Improving the Structure of a Signal Used for Real-Time Calibrating of the Receiving Channels of Digital Transceiver Modules in Digital Phased Antenna Arrays

Introduction. Modern digital phased array antenna (DPAA) systems incorporate a large number of identical transceiver modules (TMs). These modules require real-time calibration with a high level of accuracy. In a previous work, we proposed a real-time calibration method for all receiver channels, which is based on the use of a calibration signal (CalSig) of the same frequency spectrum as the reflected signal and modulated in phase and amplitude by BPSK and OOK codes, respectively. This method was found to have a number of advantages over conventional approaches. However, the use of the same CalSig sample for all receiving channels increases the noise power gain at the output of a digital beam-forming unit (DBU). To overcome this limitation, we set out to improve the structure of CalSigs by making them pseudo-orthogonal. As a result, the noise power gain at the DBU output can be significantly reduced compared to that obtained in our previous work.Aim. To propose an improved design of a controlled amplitude modulation code OOK generator, which allows creation of pseudo-orthogonal CalSigs. As a result, the noise power gain at the output will increase insignificantly, thus having no negative effect on the quality of digital beam forming, signal processing and calibration.Materials and methods. Theory of system engineering and technology; theory of digital signal processing; system analysis; mathematical modeling.Results. An improved CalSig for calibrating the receiving channels of TMs was obtained. A structural diagram allowing the formation of pseudo-orthogonal CalSigs was synthesized.Conclusions. We proposed a new approach to improving the structure of signals used for real-time calibrating the DPAA receiving channels. A structural diagram of an amplitude-modulated OOK code generator for pseudo-orthogonal CalSigs was developed.Introduction. Modern digital phased array antenna (DPAA) systems incorporate a large number of identical transceiver modules (TMs). These modules require real-time calibration with a high level of accuracy. In a previous work, we proposed a real-time calibration method for all receiver channels, which is based on the use of a calibration signal (CalSig) of the same frequency spectrum as the reflected signal and modulated in phase and amplitude by BPSK and OOK codes, respectively. This method was found to have a number of advantages over conventional approaches. However, the use of the same CalSig sample for all receiving channels increases the noise power gain at the output of a digital beam-forming unit (DBU). To overcome this limitation, we set out to improve the structure of CalSigs by making them pseudo-orthogonal. As a result, the noise power gain at the DBU output can be significantly reduced compared to that obtained in our previous work.Aim. To propose an improved design of a controlled amplitude modulation code OOK generator, which allows creation of pseudo-orthogonal CalSigs. As a result, the noise power gain at the output will increase insignificantly, thus having no negative effect on the quality of digital beam forming, signal processing and calibration.Materials and methods. Theory of system engineering and technology; theory of digital signal processing; system analysis; mathematical modeling.Results. An improved CalSig for calibrating the receiving channels of TMs was obtained. A structural diagram allowing the formation of pseudo-orthogonal CalSigs was synthesized.Conclusions. We proposed a new approach to improving the structure of signals used for real-time calibrating the DPAA receiving channels. A structural diagram of an amplitude-modulated OOK code generator for pseudo-orthogonal CalSigs was developed

Support Vector Machine for Regression of Ultimate Strength of Trusses: A Comparative Study

Thanks to the rapid development of computer science, direct analyses have been increasingly used in the design of structures in lieu of member-based design methods using the effective length factor. In a direct analysis, the ultimate strength of a whole structure can be sufficiently estimated, so that the need for member capacity checks is eliminated. However, in complicated structural design problems where many structural analyses are required, the use of direct analyses requires an excessive computation cost. In such cases, Machine Learning (ML) algorithms are used to build metamodels that can predict the structural responses without performing costly structural analysis. In this paper, the support vector machine (SVM) algorithm is employed for the first time to develop a metamodel for predicting the ultimate strength of trusses using direct analysis. Several kernel functions for the SVM model, including linear, sigmoid, polynomial, radial basis function (RBF), are considered. A planar 39-bar nonlinear inelastic steel truss is taken to study the performance of the kernel functions. The results confirm the applicability of the SVM-based metamodel for predicting the ultimate strength of trusses. In particular, the RBF appears to be the best kernel among others. This investigation also provides a deeper understanding of the effect of the parameters on the efficiency of the kernel functions

A Model for Detecting Accounting Frauds by using Machine Learning

This paper aims to develop a machine learning model that enables to predict signs of financial statement frauds by combining the domain knowledge of machine learning and accounting. Inputs of this model is a published dataset of financial statements, and outputs involve the conclusions whether the predicted financial statements indicate the signs of financial statement frauds or not. Currently, XGBoost is recognized as one of the most popular classification methods with fast performance, flexibility, and scalability. However, its default properties are not suitable for fraudulent detecting of imbalanced datasets. To overcome this drawback, this research introduces a new machine learning model based on XGBoost technique, called f(raud)-XGBoost. The proposed model not only inherits XGBoost advantages but also enables it to detect financial statement frauds. We apply the Area Under the Receiver Operating Characteristics Curve and NDCG@k to perform the evaluation process. The experimental results show that the new model performs slightly better than three existing models including logistic regression model that is based on financial ratios, Support-vector-machine model, and RUSBoost mode

Exploring the role of destination image to ecotourism intention

The destination image is a concept created by different supply and demand agents. The balance between what is expected and what is being offered is essential in promoting the destination. Tourism is a social phenomenon based on a positive destination image. The target image is the mental expression held by the individual of the venue, and the delay depends on the information received or the actual visit by the individual. However, tourism research has yet to confirm whether an integrated destination image models applicable in predicting the overall destination image and behavioural of travellers. The purpose of this study is to delineate those criteria by analysing the correlation between destination personality, destination image, and intention to visit while considering the influence of constructive attitudes and emotional values

An open database of productivity in Vietnam's social sciences and humanities for public use

This study presents a description of an open database on scientific output of Vietnamese researchers in social sciences and humanities, one that corrects for the shortcomings in current research publication databases such as data duplication, slow update, and a substantial cost of doing science. Here, using scientists’ self-reports, open online sources and cross-checking with Scopus database, we introduce a manual system and its semi-automated version of the database on the profiles of 657 Vietnamese researchers in social sciences and humanities who have published in Scopus-indexed journals from 2008 to 2018. The final system also records 973 foreign co-authors, 1,289 papers, and 789 affiliations. The data collection method, highly applicable for other sources, could be replicated in other developing countries while its content be used in cross-section, multivariate, and network data analyses. The open database is expected to help Vietnam revamp its research capacity and meet the public demand for greater transparency in science management

Dataset of Vietnamese students’ intention in respect of study abroad before and during COVID-19 pandemic

The Covid-19 Pandemic had completely disrupted the worldwide educational system. Many schools chose the online delivery mode for students in case learning losses incurred during social distance decree. However, as to these students who are currently in the study abroad planning stages, reached an intention crossroads, whether standing for certain unchanging decisions in study abroad destinations or changing swiftly due to the unexpected policies in quarantine. This case opened to interpretation, which was based on our e-survey since 3 May to 13 May 2020 with 397 responses covering a range of Vietnamese students. In this dataset, we focused on (i) Students’ Demographics; (ii) The previous intention of students to study abroad before and during the Covid-19 ravaged and (iii) Their intention afterwards

Determination of Fluoroquinolone antibiotics in sludge matrix using pressurized liquid extraction technique combined with high performance liquid chromatography/fluorescence detection

Joint Research on Environmental Science and Technology for the Eart

Impact of Accounting Information on Financial Statements to the Stock Price of the Energy Enterprises Listed on Vietnam's Stock Market

This paper studies the impact of accounting information on financial statements to the stock price of energy enterprises listed on Vietnam's stock market. By using the OLS regression model and quantile regression model (QR), the author studies the influence of factors such as return on assets (Roa), capital structure (LV), enterprise size (Size), current ratio (CR), and accounts receivable turnover (Turnover) to stock prices. Data from this study were collected from 44 energy enterprises during 2006-2016. The results show that return on assets (Roa), enterprise size (Size), current ratio (CR), and accounts receivable turnover (Turnover) are positively correlated with the stock price, with an explanation level of 48.47%. Capital structure (LV) does not affect stock prices. Based on the research results, the authors propose some recommendations for investors and enterprises and suggest other research directions as well as adding new factors to the stock price. Keywords: accounting information; financial statement; stock price; energy enterprises JEL Classifications: E44, G2, Q4