• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.120-124
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• 2022

With advancements in underwater stealth technology for naval vessels, new sensor configurations for detecting targets have been attracting increased attention. Latest underwater mines adopt multiple sensor configurations that include electric field sensors to detect targets and to help acquire accurate ignition time. An underwater electric field sensor consists of a pair of electrodes, signal processing unit, and preamplifier. For detecting underwater electric fields, the preamplifier requires low-noise amplification at ultra-low frequency bands. In this paper, the specific requirements for low-noise preamplifiers are discussed along with the experimental results of various setups of matched transistors and chopper stabilized operational amplifiers. The results showed that noise characteristics at ultra-low frequency bands were affected significantly by the voltage noise density of the chopper amplifier and the number of matched transistors used for differential amplification. The fabricated preamplifier was operated within normal design parameters, which was verified by testing its gain, phase, and linearity.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.173-182
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• 2005

In this paper, a target tracking algorithm for tracking maneuvering vehicles is presented. The overall algorithm belongs to the category of an interacting multiple-model (IMM) algorithm used to detect multiple targets using fused information from multiple sensors. First, two kinematic models are derived: a constant velocity model for linear motions, and a constant-speed turn model for curvilinear motions. Fpr the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed. The performance of the suggested algorithm using a Monte Carlo simulation under the two patterns is evaluated.

• The Korean Journal of Financial Management
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• v.14 no.3
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• pp.319-339
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• 1997

This paper provides an empirical analysis of the winner's curse in the context of corporate takeovers. The study analyzes conditions which make overpayment likely. For a sample of corporate takeovers completed between 1982 and 1993, the analysis shows that the volatility of targets relative to that of acquirers (not the uncertainty of the target or acquirer alone) has a definitive impact on the magnitude of the winner's curse. Also, the incidence is more pronounced in multiple-bidder than in single-bidder contests. Specifically, white knights are more likely to overpay than other acquirers in multiple bidding situations. Furthermore, the study finds that the process of competitive bidding is a zero sum game since the greater returns to the shareholders of target firms in multiple-bid contests come at the expense of the acquiring companies, Overall, the evidence suggests that the bidders need to become more conservative, particularly as the relative uncertainty of the target's 'true' value and the number of bidders increase.

• ETRI Journal
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• v.43 no.1
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• pp.17-30
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• 2021

At present, multiple input multiple output radars offer accurate target detection and better target parameter estimation with higher resolution in high-speed wireless communication systems. This study focuses primarily on power allocation to improve the performance of radars owing to the sparsity of targets in the spatial velocity domain. First, the radars are clustered using the kernel fuzzy C-means algorithm. Next, cooperative and noncooperative clusters are extracted based on the distance measured using the kernel fuzzy C-means algorithm. The power is allocated to cooperative clusters using the Pareto optimality particle swarm optimization algorithm. In addition, the Nash equilibrium particle swarm optimization algorithm is used for allocating power in the noncooperative clusters. The process of allocating power to cooperative and noncooperative clusters reduces the overall transmission power of the radars. In the experimental section, the proposed method obtained the power consumption of 0.014 to 0.0119 at K = 2, M = 3 and K = 2, M = 3, which is better compared to the existing methodologies-generalized Nash game and cooperative and noncooperative game theory.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.6
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• pp.628-641
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• 2014

Stepwise efficiency improvement in data envelopment analysis (DEA)-based benchmarking is a realistic and effective method by which inefficient decision making units (DMUs) can choose benchmarks in a stepwise manner and, thereby, effect gradual performance improvement. Most of the previous research relevant to stepwise efficiency improvement has focused primarily on how to stratify DMUs into multiple layers and how to select immediate benchmark targets in leading levels for lagging-level DMUs. It can be said that the sequence of benchmark targets was constructed in a myopic way, which can limit its effectiveness. To address this issue, this paper proposes an optimization approach to the construction of a sequence of benchmarks in DEA-based benchmarking, wherein two optimization criteria are employed : similarity of input-output use patterns, and proximity of input-output use levels between DMUs. To illustrate the proposed method, we applied it to the benchmarking of 23 national universities in South Korea.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.50 no.1
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• pp.21-29
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• 2014

The dynamic information of radar and automatic identification system (AIS) for targets obtained from the traffic vessels operating in the north outer harbor and surrounding waters of Busan port, Korea. The target information was analyzed to investigate the potential collision risk resulting from the invalid true heading (HDT) information of AIS and the integration ambiguity in the graphic presentation of both tracked data sets for two systems. An integrated display system (IDS) for supporting the navigator of offshore fishing vessels was also developed to find possible maneuvering solutions for collision avoidance by comparing radar data with AIS data in real-time at sea. Consequently, the multiple functions of IDS can provide additional information that is potentially valuable for taking action to avoid the collision in offshore fishing vessels. However, the integration criteria of radar and AIS targets in the IDS must be carefully established to eliminate the fusion ambiguity in the graphic presentation of both AIS and radar symbols such as the one or two physical targets which are very close to each other.

• BMB Reports
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• v.53 no.7
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• pp.349-356
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• 2020

Mass spectrometry (MS) is an ideal tool for analyzing multiple types of (bio)molecular information simultaneously in complex biological systems. In addition, MS provides structural information on targets, and can easily discriminate between true analytes and background. Therefore, imaging mass spectrometry (IMS) enables not only visualization of tissues to give positional information on targets but also allows for molecular analysis of targets by affording the molecular weights. Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) MS is particularly effective and is generally used for IMS. However, the requirement for an organic matrix raises several limitations that get in the way of accurate and reliable images and hampers imaging of small molecules such as drugs and their metabolites. To overcome these problems, various organic matrix-free LDI IMS systems have been developed, mostly utilizing nanostructured surfaces and inorganic nanoparticles as an alternative to the organic matrix. This minireview highlights and focuses on the progress in organic matrix-free LDI IMS and briefly discusses the use of other IMS techniques such as desorption electrospray ionization, laser ablation electrospray ionization, and secondary ion mass spectrometry.

• Molecules and Cells
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• v.37 no.5
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• pp.357-364
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• 2014

Medulloblastoma, the most common malignant brain tumor in children, is a disease whose mechanisms are now beginning to be uncovered by high-throughput studies of somatic mutations, mRNA expression patterns, and epigenetic profiles of patient tumors. One emerging theme from studies that sequenced the tumor genomes of large cohorts of medulloblastoma patients is frequent mutation of RNA binding proteins. Proteins which bind multiple RNA targets can act as master regulators of gene expression at the post-transcriptional level to co-ordinate cellular processes and alter the phenotype of the cell. Identification of the target genes of RNA binding proteins may highlight essential pathways of medulloblastomagenesis that cannot be detected by study of transcriptomics alone. Furthermore, a subset of RNA binding proteins are attractive drug targets. For example, compounds that are under development as anti-viral targets due to their ability to inhibit RNA helicases could also be tested in novel approaches to medulloblastoma therapy by targeting key RNA binding proteins. In this review, we discuss a number of RNA binding proteins, including Musashi1 (MSI1), DEAD (Asp-Glu-Ala-Asp) box helicase 3 X-linked (DDX3X), DDX31, and cell division cycle and apoptosis regulator 1 (CCAR1), which play potentially critical roles in the growth and/or maintenance of medulloblastoma.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.147-157
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• 2018

There are increasing demands to provide early warning against intruding drones and cope with potential threats. Commercial anti-drone systems are mostly based on simple target detection by radar reflections. In real scenario, however, it becomes essential to obtain drone radar signatures so that hostile targets are recognized in advance. We present experimental test results that micro-Doppler radar signature delivers partial information on multi-rotor platforms and exhibits limited performance in drone recognition and classification. Afterward, we attempt to generate high resolution profile of flying drone targets. To this purpose, wide bands radar signals are employed to carry out inverse synthetic aperture radar(ISAR) imaging against moving drones. Following theoretical analysis, experimental field tests are carried out to acquire real target signals. Our preliminary tests demonstrate that high resolution ISAR imaging provides effective measures to detect and classify multiple drone targets in air.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.337-345
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• 2020

The Weapon-Target Assignment(WTA) problem can be formulated as an optimization problem that minimize the threat of targets. Existing methods consider the trade-off between optimality and execution time to meet the various mission objectives. We propose a multi-agent reinforcement learning algorithm for WTA based on mean field game to solve the problem in real-time with nearly optimal accuracy. Mean field game is a recent method introduced to relieve the curse of dimensionality in multi-agent learning algorithm. In addition, previous reinforcement learning models for WTA generally do not consider weapon interference, which may be critical in real world operations. Therefore, we modify the reward function to discourage the crossing of weapon trajectories. The feasibility of the proposed method was verified through simulation of a WTA problem with multiple targets in realtime and the proposed algorithm can assign the weapons to all targets without crossing trajectories of weapons.