• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.5
• /
• pp.3074-3078
• /
• 2014

We implemented a dual-band slot-coupled patch (SCP) antenna for the external access point (AP) of the wireless local area network (WLAN) band. The antennas consist of two radiators on three layers. The first radiator is a slotted bow tie antenna operating at the 2.4-2.483 GHz band. The second radiator is a patch antenna with parasitic elements operating at 4.095-5.845 GHz. The high gain and broad bandwidth is important element of wireless access. To enhance the bandwidth, a coupled feeding was used in the first radiator and a parasitic patch was used in the second radiator. We used a parasitic patch and chock to improve the directivity and isolation in both radiators. The porposed antenna was designed by EM simulation tool and measured. The S11 of the antenna was less than -11dB (VSWR 1.8:1) at operating frequency. The peak gain was more than 6 dBi in the first antenna and more than 8 dBi in the second antenna.

• Journal of the Korea Convergence Society
• /
• v.12 no.8
• /
• pp.31-37
• /
• 2021

As the size of buildings increases due to urbanization due to the development of industry, the need to purify the air and maintain a comfortable indoor environment is also increasing. With the development of monitoring technology for refrigeration systems, it has become possible to manage the amount of electricity consumed in buildings. In particular, refrigeration systems account for about 40% of power consumption in commercial buildings. Therefore, in order to develop the refrigeration system failure diagnosis algorithm in this study, the purpose of this study was to understand the structure of the refrigeration system, collect and analyze data generated during the operation of the refrigeration system, and quickly detect and classify failure situations with various types and severity . In particular, in order to improve the classification accuracy of failure types that are difficult to classify, a three-step diagnosis and classification algorithm was developed and proposed. A model based on SVM and LGBM was presented as a classification model suitable for each stage after a number of experiments and hyper-parameter optimization process. In this study, the characteristics affecting failure were preserved as much as possible, and all failure types, including refrigerant-related failures, which had been difficult in previous studies, were derived with excellent results.

• Clean Technology
• /
• v.28 no.4
• /
• pp.323-330
• /
• 2022

Microfluidic paper-based analytical devices (μPADs) have recently been in the spotlight for their applicability in point-of-care diagnostics and environmental material detection. This study presents a double-sided printing method for fabricating 3D-μPADs, providing simple and cost effective metal ion detection. The design of the 3D-μPAD was made into an acryl stamp by laser cutting and then coating it with a thin layer of PDMS using the spin-coating method. This fabricated stamp was used to form the 3D structure of the hydrophobic barrier through a double-sided contact printing method. The fabrication of the 3D hydrophobic barrier within a single sheet was optimized by controlling the spin-coating rate, reagent ratio and contacting time. The optimal conditions were found by analyzing the area change of the PDMS hydrophobic barrier and hydrophilic channel using ink with chromatography paper. Using the fabricated 3D-μPAD under optimized conditions, Ni²⁺, Cu²⁺, Hg²⁺, and pH were detected at different concentrations and displayed with color intensity in grayscale for quantitative analysis using ImageJ. This study demonstrated that a 3D-μPAD biosensor can be applied to detect metal ions without special analysis equipment. This 3D-μPAD provides a highly portable and rapid on-site monitoring platform for detecting multiple heavy metal ions with extremely high repeatability, which is useful for resource-limited areas and developing countries.

• Journal of the Microelectronics and Packaging Society
• /
• v.24 no.4
• /
• pp.23-29
• /
• 2017

The packaged optical fiber Bragg grating sensors which were networked by multiplexing the Bragg grating sensors with WDM technology were investigated in application for the structural health monitoring of the marine trestle structure transporting the ship. The optical fiber Bragg grating sensor was packaged in a cylindrical shape made of aluminum tubes. Furthermore, after the packaged optical fiber sensor was inserted in polymeric tube, the epoxy was filled inside the tube so that the sensor has resistance and durability against sea water. The packaged optical fiber sensor component was investigated under 0.2 MPa of hydraulic pressure and was found to be robust. The number and location of Bragg gratings attached at the trestle were determined where the trestle was subject to high displacement obtained by the finite element simulation. Strain of the part in the trestle being subjected to the maximum load was analyzed to be ${\sim}1000{\mu}{\varepsilon}$ and thus shift in Bragg wavelength of the sensor caused by the maximum load of the trestle was found to be ~1,200 pm. According to results of the finite element analysis, the Bragg wavelength spacings of the sensors were determined to have 3~5 nm without overlapping of grating wavelengths between sensors when the trestle was under loads and thus 50 of the grating sensors with each module consisting of 5 sensors could be networked within 150 nm optical window at 1550 nm wavelength of the Bragg wavelength interrogator. Shifts in Bragg wavelength of the 5 packaged optical fiber sensors attached at the mock trestle unit were well interrogated by the grating interrogator which used the optical fiber loop mirror, and the maximum strain rate was measured to be about $235.650{\mu}{\varepsilon}$. The modelling result of the sensor packaging and networking was in good agreements with experimental result each other.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.127-148
• /
• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.