• Journal of Biosystems Engineering
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• v.26 no.2
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• pp.147-154
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• 2001

In Korea and Japan, dried oak mushrooms are classified into 12 to 16 different categories based on its external visual quality. And grading used to be done manually by the human expert and is limited to the randomly sampled oak mushrooms. Visual features of dried oak mushrooms dominate its quality and are distributed over both sides of the gill and the cap. The 2nd prototype computer vision based automatic grading and sorting system for dried oak mushrooms was developed based on the 1st prototype. Sorting function was improved and overall system for grading was simplified to one stage grading instead of two stage grading by inspecting both front and back sides of mushrooms. Neuro-net based side(gill or cap) recognition algorithm of the fed mushroom was adopted. Grading was performed with both images of gill and cap using neural network. A real time simultaneous discharge algorithm, which is good for objects randomly fed individually and for multi-objects located along a series of discharge buckets, was developed and implemented to the controller and the performance was verified. Two hundreds samples chosen from 10 samples per 20 grade categories were used to verify the performance of each unit such as feeding, reversing, grading, and discharging unites. Test results showed that success rates of one-line feeding, reversing, grading, and discharging functions were 93%, 95%, 94%, and 99% respectively. The developed prototype revealed successful performance such as the approximate sorting capability of 3,600 mushrooms/hr per each line i.e. average 1sec/mushroom. Considering processing time of approximate 0.2 sec for grading, it was desired to reduce time to reverse a mushroom to acquire the reversed surface image.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.3
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• pp.49-58
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• 2000

This paper deals with a systematic approach to neural network controller design for static VAR compensator (SVC) using a learning algorithm of error back propagation that accepts error and change of error as inputs, the momentum learning technique is used for reduction of learning time, to improve system stability. A SVC, one of the Flexible AC Transmission System(FACTS), constructed by a fixed capacitor(FC) and a thyristor controlled reactor(TCR), is designed and implemented to improve the damping of a synchronous generator, as well as controlling the system voltage.TO verify the robustness of the proposed method, we considered the dynamic response of generator rotor angle deviation, angular velocity deviation and generator terminal voltage by applying a power fluctuation and rotor angle fluctuation in initial point when heavy load and normal load. Thus, we prove the usefulness of proposed method to improve the stability of single machine-infinite bus with SVC system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.871-877
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• 2004

In this paper, for manufacturing a custom-made shoes, shape of foot acquired three-dimensional measurement device which makes shoe-last data for needing a custom-made shoes is founded on artificial intelligence technique and it shows method restoring to the original shape in optimized state. the developed system for this study is based on PC which uses existing three dimensional measurement method. And it gains shoe-last and data of foot shape going through 8 CCD(Charge Coupled Device) Which equipped top and bottom, right and left sides and 4 lasers which also equipped both sides and upper and lower sides. The acquired data are processed image processing algorithm using artificial intelligence technique. And result of data management is better quality of removing noise than other system not using artificial intelligence technique and it can simplify post-processing. So, this paper is constituted hardware and software system and it used neural network for determining threshold value, when input image on pre-processing step is being stage of image binarization and present that results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.774-779
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• 2020

The seismic response reduction capacity of a smart mid-story isolation system was investigated using the RNN model in this study. For this purpose, an RNN model was developed to make a dynamic response prediction of building structures subjected to seismic loads. An existing tall building with a mid-story isolation system was selected as an example structure for realistic research. A smart mid-story isolation system was comprised of an MR damper instead of existing lead dampers. The RNN model predicted the seismic responses accurately compared to those of the FEM model. The simulation time of the RNN model can be reduced significantly compared to the FEM model. After the numerical simulations, the smart mid-story isolation system could effectively reduce the seismic responses of the existing building compared to the conventional mid-story isolation system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.197-207
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• 2016

It is very important to maintain a constant chlorine concentration in the post chlorination process, which is the final step in the water treatment process (hereafter WTP) before servicing water to citizens. Even though a flow meter between the filtration basin and clear well must be installed for the post chlorination process, it is not easy to install owing to poor installation conditions. In such a case, a raw water flow meter has been used as an alternative and has led to dosage errors due to detention time. Therefore, the inlet flow to the clear well is estimated by a time series neural network for the plant without a measurement value, a new residual chlorine meter is installed in the inlet of the clear well to decrease the control period, and the proposed modeling and controller to analyze the chlorine concentration change in the well is a neuro fuzzy algorithm and cascade method. The proposed algorithm led to post chlorination and chlorination improvements of 1.75 times and 1.96 times respectively when it was applied to an operating WTP. As a result, a hygienically safer drinking water is supplied with preemptive response for the time delay and inherent characteristics of the disinfection process.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.77-88
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• 2018

This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.37-44
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• 2004

This paper represents a new approach for the protective relay of power transmission lines using a Artificial Neural Network(ANN). A different fault m transmission lines need to be detected classified and located accurately and cleared as fast as possible. However, The protection range of the distance relay is always designed on the basis of fixed settings, and unfortunately these approach do not have the ability to adapt dynamically to the system operating condition. ANN is suitable for the adaptive relaying and the detection of complex faults. The backpropagation algerian based multi-layer protection is utilized for the teaming process. It allows to make control to various protection functions. As expected, the simulation result demonstrate that this approach is useful and satisfactory.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.4
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• pp.357-367
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• 1999

When traction system of 2-motor driven electric vehicle(EV) is consisted of two motors (IPMSM) . two inverters. and one traction controller, control performances of IPMSM for an electric vehicle is affected by parameter variation b because of large current magnitude and wide current phase angle. To solve this problem, new parameter estimator for L Ld and Lq is constructed by neu때 network technique. And new vector control algorithm with parameter estimator by n neural network is proposed for IPMSM.And also. an advanced traction control algorithm is proposed using fuzzy c controller in order to enhance the driveability oftwo-wheel drive EVs with fitted with a traction control system Performances of the proposed algorithm are examined by simulations and the experimental resul않 with respect to t the prototype IPMSM and EV.

• Journal of IKEEE
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• v.27 no.4
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• pp.531-536
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• 2023

This paper proposes a sliding window method with frame feature insertion for immediate attack detection on in-vehicle networks. This method guarantees real-time attack detection by labeling based on the attack status of the current frame. Experiments show that the proposed method improves detection performance by giving more weight to the current frame in CNN computation. The proposed model was designed based on a lightweight LeNet-5 architecture and it achieves 100% detection for DoS attacks. Additionally, by comparing the complexity with conventional models, the proposed model has been proven to be more suitable for resource-constrained devices like ECUs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.605-610
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• 2020

Software Defined Networking (SDN) is setting the standard for the management of networks due to its scalability, flexibility and functionality to program the network. The Distributed Denial of Service (DDoS) attack is most widely used to attack the SDN controller to bring down the network. Different methodologies have been utilized to detect DDoS attack previously. In this paper, first the dataset is obtained by Kaggle with 84 features, and then according to the rank, the 20 highest rank features are selected using Permutation Importance Algorithm. Then, the datasets are trained and tested with Convolution Neural Network (CNN) classifier model by utilizing deep learning techniques. Our proposed solution has achieved the best results, which will allow the critical systems which need more security to adopt and take full advantage of the SDN paradigm without compromising their security.