## Complexity Control(복잡성 제어)란 무엇입니까?

Complexity Control 복잡성 제어 - Here, we study how membrane complexity controls the energetics of the first steps of membrane fusions, that is, the formation of a stalk.^{[1]}In contrast with the conventional fixed-time control schemes that typically contain the fractional powers of errors in their designs, this work develops a low-complexity control structure in the sense of removing the dependence on the need of above-mentioned fractional power terms by means of prescribed performance control (PPC) method.

^{[2]}A low-complexity control unit is implemented which is composed of power switches, comparators and logic gates and is able to supervise two supercapacitors, a small and a larger one, as well as a backup battery.

^{[3]}In this article, we propose an online learning-based multi-stage complexity control method for live video coding.

^{[4]}In this context, as a complexity control method, HMOF outperforms the state-of-the-art complexity reduction algorithms under a similar complexity reduction ratio.

^{[5]}In this paper, an approach to tune limited-complexity controllers from data for linear systems is proposed.

^{[6]}$K_{1}(s)$ and $K_{2}(s)$ are low-complexity controllers that belong to the Family of All Stabilizing Controllers (FASC) and their free control parameters are selected to achieve strong stability.

^{[7]}By means of mean-field game (MFG) analysis, we derive a reduced-complexity control solution.

^{[8]}Experiments indicate that this approach (a) is effective in recognition, with in-corpus performances comparable to other proposals in the literature but with the added value of complexity control and (b) allows an innovative way to analyze emotions conveyed by speech using possibilistic membership degrees.

^{[9]}Experimental results indicate that high efficiency (> 98 %), together with a low-complexity control operation, can be achieved using the approach presented here.

^{[10]}This article proposes and compares a new family of low-complexity control schemes for the fast charge of lithium-ion (Li-ion) battery cells accounting for degradation constraints.

^{[11]}The low-complexity controllers are designed by the dynamic surface control method as it eliminates the problem of the explosion of items.

^{[12]}To check the validity of our proposals, an LPV model-based control strategy is compared in simulations over a circuit path to another reduced computational complexity control strategy, the Inverse Kinematic Bicycle model (IKIBI), in the presence of process and measurement Gaussian noise.

^{[13]}In terms of complexity control, APGP prevailed over GP but not over GP+BC; however, GP+BC produced simpler solutions at the cost of test-set accuracy.

^{[14]}In this paper, we propose an interpretable machine learning-based complexity control method for efficiently im-plementing HEVC on live video applications with different computing capacities and limited powers.

^{[15]}This review emphasizes analytical techniques and results obtained using the Dahl salt-sensitive (S) rat as a model of hypertension by presenting results in detail for three specific chromosomal regions harboring genetic elements of increasing complexity controlling BP.

^{[16]}To address this problem, we propose a complexity controlled SI creation solution for the newly DSVC framework.

^{[17]}In order to achieve load balancing among the distributed controllers, we proposed a low-complexity controller placement algorithm, Simulated Annealing Partition-based K-Means (SAPKM), towards SDWAN.

^{[18]}These two low-complexity controllers are proposed as alternatives for the classical PD and PI controllers.

^{[19]}This paper presents a novel contribution of a low complexity control scheme for voltage control of a dynamic voltage restorer (DVR).

^{[20]}Then coupled with a model complexity control (MCC) framework, a new variant of CMA-ES, named MCC-CCG-CMAES, is presented for LSGO problems, which suffers less from curse of dimensionality and significantly reduces the computational cost compared with the standard CMA-ES.

^{[21]}In lieu of Nussbaum gain techniques, parameter estimation algorithms and switching control strategies, a continuous static low-complexity control solution is provided by means of a novel combination of smooth orientation functions and error transformation functions.

^{[22]}Lyapunov-like functions are used to characterize partial information in an abstraction for a lower complexity controller synthesis in each sub-system.

^{[23]}In this paper, we propose a complexity control method in the high-efficiency video coding intracoding to facilitate these video applications.

^{[24]}Our model simultaneously performs group selection and estimation, while our fully Bayes treatment of the mixture proportion allows for model complexity control and automatic self-adaptivity to different levels of sparsity.

^{[25]}Therefore, paratooite-(La) versus carbocernaite represents a good example of structural complexity increasing due to the increasing chemical complexity controlled by different electronic properties of mineral-forming chemical elements (transitional versus alkali and alkaline earth metals).

^{[26]}In order to handle complexity control and ill-conditioned nonlinear least squares problems, we consider in this paper two $$L_2$$ regularization algorithms for the SNLLS problems.

^{[27]}For the class of multi-input multi-output nonlinear systems, comprised of fully interconnected strict-feedback subsystems, having uncertain, though locally Lipschitz nonlinearities and input quantization equipped with a hysteretic property, the objective is to construct a closed-loop system that combines prescribed, output trajectory tracking performance attributes (maximum overshoot, minimum convergence rate, maximum steady-state error), and a low-complexity control solution.

^{[28]}The proposed approximation-free distributed controllers only utilize error variables incorporating with performance bound functions, which lead to a low-complexity control algorithm.

^{[29]}Often DR is employed for the same purpose as supervised regularization and other forms of complexity control: exploiting a bias/variance tradeoff to mitigate overfitting.

^{[30]}In particular, we discuss how nanoscale complexity controls the emergence of high temperature superconductivity (HTS), myelin functionality and formation of hybrid organic-inorganic nanostructures.

^{[31]}

여기에서 우리는 막의 복잡성이 막 융합의 첫 번째 단계, 즉 줄기 형성의 에너지를 제어하는 방법을 연구합니다.

^{[1]}일반적으로 설계에 오류의 분수 거듭제곱을 포함하는 기존의 고정 시간 제어 방식과 달리 이 작업은 다음을 통해 위에서 언급한 분수 거듭제곱 항의 필요성에 대한 의존성을 제거한다는 의미에서 복잡성이 낮은 제어 구조를 개발합니다. 규정된 성능 제어(PPC) 방법.

^{[2]}전원 스위치, 비교기 및 논리 게이트로 구성된 저복잡성 제어 장치가 구현되며 백업 배터리는 물론 소형 및 대형 슈퍼커패시터 2개를 감독할 수 있습니다.

^{[3]}본 논문에서는 라이브 비디오 코딩을 위한 온라인 학습 기반의 다단계 복잡도 제어 방법을 제안한다.

^{[4]}이러한 맥락에서 HMOF는 복잡성 제어 방법으로서 유사한 복잡성 감소 비율에서 최첨단 복잡성 감소 알고리즘보다 성능이 우수합니다.

^{[5]}이 논문에서는 선형 시스템에 대한 데이터에서 제한된 복잡성 컨트롤러를 조정하는 접근 방식을 제안합니다.

^{[6]}$K_{1}(s)$ 및 $K_{2}(s)$는 FASC(Family of All Stabilizing Controllers)에 속하는 저복잡도 컨트롤러이며 이들의 자유 제어 매개변수는 강력한 안정성을 달성하기 위해 선택됩니다.

^{[7]}MFG(mean-field game) 분석을 통해 복잡성이 감소된 제어 솔루션을 도출합니다.

^{[8]}실험에 따르면 이 접근 방식은 (a) 문헌의 다른 제안과 비슷하지만 복잡성 제어의 부가 가치가 있는 코퍼스 내 성능으로 인식에 효과적이며 (b) 가능성 멤버쉽을 사용하여 음성으로 전달되는 감정을 분석하는 혁신적인 방법을 허용합니다. 학위.

^{[9]}실험 결과는 여기에 제시된 접근 방식을 사용하여 낮은 복잡성 제어 작업과 함께 고효율(> 98%)을 달성할 수 있음을 나타냅니다.

^{[10]}이 기사에서는 성능 저하 제약을 고려한 리튬 이온(Li-ion) 배터리 셀의 고속 충전을 위한 새로운 저복잡성 제어 체계 제품군을 제안하고 비교합니다.

^{[11]}저복잡도 컨트롤러는 아이템 폭발의 문제를 없애기 때문에 동적 표면 제어 방식으로 설계되었습니다.

^{[12]}제안의 유효성을 확인하기 위해 LPV 모델 기반 제어 전략은 프로세스 및 측정 가우스 노이즈가 있는 상태에서 회로 경로를 통한 시뮬레이션에서 계산 복잡성이 감소된 또 다른 제어 전략인 IKIBI(역운동학 자전거 모델)와 비교됩니다.

^{[13]}복잡성 제어 측면에서 APGP는 GP보다 우세했지만 GP+BC보다 우세했습니다. 그러나 GP+BC는 테스트 세트 정확도를 희생시키면서 더 간단한 솔루션을 만들었습니다.

^{[14]}nan

^{[15]}nan

^{[16]}nan

^{[17]}nan

^{[18]}nan

^{[19]}nan

^{[20]}nan

^{[21]}nan

^{[22]}nan

^{[23]}nan

^{[24]}nan

^{[25]}nan

^{[26]}nan

^{[27]}nan

^{[28]}nan

^{[29]}nan

^{[30]}nan

^{[31]}

## Model Complexity Control

Then coupled with a model complexity control (MCC) framework, a new variant of CMA-ES, named MCC-CCG-CMAES, is presented for LSGO problems, which suffers less from curse of dimensionality and significantly reduces the computational cost compared with the standard CMA-ES.^{[1]}Our model simultaneously performs group selection and estimation, while our fully Bayes treatment of the mixture proportion allows for model complexity control and automatic self-adaptivity to different levels of sparsity.

^{[2]}

## complexity control method

In this article, we propose an online learning-based multi-stage complexity control method for live video coding.^{[1]}In this context, as a complexity control method, HMOF outperforms the state-of-the-art complexity reduction algorithms under a similar complexity reduction ratio.

^{[2]}In this paper, we propose an interpretable machine learning-based complexity control method for efficiently im-plementing HEVC on live video applications with different computing capacities and limited powers.

^{[3]}In this paper, we propose a complexity control method in the high-efficiency video coding intracoding to facilitate these video applications.

^{[4]}

본 논문에서는 라이브 비디오 코딩을 위한 온라인 학습 기반의 다단계 복잡도 제어 방법을 제안한다.

^{[1]}이러한 맥락에서 HMOF는 복잡성 제어 방법으로서 유사한 복잡성 감소 비율에서 최첨단 복잡성 감소 알고리즘보다 성능이 우수합니다.

^{[2]}nan

^{[3]}nan

^{[4]}

## complexity control solution

By means of mean-field game (MFG) analysis, we derive a reduced-complexity control solution.^{[1]}In lieu of Nussbaum gain techniques, parameter estimation algorithms and switching control strategies, a continuous static low-complexity control solution is provided by means of a novel combination of smooth orientation functions and error transformation functions.

^{[2]}For the class of multi-input multi-output nonlinear systems, comprised of fully interconnected strict-feedback subsystems, having uncertain, though locally Lipschitz nonlinearities and input quantization equipped with a hysteretic property, the objective is to construct a closed-loop system that combines prescribed, output trajectory tracking performance attributes (maximum overshoot, minimum convergence rate, maximum steady-state error), and a low-complexity control solution.

^{[3]}

MFG(mean-field game) 분석을 통해 복잡성이 감소된 제어 솔루션을 도출합니다.

^{[1]}nan

^{[2]}nan

^{[3]}

## complexity control scheme

This article proposes and compares a new family of low-complexity control schemes for the fast charge of lithium-ion (Li-ion) battery cells accounting for degradation constraints.^{[1]}This paper presents a novel contribution of a low complexity control scheme for voltage control of a dynamic voltage restorer (DVR).

^{[2]}

이 기사에서는 성능 저하 제약을 고려한 리튬 이온(Li-ion) 배터리 셀의 고속 충전을 위한 새로운 저복잡성 제어 체계 제품군을 제안하고 비교합니다.

^{[1]}nan

^{[2]}