• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.127-136
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• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07b
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• pp.1184-1187
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• 2003

Electrochemical capacitors are becoming attractive energy storage systems particularly for applications involving high power requirements such as hybrid systems consisting of batteries and electrochemical capacitors for electric vehicle propulsion. A new type electric double layer capacitor (EDLC) was constructed by using carbon nanofibers (CNFs) and DAAQ(1,5-diaminoanthraquinone) electrode. Carbonaceous materials are found in variety forms such as graphite, diamond, carbon fibers etc. While all the carbon nanofibers include impurities such as amorphous carbon, nanoparticles, catalytic metals and incompletely grown carbons. We have eliminated of Ni particles and some carbonaceous particles in nitric acid. Nitric acid treated CNFs could be covered with very thin DAAQ oligomer from the results of CV and TG analyses and SEM images. DAAQ oligomer film exhibited a specific capacity as 45-50 Ah/kg in 4M $H_2SO_4$. We established Process Parameters of the technique for the formation of nano-structured materials. Furthermore, improved the capacitive properties of the nano structured CNFs electrodes using controlled solution chemistry. As a result, CNFs coated by DAAQ composite electrode showed relatively good electrochemical behaviors in acidic electrolyte system with respect to specific capacity and scan rate dependency.

• Journal of ILASS-Korea
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• v.26 no.3
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• pp.135-141
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• 2021

High-efficient HEV Engine cooling systems reflects variable coolant temperature because it can decrease the hydrodynamic frictional losses of lubricated engine parts in light duty conditions. In order to safely raise the operating temperature of passenger cars to a constant higher level, and thus optimize combustion and all accompanying factors, a new thermostat technology was developed : the electronically map-controlled thermostat. In this work, various crystalline plastics such as polyphthalamide (PPA) and polyphenylenesulfide (PPS) mixed with various glass fiber amounts were introduced into plastic fittings of automotive electronic controlled thermostat for the purpose of suppressing influx of coolant into the element and undesirable opening during hot soaking. Skirt was installed around element frame of automotive electronic controlled thermostat for improving thermal sensitivity in terms of response time, hysteresis and melting temperature. To validate the effectiveness and optimum shape of skirt, thermal sensitivity test and three-dimensional CFD simulation have been performed. As a consequence, important improvement in thermal sensitivity with less than 3℃ of maximum coolant temperature between opening and engine inlet was obtained.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.65-74
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• 2022

In natural language processing, commonly used words such as prepositions are referred to as stop-words; they have no inherent meaning and are therefore ignored in indexing and retrieval tasks. The removal of stop-words from Arabic text has a significant impact in terms of reducing the size of a cor- pus text, which leads to an improvement in the effectiveness and performance of Arabic-language processing systems. This study investigated the effectiveness of applying a stop-word lists elimination with normalization as a preprocessing step. The idea was to merge statistical method with the linguistic method to attain the best efficacy, and comparing the effects of this two-pronged approach in reducing corpus size for Ara- bic natural language processing systems. Three stop-word lists were considered: an Arabic Text Lookup Stop-list, Frequency- based Stop-list using Zipf's law, and Combined Stop-list. An experiment was conducted using a selected file from the Arabic Newswire data set. In the experiment, the size of the cor- pus was compared after removing the words contained in each list. The results showed that the best reduction in size was achieved by using the Combined Stop-list with normalization, with a word count reduction of 452930 and a compression rate of 30%.

• Applied Chemistry for Engineering
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• v.33 no.2
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• pp.133-144
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• 2022

Fast-paced industrial and agricultural development generates large quantities of hazardous heavy metals (HMs), which are extremely damaging to individuals and the environment. Research in both academia and industry has been spurred by the need for HMs to be removed from water bodies. Advanced materials are being developed to replace existing water purification technologies or to introduce cutting-edge solutions that solve challenges such as cost efficacy, easy production, diverse metal removal, and regenerability. Water treatment industries are increasingly interested in activated carbon because of its high adsorption capacity for HMs adsorption. Furthermore, because of its huge surface area, abundant functional groups on surface, and optimal pore diameter, the modified activated carbon has the potential to be used as an efficient adsorbent. Metal-organic frameworks (MOFs), a novel organic-inorganic hybrid porous materials, sparked an interest in the elimination of HMs via adsorption. This is due to the their highly porous nature, large surface area, abundance of exposed adsorptive sites, and post-synthetic modification (PSM) ability. This review introduces PSM methods for MOFs, chemical modification of activated carbons (ACs), and current advancements in the elimination of Pb²⁺, Hg²⁺, and Cd²⁺ ions from water using modified MOFs and ACs via adsorption.

• Tribology and Lubricants
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• v.37 no.6
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• pp.213-223
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• 2021

Electrochemical mechanical planarization (ECMP) was developed to overcome the shortcomings of conventional chemical mechanical planarization (CMP). Because ECMP technology utilizes electrochemical reactions, it can have a higher efficiency than CMP even under low pressure conditions. Therefore, there is an advantage in that it is possible to reduce dicing and erosions, which are physical defects in semiconductor CMP. This paper summarizes the papers on ECMP published from 2003 to 2021 and analyzes research trends in ECMP technology. First, the material removal mechanisms and the configuration of the ECMP machine are dealt with, and then ECMP research trends are reviewed. For ECMP research trends, electrolyte, processing variables and pads, tribology, modeling, and application studies are investigated. In the past, research on ECMP was focused on basic research for the development of electrolytes, but it has recently developed into research on tribology and process variables and on new processing systems and applications. However, there is still a need to increase the processing efficiency, and to this end, the development of a hybrid ECMP processing method using another energy source is required. In addition, ECMP systems that can respond to the developing metal 3D printing technology must be researched, and ECMP equipment technology using CNC and robot technology must be developed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.295-311
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• 2023

Cloud computing has evolved significantly, intending to provide users with fast, dependable, and low-cost services. With its development, malicious users have become increasingly capable of attacking both its internal and external security. To ensure the security of cloud services, encryption, authorization, firewalls, and intrusion detection systems have been employed. However, these single monitoring agents, are complex, time-consuming, and they do not detect ransomware and zero-day vulnerabilities on their own. An innovative Record and Replay-based hybrid Honeynet (R2NET) system has been developed to address this issue. Combining honeynet with Record and Replay (RR) technology, the system allows fine-grained analysis by delaying time-consuming analysis to the replay step. In addition, a machine learning algorithm is utilized to cluster the logs of attackers and store them in a database. So, the accessing time for analyzing the attack may be reduced which in turn increases the efficiency of the proposed framework. The R2NET framework is compared with existing methods such as EEHH net, HoneyDoc, Honeynet system, and AHDS. The proposed system achieves 7.60%, 9.78%%, 18.47%, and 31.52% more accuracy than EEHH net, HoneyDoc, Honeynet system, and AHDS methods.

• Annual Conference of KIPS
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• 2022.05a
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• pp.385-387
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• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1916-1934
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• 2023

The phishing attack is a malicious emerging threat on the internet where the hackers try to access the user credentials such as login information or Internet banking details through pirated websites. Using that information, they get into the original website and try to modify or steal the information. The problem with traditional defense systems like firewalls is that they can only stop certain types of attacks because they rely on a fixed set of principles to do so. As a result, the model needs a client-side defense mechanism that can learn potential attack vectors to detect and prevent not only the known but also unknown types of assault. Feature selection plays a key role in machine learning by selecting only the required features by eliminating the irrelevant ones from the real-time dataset. The proposed model uses Hyperparameter Optimized Artificial Neural Networks (H-ANN) combined with a Hybrid Firefly and Grey Wolf Optimization algorithm (H-FFGWO) to detect and block phishing websites in Internet of Things(IoT) Applications. In this paper, the H-FFGWO is used for the feature selection from phishing datasets ISCX-URL, Open Phish, UCI machine-learning repository, Mendeley website dataset and Phish tank. The results showed that the proposed model had an accuracy of 98.07%, a recall of 98.04%, a precision of 98.43%, and an F1-Score of 98.24%.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.