• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.51-57
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• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.2
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• pp.23-32
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• 2020

The compression sensing technology, CAFB, was developed to obtain the raw signal of the target structure by compressing it into a signal of the intended frequency range. At this point, for compression sensing, the CAFB can be optimized for various reference signals depending on the desired frequency range of the target structure. In addition, optimized CAFB should be able to efficiently compress the effective structural answers of the target structure even in sudden/dangerous conditions such as earthquakes. In this paper, the targeted frequency range for efficient structural integrity monitoring of relatively flexible structures was set below 10Hz, and the optimization method of CAFB for this purpose and the seismic response performance of CAFB in seismic conditions were evaluated experimentally. To this end, in this paper, CAFB was first optimized using Kobe seismic waveform, and embedded it in its own wireless IDAQ system. In addition, seismic response tests were conducted on two span bridges using Kobe seismic waveform. Finally, using an IDAQ system with built-in CAFB, the seismic response of the two-span bridge was wirelessly obtained, and the compression signal obtained was cross-referenced with the raw signal. From the results of the experiment, the compression signal showed excellent response performance and data compression effects in relation to the raw signal, and CAFB was able to effectively compress and sensitize the effective structural response of the structure even in seismic situations. Finally, in this paper, the optimization method of CAFB was presented to suit the intended frequency range (less than 10Hz), and CAFB proved to be an economical and efficient data compression sensing technology for instrumentation-monitoring of seismic conditions.

• 한국체육학회지인문사회과학편
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• v.57 no.3
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• pp.345-356
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• 2018

Objective: The purpose of this study was to investigate the local stability of the lower extremity joints and muscle activation patterns of the lower extremity during walking between falling and non-falling group in the elderly women. Method: Forty women, heel strikers, were recruited for this study. Twenty subjects (age:72.55±5.42yrs; height:154.40±4.26cm; mass:57.40±6.21kg; preference walking speed:0.52±0.17m/s; fall frequency=1.70±1.26 times) had a history falls(fall group) within two years and Twenty subjects (71.90±2..90yrs; height:155.28±4.73cm; mass:56.70±5.241kg; preference walking speed: 0.56±0.13m/s) had no history falls(non-fall group). While they were walking on a instrumented treadmill at their preference speed for a long while, kinematic and EMG signals were obtained using 3-D motion capture and wireless EMG electrodes, respectively. Local stability of the ankle and knee joint were calculated using Lyapunov Exponent (LyE) and muscles activation and their co-contraction index were also quantified. Hypotheses were tested using one-way ANOVA and Mann-Whitey. Spearman rank was also used to determine the correlation coefficients between variables. Level of significance was set at p<.05. Results: Local stability in the knee joint adduction-abduction was significantly greater in fall group than non-fall group(p<.05). Activation of anterior tibials that acts on the foot segment dorsal flexion was greater in non-fall group than fall group(p<.05). CI between gastrocnemius and anterior tibials was found to be significantly different between two groups(p<.05). In addition, there was significant correlation between CI of the leg and LyE of the ankle joint flexion-extention in the fall group(p<.05). Conclusion: In conclusion, muscles that act on the knee joint abduction-adduction as well as gastrocnemius and anterior tibials that act on the ankle joint flexion-extention need to be strengthened to prevent from potential fall during walking.