• Structural Monitoring and Maintenance
• /
• 제1권1호
• /
• pp.111-130
• /
• 2014

The increased use of carbon fiber reinforced polymer (CFRP) in retrofitting reinforced concrete (RC) members has led to the need to develop non-destructive techniques that can monitor and characterize the unique damage mechanisms exhibited by such structural systems. This paper presented the damage characterization results of six CFRP retrofitted RC beam specimens tested in the laboratory and monitored using acoustic emission (AE). The focus of this study was to continuously monitor the change in AE parameters and analyze them both qualitatively and quantitatively, when brittle failure modes such as debonding occur in these beams. Although deterioration of structural integrity was traceable and can be quantified by monitoring the AE data, individual failure mode characteristics could not be identified due to the complexity of the system failure modes. In all, AE was an effective non-destructive monitoring tool that can trace the failure progression in RC beams retrofitted with CFRP. It would be advantageous to isolate signals originating from the CFRP and concrete, leading to a more clear understanding of the progression of the brittle damage mechanism involved in such a structural system. For practical applications, future studies should focus on spectral analysis of AE data from broadband sensors and automated pattern recognition tools to classify and better correlate AE parameters to failure modes observed.

• 대한기계학회논문집
• /
• 제17권1호
• /
• pp.27-37
• /
• 1993

본 연구에서는 공구의 파손 및 마멸량을 검출할 수 있는 시스템을 구축하고자 하였다. CCD(charge coupled device)카메라를 통해 공구형상의 영상을 얻고 이를 PC 로 분석하는 영상처리 기법과, 여기서 계산된 정보를 이용하여 패턴인식 기법으로 공 구의 상태를 판정하는 알고리즘을 개발하였다.

• 한국기계가공학회지
• /
• 제16권5호
• /
• pp.31-39
• /
• 2017

Research in the machine tool industry has focused on ICT-based smart machines rather than hardware technologies related to machine tools. Real-time tool-status monitoring is representative of this type of technology and has become important for measuring sensors during cutting processes. In this paper, we studied several research areas and used a round bar to conduct fundamental research into the axial displacement of the main spindle of a tool when it was subjected to a machining load. We were able to use the gap sensor to detect the axial displacement indirectly by using grooves with various shapes on the round bar and sensing the gaps between the grooves. We then determined the optimal groove shape for monitoring the tool state.

• 센서학회지
• /
• 제32권5호
• /
• pp.275-279
• /
• 2023

Throughout the 20th century, the transition of pig farms from extensive to intensive commercial operations amplified the risk of disease transmission, particularly involving African swine fever (ASF). Real-time temperature monitoring systems have emerged as essential tools for early ASF diagnosis. In this paper, we introduce new real-time temperature monitoring ear tags (RTMEs) modeled after existing ear tag designs. Our crafted Pig-Temp platforms have three primary advantages. First, they can be effortlessly attached to pig ears, ensuring superior compatibility. Second, they enable real-time temperature detection, and the data can be displayed on a personal computer or smartphone application. Furthermore, they demonstrate excellent measurement accuracy, ranging from 98.9% to 99.8% at temperatures between 2.2 and 360℃. A linear regression approach enables fever symptoms associated with ASF to be identified within 3 min using RTMEs. The communication range extends to approximately 12 m (452 m²), enabling measurements from an estimated 75 to 2,260 pigs per gateway. These newly developed Pig-Temp platforms offer singifcant enhancement of early ASF detection.

• International Journal of Computer Science & Network Security
• /
• 제22권8호
• /
• pp.249-259
• /
• 2022

Project management and deployment has gone through a long journey from traditional and agile to continuous integration, continuous deployment and continuous monitoring. Software industry benefited with the latest buzzword in the development process, DevOps that not only escalates software productivity but at the same time enhances software quality. But the implementation and assessment of DevOps practices is expository as there are no guidelines to assess and improvise DevOps application in software industries. Hence, there was a need to develop a hybrid model to assist software practitioners in DevOps implementation. The intention behind this paper is to implement the already proposed DevOps hybrid model using suggested tool chains including Jenkins, Selenium, GitLab, Ansible and Nagios automation tools through Jenkins project management environment and plugins. To achieve this implementation objective, a java application is developed with a web-based graphical interface. Further, in this paper, different challenges and benefits of Jenkins implementation shall also be outlined. The paper also presents the effectiveness of DevOps based Model implementation in software organizations. The impact of considering other automation tools and models can also be considered as a part of further research.

• International Journal of Computer Science & Network Security
• /
• 제23권6호
• /
• pp.107-114
• /
• 2023

Successful implementations of DevOps practices significantly improvise software efficiency, collaboration and security. Most of the organizations are adopting DevOps for faster and quality software delivery. DevOps brings development and operation teams together to overcome all kind of communication gaps responsible for software failures. It relies on different sets of alternative tools to automate the tasks of continuous integration, testing, delivery, deployment and monitoring. Although DevOps is followed for being very reliable and responsible environment for quality software delivery yet it lacks many quantifiable aspects to prove it on the top of other traditional and agile development methods. This research evaluates quantitative performance of DevOps and traditional/ agile development methods based on software metrics. This research includes three sample projects or code repositories to quantify the results and for DevOps integrated selective tool chain; current research considers our earlier proposed and implemented DevOps hybrid model of integrated automation tools. For result discussion and validation, tabular and graphical comparisons have also been included to retrieve best performer model. This comparative and evaluative research will be of much advantage to our young researchers/ students to get well versed with automotive environment of DevOps, latest emerging buzzword of development industries.

• 대한원격탐사학회지
• /
• 제33권5_2호
• /
• pp.617-620
• /
• 2017

농작물 작황 추정은 생산량 예측을 통한 수급 조절, 가격 예측, 농가 소득 보전을 위한 정책 수립 등에 중요한 판단자료로 활용된다. 급변하는 국내외 여건에서 작물의 안정생산과 식량안보, 생태계 지속성 평가를 위해 원격탐사 등 국가차원의 미래기술 개발 노력이 요구되고 있다. 농촌진흥청은 2010년부터 국내외 주요 곡물생산지대 작황 평가를 위한 원격탐사, 작물모형, 농업기상 분야 원천기술 개발을 위해 노력해왔다. 본 특별호는 농촌진흥청에서 지난 8년간 국내외 작황 평가를 위해 수행해 온 원격탐사, 작물모형, 농업기상 분야의 연구개발 성과 및 연계된 이들 분야 간 융복합 연구 수행 현황을 정리하고 향후 연구 방향을 제시하고자 발간하게 되었다.

• 한국통신학회논문지
• /
• 제39A권6호
• /
• pp.357-364
• /
• 2014

본 논문은 반사필터를 사용하여 FTTH-PON의 원격제어 시스템을 다루고 있다. 기존의 FTTH-PON방식은 광케이블 고장과 수동분기소자(Splitter)의 품질 저하가 발생하는 것을 실시간 인지 할 수 없다. 본 연구에서는 이를 해결하기 위해서 광섬유 브래그 격자 원리를 이용한 반사필터를 개발하여 간선망과 Splitter를 감시 할 수 있게 하였다. 반사필터는 광케이블 밴딩, 커넥터 상태와 스플리터의 품질이 점진적으로 저하되는 것을 모니터링 하고 광케이블 고장, 수동 광분배기(Splitter) 등의 고장 위치를 확인하여 OLT측에 경보를 보내서 중앙관제에서 감시할 수 있는 시스템이다. 즉, 기존 통신 국사 측 파장분기 결합부(Coupler)를 이용해 1개의 광심선으로 live 파장과 감시용 파장을 2개로 보내어 가입자 측 장치인 모뎀과 집선 스위치에 반사필터를 사용하여 원격으로 광선로를 감시하는 시스템에 관한 연구이다. 이러한 반사필터의 개발로 인해 광선로 평균 고장처리 시간 단축과 효율적 유지보수를 할 수 있을 것으로 기대한다.

• Structural Monitoring and Maintenance
• /
• 제1권3호
• /
• pp.249-271
• /
• 2014

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

• International Journal of Advanced Culture Technology
• /
• 제2권2호
• /
• pp.11-14
• /
• 2014

In the smart grid, it will be possible to communicate with the consumers for the purposes of monitoring and controlling their power consumption without disturbing their business or comfort. This will bring easier administration capabilities for the utilities. On the other hand, consumers will require more advanced home automation tools which can be implemented by using advanced sensor technologies. For instance, consumers may need to adapt their consumption according to the dynamically varying electricity prices which necessitates home automation tools. This paper tries to combine neural network and nonlinear autoregressive with exogenous variable (NARX) class for next week electric load forecasting. The suitability of the proposed approach is illustrated through an application to electric load consumption data. The suggested system provides a useful and suitable tool especially for the load forecasting.