• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.5
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• pp.27-39
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• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.

• Journal of the Korean School Mathematics Society
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• v.25 no.4
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• pp.423-446
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• 2022

This study explores and compares Korean and U.S. elementary preservice teachers' analytic approaches of students' addition and subtraction computational strategies. Twenty-six Korean and twenty U.S. elementary preservice teachers participated in the study. Participants were asked to analyze mathematical approaches and errors from students' addition and subtraction operations. Preservice teachers' written documents were analyzed by applying open coding and inductive coding based on the grounded theory. As a result, the pattern of error analysis and interpretation of students' addition computations were similar for both Korean and U.S. preservice teachers whereas there were some differences in the analysis of students' subtraction computations. Both Korean and U.S. preservice teachers had difficulties identifying students' strategies and errors for a complicated and unconventional computational approach. Results also indicated that preservice teachers' noticing and interpretation of students' strategies and errors were influenced by their K-12 mathematics curriculum and teacher education program. This study suggests implications and future directions for teacher education, more contextualized teacher preparation programs and balanced connection to the K-12 curriculum.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.476-477
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• 2022

This study deals with rotation, which is one of the expression methods of motion used in the 3D development environment. Euler angle is a rotation method introduced by Leonhard Euler to display objects in three-dimensional space. Although three angles can handle all rotations in a three dimensional coordinate space, there are serious errors in this approach. If you rotate an object with Euler angles, you will face the problem of gimbal locks that cannot rotate under certain circumstances. In contrast to this, the method to rotate an object without a gimbal lock is the quaternion rotation with quaternion. Rather than a detailed mathematical proof of quaternion, it introduces what concept is used in the current 3D development environment, and applies it to camera rotation control to implement a rotating camera without a gimbal lock.

• Earthquakes and Structures
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• v.26 no.5
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• pp.349-361
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• 2024

Excessive torsional behaviour is one of the major reasons for failure of buildings, as inferred from past earthquakes. Numerous seismic codes across the world specify a displacement-based or drift-based criterion for classifying buildings as torsionally irregular. In recent years, quite a few researchers have pointed out some of the inherent deficiencies associated with the current codal guidelines on torsional irregularity. This short communication paper aims to envisage the need for a revision of the displacement-based guidelines on torsional irregularity, and further highlight the appropriateness of a rotation-based criterion. A set of 6 reinforced concrete building models with asymmetric shear walls are analysed using ETABS v18.0.2, by varying the number of stories from 1 to 9, and the torsional irregularity coefficient of various stories is calculated using the displacement-based formula. Since rotation about the vertical axis is a direct indication of the twist experienced by a building, the calculated torsional irregularity coefficients of all stories are compared with the corresponding floor rotations. The conflicting results obtained for the torsional irregularity coefficients are projected through five categories, namely mismatch with floor rotations, inconsistency in trend, lack of clarity in incorporation of negative values, sensitivity to low values of displacement and error conceived in the mathematical formulation. The findings indicate that the irregularity coefficient does not accurately represent the torsional behaviour of buildings in a realistic sense. The Indian seismic code-based values of 1.2 and 1.4, which are used to characterize buildings as torsionally irregular are observed to be highly sensitive to the numerical values of displacements, rather than the actual degree of rotation. The study thus emphasizes the revision of current guidelines based on a more relevant rotation-based or eccentricity-based approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1659-1674
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• 2024

Conventional maritime communication struggles to provide high data rate services for Internet of Things (IoT) devices due to the variability of maritime environments, making it challenging to ensure consistent connectivity for onboard sensors and devices. To resolve this, we perform mathematical modeling of the maritime channel and compare it with real measurement data. Through the modeled channel, we verify the received beam gain at buoys on the ocean surface. Additionally, leveraging the modeled wave motions, we estimate future angles of the buoy to use the Extended Kalman Filter (EKF) for design beamforming strategies that adapt to the evolving maritime environment over time. We further validate the effectiveness of these strategies by assessing the results from an outage probability perspective. focuses on improving maritime communication by developing a dynamic model of the maritime channel and implementing a Kalman filter-based buoy motion tracking system. This system is designed to enable precise beamforming, a technique used to direct communication signals more accurately. By improving beamforming, the aim is to enhance the quality of communication links, even in challenging maritime conditions like rough seas and varying sea states. In our simulations that consider realistic wave motions, you've observed significant improvements in link quality due to the enhanced beamforming technique. These improvements are particularly notable in environments with high sea states, where communication challenges are typically more pronounced. The progress made in this area is not just a technical achievement; it has broad implications for the future of maritime communication technologies. This paper promises to revolutionize the way we approach communication in maritime environments, paving the way for more reliable and efficient information exchange on the seas.

• Steel and Composite Structures
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• v.52 no.1
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• pp.31-43
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• 2024

A very widely used analytical method (mathematical model), mentioned in Eurocode 3, to examine the connections' bending behavior is the component-based method that has certain weak points shown in the plastic behavior part of the moment-rotation curves. In the component method available in Eurocode 3, for simplicity, the effect of strain hardening is omitted, and the bending behavior of the connection is modeled with the help of a two-line diagram. To make the component method more efficient and reliable, this research proposed its advanced version, wherein the plastic part of the diagram was developed beyond the guidelines of the mentioned Regulation, implemented to connect the end plate, and verified with the moment-rotation curves found from the laboratory model and the finite element method in ABAQUS. The findings indicated that the advanced component method (the method developed in this research) could predict the plastic part of the moment-rotation curve as well as the conventional component-based method in Eurocode 3. The comparison between the laboratory model and the outputs of the conventional and advanced component methods, as well as the outputs of the finite elements approach using ABAQUS, revealed a different percentage in the ultimate moment for bolt-extended end-plate connections. Specifically, the difference percentages were -31.56%, 2.46%, and 9.84%, respectively. Another aim of this research was to determine the optimal dimensions of the end plate joint to reduce costs without letting the mechanical constraints related to the bending moment and the resulting initial stiffness, are not compromised as well as the safety and integrity of the connection. In this research, the thickness and dimensions of the end plate and the location and diameter of the bolts were the design variables, which were optimized using Particle Swarm Optimization (PSO), Snake Optimization (SO), and Teaching Learning-Based Optimization (TLBO) to minimization the connection cost of the end plate connection. According to the results, the TLBO method yielded better solutions than others, reducing the connection costs from 43.97 to 17.45€ (60.3%), which shows the method's proper efficiency.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.63-76
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• 2024

Weather forecasting has become a very popular topic nowadays among researchers because of its various effects on global lives. It is a technique to predict the future, what is going to happen in the atmosphere by analyzing various available datasets such as rain, snow, cloud cover, temperature, moisture in the air, and wind speed with the help of our gained scientific knowledge i.e., several approaches and set of rules or we can say them as algorithms that are being used to analyze and predict the weather. Weather analysis and prediction are required to prevent nature from natural losses before it happens by using a Deep Learning Approach. This analysis and prediction are the most challenging task because of having multidimensional and nonlinear data. Several Deep Learning Approaches are available: Numerical Weather Prediction (NWP), needs a highly calculative mathematical equation to gain the present condition of the weather. Quantitative precipitation nowcasting (QPN), is also used for weather prediction. In this article, we have implemented and analyzed the various distinct techniques that are being used in data mining for weather prediction.

• Steel and Composite Structures
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• v.50 no.5
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• pp.515-529
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• 2024

Cold-formed steel (CFS) is a popular choice for construction due to its low cost, durability, sustainability, resistance to high environmental and seismic pressures, and ease of installation. The beam-column connections in residential and medium-rise structures are formed using self-drilling screws that connect two CFS channel sections and a gusset plate. In order to increase the moment capacity of these CFS screwed beam-column connections, stiffeners are often placed on the web area of each single channel. However, there is limited literature on studying the effects of stiffeners on the moment capacity of CFS screwed beam-column connections. Hence, this paper proposes a new test approach for determining the moment capacity of CFS screwed beam-column couplings. This study describes an experimental test programme consisting of eight novel experimental tests. The effect of stiffeners, beam thickness, and gusset plate thickness on the structural behaviour of CFS screwed beam-column connections is investigated. Besides, nonlinear elasto-plastic finite element (FE) models were developed and validated against experimental test data. It found that there was reasonable agreement in terms of moment capacity and failure mode prediction. From the experimental and numerical investigation, it found that the increase in gusset plate or beam thickness and the use of stiffeners have no significant effect on the structural behaviour, moment capacity, or rotational capacity of joints exhibiting the same collapse behaviour; however, the capacity or energy absorption capacities have increased in joints whose failure behaviour varies with increasing thickness or using stiffeners. Besides, the thickness change has little impact on the initial stiffness.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.2974-2989
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• 2024

The one-dimensional Two-Fluid concept uses an area-average approach to simplify the time and phase-averaged Two-Fluid conservation equations, making it more suitable for addressing difficulties at an industrial scale. Nevertheless, the mathematical framework has inherent weaknesses due to the loss of details throughout the averaging procedures. This limitation makes the conventional model inappropriate for some flow regimes, where short-wavelength perturbations experience uncontrolled amplification, leading to solutions that need to be physically accurate. The critical factor in resolving this problem is the integration of closure relations. These relationships play a crucial function in reintroducing essential physical characteristics, thus correcting the loss that occurs during averaging and guaranteeing the stability of the model. To improve the accuracy of predictions, it is essential to assess the stability and grid dependence of one-dimensional formulations, which are particularly affected by closure relations and numerical schemes. The current research presented in the text focuses on improving the well-posedness of the TFM, specifically within the TRACE code, which is widely utilized for nuclear reactor safety assessments. Incorporating a bubble collision model in the momentum equations is demonstrated to enhance the TFM's resilience, especially in scenarios with high void fractions where conventional TFMs may face challenges. The analysis presents a linear stability analysis performed for the transient one-dimensional Two-Fluid Model of system code TRACE within the framework of vertically dispersed flows. The main emphasis is on evaluating the stability characteristics of the model while also acknowledging its susceptibility to closure relations and numerical techniques.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.