• ETRI Journal
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• v.40 no.2
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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.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.3 no.2
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• pp.66-78
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• 1992

Available data for the radar clutter, and the empirical and the theoretical models for the radar clutter have been collected and analyzed. Data sets and models from the remote sensing field have been studied extensively. Although the grazing angles used in remote sensing is larger than the angles normally encountered in radar clutter application, remote sensing field has the merit of abundance of data in much more detailed target classes. The remote sensing model is also superior to the normally used clutter models in the sense that each target class has its own model, rather than being generally characterized by assumed roughness parameters.

• Bulletin of the Korean Chemical Society
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• v.28 no.6
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.1
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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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• v.17 no.6
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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.

• Proceedings of the IEEK Conference
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• 2009.05a
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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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• v.10 no.1
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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.

• Journal of the military operations research society of Korea
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• v.35 no.3
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• pp.31-45
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• 2009

Reliability should be done from the initial stage of development to secure performance and safety of system. To establish and achieve target reliability of a system, reliability should be allocated into the subsystems. In the acquisition and development of a system, frequent failures will cause a negative effect on performing mission and occurs increasing operating cost. This study reviewed and evaluated the existing reliability allocation models using operation and maintenance costs to find the correlation between reliability allocation models and its operating cost. A target system reliability on the diesel engine to be developed for naval vessels is allocated into its subsystem based on the existing reliability allocation models. A selection methodology for reliability allocation models was made to minimize operating cost by using simulation based on the given operating diesel engine data for naval vessels.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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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.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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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.