• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1064-1066
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• 2003

In this paper, ground penetrating radar (GPR), which has the capability to detect non metal and plastic mines, is proposed to detect and discriminate antipersonnel (AP) landmines. The time domain GPR - Impulse radar and frequency domain GPR - SFCW (Stepped Frequency Continuous Wave) radar is utilized for metal and non-metal landmine detection and its performance is investigated. Since signal processing is vital for target reorganization and clutter rejection, we implemented the MUSIC (Multiple Signal Classification) algorithm for the signal processing of SFCW radar data and SAR (Synthetic Aperture Radar) processing method for the signal processing of Impulse radar data.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.7
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• pp.269-277
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• 1996

This paper has been presented a design of a POCSAG decoder in RT-level VHDL and implemented in a FPGA chip, and tested. In a single clock of 76.8KHz, the decoder receives all the data of the rate of 512/1200/2400bps and has maximum 2-own frames for service enhancement. To improve decoder performance, the decoder uses a preamble detection cosidering 9% frequency tolerance, a SCW detction and a ICW detection at each 4 bit. The decoder also corrects a address data and a message data up to 2 bits and proposes the PF (preamble frequency) error for saving battery. The decoder increases a battery life owing to turn off RF circuits when the preamble signal is detected with nises. The chip has been designed in RT-level VHdL, synthesized into logic gates using power view$^{TM}$ of viewlogic software. The chip has been implemented in an ALTERA EPF81188GC232-3 FPGA chip with 98% usability, and fully tested in shield room and field room. The chip has been proved that the wrong detection numbers of preamble of noises are significantly reduced in the pager system using PDI 2400 through the real field test. The receiving performance is improved by 20% of aaverage, compared with other existing systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.168-175
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• 2023

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

• Journal of Mechanical Science and Technology
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• v.20 no.2
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• pp.205-211
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• 2006

Among other critical conditions in rotor systems the large non-linear vibration excited by bearing non-linearity causes the rotor failure. For reducing this catastrophic failure and predictive detection of this phenomenon the analysis of orthotropic bearing non-linearity in rotor system using higher order frequency response functions (HFRFs) is conducted and is shown to be theoretically feasible as that of non-rotating structures. The complex HFRFs based on the Volterra series are newly developed for the process and investigated their features by using the simple forms of the FRFs associated with the forward and the backward modes.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.153-158
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• 2016

As the number of social network users increases, the information on event such as social issues and disasters receiving attention in each region is promptly posted by the bucket through social media site in real time, and its social ripple effect becomes huge. This study proposes a detection method of events that draw attention from users in specific region at specific time by using twitter data with regional information. In order to collect Twitter data, we use Twitter Streaming API. After collecting data, We implemented event detection system by analyze the frequency of a keyword which contained in a twit in a particular time and clustering the keywords that describes same event by exploiting keywords' co-occurrence graph. Finally, we evaluates the validity of our method through experiments.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.173-182
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• 2013

Typographical mistakes made in the writing process of drafts of electronic documents are more common than any other type of errors. The majority of these errors caused by mistyping are regarded as consequently still typo-errors, but a considerable number of them are developed into the grammatical errors and the semantic errors. Pseudo semantic errors among these errors due to typographical errors have more noticeable peculiarities than pure semantic errors between senses of surrounding context words within a sentence. These semantic errors can be detected and corrected by simple algorithm based on the co-occurrence frequency because of their prominent contextual discrepancy. I propose a method for detection and correction based on the co-occurrence frequency in order to detect semantic errors due to typo-errors. The co-occurrence frequency in proposed method is counted for only words with immediate dependency relation, and the cosine similarity measure is used in order to detect pseudo semantic errors. From the presented experimental results, the proposed method is expected to help improve the detecting rate of overall proofreading system by about 2~3%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.661-663
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• 2021

Network performance is degraded when the UE is disconnected because the outage occurs at the base station in a mobile communication system. Therefore it is important to detect and recover the outage. In this paper, detecting the outage base station by using the KPI and the network scanning in the neighbor base station, and increasing the transmit power and changing the frequency band to recovery the outage scheme is proposed. The proposed scheme uses not only the KPI of the base station but also the network scanning of the neighbor base stations to detect the outage base station, so that it is possible to detect the outage base station more accurately. In addition, when the outage occurred, the neighbor base station changes the transmit power and frequency band to recover the outage with less signal interference.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.2
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• pp.113-121
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• 2023

High-precision lasers and anti-aircraft radars are the main equipment to protect the Korean Peninsula, and require preemptive maintenance before signs of failure. Of the key components in the drive sector, bearings do not have a fault alarm function. Therefore, the technology for diagnosing defects in bearings before the performance degradation of equipment occurs is becoming more important. In this paper, for the experimental analysis, we measured the acceleration of the four sets of the same lot using acceleration sensors. Through periodic measurements, the factors that changed until the bearing stopped rotating were analyzed. To determine the replacement time, the main factors and threshold values of the bearing signal were analyzed. The error of the theoretical and experimental analysis results of the defect frequency was within 2.8 %, and the validity of the theoretical analysis results could be confirmed. Based on the results, it is possible to remotely transmit trouble alerts to users through the system check function.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.4
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• pp.309-314
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• 2013

This paper dealt with the radiated electromagnetic wave detection of partial discharge (PD) in oil for insulation diagnostics of oil-immersed transformers. Three types of electrode system were fabricated to simulate the insulation defects that could occur in oil-immersed transformers. Frequency components of radiated electromagnetic wave in oil was measured by broadband bi-conical antennas of 300 MHz~2 GHz and a spectrum analyzer of 9 kHz~3 GHz. Frequency component of electromagnetic waves from PD in oil were highly distributed at 500 MHz. From the result, a narrow-band monopole antenna with the center frequency of 500 MHz was fabricated. We could detect PD signal in insulation oil without an influence of external noise by a measurement system which consists of the prototype monopole antenna, a LNA (Low Noise Amplifier), an oscilloscope and a spectrum analyzer.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1799-1801
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• 2004

We have developed the external-type PD sensors for detecting of the electromagnetic waves up to ultra high frequency (UHF) bands, propagating in various type of GIS. Sufficiently high sensitivity and accuracy for practical use were achieved for the external PD sensor. The experience at the fields and the criteria for the judgment are also discussed in detail.