• Title/Summary/Keyword: Self-adaptive model

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Self-Tuning Position Control of a Remotely Operated Vehicle (원격무인 잠수정의 자기동조 위치제어)

  • Lee, Pan-Muk
    • Journal of Ocean Engineering and Technology
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    • v.3 no.2
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    • pp.51-58
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    • 1989
  • In general, a remotely operated vehicle(ROV) operates at deep sea. The control system of ROV is composed of two local loops; the first loop placed on the surface vessel monitors and manipulates the attitude of the ROV using joystick, and the second part on the ROV automatically controls thrusters and acquires positional data. This paper presents a position control simulation of a ROV using an adaptive controller and discusses the control effects of two different conditions. The design of an adaptive control system is obtained by the application of a self-tuning controller with the minimization of an appropriate cost function. The parameters of the control system are estimated by a recursive least square method(RLS). In the simulation, a Runge-Kutta method is used for the numerical integration and the generated outputs are obtained by adding measurement errors. Additionally, this paper discusses the mathematical modelling of a ROV and make a survey of control systems.

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Dynamic Decision Making for Self-Adaptive Systems Considering Environment Information (환경정보를 고려한 자가적응형 시스템을 위한 동적 의사결정 기술)

  • Kim, Misoo;Jeong, Hohyeon;Lee, Eunseok
    • Journal of KIISE
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    • v.43 no.7
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    • pp.801-811
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    • 2016
  • Self-adaptive systems (SASs) can change their goals and behaviors to achieve its ultimate goal in a dynamic execution environment. Existing approaches have designed, at the design time, utility functions to evaluate and predict the goal satisfaction, and set policies that are crucial to achieve each goal. The systems can be adapted to various runtime environments by utilizing the pre-defined utility functions and policies. These approaches, however, may or may not guarantee the proper adaptability, because system designers cannot assume and predict all system environment perfectly at the design time. To cope with this problem, this paper proposes a new method of dynamic decision making, which takes the following steps: firstly we design a Dynamic Decision Network (DDN) with environmental data and goal model that reflect system contexts; secondly, the goal satisfaction is evaluated and predicted with the designed DDN and real-time environmental information. We furthermore propose a dynamic reflection method that changes the model by using newly generated data in real-time. The proposed method was actually applied to ROBOCODE, and verified its effectiveness by comparing to conventional static decision making.

An Automated Code Generation for Dynamic reconfiguration based on Goal-Scenario (목표 시나리오 기반의 동적 재구성을 위한 코드 자동 생성 기법)

  • Baek, Su-Jin;Sim, Sung-Ho;Song, Young-Jae
    • Journal of Digital Convergence
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    • v.10 no.1
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    • pp.349-355
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    • 2012
  • Today, the computing environments is very complex, so researches that endow a system with the self-healing's ability that recognizes problem arising in a target system are being an important issues. However, the existing methodology, the goal for the new requirements for self-healing system developers to model and analyze the constraints that must be greater efforts. Therefore, in this paper are aware of problems detected by the system to solve the problem is the analysis of goal-based scenarios. In addition, there is a pre and post applying a strategy to be dynamically reconfigured to show you how to self-healing. These proposed new requirements for methodology, self-healing reduces the load on the developer's analysis.

Reasoning Non-Functional Requirements Trade-off in Self-Adaptive Systems Using Multi-Entity Bayesian Network Modeling

  • Saeed, Ahmed Abdo Ali;Lee, Seok-Won
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.3
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    • pp.65-75
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    • 2019
  • Non-Functional Requirements (NFR) play a crucial role during the software development process. Currently, NFRs are considered more important than Functional Requirements and can determine the success of a software system. NFRs can be very complicated to understand due to their subjective manner and especially their conflicting nature. Self-adaptive systems (SAS) are operating in dynamically changing environment. Furthermore, the configuration of the SAS systems is dynamically changing according to the current systems context. This means that the configuration that manages the trade-off between NFRs in this context may not be suitable in another. This is because the NFRs satisfaction is based on a per-context basis. Therefore, one context configuration to satisfy one NFR may produce a conflict with another NFR. Furthermore, current approaches managing Non-Functional Requirements trade-off stops managing them during the system runtime which of concern. To solve this, we propose fragmentizing the NFRs and their alternative solutions in form of Multi-entity Bayesian network fragments. Consequently, when changes occur, our system creates a situation specific Bayesian network to measure the impact of the system's conditions and environmental changes on the NFRs satisfaction. Moreover, it dynamically decides which alternative solution is suitable for the current situation.

Adaptive Attention Annotation Model: Optimizing the Prediction Path through Dependency Fusion

  • Wang, Fangxin;Liu, Jie;Zhang, Shuwu;Zhang, Guixuan;Zheng, Yang;Li, Xiaoqian;Liang, Wei;Li, Yuejun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.9
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    • pp.4665-4683
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    • 2019
  • Previous methods build image annotation model by leveraging three basic dependencies: relations between image and label (image/label), between images (image/image) and between labels (label/label). Even though plenty of researches show that multiple dependencies can work jointly to improve annotation performance, different dependencies actually do not "work jointly" in their diagram, whose performance is largely depending on the result predicted by image/label section. To address this problem, we propose the adaptive attention annotation model (AAAM) to associate these dependencies with the prediction path, which is composed of a series of labels (tags) in the order they are detected. In particular, we optimize the prediction path by detecting the relevant labels from the easy-to-detect to the hard-to-detect, which are found using Binary Cross-Entropy (BCE) and Triplet Margin (TM) losses, respectively. Besides, in order to capture the inforamtion of each label, instead of explicitly extracting regional featutres, we propose the self-attention machanism to implicitly enhance the relevant region and restrain those irrelevant. To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.

Prediction of the compressive strength of self-compacting concrete using surrogate models

  • Asteris, Panagiotis G.;Ashrafian, Ali;Rezaie-Balf, Mohammad
    • Computers and Concrete
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    • v.24 no.2
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    • pp.137-150
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    • 2019
  • In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of self-compacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture.

An Architecture-based Multi-level Self-Adaptive Monitoring Method for Software Fault Detection (소프트웨어 오류 탐지를 위한 아키텍처 기반의 다계층적 자가적응형 모니터링 방법)

  • Youn, Hyun-Ji;Park, Soo-Yong
    • Journal of KIISE:Software and Applications
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    • v.37 no.7
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    • pp.568-572
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    • 2010
  • Self-healing is one of the techniques that assure dependability of mission-critical system. Self-healing consists of fault detection and fault recovery and fault detection is important first step that enables fault recovery but it causes overhead. We can detect fault based on model, the detection tasks that notify system's behavior and compare normal behavior model and system's behavior are heavy jobs. In this paper, we propose architecture-based multi-level self-adaptive monitoring method that complements model-based fault detection. The priority of fault detection per component is different in the software architecture. Because the seriousness and the frequency of fault per component are different. If the monitor is adapted to intensive to the component that has high priority of monitoring and loose to the component that has low priority of monitoring, the overhead can be decreased and the efficiency can be maintained. Because the environmental changes of software and the architectural changes bring the changes at the priority of fault detection, the monitor learns the changes of fault frequency and that is adapted to intensive to the component that has high priority of fault detection.

Adaptive Beamforming Method for Turning Towed Line Array SONAR (회전하는 견인 선배열 소나의 적응 빔 형성 기법)

  • Lee, Seokjin;Park, Kyung-Min;Chung, Suk-Moon
    • The Journal of the Acoustical Society of Korea
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    • v.33 no.6
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    • pp.383-391
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    • 2014
  • In order to detect underwater acoustic signals, various SONAR array types have been developed, including towed line array SONAR system (TASS). However, the TASS suffers from performance degradation which is caused by aperture deformation during a turn, because the TASS have a long-aperture array. A parabolic array model for turning TASS have been developed to solve the degradation problem occurred during a turn. In this paper, adaptive beamforming system is developed using the parabolic TASS model to cancel interference signals. The developed beamforming system is based on generalized sidelobe canceller (GSC) structure and self-tuning adaptive algorithm.

Robust Flight Control System Using Neural Networks: Dynamic Surface Design Approach (신경 회로망을 이용한 강인 비행 제어 시스템: 동적 표면 설계 접근)

  • Yoon, Sung-Jin;Choi, Yoon-Ho;Park, Jin-Bae
    • Proceedings of the KIEE Conference
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    • 2006.07d
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    • pp.1848-1849
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    • 2006
  • The new robust controller design method is proposed for the flight control systems with model uncertainties. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides us with the ability to overcome the "explosion of complexity" problem of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of flight systems and all their weights are trained on-line. From the Lyapunov stability analysis, their adaptation laws are induced and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a high performance aircraft (F-16) are utilized to validate the good tracking performance and robustness of the proposed control system.

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Robust Control of Planar Biped Robots in Single Support Phase Using Intelligent Adaptive Backstepping Technique

  • Yoo, Sung-Jin;Park, Jin-Rae;Choi, Yoon-Ho
    • International Journal of Control, Automation, and Systems
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    • v.5 no.3
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    • pp.269-282
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    • 2007
  • This paper presents a robust control method via the intelligent adaptive backstepping design technique for stable walking of nine-link biped robots with unknown model uncertainties and external disturbances. In our control structure, the self recurrent wavelet neural network(SRWNN) which has the information storage ability is used to observe the uncertainties of the biped robots. The adaptation laws for all weights of the SRWNN are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Also, we prove that all signals in the closed-loop adaptive system are uniformly ultimately bounded. Through computer simulations of a nine-link biped robot with model uncertainties and external disturbances, we illustrate the effectiveness of the proposed control system.