• School Mathematics
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• v.18 no.3
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• pp.711-731
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• 2016

The purpose of this study is to investigate how pre-service secondary mathematics teachers modify mathematical tasks from a textbook and learning opportunities they have during the task modification. In the pursuit of this purpose, tasks was selected from derivative units in a textbook and five pre-service teachers was asked to modify the tasks. The findings from analysis are as follows. First, the cognitive demands of modified tasks were maintained or higher than those of the originals. Pre-service teachers' tendency toward conceptual understanding of derivative seems to make the result. Second, task modification provided a lot of learning opportunities for pre-service teachers. They tried to know intention of curriculum and textbook, realized the importance of predicting students' responses, and had opportunities for cooperation and reflective thinking.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.51-56
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• 2008

With growing demand for zero defects, predicting reliability of software systems is gaining importance. Software reliability models are used to estimate the reliability or the number of latent defects in a software product. Most reliability models to estimate the reliability of software in the literature are based on the development lifecycle stages. However, in the maintenance phase, the software needs to be corrected for errors and to be enhanced for the requests from users. These decrease the reliability of software. Software Reliability Growth Models (SRGMs) have been applied successfully to model software reliability in development phase. The software reliability in maintenance phase exhibits many types of systematic or irregular behaviors. These may include cyclic behavior as well as long-term evolutionary trends. The cyclic behavior may involve multiple periodicities and may be asymmetric in nature. In this paper, SGRM has been adapted to develop a reliability prediction model for the software in maintenance phase. The model is established using maintenance data from a commercial shop floor control system. The model is accepted to be used for resource planning and assuring the quality of the maintenance work to the user.

• International Journal of Railway
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• v.4 no.2
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• pp.42-49
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• 2011

Today, rolling stock has become a fast and convenient mode of transportation and has witnessed increased demand. But the speed improvement has resulted in increased aerodynamic noise and therefore residential districts near the railroad tracks are exposed to ever increasing noise level. A study on methodologies for measuring and appraising rolling stock's environmental noise has therefore become an important area of endeavor. In the case of the environmental noise, there are no changes in tone so prediction can be made by reducing areas around the railway. The present study explores estimation of the noise around the railway using scale model, and the source of the noise has been investigated as well. The scale model of rolling stock will have to be able to measure high frequency noise and it is required to be generated in a short amount of time. Since popping a balloon or firing a gun fits this requirement the present study analyzed the characteristics of these two different noise sources. Measurement was made in a large vacant lot and the reflection due to the ground was also examined. The method proposed here can be used in the future for predicting the environmental noise of railway vehicles.

• Food Science and Industry
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• v.54 no.3
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• pp.145-159
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• 2021

Conventional nutrition services involve producer-oriented approaches without considering the differences in the characteristics and circumstances of each individual, whereas personalized nutrition services are consumer-oriented concepts that provide products and services for maintaining optimal health conditions based on the genetic, physiological, and metabolic characteristics of individuals, with these products based on balanced nutrition and healthy living. Currently, methods for evaluating dietary habits, monitoring dietary behaviors, deep phenotyping, and metabotyping via microbiota profiling, as well as methods for predicting big data by using machine learning, have been previously studied in Korea and abroad. With the development of medical technology and the improvement of hygiene, the demand for personalized nutrition and health services for healthier, happier, and more satisfying lives is rapidly increasing. Therefore, based on scientific technologies, attempts are needed to advance these services into global personalized markets and to boost the global competitiveness of countries and companies.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.65-74
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• 2021

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

• Structural Engineering and Mechanics
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• v.77 no.6
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• pp.705-720
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• 2021

Reinforced concrete (RC) columns are the primary type of vertical support used in building structures that sustain vertical loads. However, their strength may be insufficient due to fire, earthquake or volatile environments. The load demand may be increased due to new functional usages of the structure. The deformability of concrete columns can be greatly reduced under high axial load conditions. In response, a novel steel encasement that distinguishes from the traditional steel jacketing that is assembled by welding or bolt is developed. This novel strengthening method features easy installation and quick strengthening because direct fastening is used to connect the four steel plates surrounding the column. This new connection method is usually used to quickly and stably connect two steel components by driving high strength fastener into the steel components. The connections together with the steel plates behave like transverse reinforcement, which can provide passive confinement to the concrete. The confined column along with the steel plates resist the axial load. By this way, the axial load capacity and deformability of the column can be enhanced. Eight columns are tested to examine the reliability and effectiveness of the proposed method. The effects of the vertical spacing between adjacent connections, thickness of the steel plate and number of fasteners in each connection are studied to identify the critical parameters which affect the load bearing performance and deformation behavior. Lastly, a theoretical model is proposed for predicting the axial load capacity of the strengthened RC columns.

• Steel and Composite Structures
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• v.43 no.5
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• pp.625-637
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• 2022

Axial compression capacity (P_u) is a significant yet complex parameter of concrete-filled steel tube (CFST) columns. This study offers a novel ensemble tool, adaptive neuro-fuzzy inference system (ANFIS) supervised by equilibrium optimization (EO), for accurately predicting this parameter. Moreover, grey wolf optimization (GWO) and Harris hawk optimizer (HHO) are considered as comparative supervisors. The used data is taken from earlier literature provided by finite element analysis. ANFIS is trained by several population sizes of the EO, GWO, and HHO to detect the best configurations. At a glance, the results showed the competency of such ensembles for learning and reproducing the P_u behavior. In details, respective mean absolute errors along with correlation values of 4.1809% and 0.99564, 10.5947% and 0.98006, and 4.8947% and 0.99462 obtained for the EO-ANFIS, GWO-ANFIS, and HHO-ANFIS, respectively, indicated that the proposed EO-ANFIS can analyze and predict the behavior of CFST columns with the highest accuracy. Considering both time and accuracy, the EO provides the most efficient optimization of ANFIS and can be a nice substitute for experimental approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3923-3942
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• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.7-16
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• 2004

The homogeneous road section is defined as one consisted of similar traffic characteristics focused on demand and supply. The criteria, in the aspect of demand, are the diverging rate and the ratio of green time to cycle time at signalized intersection, and distance between the signalized intersections. The criteria, in that or supply, are the traffic patterns such as traffic volume and its speed. In this study, the effective method to generate valuable data, pointing out the problems of removal method of obscure data, is proposed using data collected from Gonjiam IC to Jangji IC on the national highway No.3. Travel times are collected with licence matching method and traffic volume and speed are collected from detectors. Futhermore, the method of selecting homogeneous road section is proposed considering demand and supply aspect simultaneously. This method using outlier filtering algorithm can be applied to generate the travel time forecasting model and to revise the obscured of missing data transmitting from detectors. The point and link data collected at the same time on the rational highway can be used as a basis predicting the travel time and revising the obscured data in the future.