• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.6
• /
• pp.61-73
• /
• 2020

Real-time location-sensing technology using location information collected from IoT devices is being applied for safety management purposes in many industries, such as ports. On the other hand, positional error is always present owing to the characteristics of GPS. Therefore, accident-risk detection algorithms must consider positional error. This paper proposes an methodology of calibration for falling object accident-risk-zone approach detection algorithm considering GPS errors. A probability density function was estimated, with positional error data collected from IoT devices as a probability variable. As a result of the verification, the algorithm showed a detection accuracy of 93% and 77%. Overall, the analysis results derived according to the GPS error level will be an important criterion for upgrading algorithms and real-time risk managements in the future.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2005.11a
• /
• pp.324-329
• /
• 2005

This research investigates the electromechanical characteristics of a spindle motor in a free-falling mobile hard disk drive before unexpected shock. Electromechanical simulation includes a time-stepping finite element analysis of the magnetic field in a speed controlled brushless DC motor and dynamic analysis of the stationary and rotating part linked by fluid dynamic bearing under the free-failing condition. Analysis results show that the dynamic characteristics of the rotating spindle system during free-falling state have an effect on the relative motion between the stationary and rotating part of HDD. It results from the variation of reaction force in the bearing area due to the gravely force exerted on the rotating part of HDD, and the free-failing condition can be detected by observing the signal of the spindle motor and disk-head interface without using the accelerometer.

• Journal of radiological science and technology
• /
• v.21 no.2
• /
• pp.57-63
• /
• 1998

AEC was designed to assist in the production of radiographs of equal density and to aid in the production f consistently high quality radiographs. Many improvements have been made in the AEC and these have helped to increase it's acceptance. Newer system (Falling load) control kVp and mA as well as exposure time. Authors have had experiments about the characteristics of AEC by Zone System. Zone System is the method to detection of photographic image qualify by control the photo print from Fred Archer and Ansel Adams. With the Zone System theory the experiments of density control, characteristics of phantom thickness and reproducibility of occupation rate of contrast media could make quality control of X-ray photography effectively in falling load system.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.11
• /
• pp.33-38
• /
• 2012

An free-fall object is received only force of gravity. Movement that only accept gravity is free-fall movement, and a free-falling object is free falling body. In other words, free falling body is only freely falling objects under the influence of gravity, regardless of the initial state of objects movement. In this paper, we assume, ignoring the resistance of the air, and the free-fall acceleration by the height does not change within the range of the short distance in the vertical direction. Under these assumptions, we can know about time and maximum height to reach the peak point from jumping vertically upward direction, time and speed of the car return to the starting position, and time and speed when the car fall to the ground. It can be measured by jumping degree and risk of accident from car or motorcycle in telematics.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.11
• /
• pp.2546-2553
• /
• 2015

Falling can happen to anyone, anywhere at anytime and especially it is one of the risk factor that can lead causes of death of persons aged 65 and over. Recently, the study of fall detection mechanisms as a smart healthcare service based on the IoT(Internet of Things) are being actively investigated. In this paper, we implement a fall detection system using arduino as a smart sensor communicates with a smart device. When transmitting the information of the acceleration on a sensor smart sensor with a BLE(Bluetooth Low Energy), the smart device processing and analyzing this information. and determines a fall situation. A fall detection system based on the Internet of Things which using smart sensor and smart device, has the advantage of being able to overcome the mobility and portability constraints.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.9
• /
• pp.1082-1087
• /
• 2019

Video Incident Detection System is a detection system for the purpose of detection of an emergency in an unexpected situation such as a pedestrian in a tunnel, a falling object, a stationary vehicle, a reverse run, and a fire(smoke and flame). In recent years, the importance of the city center has been emphasized by the construction of underpasses in great depth underground space. Therefore, in order to apply Video Incident Detection System to a Double Deck Tunnel, it was developed to reflect the design characteristics of the Double Deck Tunnel. and In this paper especially, the fire detection technology, which is not it is difficult to apply to the Double Deck Tunnel environment because it is not supported on existing Video Incident Detection System or has a fail detect, we propose fire detection using color image analysis, silhouette spread, and statistical properties, It is verified through a real fire test in a double deck tunnel test bed environment.

• Journal of Sensor Science and Technology
• /
• v.20 no.6
• /
• pp.420-427
• /
• 2011

The population of elderly people increases rapidly as our society moves towards the aged one. Healthcare for the elderly becomes an important issue and falling down is one of the critical problems although not well recognized. In this study, a fall detection system was developed using a 3-axis accelerometer. Analyzing fall patterns, we took into account the degree of impact, posture angle, the repetitions of similar movements and the activities after a potential fall and proposed an algorithm of fall detection. Information of the fall sensor was sent to a remote healthcare server through the wireless networks of Zigbee and WLAN. Our system was designed to monitor multiples users. 12 persons participated in experiment and each one performed 24 different movements. Our proposed algorithm was compared with other reported ones. Our method produced the excellent results having a sensitivity of 96.4 % and a specificity of 100 % whereas other methods had a sensitivity range between 87.5 % and 94.8 % and a specificity range between 63.5 % and 83.3 %.

• Journal of the Society of Disaster Information
• /
• v.20 no.2
• /
• pp.302-314
• /
• 2024

Purpose: This study proposes a fall detection model based on a top-down deep learning pose estimation model to automatically determine falls of multiple workers in an underground utility tunnel, and evaluates the performance of the proposed model. Method: A model is presented that combines fall discrimination rules with the results inferred from YOLOv8-pose, one of the top-down pose estimation models, and metrics of the model are evaluated for images of standing and falling two or fewer workers in the tunnel. The same process is also conducted for a bottom-up type of pose estimation model (OpenPose). In addition, due to dependency of the falling interference of the models on worker detection by YOLOv8-pose and OpenPose, metrics of the models for fall was not only investigated, but also for person. Result: For worker detection, both YOLOv8-pose and OpenPose models have F1-score of 0.88 and 0.71, respectively. However, for fall detection, the metrics were deteriorated to 0.71 and 0.23. The results of the OpenPose based model were due to partially detected worker body, and detected workers but fail to part them correctly. Conclusion: Use of top-down type of pose estimation models would be more effective way to detect fall of workers in the underground utility tunnel, with respect to joint recognition and partition between workers.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.2
• /
• pp.960-973
• /
• 2018

This paper presents design for error correcting algorithm of the time of flight (ToF) detection value in the light detection and ranging (LIDAR) system sensor. The walk error of ToF value is generated by change of the received signal power depending on distance between the LIDAR sensor and object. The proposed method efficiently compensates the ToF value error by the independent ToF value calculation from the received signal using both rising point and falling point. A constant error of ~0.05 m is obtained after the walk error correction while an increasing error up to ~1 m is obtained with conventional method.

• Journal of Korea Multimedia Society
• /
• v.17 no.4
• /
• pp.466-477
• /
• 2014

Many image hiding schemes based on least significant bit (LSB) transformation have been proposed. One of the LSB-based image hiding schemes that employs diamond encoding was proposed in 2008. In this scheme, the binary secret data is converted into base n representation, and the converted secret data is concealed in the cover image. Here, we show that this scheme has two vulnerabilities: noticeable spots in the stego-image, i.e., a non-smooth embedding result, and inefficiency caused by rough re-adjustment of falling-off-boundary value and impractical base translation. Moreover, we propose a new scheme that is efficient and produces a smooth and high quality embedding result by restricting n to power of 2 and using a sophisticated re-adjustment procedure. Our experimental results show that our scheme yields high quality stego-images and is secure against RS detection attack.