Principal Component Analysis Method Research Articles

Sistem Presensi Pendeteksi Wajah menggunakan Metode Modified Region Convolutional Neural Network dan PCA

Attendance is an activity to collect data to find out the number of attendances at an event or activity. In order to increase effectiveness and time efficiency is important, it is necessary to have an artificial intelligence-based presence by means of face detection. This study aims to create a face detection attendance system using modified region convolutional neural network (MR-CNN) and principal component analysis (PCA) methods. This type of research is development research by applying the concept of digital image based on face recognition. This research applies a method in deep learning, namely MR-CNN through camera media to take images and the extraction process using the PCA method to reduce image resolution. Images that include various individuals who want to be recognized are stored and then used as datasets. From the dataset, the MR-CNN model was trained with training data. Our findings are in the form of a web-based facial short-attendance system. Where are the calculation results in this system has an accuracy value of 96.1%, so it can be used well for facial identification, and can be used at SMK Bhakti Anindya.

A multi-stage sub-structural damage localization approach using multi-label radial basis function neural network and auto-regressive model parameters

This paper proposes a Structural Health Monitoring (SHM) approach to detect localized damage by employing a Multi-Label Radial Basis Function Neural Network (ML-RBFNN). The proposed research methodology aims to identify structural damage more efficiently. This involves dividing the main structure into smaller sub-structures known as zones instead of employing a global search for damage across the entire structure. Each zone is further divided into smaller groups, including sub-zones, nodes, or elements. To reduce the complexities associated with feature extraction, auto-regressive (AR) model parameters extracted from raw time series are considered damage-sensitive features. Considering the simultaneous effects of multiple sensors can increase the number of inputs to the networks, making it challenging to reduce the complexity of the dataset. To address this problem, the principal component analysis (PCA) method is utilized to decrease the dimension of feature space. The effectiveness of the proposed approach is evaluated through a comprehensive comparative analysis against conventional single-stage damage detection methods, with a focus on computational costs and accuracy. Furthermore, the noise-sensitivity characteristics of the proposed method are explored, providing insights into their robustness under various conditions. The validation of the approach is carried out through numerical modeling using the ASCE benchmark structure and experimental data collected from the Qatar University Grandstand Simulator. The application of ML-RBFNN with AR parameters as features yielded a remarkable accuracy exceeding 98% in diagnosing damage occurrence and location for both multi-stage and single-stage SHM methods. Moreover, our proposed approach demonstrates enhanced computational efficiency compared to the single-stage method.

Identification of Typhoon-Vulnerable Areas and Countermeasures in High-Density Coastal Cities: The Case of Macau

Typhoons are extremely severe weather events which seriously threaten the safety of people’s lives and properties. Therefore, identifying and controlling typhoon disaster hazards have become important research topics. The spatial–temporal characteristics of typhoons are analysed using the typhoon disaster data in Macau from 2000 to 2020. Computational fluid dynamics (CFD) numerical simulation is adopted to understand the 3D urban wind environment. Moreover, the ‘exposure, sensitivity and adaptation’ evaluation model is applied to construct the study framework. To calculate urban disaster vulnerability, the Create Fishnet tool is used to divide the city of Macau into 470 grids. The principal component analysis method is used to reveal the factors that significantly affect the typhoon’s vulnerable areas. Result shows that 31.27% of grids are severely vulnerable. In addition, six principal components are identified, including indicators such as population density, building area ratio, mean elevation and wind speed. This study verifies the feasibility of wind speed data obtained by CFD in the typhoon evaluation model. Moreover, it provides a reliable reference guide for future urban microlevel studies.

Low-velocity impact analysis and multi-objective optimization of hybrid carbon/basalt fibre reinforced composite laminate

Three-dimensional in-plane progressive damage model was developed for carbon/basalt fibre interlayer hybrid composites in this study, and cohesive element model was employed to simulate interlaminar damage. Numerical simulation of low-velocity progressive impact damage of hybrid composite laminate was carried out, and its accuracy was verified by physical impact test, with the maximum error of the maximum contact force, maximum displacement and energy absorption not exceeding 6.1% and the maximum error in damage area size within 4.8%. The effects of lay-up structure and angle on the impact properties of hybrid laminate were investigated through simulation. Finally, real number coding strategy was proposed for the parametric representation of the lay-up material and angle of each layer. Multi-objective optimization of hybrid laminate based on Kriging surrogate model and hybrid algorithm composed of elitist non-dominated sorting genetic (NSGA2) algorithm and multi-objective particle swarm (MPSO) algorithm was conducted. The optimal trade-off solution was selected from the Pareto solutions using technique for ordering preferences by similarity to ideal solution (TOPSIS) coupled with principal component analysis (PCA) methods. The coefficient of variance between peak force and maximum displacement of the optimized laminate was 49.3%, 44.9% and 31.4% lower than those of the pure CFRP laminate, pure BFRP laminate, and hybrid laminate before optimization, respectively, indicating that the balance degree of impact displacement and force has been substantially improved. The progressive impact damage simulation and parametric optimization design methodology proposed in this study provide useful guidance for the design of hybrid composite laminate.

Evaluation and Selection of Manufacturing Suppliers under the Perspective of Smart Manufacturing Combined with Bilevel Programming

To address the issue of supplier evaluation and selection in the current intelligent manufacturing industry, a supplier intelligent evaluation and selection model combining bilevel programming is proposed. Firstly, based on existing research results and the current situation of the manufacturing market, a manufacturing supplier evaluation index system is constructed, and the principal component analysis method is used to simplify it; Then, the simplified indicator system is input into the back propagation neural network to build a supplier intelligent evaluation model and achieve supplier intelligent evaluation; A mathematical model for supplier selection problem is constructed based on bilevel programming, and an optimized sparrow search algorithm is put forward to solve it. A supplier intelligent selection model is constructed to achieve intelligent supplier selection. The lab findings denote that the F1 value of the supplier intelligent evaluation model is 0.953, the accuracy is 0.985, the area under the curve is 0.962, and the error of the output comprehensive evaluation value is 0.003; The output accuracy of the supplier intelligent selection model is 0.8. The above results indicate that the model raised in the article has good application effects in supplier evaluation and selection, and has a certain promoting effect on the development of the intelligent manufacturing industry.

Research on the Teaching Model of English Teaching Quality Evaluation under the Background of MOOC

In the context of informatization, online teaching is increasingly favored by people for its convenience and resource richness. Through the Open CourseWar (MOOC) platform, people can easily access learning resources and achieve efficient line length learning. However, currently MOOC online learning cannot provide targeted evaluation for the learning effectiveness of English learners, and there is relatively little research on online English quality assessment. Propose an intelligent MOOC online English teaching evaluation technology. Firstly, analyze the existing English teaching factors and construct a quality evaluation system for online English teaching using Principal Component Analysis (PCA) and expert evaluation method. Considering that the quality of English teaching is influenced by many factors, the evaluation of English teaching quality belongs to a complex nonlinear solving problem. Therefore, the advanced GA-RBF (Genetic Algorithm Radial Basis Function Neural Network, GA-RBF) model is adopted to solve the English teaching quality evaluation model. In teaching quality evaluation, BP and RBF are selected to participate in comparative testing of teaching quality evaluation. In the training loss test of multiple models, the GA-RBF model has the best convergence speed and training performance in the oral sample test, tends to converge after 270 iterations, with a loss value of 0.24. In the evaluation of English proficiency indicators, the BP model has a significant error in testing, with an error of 13 in the evaluation of reading ability scores. The error of RBF in reading ability score evaluation is 4, and the GA-RBF model performs the best. The reading ability evaluation error is 1, and the overall evaluation performance is the best. Through the above research, an intelligent method for evaluating the quality of English teaching is proposed, which will provide important technical references for MOOC online education evaluation and English teaching improvement.

The crosstalk between metabolic reprogramming and epithelial-mesenchymal transition and their synergistic roles in distant metastasis in breast cancer.

Metabolic reprogramming (MR) and epithelial-mesenchymal transition (EMT) are crucial phenomena involved in the distant metastasis of breast cancer (BRCA). This study aims to assess the risk of distant metastasis in BRCA patients based on MR and EMT processes and investigate their underlying mechanisms. Gene sets related to EMT and MR were downloaded. MR-related genes (MRG) and EMT-related genes (ERG) were obtained. Principal Component Analysis method was used to define the EMT Potential Index (EPI) and MR Potential Index (MPI) to quantify the EMT and MR levels in each tumor tissue. A linear scoring model, the Metastasis Score, was derived using the union of MRGs and ERGs to evaluate the risk of distant metastasis/recurrence in BRCA. The Metastasis Score was then validated in multiple datasets. Additionally, our study explored the underlying mechanism of the Metastasis Score and its association with tumor immunity, focusing on HPRT1 gene expression in breast cancer tissues of transfer and untransferred groups using experimental methods. A total of 59 MRGs and 30 ERGs were identified in the present study. Stratifying the dataset based on EPI and MPI revealed significantly lower survival rates (P < .05) in the MPI_high and EPI_high groups. Kaplan-Meier analysis indicated the lowest survival rate in the EPI-high + MPI-high group. The Metastasis Score demonstrated its ability to distinguish prognoses in GSE2034, GSE17705, and TCGA-BRCA datasets. Additionally, differences in mutated genes were found between the high- and the low-Metastasis Score groups, displaying significant associations with immune cell infiltration and anti-tumor immune status. Notably, the 13 genes included in the Metastasis Score showed a strong association with prognosis and tumor immunity. Immunohistochemistry and western blot results revealed high expression of the HPRT1 gene in the transfer group. This study established the Metastasis Score as a reliable tool for evaluating the risk of distant metastasis/recurrence in BRCA patients. Additionally, we identified key genes involved in MR and EMT crosstalk, offering valuable insights into their roles in tumor immunity and other relevant aspects.

Influence of the Properties of Different Types of Recycled Aggregate on the Service Properties and Leaching of Paving Blocks Manufactured at Industrial Scale.

The literature shows that a circular economy can benefit some sectors such as the construction industry. This sector demands huge amounts of raw materials and produces waste when buildings and structures are demolished. This paper explores the possibility of manufacturing at industrial scale paving blocks using different types of construction and demolition wastes as aggregates, without modifying the commonly used industrial conditions. A total of four different recycled aggregates were used in this research. Both natural and recycled aggregates have been characterized. The dosages were optimized (three different formulations). Prefabricated tests have been carried out on the products manufactured in industrial plants and the evolution of mechanical properties over time has been analysed. The results obtained were analysed statistically by applying the principal component analysis (PCA) method. To ensure the security of the elements manufactured, the ionic leaching of the materials used as recycled aggregate and of the elements produced has been tested. The main implications of this research on the construction industry show that the majority of recycled aggregates used could replace 25% of the natural aggregate in manufactured precast concrete, that the properties of the aggregates should be taken into account in the different standards and that all paving blocks manufactured in this study can be considered environmentally safe (no risk of leaching) according to the Netherland Soil Quality Decree. Therefore, it is evident that it is possible to manufacture on an industrial scale paving blocks with mixed recycled aggregates, concrete and ceramic in nature, both with the fine and coarse fractions that meet the requirements of its reference standard UNE-EN 1338 and the Netherland Soil Quality Decree that evaluates environmental risks due to leaching.

Fossil fuel prices and economic policy uncertainty– A regime-switching approach

The study investigates the impact of fossil fuel prices on the regime-switching dynamics of economic policy uncertainty for the global economy. The period of investigation spans 25 years; comprising monthly data for the period of 1998:01 to 2023:03 and, due to its propensity to accommodate shocks, swings and shifts in the data, the technique of analysis employed is the Markov Switching Dynamic Regression. The principal component analysis (PCA) method was used in obtaining a composite index for the fossil fuel prices. The results obtained show evidence of regime-switching behaviour with five (5) times persistence of low to high global economic policy uncertainty. In addition, the study finds significant counter-cyclical and pro-cyclical effects of fossil fuel prices on global economic policy uncertainty; especially under the regime of high uncertainty. These results are consistent with the results for the composite index of fossil fuel prices but with alternate persistence effects. These suggest that policymakers should be concerned in stabilizing fluctuating fossil fuel prices in order to contain its spiralling and uncertain effects on the global economic policy. More so, governments should devise series of low carbon-emission means for home and industrial uses to ultimately reduce the excessive demand for fossil fuel so as to crash its prices in the international market.

SLRfinder: A method to detect candidate sex-linked regions with linkage disequilibrium clustering.

Despite their critical roles in genetic sex determination, sex chromosomes remain unknown in many non-model organisms, especially those having recently evolved sex-linked regions (SLRs). These evolutionarily young and labile sex chromosomes are important for understanding early sex chromosome evolution but are difficult to identify due to the lack of Y/W degeneration and SLRs limited to small genomic regions. Here, we present SLRfinder, a method to identify candidate SLRs using linkage disequilibrium (LD) clustering, heterozygosity and genetic divergence. SLRfinder does not rely on specific sequencing methods or a specific type of reference genome (e.g., from the homomorphic sex). In addition, the input of SLRfinder does not require phenotypic sexes, which may be unknown from population sampling, but sex information can be incorporated and is necessary to validate candidate SLRs. We tested SLRfinder using various published datasets and compared it to the local principal component analysis (PCA) method and the depth-based method Sex Assignment Through Coverage (SATC). As expected, the local PCA method could not be used to identify unknown SLRs. SATC works better on conserved sex chromosomes, whereas SLRfinder outperforms SATC in analysing labile sex chromosomes, especially when SLRs harbour inversions. Power analyses showed that SLRfinder worked better when sampling more populations that share the same SLR. If analysing one population, a relatively larger sample size (around 50) is needed for sufficient statistical power to detect significant SLR candidates, although true SLRs are likely always top-ranked. SLRfinder provides a novel and complementary approach for identifying SLRs and uncovering additional sex chromosome diversity in nature.

Measurable parameter combinations of environmentally-dephased EMRI gravitational-wave signals

The future space-borne Laser Interferometer Space Antenna (LISA) is expected to detect gravitational waves (GW) from Extreme Mass Ratio Inspiral (EMRI) binaries which may live in nontrivial environments such as accretion disks. In this work, we apply the Fisher matrix Principal Component Analysis (PCA) method to assess how well LISA observations can jointly constrain the source parameters and environmental densities around EMRIs. Specifically, we calculate the Fisher matrix from the post-Newtonian parameters of an EMRI binary embedded in a fluid with a constant density profile. We determine that the most dominant measurable parameter combination is dominated by contributions from environmental effects, namely, gravitational drag, accretion, and gravitational pull (in order of contribution). The proposed reparameterization of the PN parameters can be used to improve the power and efficiency of future detection and parameter estimation methods.

Economic, social and institutional determinants of FDI inflows: A comparative analysis of developed and developing economies

This paper investigates the determinants of Foreign Direct Investment (FDI) inflows by classifying determinants into economic, social, and institutional categories, and examines its linkage between FDI in developed and developing economies at an aggregate level. Countries are distinguished in accordance with country income level, and each category is constructed as a composite index by utilizing the Principal Component Analysis (PCA) method. Using annual data from 1996 to 2019 and panel data approaches, the determinants of FDI are investigated for 178 countries. Main findings are, first, developing economies are largely dependent on economic indicators to attract FDI. Second, in the case of developed economies, the role of social indicators on FDI inflows is shown relatively more significant than economic indicators. Finally, the linkage between institutional indicators and FDI inflows is weak and statistically insignificant in both developed and developing economies.

Dimensionality reduction analysis of the renewable energy sector in Azerbaijan: nonparametric analyses of large datasets

Although the number of econometric analyses related to the renewable energy sector in Azerbaijan is increasing, studies on nonparametric dimensionality reduction are rather sparse. Principal component analysis (PCA) and multiple correspondence analysis (MCA) were chosen to fill this apparent research gap. As a result, a large dataset including the renewable energy sector and selected key macroeconomic indicators was evaluated. The PCA procedure yielded four distinct principle components reflecting the main macroeconomic variables, renewable energy production, industry-energy relations and natural resource revenues. The PCA method offers the possibility to examine the precise correlations and the underlying patterns between the displayed clusters of variables. Meanwhile, the MCA-based cross-country assessment of Azerbaijan’s wind, solar and hydropower has struck somewhat pessimistic notes, as the country lags behind neighbouring and other post-Soviet countries (e.g. Estonia, Iran, Latvia, Russia) in developing its green energy sector. These findings are of great interest to policymakers, businesses and academics who wish to gain deep insight into the Azerbaijani economy in terms of renewable energy production. The practical value of the present work also lies in the fact that it analyses a multidimensional and relatively longitudinal dataset (1990–2022), which is an example of a methodological application of two nonparametric approaches.

Research on the construction of multi objective coupling model and optimization method of ship form

AbstractMulti‐objective optimization of ship form can effectively reduce ship energy consumption, and is one of the important research topics of green ships. However, the computational cost of numerical simulation based on computational fluid dynamics (CFD) theory is relatively high, which affects the efficiency of optimization. Traditional subjective weighting methods mostly rely on expert's experience, which affects the scientificity of optimization. This paper effectively integrates the CFD method, the improved multi‐objective optimization algorithm and the objective weighting method to build a ship form multi‐objective optimization framework. Conduct multi‐objective optimization research on resistance and seakeeping performance of a very large crude oil carrier (KVLCC) ship. The improved bare‐bones multi‐objective particle swarm optimization (IBBMOPSO) algorithm is used to obtain the pareto front, and the kernel principal component analysis (KPCA) method is used to objectively assign the weight of each target. Finally, the optimal ship form scheme with high satisfaction was obtained. The multi‐objective optimization framework constructed in this paper can provide a certain theoretical basis and technical support for the development of ship greening and digital transformation.

Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China

Accurate forecasting of monthly runoff is essential for efficient management, allocation, and utilization of water resources. To improve the prediction accuracy of monthly runoff, the long and short memory neural networks (LSTM) coupled with variational mode decomposition (VMD) and principal component analysis (PCA), namely VMD-PCA-LSTM, was developed and applied at the Waizhou station in the Ganjiang River Basin. The process begins with identifying the main forecasting factors from 130 atmospheric circulation indexes using the PCA method and extracting the stationary components from the original monthly runoff series using the VMD method. Then, the correlation coefficient method is used to determine the lag of the above factors. Lastly, the monthly runoff is simulated by combining the stationary components and key forecasting factors via the LSTM model. Results show that the VMD-PCA-LSTM model effectively addresses the issue of low prediction accuracy at high flows caused by a limited number of samples. Compared to the single LSTM and VMD-LSTM models, this comprehensive approach significantly enhances the model’s predictive accuracy, particularly during the flood season.

Continuous wavelet transform and integration of discrete wavelet transform with principal component analysis and fuzzy inference system for the simultaneous determination of ethinyl estradiol and drospirenone in combined oral contraceptives

In this study, the spectrophotometric method integrated with continuous wavelet transform (CWT) and coupled discrete wavelet transform (DWT) with fuzzy inference system (FIS) was developed for the simultaneous determination of ethinyl estradiol (EE) and drospirenone (DP) in combined oral contraceptives (COCs). The CWT approach was performed in the linearity range of 0.6–6 µg/mL for EE and 0.9 to 18 µg/mL for DP. Biorthogonal with an order of 1.3 (bior1.3) at a wavelength of 216 nm and Daubechies with an order of 2 (db2) at a wavelength of 278 nm were selected as the best wavelet families for obtaining the best zero crossing point for EE and DP, respectively. The limit of detection (LOD) of 0.7677 and 0.3222 µg/mL and the limit of quantification (LOQ) of 2.326 and 0.9765 µg/mL were obtained for EE and DP, respectively. The mean recovery of 103.24% and 99.77%, as well as root mean square error (RMSE) of 0.1896 and 0.1969, were found for EE and DP, respectively. In the DWT, the absorption of the mixtures was decomposed using different wavelets named db4, db2, Symlet2 (sym2), and bior1.3. Each of the wavelet outputs was dimension reduced by the principal component analysis (PCA) method and considered as FIS input. The wavelet of db4 with the coefficient of determination (R2) of 0.9979, RMSE of 0.0968, and mean recovery of 100.63% was chosen as the best one for the EE, while bior1.3 with R2 of 0.9955, RMSE of 0.4055, and mean recovery of 101.93% was selected for DP. These methods were successfully used to analyze the EE and DP simultaneously in tablet pharmaceutical formulation without any separation step. The suggested methods were compared with a reference method (HPLC) using analysis of variance (ANOVA) at a 95% confidence level, and no significant difference was observed in terms of accuracy. The suggested chemometric methods are reliable, rapid, and inexpensive, and can be used as an environmentally friendly alternative to HPLC for the simultaneous estimation of the mentioned drugs in commercial pharmaceutical products.

A data-driven robust optimization method based on scenario clustering for PVC production scheduling under uncertainty

In this paper, a data-driven robust optimization method based on scenario clustering is proposed for addressing energy consumption uncertainty characterized by correlation and multi-peaked distribution within the PVC production process. Firstly, principal component analysis (PCA) and kernel density estimation (KDE) methods are used to capture the correlation and distribution information effectively across multidimensional uncertain parameters; then a modified K-means clustering method based on density peaks is applied to cluster energy consumption scenarios and the flexible uncertainty subsets is established. A two-stage robust optimization model for the vinyl chloride production section is then proposed, and the column and constraint generation algorithm is applied to solved. Finally, the effectiveness of the proposed method is validated through a PVC production case study. Comparative results demonstrate that the proposed model reduces energy consumption under uncertainty and improves the robustness of PVC production scheduling.

Analysis of the Effect of Infrastructure System at Kendari New Port Terminal on Container Customer Satisfaction Using Principal Component Analysis (PCA) Method

Analysis of the Effect of Infrastructure System at Kendari New Port Terminal on Container Customer Satisfaction Using Principal Component Analysis (PCA) Method

Key Determinants of Corporate Governance in Financial Institutions: Evidence from South Africa

The purpose of this study was to examine the key determinants of corporate governance in selected financial institutions. Using South African financial institutions as a unit of analysis, namely insurance companies and banks, the study employed a panel generalised method of moments (GMM) model using a data set for the period from 2007 to 2020, to assess key determinants of corporate governance proxies identified for the study. The study sampled 21 South African financial institutions composed of Johannesburg Securities Exchange (JSE) listed and unlisted banks and insurance companies. To measure corporate governance, the study developed a composite index employing the principal components analysis (PCA) method. The findings revealed a positive and significant association between the corporate governance index and its lagged variables. Furthermore, a significant and positive link was found between the efficiency ratio and corporate governance index and capital adequacy ratio (CAR); corporate governance index and firm size; corporate governance index and leverage ratio (LEV); and corporate governance index and return on assets (ROA). However, a negative and significant correlation was found between financial stability and the corporate governance index. The link between return on equity (ROE) and corporate governance was insignificant. A small cohort of financial institutions was excluded because it was challenging to obtain complete annual reports to extract the required data. The study was limited to only five corporate governance measures, namely board diversity, board size, board composition (independent non-executive directors and non-executive directors), and board remuneration. The findings are anticipated to persuade developing countries to pay special attention to how corporate governance is measured.

Analysis of Influencing Factors of Residents' Happiness in Smart Cities based on Principal Component Analysis

This paper analyzes the factors affecting the happiness of smart city residents, uses principal component analysis method and random forest method, and uses SPSS, SPSSPRO and other software to analyze and process the data. First, it establishes the evaluation index of the happiness of smart city residents, and then evaluates the measures that can improve the happiness of residents in the construction of smart city. Finally, reasonable development suggestions for the construction of smart cities are put forward through forecasting and processing. Research background and theoretical literature. Find out the relevant theoretical basis, and finally determine the reasonable research method. The index of resident happiness is designed from the perspective of smart economic happiness, smart political happiness, smart public service happiness, smart security management happiness, and smart ecological environment happiness, and is used as the index factor of principal component analysis. The model of influencing urban residents' happiness under smart city. According to the resident data, the principal component analysis method is constructed, and then the random forest model is used to test, and finally the weight of the influencing factors of residents' happiness under the smart city is obtained.