• ETRI Journal
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• 제40권2호
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• pp.188-196
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• 2018

This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non-conducting materials, their radar cross-section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal-to-noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.

• 한국전자파학회지:전자파기술
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• 제3권2호
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• pp.66-78
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• 1992

본 논문에서는 레이다 클러터 자료와 실험적 또는 이론적 모델을 모아 분서가였다. 원격탐사에 대한 자료와 모델이 폭넓게 연구되었는데 이는 비교적 세부적인 목표물에 대한 자료가 풍부한 장점이 있기 때문이다. 실제 레이다 설계에는 원격탐사에 사용되는 입사각 보다 큰 영역(near grazing angle)의 클러터 자료 및 모델이 필요한데 이에 대한 연구는 더 계속 되어야 한다.

• Bulletin of the Korean Chemical Society
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• 제28권6호
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• pp.947-952
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• 2007

Mycobacterium tuberculosis is a pathogen responsible for 2-3 million deaths every year worldwide. The emergence of drug-resistant and multidrug-resistant tuberculosis has increased the need to identify new antituberculosis targets. Acetohydroxy acid synthase, (AHAS, EC 2.2.1.6), an enzyme involved in branched-chain amino acid synthesis, has recently been identified as a potential anti-tuberculosis target. To assist in the search for new inhibitors and “receptor-based” design of effective inhibitors of tubercular AHAS (TbAHAS), we constructed four different structural models of TbAHAS and used one of the models as a target for virtual screening of potential inhibitors. The quality of each model was assessed stereochemically by PROCHECK and found to be reliable. Up to 89% of the amino acid residues in the structural models were located in the most favored regions of the Ramachandran plot, which indicates that the conformation of each residue in the models is good. In the models, residues at the herbicide-binding site were highly conserved across 39 AHAS sequences. The binding mode of TbAHAS with a sulfonylurea herbicide was characterized by 32 hydrophobic interactions, the majority of which were contributed by residue Trp516. The model based on the highest resolution X-ray structure of yeast AHAS was used as the target for virtual screening of a chemical database containing 8300 molecules with a heterocyclic ring. We developed a short list of molecules that were predicted to bind with high scores to TbAHAS in a conformation similar to that of sulfonylurea derivatives. Five sulfonylurea herbicides that were calculated to efficiently bind TbAHAS were experimentally verified and found to inhibit enzyme activity at micromolar concentrations. The data suggest that this time-saving and costeffective computational approach can be used to discover new TbAHAS inhibitors. The list of chemicals studied in this work is supplied to facilitate independent experimental verification of the computational approach.

• 대한산업공학회지
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• 제20권1호
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• pp.3-13
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• 1994

The problem of selecting the optimal target values in a canning process is considered for situations where there is a linear shift in the mean of the content of a can which is assumed to be normally distributed with known variance. The target values are initial process mean, length of resetting cycle and controllable upper limit. Profit models are constructed which involve give-away, rework, and resetting costs. Methods of finding the optimal target values are presented and a nemerical example is given.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.264-266
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• 2009

A skill is defined as the special ability to do something well, especially as acquired by learning and practice. To learn a skill, a Bayesian network model for representing the skill is first learned. We will regard the Bayesian network for a skill as an affordance. We propose a soft Behavior Motivation(BM) switch as a method for ordering affordances to accomplish a task. Then, a skill is constructed as a combination of an affordance and a soft BM switch. To demonstrate the validity of our proposed method, some experiments were performed with GENIBO(Pet robot) performing a task using skills of Search-a-target-object, Approach-a-target-object, Push-up-in front of -a-target-object.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.272-287
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• 2016

Recently, various and effective contrast based salient object detection models to focus on a single target have been proposed. However, there is a lack of research on detection of multiple objects, and also it is a more challenging task than single target process. In the multiple target problem, we are confronted by new difficulties caused by distinct difference between properties of objects. The characteristic of existing models depending on the global maximum distribution of data point would become a drawback for detection of multiple objects. In this paper, by analyzing limitations of the existing methods, we have devised three main processes to detect multiple salient objects. In the first stage, regional features are extracted from over-segmented regions. In the second stage, the regional features are categorized into homogeneous cluster using the mean-shift algorithm with the kernel function having various sizes. In the final stage, we compute saliency scores of the categorized regions using only spatial features without the contrast features, and then all scores are integrated for the final salient regions. In the experimental results, the scheme achieved superior detection accuracy for the SED2 and MSRA-ASD benchmarks with both a higher precision and better recall than state-of-the-art approaches. Especially, given multiple objects having different properties, our model significantly outperforms all existing models.

• 한국국방경영분석학회지
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• 제35권3호
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• pp.31-45
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• 2009

시스템의 성능과 안전성을 보장하기 위해서는 개발 초기부터 신뢰도에 대한 연구가 이루어져야 한다. 시스템의 목표 신뢰도를 수립하고, 이를 달성하기 위하여 하부시스템 및 부분품에 신뢰도를 할당해야 한다. 시스템의 획득 및 개발에 있어서 성능이 우수하고 비용이 저렴하더라도 고장이 빈번하게 발생한다면 원활한 임무 수행에 많은 영향을 미치고, 막대한 운영유지비용이 소요될 것이다. 본 연구에서는 신뢰도 할당 모형과 운영 유지비용과의 관계를 알아보기 위하여 기존의 알려진 신뢰도 할당 모형들을 검토 및 평가하였다. 신뢰도 할당모형의 평가는 차기 개발 함정용 디젤 엔진을 대상으로 하였으며, 다양한 신뢰도 할당 모형에 목표 신뢰도를 고려하여 신뢰도를 할당하고, 현재 운영하는 함정용 디젤 엔진의 자료를 바탕으로 시뮬레이션을 수행하여 운영유지비용을 최소화하는 신뢰도 할당 모형을 선정하였다.

• 산업경영시스템학회지
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• 제44권3호
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• 한국컴퓨터정보학회논문지
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• 제21권3호
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• pp.65-71
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• 2016

To reduce the expenses for development a novel drug, systems biology has been studied actively. Target prediction, a part of systems biology, contributes to finding a new purpose for FDA(Food and Drug Administration) approved drugs and development novel drugs. In this paper, we propose a classification model for predicting novel target genes based on relation between target genes and disease related genes. After collecting known target genes from TTD(Therapeutic Target Database) and disease related genes from OMIM(Online Mendelian Inheritance in Man), we analyzed the effect of target genes on disease related genes based on PPI(Protein-Protein Interactions) network. We focused on the distinguishing characteristics between known target genes and random target genes, and used the characteristics as features for building a classifier. Because our model is constructed using information about only a disease and its known targets, the model can be applied to unusual diseases without similar drugs and diseases, while existing models for finding new drug-disease associations are based on drug-drug similarity and disease-disease similarity. We validated accuracy of the model using LOOCV of ten times and the AUCs were 0.74 on Alzheimer's disease and 0.71 on Breast cancer.