• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• Nuclear Engineering and Technology
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• v.48 no.1
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• pp.129-143
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• 2016

Sometimes, we need or try to figure out somebody's thoughts from his or her behaviors such as eye movement, facial expression, gestures, and motions. In safety-critical and complex systems such as nuclear power plants, the inference of operators' thoughts (understanding or diagnosis of a current situation) might provide a lot of opportunities for useful applications, such as development of an improved operator training program, a new type of operator support system, and human performance measures for human factor validation. In this experimental study, a novel method for inference of an operator's thoughts from his or her eye movement data is proposed and evaluated with a nuclear power plant simulator. In the experiments, about 80% of operators' thoughts can be inferred correctly using the proposed method.

• Science of Emotion and Sensibility
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• v.14 no.2
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• pp.221-226
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• 2011

Although diverse researches on sensing method of human movement have been performed, there are still many limitations to the existing methods. As a part of supplementing the limitations to the existing motion sensing methods, this study aimed to execute an exploratory examination on a POF-based sleeve-shaped motion sensor for less restrictive sensing of human movement. In this study, a set of POF-based motion sensor, which was embedded in a sleeve-shaped platform was devised, and a set of exploratory experiments was performed on the possibility of sensing of human movement as diverse as in daily life, through this device. The scope of this research was limited to an exploration on the possibility and basic elements of POF-based sleeve-shaped motion sensor, while the influence of sleeve patterns, those of wearer's somatotype, those of sewing method were not studied in this study. When compared to the pre-existing methods, the POF-based motion sensor platformed on sleeve in this study, which was purposively devised to be applied to the motion sensing clothing shows some beneficial characteristics : more sensitive measurement on human motion, low cost, no timely restriction in sensing, no request for gigantic apparatus and space for sensing.

• Steel and Composite Structures
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• v.35 no.6
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• pp.779-788
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• 2020

Multi-Walled Carbon nanotubes (MWCNT) coupled with Silicone Rubber (SR) can represent applicable strain sensors with accessible materials, which result in good stretchability and great sensitivity. Employing these materials and given the fact that the combination of these two has been addressed in few studies, this study is trying to represent a low-cost, durable and stretchable strain sensor that can perform excellently in a high number of repeated cycles. Great stability was observed during the cyclic test after 2000 cycles. Ultrahigh sensitivity (GF>1227) along with good extensibility (ε>120%) was observed while testing the sensor at different strain rates and the various number of cycles. Further investigation is dedicated to sensor performance in the detection of human body movements. Not only the sensor performance in detecting the small strains like the vibrations on the throat was tested, but also the larger strains as observed in extension/bending of the muscle joints like knee were monitored and recorded. Bearing in mind the applicability and low-cost features, this sensor may become promising in skin-mountable devices to detect the human body motions.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.88-95
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• 2022

Detecting anomalies in human movement is an important task in industrial applications, such as monitoring industrial disasters or accidents and recognizing unauthorized factory intruders. In this paper, we propose an abnormal worker movement detection system based on data stream processing and hierarchical clustering. In the proposed system, Apache Spark is used for streaming the location data of people. A hierarchical clustering-based anomalous trajectory detection algorithm is designed for detecting anomalies in human movement. The algorithm is integrated into Apache Spark for detecting anomalies from location data. Specifically, the location information is streamed to Apache Spark using the message queuing telemetry transport protocol. Then, Apache Spark processes and stores location data in a data frame. When there is a request from a client, the processed data in the data frame is taken and put into the proposed algorithm for detecting anomalies. A real mobility trace of people is used to evaluate the proposed system. The obtained results show that the system has high performance and can be used for a wide range of industrial applications.

• Journal of the Health Care and Life Science
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• v.9 no.2
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• pp.297-302
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• 2021

As the price of LiDAR developed for autonomous driving has dropped dramatically, LiDAR has begun to be applied in various fields. Typical examples are vacuum cleaner robots, autonomous delivery robots, and autonomous obstacle avoidance drones. LiDAR is becoming the only means of figuring out the location of an object in real time while compensating for the weakness that 2D or 3D cameras are vulnerable to lighting. In this paper, we propose a method to monitor the movement of a patient by installing a 2D lidar in a hospital room. When a patient who needs intensive monitoring due to psychologically unstable, suicidal intention, or psychotic findings is alone in the ward, 2D LiDAR monitors the patient's movement and sends an appropriate alarm to the management room to effectively monitor the patient. devised a way to do it.

• Journal of Forest and Environmental Science
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• v.38 no.2
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• pp.128-132
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• 2022

Wild boars (Sus scrofa) not only cause crop damage and human casualties, but also facilitate the spread of many infectious diseases in domestic animals and humans. To determine the efficiency of a fencing system in blocking the movement of wild boars, long-term monitoring was performed in a fenced area in Bukhansan National Park using camera traps. Upon monitoring for a period of 46 months, there was a 72.6% reduction in the number of wild boar appearances in the fence-enclosed area, compared to that in the unenclosed area. For 20 months after the fence installation, the blocking effect of the fence was effective enough to reduce the appearance of wild boars by 92.6% in the fence-enclosed area, compared to that in the unenclosed area. The blocking effect of the fence remained effective for 20 months after its installation, after which its effectiveness decreased. Maintaining a fence for a long time is likely to lead to habitat fragmentation. It can also block the movement of other wild animals, including the endangered species - the long-tailed goral. This study suggests a 20-month retention period for the fences installed to inhibit the movement of wild boars in wide forests such as Gangwon-do in South Korea. To identify how long the blocking effect of the fences lasts, further studies are needed focusing on the length and height of the fence, and the conditions of the ground surface.

• The Journal of Korea Robotics Society
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• v.4 no.2
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• pp.130-138
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• 2009

Recently, people tracking technology is being required to various area including security application. This paper suggests a method to track people with multiple laser scanners to detect the waist part of human. Multi-target model and Kalman filter based estimation are employed to track the human movement. The proposed method is applied to a novel system to monitor the entrance area and to filter out the trespasser to pass through the door without identification. Experiments for various cases are performed to verify the usefulness of the developed system.

• International Journal of Contents
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• v.16 no.3
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• pp.1-17
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• 2020

Human motion recognition is essential for user-centric services such as surveillance-based security, elderly condition monitoring, exercise tracking, daily calories expend analysis, etc. It is typically based on the movement data analysis such as the acceleration and angular velocity of a target user. The existing motion recognition studies are only intended to measure the basic information (e.g., user's stride, number of steps, speed) or to recognize single motion (e.g., sitting, running, walking). Thus, a new mechanism is required to identify the transition of single motions for assessing a user's consecutive motion more accurately as well as recognizing the user's body and surrounding situations arising from the motion. Thus, in this paper, we collect the human movement data through Android smartphones in real time for five targeting single motions and propose a mechanism to recognize a consecutive motion including transitions among various motions and an occurred situation, with the state transition model to check if a vulnerable (life-threatening) condition, especially for the elderly, has occurred or not. Through implementation and experiments, we demonstrate that the proposed mechanism recognizes a consecutive motion and a user's situation accurately and quickly. As a result of the recognition experiment about mix sequence likened to daily motion, the proposed adoptive weighting method showed 4% (Holding time=15 sec), 88% (30 sec), 6.5% (60 sec) improvements compared to static method.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.1
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• pp.68-72
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• 2015

Ultrasonic inspection robot systems have been widely researched and developed for the real-time monitoring of structures such as power plants. However, an inspection robot that is operated in a simple pattern has limitations in its application to various structures in a plant facility because of the diverse and complicated shapes of the inspection objects. Therefore, accurate control of the robot is required to inspect complicated objects with high-precision results. This paper presents the idea that the shape and movement information of an ultrasonic inspector's hand could be profitably utilized for the accurate control of robot. In this study, a polymer flex sensor was applied to monitor the shape of a human hand. This application was designed to intuitively control an ultrasonic inspection robot. The movement and shape of the hand were estimated by applying multiple sensors. Moreover, it was successfully shown that a test robot could be intuitively controlled based on the shape of a human hand estimated using polymer flex sensors.