• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Journal of Korean Society of Transportation
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• v.20 no.6
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• pp.99-113
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• 2002

The multi-class traffic assignment problem is the most typical one of the multi-solution traffic assignment problems and, recently formulation of the models and the solution algorithm have been received a great deal of attention. The useful solution algorithm, however, has not been proposed while formulation of the multi-class traffic assignment could be performed by adopting the variational inequality problem or the fixed point problem. In this research, we developed a hybrid solution algorithm which combines GA algorithm, diagonal algorithm and clustering algorithm for the multi-class traffic assignment formulated as a variational inequality Problem. GA algorithm and clustering algorithm are introduced for the wide area and small cost. We also performed an experiment with toy network(2 link) and tested the characteristics of the suggested algorithm.

• Smart Structures and Systems
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• v.25 no.6
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• pp.765-778
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• 2020

The present study intends to find a proper solution for the estimation of the physical behaviors of enlarged piles through a combination of small-scale laboratory tests and a hybrid computational predictive intelligence process. In the first step, experimental program is completed considering various critical influential factors. The results of the best multilayer perceptron (MLP)-based predictive network was implemented through three mathematical-based solutions of dragonfly algorithm (DA), whale optimization algorithm (WOA), and ant lion optimization (ALO). Three proposed models, after convergence analysis, suggested excellent performance. These analyses varied based on neurons number (e.g., in the basis MLP hidden layer) and of course, the level of its complexity. The training R² results of the best hybrid structure of DA-MLP, WOA-MLP, and ALO-MLP were 0.996, 0.996, and 0.998 where the testing R² was 0.995, 0.985, and 0.998, respectively. Similarly, the training RMSE of 0.046, 0.051, and 0.034 were obtained for the training and testing datasets of DA-MLP, WOA-MLP, and ALO-MLP techniques, while the testing RMSE of 0.088, 0.053, and 0.053, respectively. This obtained result demonstrates the excellent prediction from the optimized structure of the proposed models if only population sensitivity analysis performs. Indeed, the ALO-MLP was slightly better than WOA-MLP and DA-MLP methods.

• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• Steel and Composite Structures
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• v.49 no.6
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.394.2-394.2
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• 2014

We fabricated highly transparent and flexible Ti doped In2O3 (TIO)/Ag nanowire(NW)/TIO (TAT) multilayer electrodes by linear facing target sputtering (LFTS) and brush-painting for used as flexible for anode organic solar cells(FOSCs). The characteristics of TAT transparent anode as a function of number of brush-painting cycles was also investigated. At optimized conditions we achieved highly flexible TAT multilayer electrodes with a low sheet resistance of $9.01{\Omega}/square$ and a high diffusive transmittance more than 80% in visible region as well as superior mechanical stability. The effective embedment of the Ag NW network between top and bottom TIO films led to a metallic conductivity, high transparency. Based on FE-SEM HRTEM, and XRD analysis, we can find that the Ag NW network was effectively embedded between top and bottom TIO layers due to good flexibility of Ag NW, the TAT multilayer showed superior flexibility than single TIO layer. Successful operation of FOSCs with high power conversion efficiency of 3.01% indicates that TAT hybrid electrode is a promising alternative to conventional ITO electrode for high performance FOSCs.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.35-42
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• 2011

Link quality assessment is a crucial part of sensor network formation to stably operate large-scale wireless sensor networks (WSNs). A stability of path consisting of several nodes strongly depends on all link quality between pair of consecutive nodes. Thus it is very important to assess the link quality on the stage of building a routing path. In this paper, we present a link quality assessment method, Hybrid Link Quality Metric (HQLM), which uses both of LQI and RSSI from RF chip of sensor nodes to minimize set-up time and energy consumption for network formation. The HQLM not only reduces the time and energy consumption, but also provides complementary cooperation of LQI and RSSI. In order to evaluate the validity and efficiency of the proposed method, we measure PDR (Packet Delivery Rate) by exchanging multiple messages and then, compare PDR to the result of HQLM for evaluation. From the research being carried out, we can conclude that the HQLM performs better than either LQI- or RSSI-based metric in terms of recall, precision, and matching on link quality.

• Korean Journal of Materials Research
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• v.7 no.3
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• pp.175-179
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• 1997

bliniaturization oi microwave circuit components is an important issue with the development in the mobile communication. Capacitors, inductors anti hybrid devices of these are building blocks of electric circuits, and the fabrication of these devices using thin film technology will influence on the miniaturization of electronic devices In this paper, we report the successful fabrication of the inductors, capacitors and LC hybrid devices using a ferroelectric and a ferromagnetic oxide thin iilm. Au, stable at high temperatures in oxidizing ambient, is patterned by lift-off process, and oxide thin films are deposited by ion beam sputtering and chemical vapor deposition. These devices are characterized by a network analyzer in 0.5-15GtIz range We got the inductance of 5nH, capacitance oi 10, 000 pF and resonant frequencies of $10^{6}-10^{9}Hz$.

• Journal of Science and Technology Studies
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• v.12 no.1
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• pp.145-183
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• 2012

Science reporting is easier to lead to failure than other news reporting because it needs higher professionalism. According to Actor-Network Theory(ANT), not only research results(artifacts) of scientists but also science articles are hybrid networks. Namely, they are connected by human actors(scientist, reporter, etc.) and nonhuman actors(press releases etc.). When the process of science reporting is examined on the view of ANT, it is the process that scientists' results translate the media via press releases as intermediaries and expand their network to the public. This study aims at making an ANT analysis of how research results of Electronics and Telecommunications Research Institute(ETRI) scientist Hyun-Tak Kim were reported by lots of media, focusing on the rhetoric of ETRI's press release. It can reveal the reason for the science reporting's failure and hint at the better science journalism.

• Mass Spectrometry Letters
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• v.15 no.2
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• pp.95-106
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• 2024

Taraxacum coreanum, known as the native Korean white dandelion, has been historically used in traditional medicine due to its various therapeutic properties. However, the specific benefits and mechanisms of white dandelion in alleviating particular symptoms or diseases remain uncertain due to the complexity of its phytochemical profile. In this study, we aimed to elucidate the phytochemical profiles of methanolic extracts of different parts of the white dandelion (flower, leaf, stem, and root) using hybrid ion-mobility Q-TOF MS. Using the trapped ion mobility-based PASEF technique, 3715 and 2114 molecular features with MS2 fragments were obtained in positive and negative ion modes, respectively, and then a total of 360 and 156 phytochemical compounds were annotated by matching with a reference spectral library in positive and negative ion modes, respectively. Subsequent feature-based molecular networking analysis revealed the phytochemical differences across the four different parts of the white dandelion. Our findings indicated that the methanolic extracts contained various bioactive compounds, including lipids, flavonoids, phenolic acids, and sesquiterpenes. In particular, lipids such as linoleic acids, lysophosphatidylcholines, and sesquiterpenoids were predominantly present in the leaf, while flavonoid glycosides and lysophosphoethanolamines were notably enriched in the flower. An assessment of the total phenolic content (TPC) and total flavonoid content (TFC) of the methanolic extracts revealed that the majority of phytochemicals were concentrated in the flower. Interestingly, despite the root extract displaying the lowest TPC and TFC values, it exhibited the highest radical scavenging rate when normalized to TPC and TFC, suggesting a potent antioxidant effect. These findings and further investigations into the biological activities and medicinal potential of the identified compounds, particularly those exclusive to specific plant parts, may contribute to the development of novel therapeutic agents derived from white dandelion.