• Title/Summary/Keyword: 반복 업데이트

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Stitching Method of Videos Recorded by Multiple Handheld Cameras (다중 사용자 촬영 영상의 영상 스티칭)

  • Billah, Meer Sadeq;Ahn, Heejune
    • Journal of Korea Society of Industrial Information Systems
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    • v.22 no.3
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    • pp.27-38
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    • 2017
  • This Paper Presents a Method for Stitching a Large Number of Images Recorded by a Large Number of Individual Users Through a Cellular Phone Camera at a Venue. In Contrast to 360 Camera Solutions that Use Existing Fixed Rigs, these Conditions must Address New Challenges Such as Time Synchronization, Repeated Transformation Matrix Calculations, and Camera Sensor Mismatch Correction. In this Paper, we Solve this Problem by Updating the Transformation Matrix Using Time Synchronization Method Using Audio, Sensor Mismatch Removal by Color Transfer Method, and Global Operation Stabilization Algorithm. Experimental Results Show that the Proposed Algorithm Shows better Performance in Terms of Computation Speed and Subjective Image Quality than that of Screen Stitching.

Adaptive Skin Color Segmentation in a Single Image using Image Feedback (영상 피드백을 이용한 단일 영상에서의 적응적 피부색 검출)

  • Do, Jun-Hyeong;Kim, Keun-Ho;Kim, Jong-Yeol
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.46 no.3
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    • pp.112-118
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    • 2009
  • Skin color segmentation techniques have been widely utilized for face/hand detection and tracking in many applications such as a diagnosis system using facial information, human-robot interaction, an image retrieval system. In case of a video image, it is common that the skin color model for a target is updated every frame for the robust target tracking against illumination change. As for a single image, however, most of studies employ a fixed skin color model which may result in low detection rate or high false positive errors. In this paper, we propose a novel method for effective skin color segmentation in a single image, which modifies the conditions for skin color segmentation iteratively by the image feedback of segmented skin color region in a given image.

A Study on High Speed LDPC Decoder Algorithm based on dc saperation (dc 분리 기반의 고속 LDPC 복호 알고리즘에 관한 연구)

  • Kwon, Hae-Chan;Kim, Tae-Hoon;Jung, Ji-Won
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.17 no.9
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    • pp.2041-2047
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    • 2013
  • In this paper, we proposed high speed LDPC decoding algorithm based on DVB-S2 standard. For implementing the high speed LDPC decoder, HSS algorithm which reduce the iteration numbers without performance degradation is applied. In HSS algorithm, check node update units are update at the same time of bit node update. HSS can be accelerated to the decoding speed because it does not need to separate calculation of the bit nodes, However, check node calculation blocks need many clocks because of just one memory is used. Therefore, this paper proposed dc-split memory structure in order to reduced the delay and high speed decoder is possible. Finally, this paper presented maximum split memory and throughput for various coding rates in DVB-S2 standard.

Fast Vehicle Detection based on Haarlike and Vehicle Tracking using SURF Method (Haarlike 기반의 고속 차량 검출과 SURF를 이용한 차량 추적 알고리즘)

  • Yu, Jae-Hyoung;Han, Young-Joon;Hahn, Hern-Soo
    • Journal of the Korea Society of Computer and Information
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    • v.17 no.1
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    • pp.71-80
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    • 2012
  • This paper proposes vehicle detection and tracking algorithm using a CCD camera. The proposed algorithm uses Haar-like wavelet edge detector to detect features of vehicle and estimates vehicle's location using calibration information of an image. After that, extract accumulated vehicle information in continuous k images to improve reliability. Finally, obtained vehicle region becomes a template image to find same object in the next continuous image using SURF(Speeded Up Robust Features). The template image is updated in the every frame. In order to reduce SURF processing time, ROI(Region of Interesting) region is limited on expended area of detected vehicle location in the previous frame image. This algorithm repeats detection and tracking progress until no corresponding points are found. The experimental result shows efficiency of proposed algorithm using images obtained on the road.

Formal Model of Extended Reinforcement Learning (E-RL) System (확장된 강화학습 시스템의 정형모델)

  • Jeon, Do Yeong;Song, Myeong Ho;Kim, Soo Dong
    • Journal of Internet Computing and Services
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    • v.22 no.4
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    • pp.13-28
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    • 2021
  • Reinforcement Learning (RL) is a machine learning algorithm that repeat the closed-loop process that agents perform actions specified by the policy, the action is evaluated with a reward function, and the policy gets updated accordingly. The key benefit of RL is the ability to optimze the policy with action evaluation. Hence, it can effectively be applied to developing advanced intelligent systems and autonomous systems. Conventional RL incoporates a single policy, a reward function, and relatively simple policy update, and hence its utilization was limited. In this paper, we propose an extended RL model that considers multiple instances of RL elements. We define a formal model of the key elements and their computing model of the extended RL. Then, we propose design methods for applying to system development. As a case stud of applying the proposed formal model and the design methods, we present the design and implementation of an advanced car navigator system that guides multiple cars to reaching their destinations efficiently.

GPU-based Parallel Ant Colony System for Traveling Salesman Problem

  • Rhee, Yunseok
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.2
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    • pp.1-8
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    • 2022
  • In this paper, we design and implement a GPU-based parallel algorithm to effectively solve the traveling salesman problem through an ant color system. The repetition process of generating hundreds or thousands of tours simultaneously in TSP utilizes GPU's task-level parallelism, and the update process of pheromone trails data actively exploits data parallelism by 32x32 thread blocks. In particular, through simultaneous memory access of multiple threads, the coalesced accesses on continuous memory addresses and concurrent accesses on shared memory are supported. This experiment used 127 to 1002 city data provided by TSPLIB, and compared the performance of sequential and parallel algorithms by using Intel Core i9-9900K CPU and Nvidia Titan RTX system. Performance improvement by GPU parallelization shows speedup of about 10.13 to 11.37 times.

Building of cyanobacteria forecasting model using transformer (Transformer를 이용한 유해남조 발생 예측 모델 구축)

  • Hankyu Lee;Jin Hwi Kim;Seohyun Byeon;Jae-Ki Shin;Yongeun Park
    • Proceedings of the Korea Water Resources Association Conference
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    • 2023.05a
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    • pp.515-515
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    • 2023
  • 팔당호는 북한강과 남한강이 합류하여 생성된 호소로 수도인 서울과 수도권인 경기도 동부지역의 물 공급을 담당하는 중요한 상수원이다. 이러한 팔당호에서 유해남조 발생은 상수원수 활용과 직접적으로 연관되어 있어 신속하고 정확한 관리 및 예측이 필요하다. 본 연구에서는 안전한 상수원 활용을 위해, 딥러닝 기법을 이용하여 유해남조 사전 예측 모델을 구축하고자 하였다. 모델 입력 변수는 2012년부터 2021년까지 10년 동안의 주간 팔당호 수질(수온, DO, BOD, COD, Chl-a, TN, TP, pH, 전기전도도, TDN, NH4N, NO3N, TDP, PO4P, 부유물질)과 수문(유입량, 총방류량), 기상 정보(평균기온, 최저기온, 최고기온, 일 강수량, 평균풍속, 평균 상대습도, 합계일조량), 그리고 북한강과 남한강 유입지점의 남조 세포 수를 사용하였다. 모델 출력 변수는 수질, 수문, 기상 요인으로 인한 남조의 성장 발현 시기를 고려하여 1주 후의 댐앞 남조 세포수를 사용하였다. 사용한 딥러닝 기법은 최근 주목받고 있는 Temporal Fusion Transformer (TFT)를 사용하였다. 모델 훈련용 데이터와 테스트용 데이터는 각각 8:2의 비율로 나누었으며, 검증용 데이터는 훈련용 데이터 내에서 훈련 데이터와 검증 데이터를 6:4 비율로 분배하였다. Lookback은 5로 설정하였고, 이는 주단위 데이터로 구성된 데이터세트의 특성을 반영한 것이다. 모델의 성능은 실측값과 예측값을 토대로 R-square와 Root Mean Squared Error (RMSE)를 계산하여 평가하였다. 모델학습은 총 154번 반복 진행되었으며, 이 중 성능이 가장 준수한 시점은 54번째 반복 시점으로 훈련손실 대비 검증손실이 가장 양호한 값을 나타냈다(훈련손실:0.443, 검증손실 0.380). R-square는 훈련단계에서 0.681, 검증단계에서 0.654였고, 테스트 단계에서 0.606으로 산출되었다. RMSE는 훈련단계에서 0.614(㎍/L), 검증단계에서 0.617(㎍/L), 테스트 단계에서 0.773(㎍/L)였다. 모델에 사용한 데이터세트가 주간 데이터라는 특성을 고려하면, 소규모 데이터를 사용하였음에도 본 연구에서 구축한 모델의 성능은 양호하다고 평가할 수 있다. 향후 연구에서 데이터세트를 보강하고 모델을 업데이트한다면, 모델의 성능을 더욱더 개선할 수 있을 것으로 기대된다.

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Model Analysis of AI-Based Water Pipeline Improved Decision (AI기반 상수도시설 개량 의사결정 모델 분석)

  • Kim, Gi-Tae;Min, Byung-Won;Oh, Yong-Sun
    • Journal of Internet of Things and Convergence
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    • v.8 no.5
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    • pp.11-16
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    • 2022
  • As an interest in the development of artificial intelligence(AI) technology in the water supply sector increases, we have developed an AI algorithm that can predict improvement decision-making ratings through repetitive learning using the data of pipe condition evaluation results, and present the most reliable prediction model through a verification process. We have developed the algorithm that can predict pipe ratings by pre-processing 12 indirect evaluation items based on the 2020 Han River Basin's basic plan and applying the AI algorithm to update weighting factors through backpropagation. This method ensured that the concordance rate between the direct evaluation result value and the calculated result value through repetitive learning and verification was more than 90%. As a result of the algorithm accuracy verification process, it was confirmed that all water pipe type data were evenly distributed, and the more learning data, the higher prediction accuracy. If data from all across the country is collected, the reliability of the prediction technique for pipe ratings using AI algorithm will be improved, and therefore, it is expected that the AI algorithm will play a role in supporting decision-making in the objective evaluation of the condition of aging pipes.

Memory-Efficient Implementation of Ultra-Lightweight Block Cipher Algorithm CHAM on Low-End 8-Bit AVR Processors (저사양 8-bit AVR 프로세서 상에서의 초경량 블록 암호 알고리즘 CHAM 메모리 최적화 구현)

  • Seo, Hwajeong
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.28 no.3
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    • pp.545-550
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    • 2018
  • Ultra-lightweight block cipher CHAM, consisting of simple addition, rotation, and eXclusive-or operations, enables the efficient implementations over both low-end and high-end Internet of Things (IoT) platforms. In particular, the CHAM block cipher targets the enhanced computational performance for the low-end IoT platforms. In this paper, we introduce the efficient implementation techniques to minimize the memory consumption and optimize the execution timing over 8-bit AVR IoT platforms. To achieve the higher performance, we exploit the partly iterated expression and arrange the memory alignment. Furthermore, we exploit the optimal number of register and data update. Finally, we achieve the high RANK parameters including 29.9, 18.0, and 13.4 for CHAM 64/128, 128/128, and 128/256, respectively. These are the best implementation results in existing block ciphers.

Realization of Haptic Collaboration System over Super-high-resolution Networked Tiled Display (초고해상도 네트워크 디스플레이 기반 촉감형 협업 시스템의 구현)

  • Son, Seok-Ho;Kim, Jong-Won
    • 한국HCI학회:학술대회논문집
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    • 2009.02a
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    • pp.572-578
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    • 2009
  • This paper introduce a structure of haptic collaboration system over high resolution tiled-display, and proposes a object based efficient display method for high resolution display in integrated system. in addition, a modeling of visualization system is defined to evaluate performance of the proposed method. Both haptic system and tiled-display system have requirements of computational power. A haptic device is unstable if haptic rendering rate is less than 1kHz. A requirement of tiled-display systems is frame rate of display. It requires update of 30 frame fer sec. If we use these systems independently, we can satisfy each requirements. However, if we integrate two systems, performance of entire system significantly decreases because of lack of resources. In this paper, therefore, we propose a segmentation-based display method for ultra high resolution display in integrated system. The proposed method reduces redundancy of display data by reducing a display rate of static objects. Finally, a modeling of visualization system is defined to evaluate performance of the proposed method.

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