• International journal of advanced smart convergence
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• v.10 no.1
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• pp.82-88
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• 2021

In this paper, we propose an automatic detection scheme of work distraction for remote management of telecommuting. The proposed scheme periodically captures two consequent computer screens and generates the difference image of these two captured images. The scheme applies the difference image to our deep learning model and makes a decision of abnormal patterns in the difference image. Our deep learning model is designed with the transfer learning technique of VGG16 deep learning. When the scheme detects an abnormal pattern in the difference image, it hides all texts in the difference images to protect disclosure of privacy-related information. Evaluation shows that the proposed scheme provides about 96% detection accuracy.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.1
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• pp.122-128
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• 2014

Remote fourier transform infrared(FTIR) chemical imaging detection system allows detection and identification of gases in the atmosphere from long distances. In this paper, the appropriate field of view(FOV) of the FTIR imaging system was examined and the main performance of the system for the interferometer was described. For the determination of the FOV, simulations of gas dispersion range were performed with the NBC reporting and modeling software(NBC-RAMS) developed by ADD. As a result, minimum 192 mrad of FOV was required for the remote FTIR imaging system to visualize chemical warfare agents dispersed in several hundred meters. At the same time, 0.75 mrad of instantaneous field of view(IFOV) for a linear interferometer proper to take a FOV for the chemical agent imaging.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.47-49
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• 2003

Automatic land use and land cover change (LUCC) detection via remotely sensed imagery has a wide application in the area of LUCC research, nature resource and environment monitoring and protection. Under the condition that one time (T1) data is existed land use and land cover maps, and another time (T2) data is remotely sensed imagery, how to detect change automatically is still an unresolved issue. This paper developed a land use and land cover class knowledge guided method for automatic change detection under this situation. Firstly, the land use and land cover map in T1 and remote sensing images in T2 were registered and superimposed precisely. Secondly, the remotely sensed knowledge database of all land use and land cover classes was constructed based on the unchanged parcels in T1 map. Thirdly, guided by T1 land use and land cover map, feature statistics for each parcel or pixel in RS images were extracted. Finally, land use and land cover changes were found and the change class was recognized through the automatic matching between the knowledge database of remote sensing information of land use & land cover classes and the extracted statistics in that parcel or pixel. Experimental results and some actual applications show the efficiency of this method.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.518-521
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• 2004

Change detection is one of the common research topics in remote sensing. In general, global change detection methods using image difference method, etc, are used in low resolution images and local change detection methods using floating windows, etc, are used in high resolution images. But, these methods have disadvantages in practical use. If changed area images are automatically produced, these images will be used in public area such as regional planning, regional development managements. In this research, we developed new change detection method applicable KOMPSAT EOC images. This method automatically produces subset images in changed area.

• Proceedings of the KSRS Conference
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• v.2
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• pp.610-614
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• 2006

In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3981-4004
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• 2019

This paper proposes a novel method for locating objects in real space from a single remote image and measuring actual distances between them by automatic detection and perspective transformation. The dimensions of the real space are known in advance. First, the corner points of the interested region are detected from an image using deep learning. Then, based on the corner points, the region of interest (ROI) is extracted and made proportional to real space by applying warp-perspective transformation. Finally, the objects are detected and mapped to the real-world location. Removing distortion from the image using camera calibration improves the accuracy in most of the cases. The deep learning framework Darknet is used for detection, and necessary modifications are made to integrate perspective transformation, camera calibration, un-distortion, etc. Experiments are performed with two types of cameras, one with barrel and the other with pincushion distortions. The results show that the difference between calculated distances and measured on real space with measurement tapes are very small; approximately 1 cm on an average. Furthermore, automatic corner detection allows the system to be used with any type of camera that has a fixed pose or in motion; using more points significantly enhances the accuracy of real-world mapping even without camera calibration. Perspective transformation also increases the object detection efficiency by making unified sizes of all objects.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.15-23
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• 2021

In most hacking attacks, hackers intrudes inside for a long period of time and attempts to communicate with the outside using a circumvent connection to achieve purpose. research in response to advanced and intelligent cyber threats has been mainly conducted with signature-based detection and blocking methods, but recently it has been extended to threat hunting methods. attacks from organized hacking groups are advanced persistent attacks over a long period of time, and bypass remote attacks account for the majority. however, even in the intrusion detection system using intelligent recognition technology, it only shows detection performance of the existing intrusion status. therefore, countermeasures against targeted bypass rwjqthrwkemote attacks still have limitations with existing detection methods and threat hunting methods. in this paper, to overcome theses limitations, we propose a model that can detect the targeted circumvent connection remote attack threat of an organized hacking group. this model designed a threat hunting process model that applied the method of verifying the origin IP of the remote circumvent connection, and verified the effectiveness by implementing the proposed method in actual defense information system environment.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3206-3208
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• 1999

This paper describes the remote fault monitoring system for tower parking facilities. This system consists of central station, remote monitoring equipments and communication equipments. The central station is developed under client-server architecture which composed a DB server, a fault detection client, a status collection client and a A/S client. And the remote monitoring systems are connected to central station by LAN using RAS(Remote Access Service) which is constructed PSTN(Public Switched Telephone Network). This system offers real-time fault detection and status data acquisition of tower parking system.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1346-1348
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• 2003

In this paper, the interferometric coherence was estimated and the amplitude intensity was extracted using the repeat-pass interferometric data, acquired by European Remote Sensing Satellite 1 and 2. Then discrimination and classification of surface land types in Zhangjiakou test site, Hebei Province were carried out based on the coherence estimation and the intensity extraction. Seven types of land were discriminated and classified, including in two different types of meadows, woodland, dry land, grassland, steppe and water body. The backscatter and coherence characteristics of these land types on the multi-temporal images were analyzed, and the change of surface features with time series was also discussed.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.409-420
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• 2007

Since 1988, pine wilt disease has spread over rapidly in Korea. It is not easy to detect the damaged pine trees by pine wilt disease from conventional remote sensing skills. Thus, many possibilities were investigated to detect the damaged pines using various kinds of remote sensing data including high spatial resolution satellite image of 2000/2003 IKONOS and 2005 QuickBird, aerial photos, and digital airborne data, too. Time series of B&W aerial photos at the scale of 1:6,000 were used to validate the results. A local maximum filtering was adapted to determine whether the damaged pines could be detected or not at the tree level from high resolution satellite images, and to locate the damaged trees. Several enhancement methods such as NDVI and image transformations were examined to find out the optimal detection method. Considering the mean crown radius of pine trees, local maximum filter with 3 pixels in radius was adapted to detect the damaged trees on IKONOS image. CIR images of 50 cm resolution were taken by PKNU-3(REDLAKE MS4000) sensor. The simulated CIR images with resolutions of 1 m, 2 m, and 4 m were generated to test the possibility of tree detection both in a stereo and a single mode. In conclusion, in order to detect the pine tree damaged by pine wilt disease at a tree level from satellite image, a spatial resolution might be less than 1 m in a single mode and/or 1 m in a stereo mode.