• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.45-52
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• 2006

This paper presents an image-space algorithm to real-time collision detection, which is run completely by GPU. For a single object or for multiple objects with no collision, the front and back faces appear alternately along the view direction. However, such alternation is violated when objects collide. Based on these observations, the algorithm propose the depth peeling method which renders the minimal surface of objects, not whole surface, to find colliding. The Depth peeling method utilizes the state-of-the-art functionalities of GPU such as framebuffer object, vertexbuffer object, and occlusion query. Combining these functions, multi-pass rendering and context switch can be done with low overhead. Therefore proposed approach has less rendering times and rendering overhead than previous image-space collision detection. The algorithm can handle deformable objects and complex objects, and its precision is governed by the resolution of the render-target-texture. The experimental results show the feasibility of GPU-based collision detection and its performance gain in real-time applications such as 3D games.

• Journal of Ocean Engineering and Technology
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• v.35 no.1
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• pp.91-97
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• 2021

Marine accidents caused by ships have brought about economic and social losses as well as human casualties. Most of these accidents are caused by small and medium-sized ships and are due to their poor conditions and insufficient equipment compared with larger vessels. Measures are quickly needed to improve the conditions. This paper discusses a video-integrated collision prediction and fall detection system to support the safe navigation of small- and medium-sized ships. The system predicts the collision of ships and detects falls by crew members using the CCTV, displays the analyzed integrated information using automatic identification system (AIS) messages, and provides alerts for the risks identified. The design consists of an object recognition algorithm, interface module, integrated display module, collision prediction and fall detection module, and an alarm management module. For the basic research, we implemented a deep learning algorithm to recognize the ship and crew from images, and an interface module to manage messages from AIS. To verify the implemented algorithm, we conducted tests using 120 images. Object recognition performance is calculated as mAP by comparing the pre-defined object with the object recognized through the algorithms. As results, the object recognition performance of the ship and the crew were approximately 50.44 mAP and 46.76 mAP each. The interface module showed that messages from the installed AIS were accurately converted according to the international standard. Therefore, we implemented an object recognition algorithm and interface module in the designed collision prediction and fall detection system and validated their usability with testing.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.107-112
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• 2022

The automotive industry is motivated to provide more and more amenities to its customers. The industry is taking advantage of artificial intelligence by increasing different sensors and gadgets in vehicles machoism is forward collision warning, at the same time road accidents are also increasing which is another concern to address. So there is an urgent need to provide an A.I based system to avoid such incidents which can be address by using artificial intelligence and global positioning system. Automotive/smart vehicles protection has become a major study of research for customers, government and also automotive industry engineers In this study a two layered novel hypothetical approach is proposed which include in-time vehicle/obstacle detection with auto warning mechanism for collision detection & avoidance and later in a case of an accident manifestation GPS & video camera based alerts system and interrupt generation to nearby ambulance or rescue-services units for in-time driver rescue.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.13-16
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• 2015

In any mobile ad hoc environment, collision amongst mobile objects is always likely to occur unless there is a certain level of intelligence to detect and avoid the collision. This phenomenon of detection and avoidance is the key attribute for safety applications in vehicle to pedestrian (V2P) communications systems. In this paper, we propose a V2P communications concept for collision detection and avoidance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.613-614
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.595-596
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• 2009

• Journal of Communications and Networks
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• v.11 no.1
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• pp.1-10
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• 2009

In this paper, we propose a chip-level detection and a spectral-slice scheme for the tunable-transmitter/fixed-receiver (TTFR)-based asynchronous spectral phase-encoded optical codedivision multiple-access (CDMA) system combined with timeencoding. The chip-level detection can enhance the tolerance of multiple access interference (MAI) because the channel collision does not occur as long as there is at least one weighted position without MAI. Moreover, the spectral-slice scheme can reduce the interference probability because the MAI with the different frequency has no adverse effects on the channel collision rate. As a result, these techniques mitigate channel collisions. We analyze the channel collision rate theoretically, and show that the proposed system can achieve a lower channel collision rate in comparison to both conventional systems with and without the time-encoding method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.296-308
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• 2015

Advanced Encryption Standard (AES) is widely used for protecting wireless sensor network (WSN). At the Workshop on Cryptographic Hardware and Embedded Systems (CHES) 2012, G$\acute{e}$rard et al. proposed an optimized collision attack and break a practical implementation of AES. However, the attack needs at least 256 averaged power traces and has a high computational complexity because of its byte wise operation. In this paper, we propose a novel double sieve collision attack based on bitwise collision detection, and an improved version with an error-tolerant mechanism. Practical attacks are successfully conducted on a software implementation of AES in a low-power chip which can be used in wireless sensor node. Simulation results show that our attack needs 90% less time than the work published by G$\acute{e}$rard et al. to reach a success rate of 0.9.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2003.11a
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• pp.203-207
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• 2003

Due to the inherent nature of the low flying UAV, obstacle detection is a fundamental requirement in the flight path to avoid the collision from obstacles as well as manned aircraft. In this paper, a preliminary sensor requirements of an obstacle detection system for UAV in low-altitude flight are analyzed, and the automated obstacle detection sensor system is proposed assessing both passive and active sensors such as EO camera, IR, Laser radar, microwave and millimeter radar. In addition, TCAS (Traffic Alert and Collision Avoidance System) are reviewed for the collision avoidance of the manned aircraft system. It is suggested that small-sized radar sensor is the best candidate for the smart UAV because an active radar can provide the real-time informations on range and range rate in the all-weather environment. However, an important constraints on small UAV should be resolved in terms of accommodation of the mass, volume, and power allocated in the payload of the UAV system design requirements.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.91-99
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• 2024

Collision avoidance system is important to improve the robustness and functional safety of autonomous vehicles. This paper proposes an object-level distance estimation method to develop a collision avoidance system, and it is applied to golfcarts utilized in country club environments. To improve the detection accuracy, we continually trained an object detection model based on pseudo labels generated by a pre-trained detector. Moreover, we propose object-aware depth estimation (OADE) method which trains a depth model focusing on object regions. In the OADE algorithm, we generated dense depth information for object regions by utilizing detection results and sparse LiDAR points, and it is referred to as object-aware LiDAR projection (OALP). By using the OALP maps, a depth estimation model was trained by backpropagating more gradients of the loss on object regions. Experiments were conducted on our custom dataset, which was collected for the travel distance of 22 km on 54 holes in three country clubs under various weather conditions. The precision and recall rate were respectively improved from 70.5% and 49.1% to 95.3% and 92.1% after the continual learning with pseudo labels. Moreover, the OADE algorithm reduces the absolute relative error from 4.76% to 4.27% for estimating distances to obstacles.