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

• Structural Engineering and Mechanics
• /
• v.19 no.3
• /
• pp.347-359
• /
• 2005

This paper attempts to develop the analytical model of estimating the fatigue damage using a linear elastic fracture mechanics method. The stress history on a welding member, when a truck passed over a bridge, was defined as a block loading and the crack closure theory was used. These theories explain the influence of a load on a structure. This study undertook an analysis of the stress range frequency considering both dead load stress and crack opening stress. A probability method applied to stress range frequency distribution and the probability distribution parameters of it was obtained by Maximum likelihood Method and Determinant. Monte Carlo Simulation which generates a probability variants (stress range) output failure block loadings. The probability distribution of failure block loadings was acquired by Maximum likelihood Method and Determinant. This can calculate the fatigue reliability preventing the fatigue failure of a welding member. The failure block loading divided by the average daily truck traffic is a predictive remaining life by a day. Fatigue reliability analysis was carried out for the welding member of the bottom flange of a cross beam and the vertical stiffener of a steel box bridge by the proposed model. Results showed that the primary factor effecting failure time was crack opening stress. It was important to decide the crack opening stress for using the proposed model. Also according to the 50% reliability and 90%, 99.9% failure times were indicated.

• Ocean Systems Engineering
• /
• v.10 no.4
• /
• pp.373-397
• /
• 2020

Heave compensation is a vital part of various marine and offshore operations. It is used in various applications, including the transfer of cargo between two vessels in the open ocean, installation of topsides of an offshore structure, offshore drilling and for surveillance, reconnaissance and monitoring. These applications typically involve a load suspended from a hydraulically powered winch that is connected to a vessel that is undergoing dynamic motion in the ocean environment. The goal in these applications is to design a winch controller to keep the load at a regulated height by rejecting the net heave motion of the winch arising from ship motions at sea. In this study, we analyze and compare the performance of various control algorithms in stabilizing a suspended load while the vessel is subjected to changing sea conditions. The KCS container ship is chosen as the vessel undergoing dynamic motion in the ocean. The negative of the net heave motion at the winch is provided as a reference signal to track. Various control strategies like Proportional-Derivative (PD) Control, Model Predictive Control (MPC), Linear Quadratic Integral Control (LQI), and Sliding Mode Control (SMC) are implemented and tuned for effective heave compensation. The performance of the controllers is compared with respect to heave compensation, disturbance rejection and noise attenuation.

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.301-307
• /
• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.1
• /
• pp.85-90
• /
• 2023

Data analysis methodology in the healthcare field is shifting from traditional statistics-oriented research methods to predictive research using machine learning. In this study, we survey various machine learning tools, and compare several programming models, which utilize R and Spark, for applying R, a statistical tool widely used in the health care field, to machine learning. In addition, we compare the performance of linear regression model using scala, which is the basic languages of Spark and R. As a result of the experiment, the learning execution time when using SparkR increased by 10 to 20% compared to Scala. Considering the presented performance degradation, SparkR's distributed processing was confirmed as useful in R as the traditional statistical analysis tool that could be used as it is.

• Steel and Composite Structures
• /
• v.48 no.5
• /
• pp.547-564
• /
• 2023

This study investigates the effect of bolt preloading on the rotational stiffness of stainless steel end-plate connections. An experimental programme incorporating 11 full-scale joint specimens are carried out comparing the behaviours of fully pre-tensioned (PT) and snug-tightened (ST) flush/extended end-plate connections, made of austenitic or lean duplex stainless steels. It is observed from the tests that the presence of bolt preloading leads to a significant increase in the rotational stiffness. A parallel finite element analysis (FEA) validated against the test results demonstrates that the geometric imperfection of end-plate has a strong influence on the moment-rotation response of preloaded end-plate connections, which is crucial to explain the observed "two-stage" behaviour of these connections. Based on the data obtained from the tests and FE parametric study, the performance of the Eurocode 3 predictive model is evaluated, which exhibits a significant deviation in predicting the rotational stiffness of stainless steel end-plate connections. A modified bi-linear model, which incorporates three key properties, is therefore proposed to enable a better prediction. Finally, the effect of bolt preloading is demonstrated at the system (structure) level considering the serviceability of semi-continuous stainless steel beams with end-plate connections.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.11a
• /
• pp.20-23
• /
• 2020

This research is to design an effective prefetching method required for DRAM-PCM hybrid main memory systems especially used for big data applications and massive-scale computing environment. Conventional prefetchers perform well with regular memory access patterns. However, workloads such as graph processing show extremely irregular memory access characteristics and thus could not be prefetched accurately. Therefore, this research proposes an efficient dynamical prefetching algorithm based on the regression method. We have designed an intelligent prefetch engine that can identify the characteristics of the memory access sequences. It can perform regular, linear regression or polynomial regression predictive analysis based on the memory access sequences' characteristics, and dynamically determine the number of pages required for prefetching. Besides, we also present a DRAM-PCM hybrid memory structure, which can reduce the energy cost and solve the conventional DRAM memory system's thermal problem. Experiment result shows that the performance has increased by 40%, compared with the conventional DRAM memory structure.

• Journal of Forest and Environmental Science
• /
• v.39 no.4
• /
• pp.195-202
• /
• 2023

In this study, we sought to compare and evaluate the accuracy and predictive performance of machine learning algorithms for estimating the growth of individual Larix kaempferi trees in Gangwon Province, Korea. We employed linear regression, random forest, XGBoost, and LightGBM algorithms to predict tree growth using monitoring data organized based on different thinning intensities. Furthermore, we compared and evaluated the goodness-of-fit of these models using metrics such as the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results revealed that XGBoost provided the highest goodness-of-fit, with an R² value of 0.62 across all thinning intensities, while also yielding the lowest values for MAE and RMSE, thereby indicating the best model fit. When predicting the growth volume of individual trees after 3 years using the XGBoost model, the agreement was exceptionally high, reaching approximately 97% for all stand sites in accordance with the different thinning intensities. Notably, in non-thinned plots, the predicted volumes were approximately 2.1 m³ lower than the actual volumes; however, the agreement remained highly accurate at approximately 99.5%. These findings will contribute to the development of growth prediction models for individual trees using machine learning algorithms.

• Geomechanics and Engineering
• /
• v.36 no.4
• /
• pp.381-390
• /
• 2024

The introduction of bio-based materials has been recommended in the geotechnical engineering field to reduce environmental pollutants such as heavy metals and greenhouse gases. However, bio-treated soil methods face limitations in field application due to short research periods and insufficient verification of engineering performance, especially when compared to conventional materials like cement. Therefore, this study aimed to develop a machine learning model for predicting the unconfined compressive strength, a representative soil property, of biopolymer-based soil treatment (BPST). Four machine learning algorithms were compared to determine a suitable model, including linear regression (LR), support vector regression (SVR), random forest (RF), and neural network (NN). Except for LR, the SVR, RF, and NN algorithms exhibited high predictive performance with an R² value of 0.98 or higher. The permutation feature importance technique was used to identify the main factors affecting the strength enhancement of BPST. The results indicated that the unconfined compressive strength of BPST is affected by mean particle size, followed by biopolymer content and water content. With a reliable prediction model, the proposed model can present guidelines prior to laboratory testing and field application, thereby saving a significant amount of time and money.

• Elastomers and Composites
• /
• v.59 no.1
• /
• pp.34-41
• /
• 2024

In this study, the effects of the type and content of silica on the mechanical and tear properties of chloroprene rubber (CR), which is mainly used as a jacket material for mining cables, were studied. The crosslinking density (ΔM) and reinforcing factor (α_f) defined using cure characteristics increased with increasing silica content, whereas the cure rate decreased. The hardness, tensile strength, and modulus of the CR compounds increased depending on the silica content and structural development. The reinforcing behavior of the silica-filled CR compounds according to the silica type and content showed the best fit with the Thomas equation of the predictive model. Tear strength was evaluated using two standard test methods, ASTM D470 and DIN VDE 0472-613, and the results were compared. The tear strength increased as the silica content increased, regardless of the test method, and the different tear strengths obtained by the two standard test methods showed a linear relationship with each other, indicating a high correlation.