• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1477-1483
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• 2020

The stability guarantee of wireless network based control systems is still challenging due to the lossy links and node failures. This paper proposes a hierarchical cluster-based network protocol called robust wireless sensor and actuator network (R-WSAN) by combining time, channel, and space resource diversity. R-WSAN includes a scheduling algorithm to support the network resource allocation and a control task sharing scheme to maintain the control stability of multiple plants. R-WSAN was implemented on a real test-bed using Zolertia RE-Mote embedded hardware platform running the Contiki-NG operating system. Our experimental results demonstrate that R-WSAN provides highly reliable and robust performance against lossy links and node failures. Furthermore, the proposed scheduling algorithm and the task sharing scheme meet the stability requirement of control systems, even if the controller fails to support the control task.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.8
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• pp.93-98
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• 2021

The complex effects of the machining parameters make it is difficult to control and predict surface roughness. The theoretical surface roughness observed during mechanical machining with a round tool is determined by the tool radius and pitch. However, it was revealed that other parameters, such as the depth of cut and cutting speed, also affect surface roughness. This study adapted the Taguchi method, which can analyze the effects of cutting parameters quantitatively with an efficient number of experiments, to optimize the parameters for better surface roughness. Experiments were designed based on an orthogonal array, and the quantitative effects on the surface roughness were analyzed using the S/N ratio. The surface roughness was affected by all parameters, especially the tool radius. The optimum cutting parameter values obtained in this study showed better surface roughness than the other combinations of the parameters.

• Journal of Hydrogen and New Energy
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• v.32 no.6
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• pp.455-463
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• 2021

In this study, CeO₂ support is synthesized from cerium hydroxy carbonate prepared using precipitation/digestion method using KOH and K₂CO₃ as the precipitants. The Cu was impregnated to CeO₂ support with the different loading (Cu loading=10-40 wt. %). The prepared Cu/CeO₂ catalysts were applied to a single stage water gas shift (WGS) reaction. Among the prepared catalysts, the 20Cu/CeO₂ catalyst contained 20 wt.% of Cu showed the highest CO conversion (Xco=68% at 400℃). This result was mainly due to a large amount of active sites. In addition, the activity of the 20 Cu/CeO₂ catalyst was maintained without being deactivated for 100 hours because of the strong interaction between Cu and CeO₂. Therefore, it was confirmed that 20 Cu/CeO₂ is a suitable catalyst for a single WGS reaction.

• Journal of Korean Port Research
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• v.11 no.2
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• pp.145-156
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• 1997

The purpose in this study is development of model for the Container Terminals of Pusan Port, First of all, Quantitive and Qualititve factors are characterized which effects on Physical Distribution System in Container Terminals. The System Dynamics method is used to develope the model by using these factor. This model is able to present the timinig of investment in Container Terminals of Pusan Port. Six models are showed by change of parameters in System Dynamics, in this paper. In the model, Five feedback loop were found. Loop 1 : Number of Liners$\rightarrow$Number of Congested ships$\rightarrow$Port's Charges$\rightarrow$Export & Import Cargo Volumes$\rightarrow$Number of Liners$\rightarrow$The will to investment of government$\rightarrow$Length of berth→Number of Liners. Negative loop was acquired. Loop 2 : Port's Charge$\rightarrow$Economic of Port$\rightarrow$The will to Private management$\rightarrow$Efficiency for Port's Operation$\rightarrow$Port's Charges. Positive loop was acquired. Loop 3 : Number of Congested ships$\rightarrow$Planning for future development$\rightarrow$Information Service$\rightarrow$Support service for port's user$\rightarrow$Number of Congested ships. Negative loop was acquired. Loop 4 : Number of Congested ships$\rightarrow$Planning for future development$\rightarrow$Extent of stacking area$\rightarrow$Number of handling equipmint$\rightarrow$Number of Congested ships. Negative loop was acquired. Loop 5 : Export & Import Cargo Volumes$\rightarrow$Number of Liners$\rightarrow$Econmic of Port$\rightarrow$Support service for port's user$\rightarrow$Export & Import Cargo Volumes. Positive loop was acquired. System's level variables were selected as followings ; Number of Liners, Number of Congested ships, Export & Import Carge Volumes, Length of berth, and Port's Charges. As result of simmulation of model, fluctuation of respective year was found in level variables. This fluctuation can be used properly to present timing of investment.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.131.1-131.1
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• 2010

This study was conducted for engineering optimization for the gasification process which is the key factor for success of Taean IGCC gasification plant which has been driven forward under the government support in order to expand to supply new and renewable energy and diminish the burden of the responsibility for the reduction of the green house gas emission. The gasification process consists of coal milling and drying, pressurization and feeding, gasification, quenching and HP syngas cooling, slag removal system, dry flyash removal system, wet scrubbing system, and primary water treatment system. The configuration optimization is essential for the high efficiency and the cost saving. For this purpose, it was designed to have syngas cooler to recover the sensible heat as much as possible from the hot syngas produced from the gasifier which is the dry-feeding and entrained bed slagging type and also applied with the oxygen combustion and the first stage cylindrical upward gas flow. The pressure condition inside of the gasifier is around 40~45Mpg and the temperature condition is up to $1500{\sim}1700^{\circ}C$. It was designed for about 70% out of fly ash to be drained out throughout the quenching water in the bottom part of the gasifier as a type of molten slag flowing down on the membrane wall and finally become a byproduct over the slag removal system. The flyash removal system to capture solid particulates is applied with HPHT ceramic candle filter to stand up against the high pressure and temperature. When it comes to the residual tiny particles after the flyash removal system, wet scurbbing system is applied to finally clean up the solids. The washed-up syngas through the wet scrubber will keep around $130{\sim}135^{\circ}C$, 40~42Mpg and 250 ppmv of hydrochloric acid(HCl) and hydrofluoric acid(HF) at maximum and it is turned over to the gas treatment system for removing toxic gases out of the syngas to comply with the conditions requested from the gas turbine. The result of this study will be utilized to the detailed engineering, procurement and manufacturing of equipments, and construction for the Taean IGCC plant and furthermore it is the baseline technology applicable for the poly-generation such as coal gasification(SNG) and liquefaction(CTL) to reinforce national energy security and create new business models.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.221-230
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• 2020

Supercomputer that shares limited resources to multiple users needs a way to optimize the execution of application. For this, it is useful for system administrators to get prior information and hint about the applications to be executed. In most high-performance computing system operations, system administrators strive to increase system productivity by receiving information about execution duration and resource requirements from users when executing tasks. They are also using profiling techniques that generates the necessary information using statistics such as system usage to increase system utilization. In a previous study, we have proposed a scheduling optimization technique by developing a hardware performance counter-based profiling technique that enables characterization of applications without further understanding of the source code. In this paper, we constructed a profiling testbed cluster to support optimal execution of the supercomputer and experimented with the scalability of the profiling method to analyze application characteristics in the built cluster environment. Also, we experimented that the profiling method can be utilized in actual scheduling optimization with scalability even if the application class is reduced or the number of nodes for profiling is minimized. Even though the number of nodes used for profiling was reduced to 1/4, the execution time of the application increased by 1.08% compared to profiling using all nodes, and the scheduling optimization performance improved by up to 37% compared to sequential execution. In addition, profiling by reducing the size of the problem resulted in a quarter of the cost of collecting profiling data and a performance improvement of up to 35%.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.429-434
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• 2012

This paper presents a technology for manipulating external devices by Brain Computer Interface (BCI) system. Recently, BCI based rehabilitation and assistance system for disabled people, such as patient of Spinal Cord Injury (SCI), general paralysis, and so on, is attracting tremendous interest. Especially, electroencephalogram (EEG) signal is used to organize the BCI system by analyzing the signals, such as evoked potential. The general findings of neurophysiology support an availability of the EEG-based BCI system. We concentrate on the event-related synchronization of motor imagery EEG signal, which have an affinity with an intention for moving control of external device. To analyze the brain activity, short-time Fourier transform and particle swarm optimization are used to optimal feature selection from the preprocessed EEG signals. In our experiment, we can verify that the power spectral density correspond to range mu-rhythm(${\mu}8$~12Hz) have maximum amplitude among the raw signals and most of particles are concentrated in the corresponding region. Result shows accuracy of subject left hand 40% and right hand 38%.