• Journal of Korean Society for Atmospheric Environment
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• v.30 no.4
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• pp.327-338
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• 2014

The objective of this study is to enhance the effectiveness of Korean Inspection and Maintenance (I/M) program. Three main tasks are: to measure pollutant emissions of in-use light-duty diesel vehicles (LDVs); to evaluate the validity of existing smoke control scheme for low-smoke-emitting vehicles, which have diesel particulate filters, DPF, to meet stringent Euro-5 emission limits; and to assess the necessity and the benefit of $NO_x$ inspection, which is not involved in current I/M program. We measured second-by-second smoke, particulate and gaseous emissions of 27 LDVs using opacity smoke meter, photo-acoustic soot sensor, and portable emissions measurement system, respectively, under the Korean I/M test driving cycle, KD-147. We find that the DPF plays a key role in controlling soot, which can be considered as black carbon contained in particulate matter. Thus, from an I/M perspective, we believe smoke inspection strategies for Euro-5 diesel vehicles should be more focused on the capability of detecting DPF malfunctions or failures, in order to keep DPF properly functional. Fleet averaged distance-specific $NO_x$ emissions are consistently higher than corresponding emission limits, and the values are similar among pre-Euro-3, Euro-3, and Euro-4 vehicle fleets. These findings indicate that the $NO_x$ inspection should be incorporated into current I/M program in order to manage urban $NO_x$ emissions. This research allows the Korean I/M program keep pace with developments in vehicle technologies, as well as the increased emphasis on $NO_x$ with respect to air quality and human health.

• Journal of Astronomy and Space Sciences
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• v.25 no.4
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• pp.435-444
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• 2008

High energy charged particles are trapped by geomagnetic field in the region named Van Allen Belt. These particles can move to low altitude along magnetic field and threaten even low altitude spacecraft. Space Radiation can cause equipment failures and on occasions can even destroy operations of satellites in orbit. Sun sensors aboard Science and Technology Satellite (STSAT-l) was designed to detect sun light with silicon solar cells which performance was degraded during satellite operation. In this study, we try to identify which particle contribute to the solar cell degradation with ground based radiation facilities. We measured the short circuit current after bombarding electrons and protons on the solar cells same as STSAT-1 sun sensors. Also we estimated particle flux on the STSAT-l orbit with analyzing NOAA POES particle data. Our result clearly shows STSAT-l solar cell degradation was caused by energetic protons which energy is about 700keV to 1.5MeV. Our result can be applied to estimate solar cell conditions of other satellites.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.94-105
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• 2008

Numerous platforms have been developed for ZigBee-based network embedded systems. Also, operating systems like TinyOS have been installed to facilitate efficient implementation of wireless sensor network applications which collect data, and/or execute commands. First of all, porting an operating system on a new platform may need invention of a substitute for a required but unsupported hardware component. This paper presents a multiplexed virtual system timer for a platform without a counter comparator which we have contrived to emulate by using an extra counter. Such porting also injects unexpected faults which cause a variety of painful failures. Unfortunately, TinyOS requires to handle a lot of asynchronous hardware interrupts which are hard to trace during debugging. Besides, simulators are not available for a new platform since the models of hardware on the platform are not usually developed, yet. We propose novel instrumentation techniques which can be used to effectively trace the bugs in such lack of debugging environment. These techniques are used to identify and fix a great deal of nasty issues in porting TinyOS 2.0 on MG2400 and MG2455 platforms made by RadioPulse Inc.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.737-746
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• 2019

Most landslides in the country are shallow failures triggered by intense rainfall. Many researchers have revealed the possibility of predicting shallow failure through the volumetric water content (VWC). This study examined how to determine shallow failure using the gradient characteristics of the volumetric water content. For this, flume experiments were conducted using weathered granite soil. To confirm the saturation state of the surface layer under a rainfall intensity of 30 and 50mm/hr, VWC sensors were installed at depths of 10 and 20 cm on the upper, middle and lower slope. The test results showed that a shallow failure determination using VWC could be applied limitedly according to the slope degree. In addition, the effective cumulative rainfall due to the rainfall infiltration velocity is considered the main factor for the failure time. The failure prediction using the gradient of the VWC depends on the installation location and depth of the sensor. According to the experimental data, the measured value at 20 cm below the slope was most effective. Therefore, an analysis method of VWC and the method of selecting the installation location confirmed through this study can provide important data for presenting the measurement criteria using VWC in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.306-316
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• 2016

In this paper, an NMOS-diode eFuse OTP (One-Time Programmable) memory cell is proposed using a parasitic junction diode formed between a PW (P-Well), a body of an isolated NMOS (N-channel MOSFET) transistor with the small channel width, and an n+ diffusion, a source node, in a DNW (Deep N-Well) instead of an NMOS transistor with the big channel width as a program select device. Blowing of the proposed cell is done through the parasitic junction formed in the NMOS transistor in the program mode. Sensing failures of '0' data are removed because of removed contact voltage drop of a diode since a NMOS transistor is used instead of the junction diode in the read mode. In addition, a problem of being blown for a non-blown eFuse from a read current through the corresponding eFuse OTP cell is solved by limiting the read current to less than $100{\mu}A$ since a voltage is transferred to BL by using an NMOS transistor with the small channel width in the read mode.

• Journal of Navigation and Port Research
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• v.27 no.1
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• pp.97-102
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• 2003

Wire ropes are used in a myriad of various industrial applications such as elevator, mine hoist, construction machinery, lift, and suspension bridge. Especially, the wire rope of crane is important component to container transfer. If it happens wire rope failures during the operation, it may lead to safety accident, economic loss by productivity decline and so on. To solve this problem, we developed remote wire rope fault detecting system, and this system is consisted of 3 parts that portable fault detecting part, signal processing part and remote monitoring part. All detected signal has external noise or disturbance according to circumstances. So, we applied to discrete wavelet transform to extract a signal from noisy data. It is verified that the detecting system by de-noising has good efficiency for inspecting faults of wire ropes in service. As a result, by developing this system, container terminal could reduce expense because of extension fo wire ropes exchange period and could competitive power. Also, this system is possible to apply in several field such as elevator, lift and so on.

• 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 Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.22-28
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• 2021

Multivariate processes, such as chemical and mechanical process, power plants are operated in a state where several facilities are complexly connected, the fault of a particular system can also have fatal consequences for the entire process. In addition, since process data is measured in an unstable environment, outlier is likely to be include in the data. Therefore, monitoring technology is essential, which can remove outlier from measured data and detect failures in advance. In this paper, data obtained from dynamic and multivariate process models was used to detect fault in various type of processes. The dynamic process is a simulation of a process with autoregressive property, and the multivariate process is a model that describes a situation when a specific sensor fault. Mahalanobis distance was used to remove outlier contained in the data generated by dynamic process model and multivariate process model, and fault detection was performed using ICA. For comparison, we compared performance with and a conventional single ICA method. The proposed fault detection method improves performance by 0.84%p for bias data and 6.82%p for drift data in the dynamic process. In the case of the multivariate process, the performance was improves by 3.78%p, therefore, the proposed method showed better fault detection performance.