• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2007년도 제38회 하계학술대회
• /
• pp.1009-1010
• /
• 2007

This paper presents Fuzzy-Neuro PI controller of IPMSM drive using fuzzy and neural-network. In general, PI controller in computer numerically controlled machine process fixed gain. To increase the robustness, fixed gain PI controller, Fuzzy-Neuro PI controller proposes a new method based fuzzy and neural-network. Fuzzy-Neuro PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.69.2-69
• /
• 2001

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2004년도 춘계학술발표대회
• /
• pp.1259-1262
• /
• 2004

Detecting environmental hazards and monitoring remote terrain are among many sensor network applications. In case of fire detection, it is significantly valuable to monitor fire-spot's shape and trend in time. Mobile ad hoc sensor nodes right round are responsible for sensoring, processing and networking packets, or even launching extinguisher. In this paper, we proposed a ring-oriented Multicast architecture based on "Fisheye State Routing" (MFSR) to organize a group of mobile ad hoc sensor nodes in a multicast way. It is familiar with traditional mesh-based multicast protocol [1] in mobile ad hoc network, trying to concentrates on efficiency and robustness simultaneously. Certain applications-based solution for hazards is proposed, quantitative results including architecture and recovery algorithms of MFSR are also investigated in this paper.

• International journal of advanced smart convergence
• /
• 제7권4호
• /
• pp.174-184
• /
• 2018

The efficiency and safety of social-overhead capital (SOC) public infrastructures have become an eminent social concern. In this regard, a continuous structural health monitoring has been widely implemented to oversee the robustness of such public infrastructures for the safety of the public. This paper deals with the analysis of a distributed mobility management (DMM) support for wireless sensor network (WSN) based information transmission system. The partial DMM support separates the data and control plane infrastructures, wherein, the control plane is managed by a particular mobility management network entity, while the data plane is distributed by the mobility anchors. The system will be able to optimize the information transmission for a wireless structural health monitoring of SOC public infrastructures specifically designed for bridges, and thus, guarantees the safety of public commuters.

• 한국정보통신학회논문지
• /
• 제25권12호
• /
• pp.1960-1963
• /
• 2021

In this paper, we propose a multiplay Thompson sampling algorithm in multipath communication system. Multipath communication system has advantages on communication capacity, robustness, survivability, and so on. It is important to select appropriate network path according to the status of individual path. However, it is hard to obtain the information of path quality simultaneously. To solve this issue, we propose Thompson sampling which is popular in machine learning area. We find some issues when the algorithm is applied directly in the proposal system and suggested some modifications. Through simulation, we verified the proposed algorithm can utilize the entire network paths. In summary, our proposed algorithm can be applied as a path allocation in multipath-based communications system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권1호
• /
• pp.60-79
• /
• 2022

The U-Net architecture-based segmentation models attained remarkable performance in numerous medical image segmentation missions like skin lesion segmentation. Nevertheless, the resolution gradually decreases and the loss of spatial information increases with deeper network. The fusion of adjacent layers is not enough to make up for the lost spatial information, thus resulting in errors of segmentation boundary so as to decline the accuracy of segmentation. To tackle the issue, we propose a new deep learning-based segmentation model. In the decoding stage, the feature channels of each decoding unit are concatenated with all the feature channels of the upper coding unit. Which is done in order to ensure the segmentation effect by integrating spatial and semantic information, and promotes the robustness and generalization of our model by combining the atrous spatial pyramid pooling (ASPP) module and channel attention module (CAM). Extensive experiments on ISIC2016 and ISIC2017 common datasets proved that our model implements well and outperforms compared segmentation models for skin lesion segmentation.

• 국제학술발표논문집
• /
• The 3th International Conference on Construction Engineering and Project Management
• /
• pp.257-262
• /
• 2009

Image processing has been utilized for assessment of infrastructure surface coating conditions for years. However, there is no robust method to overcome the non-uniform illumination problem to date. Therefore, this paper aims to deal with non-uniform illumination problems for bridge coating assessment and to achieve automated rust intensity recognition. This paper starts with selection of the best color configuration for non-uniformly illuminated rust image segmentation. The adaptive-network-based fuzzy inference system (ANFIS) is adopted as the framework to develop the new model, the box-and-ellipse-based neuro-fuzzy approach (BENFA). Finally, the performance of BENFA is compared to the Fuzzy C-Means (FCM) method, which is often used in image recognition, to show the advantage and robustness of BENFA.

• International Journal of Computer Science & Network Security
• /
• 제23권7호
• /
• pp.210-218
• /
• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

• Journal of Information Processing Systems
• /
• 제19권5호
• /
• pp.663-672
• /
• 2023

Most vehicle detection methods have poor vehicle feature extraction performance at night, and their robustness is reduced; hence, this study proposes a night vehicle detection method based on style transfer image enhancement. First, a style transfer model is constructed using cycle generative adversarial networks (cycleGANs). The daytime data in the BDD100K dataset were converted into nighttime data to form a style dataset. The dataset was then divided using its labels. Finally, based on a YOLOv5s network, a nighttime vehicle image is detected for the reliable recognition of vehicle information in a complex environment. The experimental results of the proposed method based on the BDD100K dataset show that the transferred night vehicle images are clear and meet the requirements. The precision, recall, mAP@.5, and mAP@.5:.95 reached 0.696, 0.292, 0.761, and 0.454, respectively.

• 한국산학기술학회논문지
• /
• 제10권12호
• /
• pp.3622-3625
• /
• 2009

본 논문은 고정 슬라이딩면을 갖는 가변 구조 제어기의 단점인 도달영역에서의 파리미터의 불확실성과 외부 외란에 대한 민감성을 감소시키는 방안으로 고정 슬라이딩면 대신 비선형 슬라이딩면을 제시한다. 비선형 슬라이딩면을 통하여 시스템 상태 궤적이 초기 위치에서부터 평형점에 이르기까지 외란과 파라미터의 불확실성에 강인하게 되며 아울러 고정 슬라이딩면까지의 도달시간 뿐만 아니라 평형점까지의 도달시간도 감소하게 되는 특성을 보이고자한다. 제안된 제어 구조의 효과는 시뮬레이션을 통해 증명하였다.