Amirkabir University of TechnologyAUT Journal of Modeling and Simulation2588-295354220221201Data mining approach for prediction umbilical cord wrapping around the fetus and investigating effective factors131140498010.22060/miscj.2022.21360.5283ENNajmehAbediniZand higher education, Shiraz, IranFatemehMoayyediDepartment of Computer Engineering, Larestan Higher Education Complex, Lar, IranSayed EbrahimDashtiDepartment of Computer Engineering, Jahrom Branch, Islamic Azad University, Jahrom, IranJournal Article20220502Today, in medical knowledge, data collection on various diseases is very important. One of the important issues in the medical world is the baby’s birth and its related issues. The relationship between mother and fetus is by the umbilical cord which is responsible for the development of the fetus. In this article, using data mining methods, the occurrence of umbilical cord torsion around the fetus is predicted, we also investigated some factors that can affect this event. Based on the studying articles on fetus birth and its factors, and consultation with gynecologists, the new and comprehensive questionnaire was designed on factors affecting the wrapping of the umbilical cord around the fetus, including 31 features that were completed by 140 samples of pregnant mothers. Then, the questionnaire was evaluated by Cronbach’s Alpha. Since the obtained dataset was imbalanced it was balanced with SMOTE technique. We compared different classification methods, including SVM, Random Forest, KNN, and Naïve Base for prediction, which KNN had the best result accuracy of 81%. Finally, to extract effective factors some association rule mining methods such as Predictive Apriori, and FP-growth were applied. the results show nutrition, blood pressure, diabetes, fetus number, and Internet usage can have more impact on wrapping the umbilical cord around the fetus.https://miscj.aut.ac.ir/article_4980_4ffbd5c8221d7c147f8363ccdc9a2a37.pdfAmirkabir University of TechnologyAUT Journal of Modeling and Simulation2588-295354220221201Disturbance rejection of non-minimum phase MIMO systems: An iterative tuning approach141160500610.22060/miscj.2022.21598.5291ENSaeedrezaTofighiDepartment of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran0000-0002-7667-068XFarshadMerrikh-BayatDepartment of Electrical and Computer Engineering, University of Zanjan, Zanjan, IranFarhadBayatDepartment of Electrical and Computer Engineering, University of Zanjan, Zanjan, IranJournal Article20220715An iterative tuning method is presented to obtain the multi-input multi-output (MIMO) feedforward controller coefficients to improve disturbance rejection in non-minimum phase (NMP) MIMO systems. In the NMP systems, eliminating the effect of disturbances may cause instability and also can impose extra costs to control the entire system. For this purpose, a simple feedforward controller structure is proposed. The unknown variables of the feedforward controller are calculated using LMIs such that the H<sub>∞</sub> norm of the transfer function matrix from disturbance to output is minimized. By taking advantage of the frequency sampling techniques into account and using some iterative algorithms, a new tractable method is constructed to solve the problem. Also, a condition based on the right half plane (RHP) zero direction for the NMP system has been proposed to improve the disturbance rejection property of these systems. To obtain optimal coefficients, the algorithm is repeated several times to reach the best answer. The method employs convex technics and CVX software to perform calculations. The efficiency of the method is shown in various practical examples using different performance indicators such as integral of absolute error (IAE), integral of squared error (ISE), integral of time multiplied by absolute error (ITAE), integral of time multiplied by squared error (ITSE).https://miscj.aut.ac.ir/article_5006_2c27a260f16ad3098393cc529f391f4a.pdfAmirkabir University of TechnologyAUT Journal of Modeling and Simulation2588-295354220221201Tilt estimation using pressure sensors for unmanned underwater vehicle navigation161172501410.22060/miscj.2022.21580.5290ENMostafaAmueiFaculty of Electrical and Computer Engineering, Malek-Ashtar University of technology, Tehran, Iran0000-0003-1436-2603Seyyed Mohammad MehdiDehghanFaculty of Electrical and Computer Engineering, Malek-Ashtar University of technology, Tehran, Iran0000-0002-3835-0212HossienNourmohammadiNorthern Research Center for Science and Technology, Malek-Ashtar University of technology, Fereydunkenar, Iran0000-0003-4885-4964Mohammad AliAlirezaPouriFaculty of Electrical and Computer Engineering, Malek-Ashtar University of technology, Tehran, Iran0000-0002-9470-4089Journal Article20220710Since the Unmanned Underwater Vehicles (UUVs) don’t receive the Global Navigation Satellite System (GNSS) signals under the water, other aided measurements are needed to provide the required accuracy in tilt estimation including roll and pitch angle estimation. Conventional approaches for pressure-based tilt estimation, only consider the relation between the static pressure and the tilt as the measurement model. However, the performance of this approach depends on the dynamic pressure which is caused by the sea waves. This paper improves the accuracy of pressure-based tilt estimation using the more accurate of the measurement model. Also, the proposed approach considers the coupling between the axes of UUV. Due to the cost of the approach and the hardware limitations of installation pressure sensors, the proposed approach is implemented using two pressure sensors. An Extended Kalman Filter (EKF) is used for simultaneous tilt and gyroscopes measurement errors estimation. A Monte-Carlo simulation is developed to evaluate the performance of the proposed approach in comparison with INS only and the conventional static pressure-based tilt estimation. The simulation results show that tilt estimation performance of conventional approach is better than the INS only performance and the performance of proposed approach is better than the both of them.https://miscj.aut.ac.ir/article_5014_4424d2deec2f9468fb61e2db07ecd6b6.pdfAmirkabir University of TechnologyAUT Journal of Modeling and Simulation2588-295354220221201Modeling of the Earth’s rotation variations using a novel approach inspired by the brain emotional learning173184504710.22060/miscj.2023.21110.5271ENAlirezaHakimiDepartment of AI, Faculty of Computer Engineering, University of Isfahan, Isfahan, IranS. AmirhassanMonadjemiDepartment of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, IranSaeedSetayeshiSchool of Continuing and Lifelong Education, National University of Singapore, SingaporeJournal Article20220216DT is a quantity that converts universal time (UT; defined by the Earth’s rotation) to terrestrial time (TT; independent of Earth’s rotation). The DT values during the time show the Earth’s rotation variations. Solar activities and the gravitational force of major solar system components are known as astronomical-based factors that can provide these variations. Recently, several models have been proposed to interpolate and forecast the DT values. Structurally, all mentioned methods have just used past DT values for modeling.
In this paper, we propose a novel approach for modeling DT based on the brain’s emotional learning with respect to astronomical-origin-based factors effective on the Earth’s rotation as the emotional input signals. This model, which employs memory units in the amygdala and orbitofrontal parts, can be named Memory-Based Brain Emotional Learning (MBBEL). MBBEL was run using the data from 1900 to 2000 and 2000 to 2019 as training and testing stages, respectively. After the modeling process, the mean absolute error (MSE) and maximum absolute error (MaxAE) of the train and test stages were 0.011, 0.051, 0.10, and 0.295, respectively. Comparing the MBBEL results against those of eight prior models revealed that MBBEL results considerably improved compared to those of the previous models.https://miscj.aut.ac.ir/article_5047_1dba3025b159cd9354da65e2d0436a31.pdfAmirkabir University of TechnologyAUT Journal of Modeling and Simulation2588-295354220221201A novel fuzzy Bayesian network-based approach for solving the project time-cost-quality trade-off problem185196504610.22060/miscj.2023.20752.5266ENMohammadhosseinHaghighiAmirkabir University of Technology, Department of Industrial Engineering and Management Systems,
Tehran, Iran.0000-0001-7038-3607MaryamAshrafiAmirkabir University of Technology, Department of Industrial Engineering and Management Systems,
Tehran, Iran.EhsanNazerfardAmirkabir University of Technology, Department of Computer Engineering, Tehran, Iran.Journal Article20211113To successfully complete projects, it is essential to meet all the goals of the criteria that affect the project, such as time, cost, and quality. The time-cost-quality trade-off (TCQT) approach is considered a practical technique when project managers or customers tend to crash the total time of a project and create a balance within these criteria. On the other hand, due to the unique inherent of projects and various risks in the real world, using a certain framework for project management problems does not seem efficient. This paper presents a novel fuzzy Bayesian network-based approach to schedule a project and control real-world uncertainties. This novel approach applies the fuzzy opinions of several experts with regard to their weight. The presented fuzzy Bayesian model can calculate a project’s total cost and duration in various uncertain situations. Consequently, this profound knowledge about the project’s various conditions helps managers be aware of the different probable scenarios. To demonstrate the efficiency and application of the proposed model, a modified project example from the literature review is adopted and solved. A common technique in project management called PERT is applied to verify the proposed approach, and the results are compared. Finally, a comparative analysis with a recent related paper is presented.https://miscj.aut.ac.ir/article_5046_8cea559c47e4fbdb73b23e0223d04e79.pdfAmirkabir University of TechnologyAUT Journal of Modeling and Simulation2588-295354220221201ISPREC++: Learning Edge Type Importance in Network-Oriented Paper Recommendation197210515210.22060/miscj.2023.21212.5275ENElahehJafariFaculty of new sciences and technologies, University of Tehran, Tehran, IranBitaShamsFaculty of Mathematical sciences, Alzahra University, Tehran, IranSamanHaratizadehFaculty of new sciences and technologies, University of Tehran, Tehran, Iran0000-0001-6376-7637Journal Article20220313With the spread of the Internet and the possibility of online access to articles, a wide range of scientific articles are available to researchers, while finding relevant articles among this substantial number of articles turns out to be a real dilemma. To solve this problem, several scientific paper recommendation algorithms have been proposed. Most of these algorithms suffer from some drawbacks that limit their usability. For example, many of these recommendation methods are designed to recommend papers only to users who had published articles before and can’t support new researchers. Also, they usually do not utilize many important features of articles each of which can have a role in determining the relevance of the articles to users. To address these concerns, in this paper, we present the novel method of Integrated Scientific Paper Recommendation with an edge-weight learning approach, called ISPREC++, as an extended version of ISPREC that focuses on learning the weights of edge types in Heterogeneous Information Networks based on users' preferences. ISPREC++ sets the weights of edges in SPIN using a Bayesian Personalized Ranking (BPR) based method and utilizes Gradient Descent to optimize its objective function. Thereafter, it exploits a limited random-walk algorithm for a Top-N recommendation. Extensive experiments on a real-world dataset demonstrate the significant performance superiority of ISPREC++ compared to the state-of-the-art scientific paper recommendation algorithms.https://miscj.aut.ac.ir/article_5152_876e1c59023b1a0e95808168e1a8ff89.pdf