등 모바일 애플리케이션의 도입에 따른 영향으로 교통법규 위반 공익신고가 급증하였으며, 이로 인해 이를 처리할 담당 경찰 인력은 부족한 상황이 되었다. 본 논문에서는 교통법규 위반 공익신고 영상 중, 가장 많은 비중을 차지하는 차선위반에 대해 딥러닝 알고리즘을 활용하여 자동 검출할 수 있는 시스템의 개발내용에 관해 기술한다. 본 연구에서는 YOLO 모델과 Lanenet 모델을 사용하여 차량과 실선 객체를 인식하고 deep sort 알고리즘을 사용하여 객체를 개별로 추적하는 방법, 그리고 차량 객체의 바운딩 박스와 실선 객체의 범위가 겹치는 부분을 인식하여 진로변경 위반을 검출하는 방법을 제안한다. 본 시스템을 통해 신고된 영상에 대해 교통법규 위반 여부를 자동 분석해줌으로써 담당 경찰 인력 부족난을 해소할 수 있을 것으로 기대한다.

• International Journal of Computer Science & Network Security
• /
• 제23권2호
• /
• pp.173-182
• /
• 2023

The UAE government has set its sights on creating a smart, electronic-based government system that utilizes AI. The country's collaboration with India aims to bring substantial returns through AI innovation, with a target of over $20 billion in the coming years. To achieve this goal, the UAE launched its AI strategy in 2017, focused on improving performance in key sectors and becoming a leader in AI investment. To ensure public safety as the role of AI in government grows, the country is working on developing integrated cyber security solutions for SCADA systems. A questionnaire-based study was conducted, using the AI IQ Threat Scale to measure the variables in the research model. The sample consisted of 200 individuals from the UAE government, private sector, and academia, and data was collected through online surveys and analyzed using descriptive statistics and structural equation modeling. The results indicate that the AI IQ Threat Scale was effective in measuring the four main attacks and defense applications of AI. Additionally, the study reveals that AI governance and cyber defense have a positive impact on the resilience of AI systems. This study makes a valuable contribution to the UAE government's efforts to remain at the forefront of AI and technology exploitation. The results emphasize the need for appropriate evaluation models to ensure a resilient economy and improved public safety in the face of automation. The findings can inform future AI governance and cyber defense strategies for the UAE and other countries.

• 반도체디스플레이기술학회지
• /
• 제22권1호
• /
• pp.134-138
• /
• 2023

This study focuses on the analysis of the results of computational fluid dynamics simulations of mist-chemical vapor deposition for the growth of an epitaxial wafer in power semiconductor technology using artificial intelligence techniques. The conventional approach of predicting the uniformity of the deposited layer using computational fluid dynamics and design of experimental takes considerable time. To overcome this, artificial intelligence method, which is widely used for optimization, automation, and prediction in various fields, was utilized to analyze the computational fluid dynamics simulation results. The computational fluid dynamics simulation results were analyzed using a supervised deep neural network model for regression analysis. The predicted results were evaluated quantitatively using Euclidean distance calculations. And the Bayesian optimization was used to derive the optimal condition, which results obtained through deep neural network training showed a discrepancy of approximately 4% when compared to the results obtained through computational fluid dynamics analysis. resulted in an increase of 146.2% compared to the previous computational fluid dynamics simulation results. These results are expected to have practical applications in various fields.