A note on kernel principal component regression

Kernel principal component regression (KPCR) was studied by Rosipal et al. [18, 19, 20], Hoegaerts et al. [7], and Jade et al. [8]. However, KPCR still encounters theoretical difficulties in the procedure for constructing KPCR and in the choice rule for the retained number of principal components. In this paper, we revise the method of KPCR to overcome the difficulties. The performance of the revised method is compared to linear regression, nonlinear regression based on Gompertz function, and nonparametric Nadaraya-Watson regression, and gives better results than those of the three methods

Robust Prediction in Kernel Principal Component Regression Based on M-Estimation

Robust regression is an important tool for analyzing data that are contaminated with outliers. Fomengko et al. [5] proposed a nonlinear robust predictionbased on the M-estimation; their method, however, needs a specific nonlinearregression model in advance. In this paper, we propose a method to obtain anonlinear robust prediction without specifying a nonlinear model in advance.We combine M-estimation and kernel principal component regression to obtainthe nonlinear prediction. Then, we compare the proposed method with someother methods

Sentiment Analysis of Twitter User’s Opinions on Government’s Performance in dealing with COVID-19 in Indonesia

Saat ini, cukup banyak masyarakat Indonesia yang menggunakan Twitter, sebuah jejaring sosial media yang menyediakan informasi berupa produk, iklan, dan promosi mengenai kritik, saran suatu isu, dan opini publik. Penelitian ini bertujuan untuk menyederhanakan dan meningkatkan pendeteksian suatu opini tanpa menggunakan metode yang memakan waktu, seperti kuesioner. Selain itu, ia membuat kumpulan data berdasarkan tweet pengguna berbahasa Indonesia. Label data dikumpulkan menggunakan metode k-fold cross-validation yang dibagi menjadi 10 bagian. Metode klarifikasi analisis sentimen dilakukan melalui studi banding antara tiga metode, yaitu Naive Bayes (NB), Support Vector Machine (SVM), dan Long Short-Term Memory (LSTM). Ketiga metode memberikan hasil yang sesuai untuk setiap sifat kepribadian tetapi SVM sedikit mengungguli yang lain

Analytical modeling of 3D-printed reinforced concrete beams

Three-dimensional (3D) printing for cementitious materials such as concrete has become increasingly popular. Numerous research efforts have been undertaken to fabricate structural elements, such as beams, using 3D-printed concrete. Because the behavior of 3D-printed reinforced concrete (RC) beams is not well understood, research in this area is still ongoing to investigate the behavior and compare it with conventional RC beams. In this paper, the results of an analytical study using finite element software on 3D-printed RC beams are presented. The main challenge was to determine a constitutive material model of the 3D-printed concrete for nonlinear analysis that was quite different from normal concrete. The developed model was validated using the results from several past experimental tests on 3D-printed RC beams. The results showed that the analytical model can accurately predict the maximum flexural strengths of the 3D-printed RC beams. However, the analytical model overestimated the initial stiffness of the beams. Furthermore, several local failures, such as shear failure of nodal points and bond failure between rebars and concrete, could not be well simulated by the analytical model. Thus, future research is needed to correctly define the constitutive material model for 3D-printed RC beams

Analytical modeling of 3D-printed reinforced concrete beams

Three-dimensional (3D) printing for cementitious materials such as concrete has become increasingly popular. Numerous research efforts have been undertaken to fabricate structural elements, such as beams, using 3D-printed concrete. Because the behavior of 3D-printed reinforced concrete (RC) beams is not well understood, research in this area is still ongoing to investigate the behavior and compare it with conventional RC beams. In this paper, the results of an analytical study using finite element software on 3D-printed RC beams are presented. The main challenge was to determine a constitutive material model of the 3D-printed concrete for nonlinear analysis that was quite different from normal concrete. The developed model was validated using the results from several past experimental tests on 3D-printed RC beams. The results showed that the analytical model can accurately predict the maximum flexural strengths of the 3D-printed RC beams. However, the analytical model overestimated the initial stiffness of the beams. Furthermore, several local failures, such as shear failure of nodal points and bond failure between rebars and concrete, could not be well simulated by the analytical model. Thus, future research is needed to correctly define the constitutive material model for 3D-printed RC beams

Condition diagnosis of bearing system using multiple classifiers of ANNs and adaptive probabilities in genetic algorithms

Condition diagnosis in bearing systems needs an effective and precise method to avoid unacceptable consequences from total system failure. Artificial Neural Networks (ANNs) are one of the most popular methods for classification in condition diagnosis of bearing systems.Regarding to ANNs performance, ANNs parameters have important role especially connectivity weights.In several running of learning processes with the same structure of ANNs, we can obtain different accuracy significantly since initial weights are selected randomly. Therefore, finding the best weights in learning process is an important task for obtaining good performance of ANNs.Previous researchers have proposed some methods to get the best weights such as simple average and majority voting.However, these methods have some limitations in providing the best weights especially in condition diagnosis of bearing systems.In this paper, we propose a hybrid technique of multiple classifier-ANNs (mANNs) and adaptive probabilities in genetic algorithms (APGAs) to obtain the best weights of ANNs in order to increase the classification performance of ANNs in condition diagnosis of bearing systems. The mANNs are used to provide several best initial weights which are optimized by APGAs.The set optimized weights from APGAs, afterward, are used as the best weights for condition diagnosis. Our experiment shows mANNs-APGAs give better results than of the simple average and majority voting in condition diagnosis of bearing systems.This experiment also shows the distinction of maximum and minimum accuracy in mANNs-APGAs is lower than the two existing methods

BIBLIOMETRIC ANALYSIS OF NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES (N-BEATS) FOR RESEARCH TREND MAPPING

Bibliometrics is the statistical analysis of articles, books, and other forms of publication. The bibliometrics analysis is performed with data on the number and authorship of scientific publications and articles, and citations to measure the work of individuals or groups of researchers, organizations, and countries to identify national and international networks and map developments in new multidisciplinary fields of science and technology. In addition, bibliometrics assesses and maps the research, organization, and country of researchers at a given time period. The Bibliometric analysis also has advantages which include mapping relationships between concepts, mapping research directions or trends, mapping state of the art (the novelty of the results of research conducted), and providing insights related to fields, topics, and research problems for future works. This study aims to determine the growth and development of N-BEATS publications, their distribution, variable keywords, and author collaboration using a bibliometric network. The research method used in this paper, through screening of articles obtained from the Scopus database page in 2008-2022, is used for citations in the form of metrics. At the same time, they are visualizing the metadata with VOSviewer. Data was collected from the direct science database with the keyword N-BEATS. The results show that 2022 has the highest number of publications, reaching 310 publications (14.90%). The distribution of research publications on N-BEATS shows a perfect distribution. Terms in the N-BEATS variable that often appear and are associated with other variables

Knowledge Discovery Database (KDD)-Data Mining Application in Transportation

In this paper, an understanding and a review of data mining (DM) development and its applications in logistics and specifically transportation are highlighted. Even though data mining has been successful in becoming a major component of various business processes and applications, the benefits and real-world expectations are very important to consider. It is also surprising to note that very little is known to date about the usefulness of applying data mining in transport related research. From the literature, the frameworks for carrying out knowledge discovery and data mining have been revised over the years to meet the business expectations. In this paper, we apply CRISP-DM for formulating effective tire maintenance strategy within the context of a Malaysian’s logistics company. The results of applying CRISP-DM for tire maintenance decisions are presented and discussed

Using Calcium Oxide and Accelerator to Control the Initial Setting Time of Mortar in 3D Concrete Printing

In recent years, 3D printing has attracted a lot of attention in the construction industry. Compared with general concrete construction, 3D concrete printing has higher flexibility in creating concrete’s shape and design. 3D concrete printing requires the precise control of fresh concrete properties such as flowability, extrudability, and resistance to segregation during printing process. The initial setting time of the concrete also needs to be controlled as it needs to adhere to the next layer and then harden rapidly in order to support the upper layer. This study proposes a method to control the initial setting time of the concrete for the 3D printing process by using a mixture of calcium oxide powder and accelerators. The study showed that using 5–10% calcium oxide and 2–4% accelerator by mass of cement, the initial setting of the concrete can be varied. It is also shown that adding only accelerator prolongs the setting time of the mixture due to the plasticizer contained therein. By using calcium oxide power, the initial setting time of the concrete can be hastened and the combination of calcium oxide powder and accelerator can reduce the initial setting time while maintaining good workability of the mixture. The addition of accelerator also increases the early compressive strength of the concrete mixture

A rectification strategy in genetic algorithms for academic timetabling problem

The university course timetabling problem is both an NP-hard and NP-complete scheduling problem. The nature of the problem concerns with the assignment of lecturers-courses to available teaching space in an academic institution and may take on the form of high school timetabling, examination timetabling or university course timetabling. In this paper, the authors attempt to construct a feasible timetable for a faculty department in a local university in Malaysia which at the present moment; the scheduling task is performed manually by an academic registrar. The feasible timetable is constructed by means of Genetic Algorithm, embedded with a rectification strategy which transforms infeasible timetables into feasible timetables