• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.51.2-51.2
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• 2010

We have investigated 63 intense geomagnetic storms (Dst $\leq$ -100 nT) that occurred from 1998 to 2006. Using these events, we compared Dst forecast models: Burton et al. (1975), Fenrich and Luhmann (1998), O'Brien and McPherron (2000a), Wang et al. (2003), and Temerin and Li (2002, 2006) models. For comparison, we examined a linear correlation coefficient, RMS error, the difference of Dst minimum value (${\Delta}$peak), and the difference of Dst minimum time (${\Delta}$peak_time) between the observed and the predicted during geomagnetic storm period. As a result, we found that Temerin and Li model is mostly much better than other models. The model produces a linear correlation coefficient of 0.94, a RMS (Root Mean Square) error of 14.89 nT, a MAD (Mean Absolute Deviation) of ${\Delta}$peak of 12.54 nT, and a MAD of ${\Delta}$peak_time of 1.44 hour. Also, we classified storm events as five groups according to their interplanetary origin structures: 17 sMC events (IP shock and MC), 18 SH events (sheath field), 10 SH+MC events (Sheath field and MC), 8 CIR events, and 10 nonMC events (non-MC type ICME). We found that Temerin and Li model is also best for all structures. The RMS error and MAD of ${\Delta}$peak of their model depend on their associated interplanetary structures like; 19.1 nT and 16.7 nT for sMC, 12.5 nT and 7.8 nT for SH, 17.6 nT and 15.8 nT for SH+MC, 11.8 nT and 8.6 nT for CIR, and 11.9 nT and 10.5 nT for nonMC. One interesting thing is that MC-associated storms produce larger errors than the other-associated ones. Especially, the values of RMS error and MAD of ${\Delta}$peak of SH structure of Temerin and Li model are very lower than those of other models.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.441-447
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• 2012

This paper presents a design technique of a Takagi-Sugeno (T-S) fuzzy-model-based $H_{\infty}$ controller for autonomous underwater vehicles (AUVs). The design procedure aims to render the stabilizing controller which satisfies performance of the diving control for AUVs in the presence of the disturbance. A nonlinear AUV is modeled by the T-S fuzzy system through the sector nonlinearity. By using Lyapunov function, the sufficient conditions are derived to guarantee the performance of robust depth control in the format of linear matrix inequality (LMI). To succeed for diving control of AUV, we add the constraints on the diving and pitch angles in the LMI conditions. Through the simulation, we confirm the effectiveness of the proposed methodology.

• The Journal of Fisheries Business Administration
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• v.40 no.3
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• pp.169-187
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• 2009

This study analyzes the long and short term effect of exchange rate on the import of Korea's fisheries focussed on main fisheries imported from China. The estimation models consist of the following contents. The first model consists of one dependent variable-import quantity of fisheries imported from China(${IMQ_t}^{CHO}$) and three independent variables-${RP_t}^{CHO}$, $EXC_t$ and $GDP_t$. The second one-one dependent variable-import quantity of fisheries imported from China(${JMQ_t}^{NAG})$ and three independent variables-${RP_t}^{NAG}$, $EX_t$ and $GDP_t$. the third one-one dependent variable-import quantity of fisheries imported from China(${IMQ_t}^{AH}$) and three independent variables-${RP_t}^{AH}$, $EX_t$ and $GDP_t$. the forth one-one dependent variable-import quantity of fisheries imported from China(${IMQ_t}^{KO}$) and three independent variables-${RP_t}^{KO)$, $EX_t$ and $GDP_t$. the last one is made up of one dependent variable-import quantity of fisheries imported from China(${IMQ_t}^{GAL}$) and three independent variables-, ${RP_t}^{GAL}$, $EX_t$ and $GDP_t$. and. The estimation results show that exchange rate of the independent variables are statistically significant in only the first model. The figure is elastic. Especially, the effect of exchange rate in first model is grater than that of the. However, the effect of exchange rate, one of independent variables in the ECM, is not statistically significant.

• Korean Journal of Food Science and Technology
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• v.36 no.2
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• pp.349-354
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• 2004

Predictive growth model of Vibrio parahaemolyticus in modified surimi-based imitation crab broth was investigated. Growth curves of V. parahaemolyticus were obtained by measuring cell concentration in culture broth under different conditions ($Initial\;cell\;level,\;1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}\;colony\;forming\;unit\;(CFU)/mL$; temperature, 15, 25 37, and $40^{\circ}C$; pH 6, 7, and 8) and applying them to Gompertz model. Microbial growth indicators, maximum specific growth rate (k), lag time (LT), and generation time (GT), were calculated from Gompertz model. Maximum specific growth rate (k) of V. parahaemolyticus increased with increasing temperature, reaching maximum rate at $37^{\circ}C$. LT and GT were also the shortest at $37^{\circ}C$. pH and initial cell number did not influence k, LT, and GT values significantly (p>0.05). Polynomial model, $k=a{\cdot}\exp(-0.5{\cdot}((T-T_{max}/b)^{2}+((pH-pH_{max)/c^{2}))$, and square root model, ${\sqrt{k}\;0.06(T-9.55)[1-\exp(0.07(T-49.98))]$, were developed to express combination effects of temperature and pH under each initial cell number using Gauss-Newton Algorism of Sigma plot 7.0 (SPSS Inc.). Relative coefficients between experimental k and k Predicted by polynomial model were 0.966, 0.979, and 0.965, respectively, at initial cell numbers of $1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}CFU/mL$, while that between experimental k and k Predicted by square root model was 0.977. Results revealed growth of V. parahaemolyticus was mainly affected by temperature, and square root model showing effect of temperature was more credible than polynomial model for prediction of V. parahaemolyticus growth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2925-2948
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• 2019

In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.974-987
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• 2021

This paper presents ductile fracture simulation of full-scale cracked pipe for nuclear piping materials using the cohesive zone model (CZM). The main objective of this study is to investigate the applicability of CZM to predict ductile fracture of cracked pipes with various crack shapes and under quasi-static/dynamic loadings. The transferability of the traction-separation (T-S) curve from a small-scale specimen to a full-scale pipe is demonstrated by simulating small- and full-scale tests. T-S curves are calibrated by comparing experimental data of compact tension specimens with finite element analysis results. The calibrated T-S curves are utilized to predict the fracture behavior of cracked pipes. Three types of full-scale pipe tests are considered: pipe with circumferential through-wall crack under quasistatic/dynamic loadings, and with 360° internal surface crack under quasi-static loading. Computational results using the calibrated T-S curves show a good agreement with experimental data, demonstrating the transferability of the T-S curves from small-scale specimen.

• Journal of Magnetics
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• v.2 no.3
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• pp.101-104
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• 1997

Vector Moving Preisach model has been applied to the unoriented Co-based alloy thin film media. In the model, the out-of plane easy axis distribution of the particles was derived directly from the texture coefficient phkl obtained from XRD analysis, which corresponds to the fraction of the grains that have the {hkl} plane lying parallel to in-plane direction. The model was validated, by its prediction of a variety of responses, including major loop, minor loop, and the angular dependence of coercivities.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.328-342
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• 2022

The Internet of Things (IoT) is a technology that offers lucrative services in various industries to facilitate human communities. Important information on people and their surroundings has been gathered to ensure the availability of these services. This data is vulnerable to cybersecurity since it is sent over the internet and kept in third-party databases. Implementation of data encryption is an integral approach for IoT device designers to protect IoT data. For a variety of reasons, IoT device designers have been unable to discover appropriate encryption to use. The static support provided by research and concerned organizations to assist designers in picking appropriate encryption costs a significant amount of time and effort. IoTES is a web app that uses machine language to address a lack of support from researchers and organizations, as ML has been shown to improve data-driven human decision-making. IoTES still has some weaknesses, which are highlighted in this research. To improve the support, these shortcomings must be addressed. This study proposes the "IoTES with Security" model by adding support for the security level provided by the encryption algorithm to the traditional IoTES model. We evaluated our technique for encryption algorithms with available security levels and compared the accuracy of our model with traditional IoTES. Our model improves IoTES by helping users make security-oriented decisions while choosing the appropriate algorithm for their IoT data.

• ETRI Journal
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• v.45 no.4
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2867-2871
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• 2012

Background: Multiple studies have reported associations between the PSCA rs2294008 C > T polymorphism and GC, but susceptibility has proven inconsistent. Therefore, we estimates the relationship between the rs2294008 C > T polymorphism and GC by meta-analysis. Methods: PubMed, Embase and Web of Science databases were searched and nine independent case-control studies were included in this meta-analysis. Crude ORs with 95% CIs were extracted according to the Mantal-Haenszel method and pooled to assess the strength of the association. Results: We observed that the PSCA rs2294008 C > T polymorphism was significantly correlated with GC risk when all studies were pooled into the meta-analysis. Further subgroup analysis showed the polymorphism to be linked with diffuse and noncardia GC in the allele contrast model, homozygote codominant model, dominant model, and recessive model. However, no connection was apparent for intestinal and cardia GC. In the stratified analysis by ethnicity, significant associations were observed in Asians for the recessive model. Interestingly, the relationship was particularly significant in the Chinese population. Conclusions: Our findings suggest that the PSCA rs2294008 C > T polymorphism is a risk factor for GC, especially in diffuse and noncardia GC and in Chinese.