• Journal of Ocean Engineering and Technology
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• v.18 no.4 s.59
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• pp.1-7
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• 2004

A simultaneous measurement system that can analyze the flow-structure interaction has been developed. This system consists of four CCD cameras, two for capturing instantaneous flow fields and two for tracking a solid body. The three-dimensional vector fields around a cylinder are measured, while the motion of the cylinder forced by the flow field is measured, simultaneously, with the constructed system. The cylinder is suspended in the working fluid of a water channel, and the surface of the working fluid is forced sinusoidally to make the cylinder bounced. Reynolds number for the mean main stream is about 3500. The interaction between the flow fields and the cylinder motion is examined quantitatively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.111-122
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• 2015

This study purposed on introducing a new automated solution for detecting railway tracks and reconstructing track models from the mobile laser scanning data. The proposed solution completes following procedures; the study initiated with detecting a potential railway region, called Region Of Interest (ROI), and approximating the orientation of railway track trajectory with the raw data. At next, the knowledge-based detection of railway tracks was performed for localizing track candidates in the first strip. In here, a strip -referring the local track search region- is generated in the orthogonal direction to the orientation of track trajectory. Lastly, an initial track model generated over the candidate points, which were detected by GMM-EM (Gaussian Mixture Model-Expectation & Maximization) -based clustering strip- wisely grows to capture all track points of interest and thus converted into geometric track model in the tracking by detection framework. Therefore, the proposed railway track tracking process includes following key features; it is able to reduce the complexity in detecting track points by using a hypothetical track model. Also, it enhances the efficiency of track modeling process by simultaneously capturing track points and modeling tracks that resulted in the minimization of data processing time and cost. The proposed method was developed using the C++ program language and was evaluated by the LiDAR data, which was acquired from MMS over an urban railway track area with a complex railway scene as well.

• Agricultural and Biosystems Engineering
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• v.6 no.2
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• pp.47-53
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• 2005

AMachine vision is a promising tool for the autonomous guidance of farm machinery. Conventional CCD camera for the machine vision needs a desktop PC to install a frame grabber, however, a web camera is ready to use when plugged in the USB port. A web camera with a notebook PC can replace existing camera system. Autonomous steering control system of this research was intended to be used for combine harvester. If the web camera can recognize cut/uncut edge of crop, which will be the reference for steering control, then the position of the machine can be determined in terms of lateral offset and heading angle. In this research, a white line was used as a cut/uncut edge of crop for steering control. Image processing algorithm including capturing image in the web camera was developed to determine the desired travel path. An experimental vehicle was constructed to evaluate the system performance. Since the vehicle adopted differential drive steering mechanism, it is steered by the difference of rotation speed between left and right wheels. According to the position of vehicle, the steering algorithm was developed as well. Evaluation tests showed that the experimental vehicle could travel within an RMS error of 0.8cm along the desired path at the ground speed of $9\sim41cm/s$. Even when the vehicle started with initial offsets or tilted heading angle, it could move quickly to track the desired path after traveling $1.52\sim3.5m$. For turning section, i.e., the curved path with curvature of 3 m, the vehicle completed its turning securely.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.959-968
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• 2019

In the agricultural industry, wireless sensor network technology has being applied by utilizing various sensors and embedded systems. In particular, a lot of researches are being conducted to diagnose diseases of crops early by using sensor network. There are some difficulties on traditional research how to diagnose crop diseases is not practical for agriculture. This paper proposes the algorithm which enables to investigate and analyze the crop leaf image taken by image camera and detect the infected area within the image. We applied the enhanced k-means clustering method to the images captured at horticulture facility and categorized the areas in the image. Then we used the edge detection and edge tracking scheme to decide whether the extracted areas are located in inside of leaf or not. The performance was evaluated using the images capturing tomato leaves. The results of performance evaluation shows that the proposed algorithm outperforms the traditional algorithms in terms of classification capability.

• Smart Media Journal
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• v.12 no.7
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• pp.59-67
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• 2023

Recently, research on behavior analysis tracking human posture and movement has been actively conducted. In particular, OpenPose, an open-source software developed by CMU in 2017, is a representative method for estimating human appearance and behavior. OpenPose can detect and estimate various body parts of a person, such as height, face, and hands in real-time, making it applicable to various fields such as smart healthcare, exercise training, security systems, and medical fields. In this paper, we propose a method for classifying four exercise movements - Squat, Walk, Wave, and Fall-down - which are most commonly performed by users in the gym, using OpenPose-based deep learning models, DNN and CNN. The training data is collected by capturing the user's movements through recorded videos and real-time camera captures. The collected dataset undergoes preprocessing using OpenPose. The preprocessed dataset is then used to train the proposed DNN and CNN models for exercise movement classification. The performance errors of the proposed models are evaluated using MSE, RMSE, and MAE. The performance evaluation results showed that the proposed DNN model outperformed the proposed CNN model.

• Journal of Biomedical Engineering Research
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• v.44 no.4
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• pp.255-263
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• 2023

Clinical ultrasound (US) is a widely used imaging modality with various clinical applications. However, capturing a large field of view often requires specialized transducers which have limitations for specific clinical scenarios. Panoramic imaging offers an alternative approach by sequentially aligning image sections acquired from freehand sweeps using a standard transducer. To reconstruct a 3D volume from these 2D sections, an external device can be employed to track the transducer's motion accurately. However, the presence of optical or electrical interferences in a clinical setting often leads to incorrect measurements from such sensors. In this paper, we propose a deep learning (DL) framework that enables the prediction of scan trajectories using only US data, eliminating the need for an external tracking device. Our approach incorporates diverse data types, including correlation volume, optical flow, B-mode images, and rawer data (IQ data). We develop a DL network capable of effectively handling these data types and introduce an attention technique to emphasize crucial local areas for precise trajectory prediction. Through extensive experimentation, we demonstrate the superiority of our proposed method over other DL-based approaches in terms of long trajectory prediction performance. Our findings highlight the potential of employing DL techniques for trajectory estimation in clinical ultrasound, offering a promising alternative for panoramic imaging.

• Journal of Environmental Science International
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• v.22 no.9
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• pp.1227-1234
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• 2013

Between July and October 2011, radio-tracking was used to analyze the characteristics of home ranges and day roosts of Myotis aurascens by using 3 individuals (male: 2, female: 1). Bat capturing was conducted at a bridge and a nearby forest in Ulju-gun, Ulsan-si. We attached radio transmitters (0.32 g) to the bats and monitored them by using a radio receiver with a Yagi antenna. Home-range analysis of M. aurascens by using 100% minimum convex polygon (MCP) and 95% MCP showed an average of 106.5 ha and 89.3 ha, respectively, and 50% kernel home range (KHR) showed an average of 8.4 ha. Home range overlap of the 3 bats was observed at the bridge and at nearby water bodies as the core areas, and the size of the home range overlap was 7.3 ha by 100% MCP, 5.9 ha by 95% MCP, and 1.6 ha by 50% KHR. The home range for each bat consisted of the main foraging sites, and the types of foraging sites were similar. M. aurascens-01(M-01) used the bridge and nearby water bodies as the nightly main core areas, M. aurascens-02(M-02) used rice fields and water bodies adjacent to the forest as core areas, and M. aurascens-03(M-03) used water bodies and resident areas as core areas. Although rice fields and resident sites represented the core areas of the home ranges of M-02 and M-03, habitat use was the highest near water bodies as the core area for all the 3 bats. The types of day roosts in this study were a wooden house, canopies of a broad-leaved woodland, and banks of rice fields. The roosts in the wooden house and canopies of the broad-leaved woodland were located within the forest, and the roost in the banks of rice fields was also adjacent to the forest. Our results revealed that the main home range and foraging sites of M. aurascens were located near water bodies as the core area, and forests and places adjacent to the forests were used as day roosts.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.223-240
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• 2013

RFID(Radio Frequency Identification) is use of an RFID tag applied to object for the purpose of identification and tracking using radio waves. Recently, it is being actively researched and introduced in logistics and manufacturing. RFID portals in supply chains are meant to identify all the tags within a given interrogation zone. Hence the hardware and software mechanisms for RFID tag identification mostly focus on successful read of multiple tags simultaneously. Such mechanisms, however, are inefficient for determining moving direction of tags, sequence of consecutive tags, and validity of the tag reads from the viewpoint of workflow. These types of problems usually cause many difficulties in RFID portal implementation in manufacturing environment, there by having RFID-system developers waste a considerable amount of time. In this research, we designated an RFID portal system with SDO(Sequence, Direction, and Object-flow)-perception capability by using fundamental data supplied by ordinary RFID readers. Using our work, RFID system developers can save a great amount of time building RFID data-capturing applications in manufacturing environment.

• Journal of Science and Technology Studies
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• v.16 no.2
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• pp.1-31
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• 2016

This essay examines the recent proliferation of location-based services (LBS) within the context the expansion of the technologies of remote identification, monitoring, and tracking. Following the spatial turn in the social sciences, this essay aims to analyze LBS as a surveillance technology that can re-shape the spatial configuration of its users and their identity. The analytic focus of this essay is on LBS within the global information infrastructure, and it utilizes key LBS examples in the US and South Korea. First, as a way to discuss the technical possibilities of LBS for spatial coordination and surveillance, this essay investigates its technical architecture in terms of information flow. It then discusses the issue of privacy in LBS by analyzing some of its key legal and regulatory issues. The combination of the global information infrastructure with location-related technologies has enabled LBS companies to expand the scope of surveillance over the ever-increasing computer-mediated activities, prompting heated discussions over whether LBS is capturing "Every Moment in Your Life." This essay concludes with a discussion on how location technologies have provided a key platform for the rise of surveillance capitalism through the creation of what Marc $Aug{\acute{e}}$ called a "non-place," a place where the identified self is constituted by LBS.