• The Journal of Natural Sciences
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• v.5 no.1
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• pp.87-93
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• 1992

A robotically assisted field material handling system designed for loading and unloading of a planar pallet with a forklift in unstructured field environment is presented. The system uses combined acoustic/visual sensing data to define the position/orientation of the pallet and to determine the specific locations of the two slots of the pallet, so that the forklift can move close to the slot and engage it for transport. In order to reduce the complexity of the material handling operation, we have developed a method based on the integration of 2-D range data of Poraloid ultrasonic sensor along with 2-D visual data of an optical camera. Data obtained from the two separate sources complements each other and is used in an efficient algorithm to control this robotically assisted field material handling system . Range data obtained from two linear scannings is used to determine the pan and tilt angles of a pallet using least mean square method. Then 2-D visual data is used to determine the swing angle and engagement location of a pallet by using edge detection and Hough transform techniques. The limitations of the pan and tilt orientation to be determined arc discussed. The system developed is evaluated through the hardware and software implementation. The experimental results are presented.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.485-490
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• 2020

Recently, amid active research on domestic shipbuilding industry and IT convergence technology, with the development of satellite detection technology for ship safety operation, ships monitored the movement of ships with the mandatory long-range identification & tracking of vessels and automatic identification system. It is possible to help safe navigation, but it is necessary to develop safety device that alert the marine officer who rely on radar to correct conditions in case of weightlessness. Therefore, an ocean fog alarm system was developed to detect and inform using photo sensors. The fabricated ocean fog detect and alarm system consists of a small, low-power optical sensor transceiver and data sensing processing module. Through experiment, it is confirmed that the fabricated ocean fog detect and alarm system measure the corresponding concentration of ocean fog for fogless circumstance and fogbound circumstance, respectively. Furthermore, the fabricated system can control RPM of ship engine according to the concentration of ocean fog, and consequently, the fabricated system can be applied to assistant device for ship safety operation.

• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.109-115
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• 2022

The CNN (Convolution Neural Network) algorithm which combines a deep learning technique, and a computer vision technology, makes image classification feasible with the high-performance computing system. In this thesis, the CNN algorithm is applied to the classification problem, by using a typical deep learning framework of TensorFlow and machine learning techniques. The data set required for supervised learning is generated with the same type of bolts. some of which have undamaged threads, but others have damaged threads. The learning model with less quantity data showed good classification performance on detecting damage in a bolt image. Additionally, the model performance is reviewed by altering the quantity of convolution layers, or applying selectively the over and under fitting alleviation algorithm.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.33-41
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• 2011

In the information communication technology, it is world-widely apparent that trend movement from internet web to smartphone app by users demand and developers environment. So it needs kinds of appropriate technological responses from geo-spatial domain regarding this trend. However, most cases in the smartphone app are the map service and location recognition service, and uses of geo-spatial contents are somewhat on the limited level or on the prototype developing stage. In this study, app for extraction of corner point features using geo-spatial imagery and their linkage to database system are developed. Corner extraction is based on Harris algorithm, and all processing modules in database server, application server, and client interface composing app are designed and implemented based on open source. Extracted corner points are applied LOD(Level of Details) process to optimize on display panel. Additional useful function is provided that geo-spatial imagery can be superimposed with the digital map in the same area. It is expected that this app can be utilized to automatic establishment of POI (Point of Interests) or point-based land change detection purposes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.11
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• pp.1077-1083
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• 2012

The jamming countermeasures in GNSS includes anti-jamming technique and jammer localization technique. In both techniques, direction of jamming signal is important and generally the MUSIC algorithm is used to find the direction of jamming signal. The MUSIC is super-resolution algorithm for detecting incident direction of signal. But, the search time of MUSIC algorithm is too long because all candidates of incidence angle are searched. This paper proposes the new method that has less computational burdens and therefore faster than the conventional MUSIC algorithm. The proposed method improves performance speed by reducing unnecessary calculations. In the proposed method, the cost function of conventional MUSIC algorithm is decomposed into the sum of squares and if the partial sum of cost function is larger than the minimum cost function so far, then the candidate is rejected and next candidates are searched. If the computed cost function is less than the minimum cost function so far, the minimum cost function so far is replaced with newly computed value. The performance of the proposed method was compared with the conventional MUSIC algorithm using the simulation. The accuracy of the estimaed direction of jamming signal was same as the conventional MUSIC while the search speed of the proposed method was 1.15 times faster than the conventional MUSIC.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.2
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• pp.120-134
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• 2000

In this paper we present a Distributed Object oriented Virtual computing Environment, called DOVE which consists of autonomous distributed objects interacting with one another via method invocations based on a distributed object model. DOVE appears to a user logically as a single virtual computer for a set of heterogeneous hosts connected by a network as if objects in remote site reside in one virtual computer. By supporting efficient parallelism, heterogeneity, group communication, single global name service and fault-tolerance, it provides a transparent and easy-to-use programming environment for parallel applications. Efficient parallelism is supported by diverse remote method invocation, multiple method invocation for object group, multi-threaded architecture and synchronization schemes. Heterogeneity is achieved by automatic data arshalling and unmarshalling, and an easy-to-use and transparent programming environment is provided by stub and skeleton objects generated by DOVE IDL compiler, object life control and naming service of object manager. Autonomy of distributed objects, multi-layered architecture and decentralized approaches in hierarchical naming service and object management make DOVE more extensible and scalable. Also,fault tolerance is provided by fault detection in object using a timeout mechanism, and fault notification using asynchronous exception handling methods

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• Journal of the Korean earth science society
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• v.21 no.4
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• pp.408-422
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• 2000

Multiple geophysical methods were applied over the Manjang cave area in Cheju Island to compare and contrast the effectiveness of each method for exploration of underground cavities. The used methods are gravity, magnetic, electrical resistivity and GPR(Ground Pentrating Radar) survey, of which instruments are portable and operations are relatively economical. We have chosen seven survey lines and applied appropriate multiple surveys depending on the field conditions. In the case of magnetic method. two-dimensional grid-type surveys were carried out to cover the survey area. The geophysical survey results reveal the characteristic responses of each method relatively well. Among the applied methods, the electric resistivity methods appeared to be the most effective ones in detecting the Manjang Cave and surrounding miscellaneous cavities. Especially, on the inverted resistivity section obtained from the dipole-dipole array data, the two-dimensional distribution of high resistivity cavities are revealed well. The gravity and magnetic data are contaminated easily by various noises and do not show the definitive responses enough to locate and delineate the Manjang cave. But they provide useful information in verifying the dipole-dipole resistivity survey results. The grid-type 2-D magnetic survey data show the trend of cave development well, and it may be used as a reconnaissance regional survey for determining survey lines for further detailed explorations. The GPR data show very sensitive response to the various shallow volcanic structures such as thin spaces between lava flows and small cavities, so we cannot identify the response of the main cave. Although each geophysical method provides its own useful information, the integrated interpretation of multiple survey data is most effective for investigation of the underground caves.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.67-72
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• 2005

To investigate the feasibility of a new concept of storing Liquefied Natural Gas (LNG) in a lined hard rock cavern, and to develop essential technologies for constructing underground LNG storage facilities, a small pilot plant storing liquid nitrogen (LN2) has been constructed at the Korea Institute of Geoscience and Mineral Resources (KIGAM). The LN2 stored in the cavern will subject the host rock around the cavern to very low temperatures, which is expected to cause the development of an ice ring and the change of ground condition around the storage cavern. To investigate and monitor changes in ground conditions at this pilot plant site, geophysical, hydrogeological, and rock mechanical investigations were carried out. In particular, geophysical methods including borehole radar and three-dimensional (3D) resistivity surveys were used to identify and monitor the development of an ice ring, and other possible changes in ground conditions resulting from the very low temperature of LN2 in the storage tank. We acquired 3D resistivity data before and after storing the LN2, and the results were compared. From the 3D images obtained during the three phases of the resistivity monitoring survey, we delineated zones of distinct resistivity changes that are closely related to the storage of LN2. In these results, we observed a decrease in resistivity at the eastern part of the storage cavern. Comparing the hydrogeological data and Joint patterns around the storage cavern, we interpret this change in resistivity to result from changes in the groundwater flow pattern. Freezing of the host rock by the very low temperature of LN2 causes a drastic change in the hydrogeological conditions and groundwater flow patterns in this pilot plant.