• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.983-986
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• 2007

In this paper, we addressed the IEEE 1451.x that can organize a sensor network for efficient measurement system. IEEE 1451 provides standard interface, specification and Object model for example Network Capable Application Processor(NCAP), Transducer Electronic Data Sheet(TEDS), Smart Transducer Interface Module (STIM) and so on. Especially IEEE 1451.2 defines the TEDS Formats and STIM. The TEDS makes transducer to be used independently from device. NCAP makes the component of measurement system to be handled as an object. Therefore each function block constructs system by using Add-on. IEEE 1451.x can be expend the system with Add-on and Plug-and-Play by using smart sensor and connected with current network. We expect that this method can provide the efficiency and convenience when using the measurement system.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.131-136
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• 2007

본 논문에서는 모바일 증강현실에서 스마트 오브젝트 인식 및 트래킹을 위한 임베디드 마커 시스템을 제안한다. 기존의 증강 현실 연구에서 주로 사용하는 마커는 임의의 패턴을 포함하고 대상 오브젝트와는 분리되어 있다. 이는 부자연스러운 시각적 장애 요인으로 작용한다. 또한 특정한 마커를 사용하기 위해 학습 과정을 거친 후 그 결과를 인식 모듈에서 일일이 등록해야 하는 번거로움이 있다. 이러한 문제점을 해결하기 위해 제안하는 임베디드 마커는 디스플레이 장치의 유무에 따라 고정형 또는 가변 형으로 분류된 스마트 오브젝트의 특성을 고려하여 오브젝트와 마커를 결합한다. 또한 통합된 학습과 인식 모듈을 통해 오브젝트의 추가 및 시스템 확장을 용이하게 한다. 제안된 시스템은 스마트 홈 테스트베드인 ubiHome 에 적용되었다. 또한 사용 성 평가를 통해 그 효용성을 분석하였다. 이러한 임베디드 마커를 사용하면 사용자는 보다 직관적으로 마커의 용도를 예측할 수 있고 대상물과의 시선을 일치시켜 자연스러운 증강현실을 경험할 수 있을 것으로 기대된다.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.3
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• pp.167-172
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• 2015

In this paper, we propose a new approach to design and control a smart gripper of robot system. A control method for flexible grasping a object in partially unknown environment was proposed, where a proximate sensor detecting the distance between the fingertip and object was used. Based on the proximate sensor signal the finger motion controller could plan the grasping process divided in three phases. The first step is scanning process which two first joints were moved to mid-position of the detected range by a state-variable feedback position controller, after the scanning was finished. The contact force of fingertip was then controlled using the detection sensor of the servo controller for finger joint control. The proposed grasping planning was tested on rectangular bar.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.18-23
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• 2019

The image projected onto the screen of the floating hologram is no more than a two-dimensional image. Although it creates an illusion that an object appears to float in space as it moves around while showing its different parts. This paper has proposed a novel method of floating 3D hologram display to view stereoscopic three-dimensional images without putting on glasses. The system is comprised of a sharkstooth scrim screen, projector, polarizing filter for the projector, and a polarizing film to block the image projected from the sham screen. As part of the polarization characteristics, the background image and the front object have completely been separated from each other with the stereoscopic 3D effect successfully implemented by the binocular disparity caused by the distance between the two screens.

• Smart Media Journal
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• v.13 no.4
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• pp.65-75
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• 2024

This paper proposes a new approach to video surveillance systems, which have become essential components in modern urban management. By utilizing blockchain and IPFS, it enhances data integrity and privacy protection. Additionally, anomaly detection and automatic video storage are enabled through object detection technology, thus improving urban safety and security. This integrated approach serves as an efficient management methodology for surveillance systems, providing city administrators and citizens with a safer and more effective monitoring environment.

• Journal of Broadcast Engineering
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• v.24 no.7
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• pp.1266-1275
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• 2019

With increasing criminal rates and number of CCTVs, much attention has been paid to intelligent surveillance system on the horizon. Object detection and tracking algorithms have been developed to reduce false alarms and accurately help security agents immediately response to undesirable changes in video clips such as crimes and accidents. Many studies have proposed a variety of algorithms to improve accuracy of detecting and tracking objects outside tunnels. The proposed methods might not work well in a tunnel because of low illuminance significantly susceptible to tail and warning lights of driving vehicles. The detection performance has rarely been tested against the tunnel environment. This study investigated a feasibility of object detection and tracking in an actual tunnel environment by utilizing YOLOv3 and Kernelized Correlation Filter. We tested 40 actual video clips to differentiate pedestrians and motorcycles to evaluate the performance of our algorithm. The experimental results showed significant difference in detection between pedestrians and motorcycles without false positive rates. Our findings are expected to provide a stepping stone of developing efficient detection algorithms suitable for tunnel environment and encouraging other researchers to glean reliable tracking data for smarter and safer City.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.241-244
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• 2014

Wireless Sensor Network(WSN) has been attracting lots of interest in recent years for smart surveillance systems. WSN-based surveillance systems need to figure out the occurrence or existence of events or objects and to find out where the events have occurred or the objects are present. In our surveillance system, it is needed to give an alarm only when the detected object is human (not pets or rodents) for reducing false alarms and improving the system reliability. In this paper, we propose a height prediction mechanism to determine if the detected object is human using Heron's formula. Finally, we verify the performance of our proposed mechanism through various experiments.

• Smart Media Journal
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• v.4 no.1
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• pp.39-43
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• 2015

A vehicle black box helps to investigate the cause of accident by recording time, and videos as wells as shock information of the time of accident Lately, intelligent black box with accident prevention as well as existing functions is being studied. This paper proposes an applicable algorithm for vehicle black boxes that prevent any accident likely to occur while a car is parked, like robbery, theft or hit-and-run. Proposed algorithm provides object recognition, face detection and alarm as the object approaches car. Tests on the algorithm prove that it can recognize an approaching object, identify and set alarm if needed, depending on each risk level.

• Smart Media Journal
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• v.5 no.3
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• pp.93-98
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• 2016

The literal meaning of loitering is "to lingering aimlessly or as if aimless in or about a place". And most criminals show this kind of act before they actually commit crime. Therefore, detecting this kind of loitering can effectively prevent a variety of crime. In this paper, we propose a loitering-detection algorithm using the Raspberry Pi. Proposed algorithm uses an adaptive difference image to detect moving objects and morphology opening operation to enhance the accuracy of detection. The loitering- behavior is being detected by using the center of gravity of the object to see the changes of angle; and pixel movement distance to determine the height of the object. When the loitering-behavior is detected, it outputs the alarm to tell the users by using the Raspberry Pi.

• Journal of the Semiconductor & Display Technology
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• v.17 no.2
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• pp.76-80
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• 2018

In recent years, with the development of automobile technology, smart system technology that assists safe driving has been developed. A camera is installed on the front and rear of the vehicle as well as on the left and right sides to detect and warn of collision risks and hazards. Beyond the technology of simple black-box recording via cameras, we are developing intelligent systems that combine various computer vision technologies. However, most related studies have been developed to optimize performance in laboratory-like environments that do not take environmental factors such as weather into account. In this paper, we propose a method to detect object by restoring visibility in image with degraded image due to weather factors such as fog. First, the image quality degradation such as fog is detected in a single image, and the image quality is improved by restoring using an intermediate value filter. Then, we used an adaptive feature extraction method that removes unnecessary elements such as noise from the improved image and uses it to recognize objects with only the necessary features. In the proposed method, it is shown that more feature points are extracted than the feature points of the region of interest in the improved image.