• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1999-2005
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• 2005

Collaboration in wireless sensor networks must be fault-tolerant due to the harsh environmental conditions in which such networks can be deployed. This paper focuses on finding signal processing algorithms for collaborative target detection based on the generalized approach to signal processing in the presence of noise that are efficient in terms of communication cost, precision, accuracy, and number of faulty sensors tolerable in the wireless sensor network. Two algorithms, namely, value fusion and decision fusion constructed according to the generalized approach to signal processing in the presence of noise, are identified first. When comparing their performance and communication overhead, decision fusion is found to become superior to value fusion as the ratio of faulty sensors to fault free sensors increases. The use of the generalized approach to signal processing in the presence of noise under designing value and decision fusion algorithms in wireless sensor networks allows us to obtain the same performance, but at low values of signal energy, as under the employment of universally adopted signal processing algorithms widely used in practice.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 1993년도 춘계학술대회 논문집
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• pp.6-10
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• 1993

빛을 통신매체로 사용한 광통신은 저손실, 광대역성, 대용량화등의 장점 때문에 많은 중장거리 통신을 점유하고 있으며, 대형가입자군을 위한 광가입자망이나 LAN등에도 시험적용되고 있다. 따라서 광통신에 있어 광FIBER를 연결시키는 CERAMIC단자를 연구개발하는데 그목적을 두었으며, CERAMIC 단자의 재질로서는 구조재료로 주로 사용되는 ZIRCONIA(psz)를 선정하였으며 또한 정밀성형 및 소결등을 검토하였다.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• 반도체공학회 논문지
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• 제2권1호
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• pp.17-20
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• 2024

본 논문은 전통적인 자원 집약적인 컴퓨터 비전 모델의 한계를 해결하기 위해 저전력 엣지 장치에 최적화된 새로운 경량 객체 검출 모델을 제안합니다. 제안된 검출기는 Single Shot Detector (SSD)에 기반하여 소형이면서도 견고한 네트워크를 설계하였고, 작은 객체를 효율적으로 감지하는 데 있어 효율성을 크게 향상시키도록 모델을 구성하였다. 이 모델은 주로 두 가지 구성요소로 구성되어 있습니다: Depthwise 와 Pointwise Convolution 레이어를 사용하여 효율적인 특징 추출을 위한 Light_Block, 그리고 작은 객체의 향상된 감지를 위한 Enhancer_Block 으로 나누었다. 우리의 모델은 300x480 의 이미지 크기를 가진 Udacity 주석이 달린 데이터셋에서 처음부터 훈련되었으며, 사전 훈련된 분류 가중치의 필요성을 제거하였다. 약 0.43M 의 파라미터로 5.5MB 만의 무게를 가진 우리의 검출기는 평균 정밀도 (mAP) 27.7%와 140 FPS 의 처리 속도를 달성하여, 정밀도와 효율성 모두에서 기존 모델을 능가하였다. 따라서, 본 논문은 추론의 정확성을 손상시키지 않으면서 엣지 장치를 위한 객체 검출에서의 효과적인 경량화를 보여주고 있다.

• 전기전자학회논문지
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• 제22권3호
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• pp.805-809
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• 2018

본 연구에서는 이진 가중치 신경망(BWN)을 부동소수점 데이터를 사용하여 학습시킨 후에, 학습된 파라미터와 주요연산을 고정소수점으로 근사화시키는 과정에서 정확도의 변화를 분석하였다. 신경망을 이루고 있는 각 계층의 입력 데이터와 컨볼루션 연산의 계산에 고정소수점 수를 사용했으며, 이때 고정소수점 수의 전체 bit 수와 소수점 이하 bit 수에 변화를 주면서 정확도 변화를 관찰하였다. 각 계층의 입력 값과 중간 계산값의 정수 부분의 손실이 발생하지 않으면 고정소수점 연산을 사용해도 부동소수점 연산에 비해 큰 정확도 감소가 없었다. 그리고 오버플로가 발생하는 경우에 고정소수점 수의 최대 또는 최소값으로 근사시켜서 정확도 감소를 줄일 수 있었다. 이 연구결과는 FPGA 기반의 BWN 가속기를 구현할 때에 필요한 메모리와 하드웨어 요구량을 줄이는 데 사용될 수 있다.

• 로봇학회논문지
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• 제13권1호
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• pp.26-30
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• 2018

Planetary global localization is necessary for long-range rover missions in which communication with command center operator is throttled due to the long distance. There has been number of researches that address this problem by exploiting and matching rover surroundings with global digital elevation maps (DEM). Using conventional methods for matching, however, is challenging due to artifacts in both DEM rendered images, and/or rover 2D images caused by DEM low resolution, rover image illumination variations and small terrain features. In this work, we use train CNN discriminator to match rover 2D image with DEM rendered images using conditional Generative Adversarial Network architecture (cGAN). We then use this discriminator to search an uncertainty bound given by visual odometry (VO) error bound to estimate rover optimal location and orientation. We demonstrate our network capability to learn to translate rover image into DEM simulated image and match them using Devon Island dataset. The experimental results show that our proposed approach achieves ~74% mean average precision.

• 한국통신학회논문지
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• 제36권12B호
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• pp.1611-1622
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• 2011

동기 획득은 펨토셀 기지국의 안정적인 동작을 위해 가져야 할 주요 기술적 과제 중 하나이다. 특히 펨토셀 기지국의 경우는 설치되는 환경적 특수성에 의해 제약을 받는다. 첫째 펨토셀 기지국이 위치하는 실내 환경에서의 전파 감쇄와 상호 간섭등의 제약 요인이다. 둘째, 펨토셀 기지국이 연결되는 백홀 네트워크에 따른 제약이 있다. 이 밖에도, 무선 연결성을 제공하는 다른 기술의 AP와 같이 기술 구성에 있어서 저 비용이 요구되는 기술이어야 한다. 본 논문에서는 실내에서 위치 측위와 타이밍 획득을 위한 기술들을 살펴보고 AGNSS-Packet Timing 하이브리드 기술의 필요성 및 진행 중인 관련 표준화와 연관된 내용을 다루도록 한다.

• 전자통신동향분석
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• 제35권5호
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• pp.14-29
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• 2020

The 6G mobile communication system will become a backbone infrastructure around 2030 for the future digital world by providing distinctive services such as five-sense holograms, ultra-high reliability/low-latency, ultra-high-precision positioning, ultra-massive connectivity, and gigabit-per-second data rate for aerial and maritime terminals. The recent remarkable advances in machine learning (ML) technology have recognized its efficiency in wireless networking fields such as resource management and cell-configuration optimization. Further innovation in ML is expected to play an important role in solving new problems arising from 6G network management and service delivery. In contrast, an approach to apply ML to a physical-layer (PHY) target tackles the basic problems in radio links, such as overcoming signal distortion and interference. This paper reviews the methodologies of ML-based PHY, relevant industrial trends, and candiate technologies, including future research directions and standardization impacts.

• Journal of information and communication convergence engineering
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• 제21권2호
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• pp.110-116
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• 2023

Recently, as various examples of machine learning have been applied in the healthcare field, deep learning technology has been applied to various tasks, such as electrocardiogram examination and body composition analysis using wearable devices such as smart watches. To utilize deep learning, securing data is the most important procedure, where human intervention, such as data classification, is required. In this study, we propose a model that uses a clustering algorithm, namely, the K-means clustering, to label body fat according to gender and age considering body size aspects, such as chest circumference and waist circumference, and classifies body fat into five groups from high risk to low risk using a convolutional neural network (CNN). As a result of model validation, accuracy, precision, and recall results of more than 95% were obtained. Thus, rational decision making can be made in the field of healthcare or obesity analysis using the proposed method.

• Journal of Biosystems Engineering
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• 제29권1호
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• pp.65-70
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• 2004

This study was carried out to verify and predict the soil informations such as the contents of organic matter(OM) and Mg and pH of the soil. The predictability of spacial variation in the paddy field was examined by analyzing the various soil information. The prediction models for the OM pH, and Mg, were developed using inverse distance weighted (IDW), triangulated irregular network(TIN) and Kriging model. The determination of coefficients of linear and spherical Kriging models were 0.756 and 0.578, respectively, and were very low in comparison with other soil information. For IDW and TIN model, the determination of coefficients were 1.000 and hence the performance of the models was found to be excellent. The developed models were validated using unknown soil sample obtained In 2000 and 2001. From the analysis of relationship between the measured pH and predicted 0.9353. For prediction of Mg, the determination of coefficient is more than 0.8. Since the determination of coefficients of developed models for OM were relatively low, it may be difficult to predict the content of OM using the developed models. For further study, the additional works to enhance the performance of the prediction models for soil information are required.