• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.847-857
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• 2020

As IoT equipment is commercialized, Bluetooth or wireless networks will be built into general living devices such as IP cameras, door locks, cars and TVs. Security for IoT equipment is becoming more important because IoT equipment shares a lot of information through the network and collects personal information and operates the system. In addition, web-based attacks and application attacks currently account for a significant portion of cyber threats, and security experts are analyzing the vulnerabilities of cyber attacks through manual analysis to secure them. However, since it is virtually impossible to analyze vulnerabilities with only manual analysis, researchers studying system security are currently working on automated vulnerability detection systems, and Firm-AFL, published recently in USENIX, proposed a system by conducting a study on fuzzing processing speed and efficiency using a coverage-based fuzzer. However, the existing tools were focused on the fuzzing processing speed of the firmware, and as a result, they did not find any vulnerability in various paths. In this paper, we propose IoTFirmFuzz, which finds more paths, resolves constraints, and discovers more crashes by strengthening the mutation process to find vulnerabilities in various paths not found in existing tools.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.519-532
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• 2021

Recently, in various fields such as games, movies, and animation, content that uses motion capture to build body models and create characters to express in 3D space is increasing. Studies are underway to generate animations using RGB-D cameras to compensate for problems such as the cost of cinematography in how to place joints by attaching markers, but the problem of pose estimation accuracy or equipment cost still exists. Therefore, in this paper, we propose a system that inputs RGB images into a joint estimation network and converts the results into 3D data to create FBX format animations in order to reduce the equipment cost required for animation creation and increase joint estimation accuracy. First, the two-dimensional joint is estimated for the RGB image, and the three-dimensional coordinates of the joint are estimated using this value. The result is converted to a quaternion, rotated, and an animation in FBX format is created. To measure the accuracy of the proposed method, the system operation was verified by comparing the error between the animation generated based on the 3D position of the marker by attaching a marker to the body and the animation generated by the proposed system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.141-148
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• 2021

This paper presents a method to detect zebra-crossing using deep learning which combines SegNet and ResNet. For the blind, a safe crossing system is important to know exactly where the zebra-crossings are. Zebra-crossing detection by deep learning can be a good solution to this problem and robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors. These traditional methods have achieved significant results with relatively long processing times, and enhanced the zebra-crossing perception to a large extent. However, running all detectors jointly incurs a long latency and becomes computationally prohibitive on wearable embedded systems. In this paper, we propose a model for fast and stable segmentation of zebra-crossing from captured images. The model is improved based on a combination of SegNet and ResNet and consists of three steps. First, the input image is subsampled to extract image features and the convolutional neural network of ResNet is modified to make it the new encoder. Second, through the SegNet original up-sampling network, the abstract features are restored to the original image size. Finally, the method classifies all pixels and calculates the accuracy of each pixel. The experimental results prove the efficiency of the modified semantic segmentation algorithm with a relatively high computing speed.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.762-767
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• 2015

An Internet of Things (IoT) is a system where various "things" are connected to each other via a wired/wireless network, and where information is gathered, processed, and interchanged/shared. One of the important actors in IoT is a mobile IoT device (such as a smartphone or tablet). These devices tend to consume a large amount of power in order to provide various high performance application services; however, as the devices cannot be supplied with power all the time, efficient power management is necessary. Power management of mobile IoT devices involves complex relationships between various entities such as application processors (APs), HW modules inside/outside AP, operating systems, mobile IoT platforms, and applications. In order to develop power-efficient mobile IoT devices, a method is needed to systematically analyze these relationships and manage power based on a clear understanding of them. To address this problem, software engineering principles for the development of power-efficient mobile IoT devices are presented in this paper. The feasibility of the proposed principles have been validated in the domain of smartphone camera power management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.741-746
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• 2015

Recently, as mobile operating system market and wireless communication technology have been rapidly developed, many devices such as smart phones, air conditioners, smart TVs, cleaning robot, and cameras become available with android operating system. Accordingly, collecting variety of information through many everyday use devices with network connections is now enabled. However, in the current market, most devices are controlled individually developed applications, and there is growing need to develop a master application that can control multiple devices. In this paper, we propose and implement a system that can control multiple android-based devices on a Wired/Wireless router(AP) registered through web server. we expect such an effort can attribute to future IoT researches.

• Journal of Digital Convergence
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• v.11 no.6
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• pp.151-157
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• 2013

This research is about set-up own posture standard and enables to compare the set-up and own posture; therefore, this posture training system leads the user's active learning through the method of self-directed learning. This system includes the camera part that shoots user's posture and save the movie clip for comparison about the user and target, the training server that provides a comparison screen playing clips simultaneously by user's input, and the terminal equipment that connects the training server and a network, transmits the user's input to the server, and displays them from the comparison screen. The Journal of Digital Policy & Management. This space is for the abstract of your study in English.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.37-42
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• 2017

Over the next decade, there are estimated to be 250 billion IoT devices in the future, and a variety of new services and device markets are expected to be created via a new network connection. However, it is difficult to accommodate various sensors and IoT devices, such as product and installation environments, for existing IP/SIP/IMS cameras and video door phone. Additionally, recently, sensors and actuators that can accommodate IoT technology and standards are continually updated. In addition, this paper has developed an SIP/IMS intelligent gateway to flexibly accommodate IoT related equipments and to interface with ZigBee/Z-Wave/NFC endpoints in addition to the gateway basic functions. This intelligent gateway will contribute to the development of the IMS system to replace the VoIP system with the IMS equipment due to the growth of the IMS market.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.5C
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• pp.464-471
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• 2010

The demand for high quality of multimedia applications, which far surpasses the rapid evolution of transmission and storage technologies, makes better compression coding capabilities ever increasingly more important. In order to provide enhanced video coding performance, this paper proposes an efficient competition-based motion vector coding scheme. The proposed algorithm adaptively forms the motion vector predictors based on the contexts of scene characteristics such as camera motion and nearby motion vectors, providing more efficient candidate predictors than the previous competition-based motion vector coding schemes which resort to the fixed candidates optimized by extensive simulations. Up to 200% of compression gain was observed in the experimental results for the proposed scheme applied to the motion vector selection for skip mode processing.

• Journal of IKEEE
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• v.23 no.4
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• pp.1295-1301
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• 2019

In this paper, we propose a novel method of denoising human motion using a bidirectional recurrent neural network (BRNN) with an attention mechanism. The corrupted motion captured from a single 3D depth sensor camera is automatically fixed in the well-established smooth motion manifold. Incorporating an attention mechanism into BRNN achieves better optimization results and higher accuracy than other deep learning frameworks because a higher weight value is selectively given to a more important input pose at a specific frame for encoding the input motion. Experimental results show that our approach effectively handles various types of motion and noise, and we believe that our method can sufficiently be used in motion capture applications as a post-processing step after capturing human motion.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1683-1691
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• 2013

Concrete surface grinding is frequently used for flatness of concrete surface, concrete pavement rehabilitation, and adhesiveness in pavement construction. The procedure is, however, labor intensive and has a hazardous work condition. Also, the productivity and the quality of concrete surface grinding highly depend on the skills of worker. Thus, the development of remote controlled concrete surface grinding equipment is necessary to prevent the environmental pollution and to protect the workers from hazardous work condition. However, it is difficult to evaluate the grinded surface objectively in a remote controlled system. Also, The machine vision system developed in this study takes the images of grinded surface with the network camera for image processing. Then, by representing the quality test results to the integrated program of the remote control station, the quality control system is constructed. The machine vision algorithm means the image processing algorithm of grinded concrete surface and this paper presents the objective quality control standard of grinded concrete surface through the application of the suggested algorithm.