Helmet Mounted Display(HMD) has great advantages on the navigation and mission symbologies for the pilot's forward looking display and, therefore, has been remarkably drawing attention as the up coming display of the next generation aircraft. The essential technology to process the Line of Sight-Foward(LOS-F) data in real-time is to estimate exact helmet situation and position. In this paper, we research a acoustic helmet tracking technique. For the reason that mechanical acoustic noises might interfere with Helmet Tracking System(HTS) and unnecessary acoustic noises are inevitable when using acoustic technique, this approach has not been adapted. In order to overcome this problem. We propose that acoustic wave of which the wave length is longer than audio frequency and, especially, we used beating signal envelope which is composed of two close high frequency.
The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.
For an underground excavation at depth in highly stressful conditions, it is important to mitigate the risk of stress-induced failure, e.g., rockburst, and improve miner safety concerning the stability of underground workplaces and the prevention of fatalities. In general, the cause of rockburst is classified into three categories: strainburst due to stress-induced fracturing, rock ejection by seismic energy transfer, and rockfall associated with mining-induced seismicity. In this study, the Split Hopkinson Pressure Bar (SHPB) modified configuration of bar drop apparatus was developed by attaching a direct shear test box and a long bar. As a result, the modified bar drop system enabled to replicate and control of a seismic velocity that was an incident on the joint rock surfaces installed in the direct shear testing box. The long bar installed in the modified bar drop system provides a longer stress wavelength to overcome the relatively shorter duration of the stress waves in the SHPB system. The dynamic shear test on the jointed rock samples using the bar drop apparatus also provided the information to estimate the rock joint shear strengths. 1. INTRODUCTION The rock structures stability depends on the distribution characteristics and mechanical characteristics of discontinuous surfaces such as faults and joints in the rock. Underground excavation has high-stress conditions, and such workplaces are causing stability and fatalities of underground workplaces due to rockburst. In general, the cause of rockburst is classified into three categories: strainburst due to stress-induced fracturing, rock ejection by seismic energy transfer, and rockfall associated with mining-induced seismicity. In order to understand the behavior and stability of such rock structures, it is necessary to understand the shear characteristics of the joint surfaces. The direct shear test and multi-stage shear test are methods to measure the shear characteristics of the joint surfaces. The direct shear test measures the shear characteristics of a joint surface by generating shear displacement with different load conditions from each specimen and requires samples with the same roughness. However, it is difficult to obtain samples with the same roughness in the field, so a multi-stage shear test is conducted. A constant normal load section is set as a specimen in the multi-stage shear test. The shear characteristics for each normal stress stage are measured by generating shear displacement while increasing the normal load. In the case of the multi-stage shear test, the International Society of Rock Mechanics and Rock Engineering (ISRM, 1981) recommends the discontinuous surface test method for rock. However, it has been pointed out that the joint surface of the specimen is damaged due to an increase in shear load due to the normal load, resulting in a lower shear strength than the direct shear test (Zhao and Zhou, 1992).
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