• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.501-512
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• 2018

Radio frequency identification (RFID) sensor tag provides several advantages including battery-less operation and low cost, which are suitable for long-term monitoring. This paper presents a self-powered RFID temperature sensor tag for online temperature monitoring in substation. The proposed sensor tag is used to measure and process the temperature of high voltage equipments in substation, and then wireless deliver the data. The proposed temperature sensor employs a novel phased-locked loop (PLL)-based architecture and can convert the temperature sensor in frequency domain without a reference clock, which can significantly improve the temperature accuracy. A two-stage rectifier adopts a series of auxiliary floating rectifier to boost its gate voltage for higher power conversion efficiency. The sensor tag chip was fabricated in TSMC $0.18{\mu}m$ 1P6M CMOS process. The measurement results show that the proposed temperature sensor tag achieve a resolution of $0.15^{\circ}C$/LSB and a temperature error of $-0.6/0.7^{\circ}C$ within the range from $-30^{\circ}C$ to $70^{\circ}C$. The proposed sensor tag achieves maximum communication distance of 11.8 m.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.138-145
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• 2018

In the field of optimization, many studies have been performed on various types of Vehicle Routing Problem (VRP) for a long time. A variety of models have been derived to extend the basic VRP model, to consider multiple truck terminal, multiple pickup and delivery, and time windows characteristics. A lot of research has been performed to find better solutions in a reasonable time for these models with heuristic approaches. In this paper, by considering realtime traffic characteristics in Map Navigation environment, we proposed a method to manage realistic optimal path allocation for the logistics trucks and cargoes, which are dispersed, in order to realize the realistic cargo mixing allowance and time constraint enforcement which were required as the most important points for an online logistics brokerage service company. Then we developed a prototype system that can support above functionality together with delivery status monitoring on Map Navigation environment. First, through Map Navigation system, we derived information such as navigation-based travel time required for logistics allocation scheduling based on multiple terminal multiple pickup and delivery models with time constraints. Especially, the travel time can be actually obtained by using the Map Navigation system by reflecting the road situation and traffic. Second, we made a mathematical model for optimal path allocation using the derived information, and solved it using an optimization solver. Third, we constructed the prototype system to provide the proposed method together with realtime logistics monitoring by arranging the allocation results in the Map Navigation environment.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 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.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.589-598
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• 2018

To maintain safety and reliability of reactors, redundant sensors are usually used to measure critical variables and estimate their averaged time-dependency. Nonhealthy sensors can badly influence the estimation result of the process variable. Since online condition monitoring was introduced, the online cross-calibration method has been widely used to detect any anomaly of sensor readings among the redundant group. The cross-calibration method has four main averaging techniques: simple averaging, band averaging, weighted averaging, and parity space averaging (PSA). PSA is used to weigh redundant signals based on their error bounds and their band consistency. Using the consistency weighting factor (C), PSA assigns more weight to consistent signals that have shared bands, based on how many bands they share, and gives inconsistent signals of very low weight. In this article, three approaches are introduced for improving the PSA technique: the first is to add another consistency factor, so called trend consistency (TC), to include a consideration of the preserving of any characteristic edge that reflects the behavior of equipment/component measured by the process parameter; the second approach proposes replacing the error bound/accuracy based weighting factor ($W^a$) with a weighting factor based on the Euclidean distance ($W^d$), and the third approach proposes applying $W^d$, TC, and C, all together. Cold neutron source data sets of four redundant hydrogen pressure transmitters from a research reactor were used to perform the validation and verification. Results showed that the second and third modified approaches lead to reasonable improvement of the PSA technique. All approaches implemented in this study were similar in that they have the capability to (1) identify and isolate a drifted sensor that should undergo calibration, (2) identify a faulty sensor/s due to long and continuous missing data range, and (3) identify a healthy sensor.

• Journal of Korea Game Society
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• v.15 no.4
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• pp.69-78
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• 2015

As the online game industry has been growing rapidly, more and more malicious activities to gain economic benefits have been reported as well. Game bot is one of the biggest problems in the online game industry. So we proposed a bot detection method based on the ERG theory of motivation for the first time. Most of the previous studies focused on behavior-based detection by monitoring patterns of the specific actions. In this paper, we applied the motivation theory to analyze user behaviors on a real game dataset. The result shows that normal users in the game followed the ERG theory of motivation in the same way as it works in real world. But in the case of game bots, the theory could not be applied because the game bot has specific reasons, unlike normal game users. We applied the ERG theory to users to distinguish game bot users from normal users. We detected the game bot with high accuracy of 99.78% by applying the theory.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.93-99
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• 2020

Text mining is one of the big data analysis methods that extracts meaningful information from atypical large-scale text data. In this study, text mining was used to monitor citizens' opinions on the policies and systems being implemented. We collected 5,108 newspaper articles and 748 online cafe posts related to 'Gyeonggi Lacal Currency' and performed frequency analysis, TF-IDF analysis, association analysis, and word tree visualization analysis. As a result, many articles related to the purpose of introducing local currency, the benefits provided, and the method of use. However, the contents related to the actual use of local currency were written in the online cafe posts. In order to revitalize local currency, the news was involved in the promotion of local currency as an informant. Online cafe posts consisted of the opinions of citizens who are local currency users. SNS and text mining are expected to effectively activate various policies as well as local currency.

• Korean Journal of Community Nutrition
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• v.16 no.1
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• pp.136-144
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• 2011

Assessment of quality of life (QOL) is a new method to investigate the effectiveness of dietary regimen. Particularly, diet-related QOL is the most appropriate method to estimate social and psychological problems originated from dietary regimen practice. The purpose of this study was to evaluate the diet-related QOL and the correlation between diet-related QOL and health-related QOL, and dietary regimen practice in online diabetes self-help patients who practice the dietary regimen. Sixty one subjects who intended to practice dietary regimen were recruited from online diabetes self-help community, and instructed to fill-up the self report questionnaires. Contents of questionnaire were comprised of general characteristics, clinical characteristics, dietary compliance, and dietary regimen practice. As a result, the mean score of the 'Dietary impact' among the diet-related QOL sub-scales was the lowest suggesting most of the subjects suffer from burden of dietary regimen practice. The "Dietary impact" was correlated with "Taste", "Convenience" and "Cost" (p < 0.05). "Self-care" and "Satisfaction" were positively associated with well-controlled blood glucose and dietary regimen compliance, but negatively associated with "Dietary impact". Diet-related QOL was significantly correlated with the Health-related QOL, particularly the mental and social component (p < 0.05). Diet-related QOL was negatively associated with BMI, and self monitoring blood glucose was negatively correlated with "Self-care" (p < 0.05). In conclusion, Diet-related QOL might be appropriate to evaluate the effects of dietary regimen or nutrition education. The need for dietary education of cognitive-behavioral strategies and problem-solving ability is required.

• The Korean Journal of Pesticide Science
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• v.19 no.1
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• pp.22-31
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• 2015

This study was carried out to monitor the current status of pesticide residues in selling agricultural products via online and assessed their safety in 2014. A total of 124 samples were purchased six times from March to August 2014 twenty online shopping malls randomly. These samples were analysed 208 pesticides by multiresidue method using a GC-ECD/NPD and a LC-MS/MS and confirmed by a GC-MSD. As a result of analysis, residual pesticides samples were 11 (8.9%) such as leek, young radish, welsh onion etc, of which 2 samples (1.6%) such as sesame bud (Chlorothalonil), artemisia (Chlorpyrifos) were violated Korea Maximum Residue limits (MRLs). 11 kinds of pesticides (19 times) were detected in 11 samples. Risk assessment evaluated human health exposure with the ratio of EDI (Estimated daily intake) to ADI (Acceptable daily intake) of pesticides detected. %ADI (the ratios of EDI to ADI) were 0.04~95.70% and some samples represented a fairly dangerous levels. In particular, Chlorothalonil in the sesame bud was shown as a significant risk close to 100% of %ADI. Accordingly, it is recommended to strengthen a safety check on agricultural products in online sales.

• Journal of Food Hygiene and Safety
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• v.36 no.4
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• pp.342-346
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• 2021

Cold brew coffee extracted from cold water for a long time has drawn public concern over hygiene. This study was carried out to investigate the microbiological contamination levels and caffeine contents in cold brew coffee. A total of 75 cold brew coffees were purchased from offline and online sources. As a result, the average number of bacteria in samples purchased online was 1.14 log CFU/mL (0-6.57 log CFU/mL), while bacteria were not detected in samples purchased offline. Therefore, stricter surveys are required to avoid the food contamination. However, Esherichia coli and nine types of foodborne pathogens were not detected in all samples. The average caffeine content of the samples was 1.6 mg/mL (384 mg/240 mL), so the caffeine almost reached to acceptable daily intake levels (400 mg for adults). However, ten products did not provide any precautions for consumer safety, so improvement of the system is needed. This monitoring data can contribute to the protection of consumer rights and improvement in the safety of cold brew coffee.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.