• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.48 no.5
• /
• pp.354-360
• /
• 1999

PCN-PZT piezoelectric acceleration sensors of annular shear mode voltage type were fabricated and their characteristics have been investigated. Field tests are also carried out. To avoid noise problems from the environmental conditions, acceleration sensors employed solid state micro-electronics for pre-amplifier. The calibration procedures based on the principle of the comparison method were adopted for investigating the characteristics of fabricated acceleration sensors. The voltage sensitivity and resonant frequency of fabricated acceleration sensors were 83mv/g, 23kHz, respectively. The lower and upper frequency limit were 4Hz and 9kHz, respectively. The variation of the voltage sensitivity showed 10% at $-406{\circ}C\; and\; 9%\; at\; 121^{\circ}C$ compared to that of reference temperature at $40^{\circ}C$.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.233-236
• /
• 2010

This paper describes the packet traffic management of the Ubiquitous Healthcare System. In this system, ECG signal and accelerometer signal is transmitted from a wearable health shirt (WHS) to the base station. However, with the increment of users in this system, traffic over-load issue occurs. The main aim of this paper is to reduce the traffic over-load issue between sensor nodes by only transmitting the required signals to the base station when irregular activities are observed. In order to achieve this, in-network processing is adapted where the process of observation is conducted inside the sensor node of WHS. Results shows that irregular activities such as fall can be detected on real-time inside the sensor node and thus resolves traffic over-load issue.

• Structural Monitoring and Maintenance
• /
• v.6 no.1
• /
• pp.33-46
• /
• 2019

In present study, Operational Modal Analysis (OMA) was employed to carry out the dynamic and vibration analysis of the threshing unit of the combine harvester thresher as a mechanical component. The main study is to find the causes of vibration and to decrease it to enhance the lifetime and efficiency of the threshing unit. By utilizing OMA, structural modal parameters such as mode shapes, natural frequencies, and damping ratio was calculated. The combine harvester was excited by engine to vibrate different parts and accelerometer sensor collected acceleration signals at different speeds, and OMA was utilized by nonparametric and frequency analysis methods to obtain modal parameters while vibrating in real working conditions. Afterwards, finite element model was designed from the thresher and updated using the data obtained from the modal analysis. Using the conducted analyses, it was specified that proximity of the thresher pass frequency to one of the natural frequencies (16.64 Hz) was the most important effect of vibration in the thresher. Modification process of the structure was carried out by increasing mass required for changing the natural frequency location of the first mode to 12.4 Hz in order to reduce resonance and vibration of the thresher.

• Structural Engineering and Mechanics
• /
• v.69 no.3
• /
• pp.347-359
• /
• 2019

This paper aims to evaluate the behavior of Dorim-Goh bridge in Seoul, Korea, under static and dynamic loads effects by ambient trucks. The prestressed concrete (PSC) girders and reinforcement concrete (RC) slab of the bridge are evaluated and assessed. A short period monitoring system is designed which comprises displacement, strain and accelerometer sensors to measure the bridge performance under static and dynamic trucks loads. The statistical analysis is used to assess the static behavior of the bridge and the wavelet analysis and probabilistic using Weibull distribution are used to evaluate the frequency and reliability of the dynamic behavior of the bridge. The results show that the bridge is safe under static and dynamic loading cases. In the static evaluation, the measured neutral axis position of the girders is deviated within 5% from its theoretical position. The dynamic amplification factor of the bridge girder and slab are lower than the design value of that factor. The Weibull shape parameters are decreased, it which means that the bridge performance decreases under dynamic loads effect. The bridge girder and slab's frequencies are higher than the design values and constant under different truck speeds.

• Tunnel and Underground Space
• /
• v.28 no.6
• /
• pp.547-554
• /
• 2018

Using crowdsourced sensor data collection technique, it is possible to collect high-density ground vibration data which is difficult to obtain by conventional methods. In this study, we have developed a crowdsourced ground vibration data collection system using MEMS sensors mounted on small electronic devices including smartphones, and implemented client and server based on the proposed infrastructure system design. The system is designed to gather vibration data quickly through Android-based smartphones or fixed devices based on Android Things, minimizing the usage of resource like power usage and data transmission traffic of the hardware.

• ETRI Journal
• /
• v.44 no.2
• /
• pp.300-312
• /
• 2022

Recently, public safety services have attracted significant attention for their ability to protect people from crimes. Rapid detection of dangerous situations (that is, abnormal situations where someone may be harmed or killed) is required in public safety services to reduce the time required to respond to such situations. This study proposes a novel danger detection technology based on multimodal data, which includes data from multiple sensors (for example, accelerometer, gyroscope, heart rate, air pressure, and global positioning system sensors), and multilog data, which includes contextual logs of humans and places (for example, contextual logs of human activities and crime-ridden districts) over time. To recognize human activity (for example, walk, sit, and punch), the proposed technology uses multimodal data analysis with an attitude heading reference system and long short-term memory. The proposed technology also includes multilog data analysis for detecting whether recognized activities of humans are dangerous. The proposed danger detection technology will benefit public safety services by improving danger detection capabilities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.391-393
• /
• 2021

In this paper, we introduce a long short-term memory (LSTM)-based fall detection system using TensorFlow that can detect falls occurring in the elderly in daily living. 3-axis accelerometer data are aggregated for fall detection, and then three types of parameter are calculated. 4 types of activity of daily living (ADL) and 3 types of fall situation patterns are classified. The parameterized data applied to LSTM. Learning proceeds until the Loss value becomes 0.5 or less. The results are calculated for each parameter θ, SVM, and GSVM. The best result was GSVM, which showed Sensitivity 98.75%, Specificity 99.68%, and Accuracy 99.28%.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.2
• /
• pp.473-482
• /
• 2024

This study utilized virtual reality to construct a simulated public restroom environment to identify potential hazards. The objective was to discern actual risks in real-world public restrooms through this virtual setup. During the virtual restroom experience, data from the built-in 3-axis accelerometer and gyroscope sensors of testor's smart phones were collected. Analysis of this data helped in identifying spatio temporal factors impacting the users. The determination of these factors as risk elements was based on an evaluation algorithm grounded in data analysis.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.3
• /
• pp.121-138
• /
• 2024

Artificial intelligence is crucial to manufacturing productivity. Understanding the difficulties in producing disruptions, especially in linear feed robot systems, is essential for efficient operations. These mechanical tools, essential for linear movements within systems, are prone to damage and degradation, especially in the LM guide, due to repetitive motions. We examine how explainable artificial intelligence (XAI) may diagnose wafer linear robot linear rail clearance and ball screw clearance anomalies. XAI helps diagnose problems and explain anomalies, enriching management and operational strategies. By interpreting the reasons for anomaly detection through visualizations such as Class Activation Maps (CAMs) using technologies like Grad-CAM, FG-CAM, and FFT-CAM, and comparing 1D-CNN with 2D-CNN, we illustrates the potential of XAI in enhancing diagnostic accuracy. The use of datasets from accelerometer and torque sensors in our experiments validates the high accuracy of the proposed method in binary and ternary classifications. This study exemplifies how XAI can elucidate deep learning models trained on industrial signals, offering a practical approach to understanding and applying AI in maintaining the integrity of critical components such as LM guides in linear feed robots.

• International Journal of Automotive Technology
• /
• v.8 no.3
• /
• pp.259-268
• /
• 2007

A knock detection circuit that is based on the signal of an accelerometer installed on the engine block of a spark ignition automotive engine has a band-pass filter with a certain frequency as a parameter to be calibrated. A new statistical method for the determination of the frequency which is the most suitable for the knock detection in real-time applications is proposed. The method uses both the cylinder pressure and block vibration signals and is divided into two steps. In both steps, a new recursive trigonometric interpolation method that calculates the frequency contents of the signals is applied. The new trigonometric interpolation method developed in this paper improves the performance of the Discrete Fourier Transformation, allowing a flexible choice of the size of the moving window. In the first step, the frequency contents of the cylinder pressure signal are calculated. The knock is detected in the cylinder of the engine cycle for which at least one value of the maximal amplitudes calculated via the trigonometric interpolation method exceeds a threshold value indicating a considerable amount of oscillations in the pressure signal; this cycle is selected as a knocking cycle. In the second step, the frequency analysis is performed on the block vibration signal for the cycles selected in the previous step. The knock detectability, which is an individual cylinder attribute at a certain frequency, is verified via a statistical hypothesis test for testing the equality of two mean values, i.e. mean values of the amplitudes for knocking and non-knocking cycles. Signal-to-noise ratio is associated in this paper with the value of t-statistic. The frequency with the largest signal-to-noise ratio (the value of t-statistic) is chosen for implementation in the engine knock detection circuit.