• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2278-2282
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• 2003

In this paper the structure of 2-DOF controller based on artificial immune network algorithms has been suggested for nonlinear system. Up to present time, a number of structures of the 2-DOF controllers are considered as 2-DOF (2-Degrees Of Freedom) control functions. However, a general view is provided that they are the special cases of either the state feedback or the modification of PID controllers. On the other hand, the immune network system possesses a self organizing and distributed memory, also it has an adaptive function by feed back law to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation, since antibody recognizes specific antigens which are the foreign substances that invade living creatures. Therefore, it can provide optimal solution to external environment. Simulation results by immune based 2-DOF controller reveal that immune algorithm is an effective approach to search for 2-DOF controller.

• Proceedings of the Korea Database Society Conference
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• 1995.12a
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• pp.3-19
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• 1995

시스템 Technology ★ Server Technology - 운영환경구축 ★ Network 구성설계 - ATM, FDDI, NMS ★ Client/Server시스템 구성별 Bench Marking ★ Windows 메뉴 및 GUI 설계 ★다기능 PC 운영환경 설정 시스템 Technology ★ Data Base Technology - DB Administration - BB Performance Tuning ★ System Integration Technology - Application Integration - System Flow Control - Task Control - Applicational Interface - S/W Down Load 시스템 Technology ★ Memory Optimization ★ IBM/Facom Host API ★ 영상전화 Customizing - Intel Proshare ★ Auto Dialing - CTI Link ★ IC-Card Interface 시스템 Technology ★ Sound 처리 - Voice Mail - 음절 처리 ★ Image 처리 ★도움말 처리 - Hyper Text 시스템 Technology ★ Socket Programming - 긴급메일 - Peer to peer message switching ★ Set Up Programming -Install Shield ★ DB Access Programming - DB-Library ★ TCP/IP Programming(중략)

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1137-1147
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• 2004

In this paper, we proposed two types of adaptive control mechanism which is named HIA(Humoral Immune Algorithm) PID and CMIA(Cell-Mediated Immune Algorithm) controller based on biological immune system under engineering point of view. The HIA PID which has real time control scheme is focused on the humoral immunity and the latter which has the self-tuning mechanism is focused on the T-cell regulated immune response. To verify the performance of the proposed controller, some experiments for the control of AGV which is used for the port automation to carry container without human are performed. The experimental results for the control of steering and speed of an AGV system illustrate the effectiveness of the proposed control scheme. Moreover, in that results, proposed controllers have better performance than other conventional PID controller and intelligent control method which is the NN(neural network) PID controller.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.99-104
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• 2001

The neural network method has been introduced to design a controller for linear motor feed system and system operation status was monitored. It is most difficult to achieve controller gain tuning because of the information limit. Regardless of the system structure, conventional control gain could be adjusted minimizing the resulting error for both position and velocity using the proposed method. Slight performance deterioration was observed at the small value of training epoch. Different controller performance for position was observed with respect changed sampling time. Actuated system performance was monitored using neural network signal processing and operational status was predicted with the rate of 80% approximately.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2188-2190
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• 2004

This paper presents an implementation of power system stabilizer using inverse dynamic neuro controller. Traditionally, mutilayer neural network is used for a universal approximator and applied to a system as a neuro-controller. In this case, at least two neural networks are used and continuous tuning of neuro-controller is required. Moreover, training of neural network is required considering all possible disturbances, which is impractical in real situation. In this paper, Taylor Model Based Inverse Dynamic Neuro Model (TMBIDNM) is introduced to avoid this problem. Inverse Dynamic Neuro Controller (IDNC) consists of TMBIDNM and Error Reduction Neuro Model (ERNM). Once the TMBIDNM is trained, it does not require retuning for cases with other types of disturbances. The controller is tested for one machine and infinite-bus power system for various operating conditions.

• The Journal of Korean Association of Computer Education
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• v.12 no.2
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• pp.77-86
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• 2009

Large-scale applications, that needs large-capacity R&D resources and realtime data transmission, have demanded more stable and high-performance network environment than current Internet environments. Recently, global R&D networks have focuses on utilizing Lambda networking technologies and resource reservation systems to be satisfied with various applications' requirements. In this paper, we modify the existing DRS (Dynamic Right-Sizing) model to reflect various advantages in terms of the stability and high-capacity of Lambda network. In addition, we suggest the design methodology of high-performance Lambda network, which can integrate NRPS (Network Resource Provisioning System) into our modified DRS model.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.199-211
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• 2009

Recent advancements in wireless networks have enabled support for mobile applications to transfer data over heterogeneous wireless paths in parallel using data striping technique [2]. Traditionally, high performance data transfer requires tuning of multiple TCP sockets, at sender's end, based on bandwidth delay product (BDP). Moreover, traditional techniques like Automatic TCP Buffer Tuning (ATBT), which balance memory and fulfill network demand, is designed for wired infrastructure assuming single flow on a single socket. Hence, in this paper we propose a buffer tuning technique at senders end designed to ensure high performance data transfer by striping data at transport layer across heterogeneous wireless paths. Our mechanism has the capability to become a resource management system for transport layer connections running on multi-homed mobile host supporting features for wireless link i.e. mobility, bandwidth fluctuations, link level losses. We show that our proposed mechanism performs better than ATBT, in efficiently utilizing memory and achieving aggregate throughput.

• ETRI Journal
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• v.40 no.4
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• pp.421-434
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• 2018

The application of deep neural networks (DNNs) to connect the world with cyber physical systems (CPSs) has attracted much attention. However, DNNs require a large amount of memory and computational cost, which hinders their use in the relatively low-end smart devices that are widely used in CPSs. In this paper, we aim to determine whether DNNs can be efficiently deployed and operated in low-end smart devices. To do this, we develop a method to reduce the memory requirement of DNNs and increase the inference speed, while maintaining the performance (for example, accuracy) close to the original level. The parameters of DNNs are decomposed using a hybrid of canonical polyadic-singular value decomposition, approximated using a tensor power method, and fine-tuned by performing iterative one-shot hybrid fine-tuning to recover from a decreased accuracy. In this study, we evaluate our method on frequently used networks. We also present results from extensive experiments on the effects of several fine-tuning methods, the importance of iterative fine-tuning, and decomposition techniques. We demonstrate the effectiveness of the proposed method by deploying compressed networks in smartphones.

• Proceedings of the KIEE Conference
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• 2005.10a
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• pp.136-138
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• 2005

The conventional self-tuning methods have the speed control problem of nonlinear BLDC motor which can't adapt against any kinds of noise or operation circumstances. In this paper, supposed to solve these problem to PI parameters controller algorithm using ANN. In the proposed algorithm, the parameters of the controller were adjusted to reduce by on-line system the error of the speed of BLDC motor. In this process, EBPA NN was constituted to an output error value of a BLDC motor and conspired an input and output. The performance of the self-tuning controller is compared with that of the PI controller tuned by conventional method(Z&N). The effectiveness of the proposed control method IS verified thought the Matlab Simulink.