• Journal of Information Technology Services
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• v.10 no.4
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• pp.243-258
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• 2011

With increasing interest in ubiquitous computing technology, an infrastructure for the short-distance wireless communication has been extended socially, bringing spotlight to the security system using the image or location. In case of existing security system, there have been issues such as the occurrences of blind spots, difficulty in recognizing multiple objects and storing of the unspecified objects. In order to solve this issue, zone-based location-estimation search system for the image have been suggested as an alternative based on the real-time location determination technology combined with image. This paper intends to suggest the search service for the image zone-based location-estimation. For this, it proposed the location determination algorism using IEEE 802.15.4/ZigBee's RSSI and for real-time image service, the RTP/RTCP protocol was applied. In order to combine the location and image, at the event of the entry of the specified target, the record of the time for image and the time of occurrence of the event on a global time standard, it has devised a time stamp, applying XML based meta data formation method based on the media's feature data based in connection with the location based data for the events of the object. Using the proposed meta data, the service mode which can search for the image from the point in time when the entry of the specified target was proposed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.780-788
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• 2011

This paper suggests one model, which can be monitor and control target system at anytime and anywhere by a WAP and ME based personal cellular phone(Feature phone) and internet connection. The suggesting model tried to overcome constraints of distance and mobility of conventional methods, TCP/IP based remote data monitoring system, which combine PDA and WLAN technologies. For the experiments of suggesting model, the target systems are constructed with individual modules, which need AC or DC power control. The development software for the control and monitor of local system use NI Labview for the easy-programming and confidence. Also, web server use APM setup for the general user. The mobile connection environments of personal cellular phone are programmed by use WML and mHTML language for the general access. Through the experiments, we have showed that suggesting model can overcome constraints of distance and mobility of conventional system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1477-1484
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• 2015

This paper proposes a robust method for object tracking based on Kalman filters algorithm and covariance matrix. As a feature of the object to be tracked, covariance matrix ensures the continuity of the moving target tracking in the image frames because the covariance is addressed spatial and statistical properties as well as the correlation properties of the features, despite the changes of the form and shape of the target. However, if object moves faster than operation time, real time tracking is difficult. In order to solve the problem, Kalman filters are used to estimate the area of the moving object and covariance matrices as a feature vector are compared with candidate regions within the estimated Kalman window. The results show that the tracking rate of 96.3% achieved using the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.1141-1144
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• 2005

In this paper, we propose an automatic displacement system for testing stability of structure. Photogrammetry is a method which can measure accurate 3D data from 2D images taken from different locations and which is suitable for analyzing and measuring the displacement of structure. This paper consists of camera calibration, feature extraction using coded target & retro-reflective circle, 3D reconstruction and analyzing accuracy. Multi-view camera which is used for measuring displacement of structure is placed with different location respectively. Camera calibration calculates trifocal tensor from corresponding points in images, from which Euclidean camera is calculated. Especially, in a step of feature extraction, we utilize sub-pixel method and pattern recognition in order to measure the accurate 3D locations. Scale bar is used as reference to measure. the accurate value of world coordinate..

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.819-824
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• 2021

As technologies related to image processing such as autonomous driving and CCTV develop, fog removal algorithms using a single image are being studied to improve the problem of image distortion. As a method of predicting fog density, there is a method of estimating the depth of an image by generating a depth map, and various fog features may be used as training data of the depth map. In addition, it is essential to implement a hardware capable of processing high-definition images in real time in order to apply the fog removal algorithm to actual technologies. In this paper, we implement NLCV (Normalize Local Coefficient of Variation), a feature of fog based on coefficient of variation, in hardware. The proposed hardware is an FPGA implementation of Xilinx's xczu7ev-2ffvc1156 as a target device. As a result of synthesis through the Vivado program, it has a maximum operating frequency of 479.616MHz and shows that real-time processing is possible in 4K UHD environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1227-1236
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• 2021

Adversarial attack, one of the attacks on deep learning classification model, is attack that add indistinguishable perturbations to input data and cause deep learning classification model to misclassify the input data. There are various adversarial attack algorithms. Accordingly, many studies have been conducted to detect adversarial attack but few studies have been conducted to classify what adversarial attack algorithms to generate adversarial input. if adversarial attacks can be classified, more robust deep learning classification model can be established by analyzing differences between attacks. In this paper, we proposed a model that detects and classifies adversarial attacks by constructing a random forest classification model with input features extracted from a target deep learning model. In feature extraction, feature is extracted from a output value of hidden layer based on class predicted by the target deep learning model. Through Experiments the model proposed has shown 3.02% accuracy on clean data, 0.80% accuracy on adversarial data higher than the result of pre-existing studies and classify new adversarial attack that was not classified in pre-existing studies.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.877-885
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• 2022

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.782-787
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• 2004

Recently, many enterprises are introducing EAI(Enterprise Application Integration) technologies for integrating heterogeneous enterprise information systems. Among EAI levels, data-level integration is relatively straightforward and most popular. However, current commercial solutions have complex functionalities and are expensive for implementing the data integration tasks. Also, they have their own proprietary architectures and have a restricted interoperability. Proposed in this paper is the development of data integration middleware for facilitating data exchanges between the heterogeneous information systems. The main feature of this middleware is a explicit mapping of meta data about the relationships between source and target data. Based on this mapping, users who do not have expertise in information technology at the small & medium enterprise can easily handle data exchange tasks between information systems.

• IE interfaces
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• v.17 no.4
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• pp.407-413
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• 2004

Recently, many enterprises are adopting EAI (Enterprise Application Integration) technologies for integrating heterogeneous enterprise information systems. Among EAI levels, data-level integration is relatively straightforward and most popular. However, most commercial solutions provide complex functionalities and are expensive for implementing the data integration tasks at the small & medium enterprises. Also, they have their own proprietary architectures and have a restricted interoperability. Proposed in this paper is the development of a data integration middleware for facilitating data exchanges between the heterogeneous information systems. The main feature of this middleware is a explicit mapping of meta data about the relationships between source and target data. Based on this explicit mapping, users who do not have expertise in information technology at the small & medium enterprises can easily execute data exchange tasks among various information systems.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.940-943
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• 1996

In teleoperating, as seeing the monitor screen obtained from a camera instituted in the working environment, human operator generally controls the slave arm. Because we can see only 2-D image in a monitor, human operator does not know the depth information and can not work with high accuracy. In this paper, we proposed a traded control method using an visual sensor for the purpose of solving this problem. We can control a teleoperation system with precision when we use the proposed algorithm. Not only a human operator command but also an autonomous visual sensor feedback command is given to a slave arm for the purpose of coincidence current image features and target image features. When the slave arm place in a distant place from the target position, human operator can know very well the difference between the desired image features and the current image features, but calculated visual sensor command have big errors. And when the slave arm is near the target position, the state of affairs is changed conversely. With this visual sensor feedback, human does not need coincide the detail difference between the desired image features and the current image features and proposed method can work with higher accuracy than other method without, sensor feedback. The effectiveness of the proposed control method is verified through series of experiments.