• ETRI Journal
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• v.35 no.2
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• pp.226-233
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• 2013

This paper presents a two-stage power-efficient class-AB operational transconductance amplifier (OTA) based on an adaptive biasing circuit suited to low-power dissipation and low-voltage operation. The OTA shows significant improvements in driving capability and power dissipation owing to the novel adaptive biasing circuit. The OTA dissipates only $0.4{\mu}W$ from a supply voltage of ${\pm}0.6V$ and exhibits excellent high driving, which results in a slew rate improvement of more than 250 times that of the conventional class-AB amplifier. The design is fabricated using $0.18-{\mu}m$ CMOS technology.

• JSTS:Journal of Semiconductor Technology and Science
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• v.5 no.3
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• pp.168-172
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• 2005

In this paper, we present a new adaptive biasing scheme for CMOS op-amps. The designed circuit has been used in an Operational Transconductance Amplifier (OTA) with ${\pm}1$ V power supply, and it has improved the positive and negative slew rates from 2.92 V/msec to 1242 V/msec and from 1.56 V/msec to 133 V/msec respectively, while maintaining all the small-signal performance parameter values the same as that without adaptive biasing (as expected), however, there was a marginal decrease of the dynamic range. The most useful features of the proposed circuit are that it uses a very low number of components (thus not creating severe area penalty) and requires only 25 nW of extra stand-by power.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.56-58
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• 2004

In this paper, we propose an adaptive beamforming algorithm for array antenna. The proposed beamforming algorithm is based on EDS (Euclidean Direction Search) algorithm. Generally LMS algorithm has a much slower rate of convergence, but its low computational complexity and robustness make it a representative method of adaptive beamforming. Although the RLS algorithm is known for its fast convergence to the optimal Wiener solution, it still suffers from high computational complexity and poor performance. The proposed EDS algorithm has a rapid convergence better than LMS algorithm, and has a computational more simple complexity than RLS algorithm. In this paper we compared the efficiency of the EDS algorithm with a standard LMS algorithm.

• Proceedings of the KSPS conference
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• 2003.05a
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• pp.203-206
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• 2003

In this paper, we employ the adaptive comb filtering for effective noise reduction in mobile communication environment. Adaptive comb filtering is a well- known method for noise reduction, but requires the correct pitch period and must be applied just in voiced speech frames. To satisfy these requirements we use two kinds of information extracted from speech packets, one of which is the pitch period information measured precisely by a speech coder and the other is the frame rate information related to a decision on speech or silence frame. Experiments on speech recognition system confirm the efficiency of this method. Feature parameters employing this method give superior performance in noise environment to those extracted directly from output speech.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1559-1562
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• 2015

최근 3 차원 깊이 정보를 활용하는 분야가 많아짐에 따라, 정확한 깊이 정보를 추출하기 위한 연구가 계속 진행되고 있다. 특히 ASW(Adaptive Support Weight)는 기존의 영역 기반 알고리즘의 정확도를 향상시키기 위한 방법으로 많이 이용되고 있다. 그 중에서 ACT(Adaptive Census Transform)는 폐백 영역이나 경계 영역에서 정확도가 낮다는 단점이 있었다. 본 논문에서는 정확한 깊이 맵 (depth map)을 추출하기 위해, 기존의 ACT를 개선한 스테레오 정합 알고리즘을 제안한다. 이는 잡음에 강하고 재사용성이 높은 MSW(Multiple Sparse Windows)를 기반으로, TAD(Truncated Absolute Difference)와 ACT 두 개의 정합 알고리즘을 동시에 사용하여 폐색 영역과 울체의 경계 영역에서 정확도가 낮은 기존의 방법을 개선한다. Middlebury에서 제공하는 영상을 사용한 시뮬레이션 결과는 제안한 방법이 기존의 방법보다 평균적으로 약 1.9% 낮은 에러율(error rate)을 가짐을 보여준다.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.25-33
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• 2021

Energy-harvesting wireless sensor networks(EH-WSNs) can collect energy from the environment and overcome the technical limitations of existing power. Since the transmission distance in a wireless sensor network is limited, the data are delivered to the destination node through multi-hop routing. In EH-WSNs, the routing protocol should consider the power situations of nodes, which is determined by the remaining power and energy-harvesting rate. In addition, in applications such as environmental monitoring, when there are urgent data, the routing protocol should be able to transmit it stably and quickly. This paper proposes an adaptive routing protocol that satisfies different requirements of normal and urgent data. To extend network lifetime, the proposed routing protocol reduces power imbalance for normal data and also minimizes transmission latency by controlling the transmission power for urgent data. Simulation results show that the proposed adaptive routing can improve network lifetime by mitigating the power imbalance and greatly reduce the transmission delay of urgent data.

• ETRI Journal
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• v.45 no.5
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• pp.811-821
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• 2023

We propose an adaptive unmanned aerial vehicle (UAV)-assisted object recognition algorithm for urban surveillance scenarios. For UAV-assisted surveillance, UAVs are equipped with learning-based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self-adaptive control strategy to maximize the time-averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real-world data demonstrate that the proposed algorithm achieves the desired performance improvements.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.111-120
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• 2015

Recently, self-adaptive system researches have been done to solve the problems occurred in the dynamic environment. Designing requirement in the self-adaptive system is necessary to recognize and solve the problem for the system, and if a developer reuses existing adaptation strategy to design the requirement, the designing time and cost would be reduced. Therefore, this paper proposes the system which extracts reusable adaptation strategy from the existing self-adaptive system. For the proposal, this paper conceptualizes the self-adaptation elements, defines the adaptation strategy ontology and target system ontology, and presents the process of extracting reusable strategy. This paper also implements proposed system and evaluates the reuse rate of the extracted strategy. As a result, the adaptation strategies extracted by proposed system are exactly operated, and the extraction method of proposed system shows higher reuse rate than a previous method.

• Journal of Drive and Control
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• v.14 no.2
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.366-369
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• 2005

Background subtraction is mainly used for the real-time extraction and tracking of moving objects from image sequences. In the outdoor environment, there are many changeable factor such as gradually changing illumination, swaying trees and suddenly moving objects, which are to be considered for the adaptive processing. Normally, GMM(Gaussian Mixture Model) is used to subtract the background adaptively considering the various changes in the scenes, and the adaptive GMMs improving the real-time performance were worked. This paper, for on-line background subtraction, applied the improved adaptive GMM, which uses the small constant for learning rate ${\alpha}$ and is not able to speedily adapt the suddenly movement of objects, So, this paper proposed and evaluated the dynamic control method of ${\alpha}$ using the adaptive selection of the number of component distributions and the global variances of pixel values.