A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Mantle sources of Cenozoic volcanoes around the South China Sea revealed by geochemical and isotopic data using the principal component analysis

Principal component analysis(PCA)was employed to determine the implications of geochemical and isotopic data from Cenozoic volcanic activities in the Southeast Asian region,including China(South China Sea(SCS),Hainan Island,Fujian-Zhejiang coast,Taiwan Island),and parts of Vietnam and Thailand.We analyzed 15 trace element indicators and 5 isotopic indicators for 623 volcanic rock samples collected from the study region.Two principal components(PCs)were extracted by PCA based on the trace elements and Sr-Nd-Pb isotopic ratios,which probably indicate an enriched oceanic island basalt-type mantle plume and a depleted mid-ocean ridge basalt-type spreading ridge.The results show that the influence of the Hainan mantle plume on younger volcanic activities(<13 Ma)is stronger than that on older ones(>13 Ma)at the same location in the Southeast Asian region.PCA was employed to verify the mantle-plume-ridge interaction model of volcanic activities beneath the expansion center of SCS and refute the hypothesis that the tension of SCS is triggered by the Hainan plume.This study reveals the efficiency and applicability of PCA in discussing mantle sources of volcanic activities;thus,PCA is a suitable research method for analyzing geochemical data.

Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

Analysis of the Employment Situation of Non Private Enterprises in Various Regions of China

In the past 30 years, Chinese enterprises have been a hot topic of discussion and concern among the general public in terms of economic and social status, ownership structure, business mechanism, and management level. Solving the problem of employment for the people is an important prerequisite for their peaceful living and work, as well as a prerequisite and foundation for building a harmonious society. The employment situation of private enterprises has always been of great concern to the outside world, and these two major jobs have always occupied an important position in the employment field of China that cannot be ignored. With the establishment of the market economy system, individual and private enterprises have become important components of the socialist economy, making significant contributions to economic development and social progress. The rapid development of China’s economy, on the one hand, is the embodiment of the superiority of China’s socialist market economic system, and on the other hand, it is the role of the tertiary industry and private enterprises in promoting the national economy. Since the 1990s, China’s private enterprises have become a new economic growth point for local and even national countries, and are one of the important ways to arrange employment and achieve social stability. This paper studies the employment of private enterprises and individuals from the perspective of statistics, extracts relevant data from China statistical Yearbook, uses the relevant knowledge of statistics to process the data, obtains the conclusion and puts forward relevant constructive suggestions.

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.

A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

Wireless Sensor Network-based Detection of Poisonous Gases Using Principal Component Analysis

This work utilizes a statistical approach of Principal Component Ana-lysis(PCA)towards the detection of Methane(CH_(4))-Carbon Monoxide(CO)Poi-soning occurring in coal mines,forestfires,drainage systems etc.where the CH_(4) and CO emissions are very high in closed buildings or confined spaces during oxi-dation processes.Both methane and carbon monoxide are highly toxic,colorless and odorless gases.Both of the gases have their own toxic levels to be detected.But during their combined presence,the toxicity of the either one goes unidentified may be due to their low levels which may lead to an explosion.By using PCA,the correlation of CO and CH_(4) data is carried out and by identifying the areas of high correlation(along the principal component axis)the explosion suppression action can be triggered earlier thus avoiding adverse effects of massive explosions.Wire-less Sensor Network is deployed and simulations are carried with heterogeneous sensors(Carbon Monoxide and Methane sensors)in NS-2 Mannasim framework.The rise in the value of CO even when CH_(4) is below the toxic level may become hazardous to the people around.Thus our proposed methodology will detect the combined presence of both the gases(CH_(4) and CO)and provide an early warning in order to avoid any human losses or toxic effects.

Application of Principal Component Analysis as Properties and Sensory Assessment Tool for Legume Milk Chocolates

Principal component analysis (PCA) was employed to examine the effect of nutritional and bioactive compounds of legume milk chocolate as well as the sensory to document the extend of variations and their significance with plant sources. PCA identified eight significant principle components, that reduce the size of the variables into one principal component in physiochemical analysis interpreting 73.5% of the total variability with/and 78.6% of total variability explained in sensory evaluation. Score plot indicates that Double Bean milk chocolate in-corporated with MOL and CML in nutritional profile have high positive correlations. In nutritional evaluation, carbohydrates and fat content shows negative/minimal correlations whereas no negative correlations were found in sensory evaluation which implies every sensorial variable had high correlation with each other.

Assessment of the Water Quality in the Ariake Sea Using Principal Component Analysis

The aim of this study is to investigate the condition of the Ariake Sea, Japan, which has been suffering from severe environmental issues for the past few decades. Water quality data have been generated from several points in this area for over 30 years by the Fukuoka, Saga, Kumamoto, and Nagasaki prefectures. In order to understand the characteristics of this sea, principal component analysis (PCA) was utilized using 11 water quality parameters;transparency, temperature, salinity, dissolved oxygen (DO), chemical oxygen demand (COD), dissolved inorganic nitrogen (DIN), ammonium-nitrogen (NH4<sup style='margin-left:-7px;'>+-N), nitrate-nitrogen (NO3<sup style='margin-left:-7px;'>--N), nitrite-nitrogen (NO2<sup style='margin-left:-7px;'>--N), phosphate-phosphorus, (PO4<sup style='margin-left:-7px;'>3--P) and silica. PCA conveyed the amount of nutrients originating from the river, the organic pollution level, and seasonal changes. Subsequently, principal component scores were calculated for each point. It was concluded that the Ariake Sea environment has been affected by two main factors, which are the nutrients from the Chikugo River and anticlockwise tidal residual flow. These two factors must be considered for the environmental restoration of the Ariake Sea.

Extracting the core indicators of pulverized coal for blast furnace injection based on principal component analysis

An updated approach to refining the core indicators of pulverized coal used for blast furnace injection based on principal component analysis is proposed in view of the disadvantages of the existing performance indicator system of pulverized coal used in blast furnaces. This presented method takes into account all the performance indicators of pulverized coal injection, including calorific value, igniting point, combustibility, reactivity, flowability, grindability, etc. Four core indicators of pulverized coal injection are selected and studied by using principal component analysis, namely, comprehensive combustibility, comprehensive reactivity, comprehensive flowability, and comprehensive grindability. The newly established core index system is not only beneficial to narrowing down current evaluation indices but also effective to avoid previous overlapping problems among indicators by mutually independent index design. Furthermore, a comprehensive property indicator is introduced on the basis of the four core indicators, and the injection properties of pulverized coal can be overall evaluated.

Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

Laser-induced breakdown spectroscopy（LIBS） is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis（PCA） and support vector machine（SVM） is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy（91.38% for the real rock） is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.

Prioritization of Semi-Arid Agricultural Watershed Using Morphometric and Principal Component Analysis, Remote Sensing, and GIS Techniques, the Zerqa River Watershed, Northern Jordan

Remote sensing and GIS techniques were employed for prioritization of the Zerqa River watershed. Forty-three 4th order sub-watersheds were prioritized based on morphometric and Principal Component Analysis (PCA), in order to examine the effectiveness of morphometric parameters in watershed prioritization. A comparison has been carried out between the results achieved through applying the two methods of analysis (morphometric and PCA). Afterwards, suitable measures are proposed for soil and water conservation. Topo sheets and ASTER DEM have been employed to demarcate the 43 sub-watersheds, to extract the drainage networks, and to compile the required thematic maps such as slope categories and elevation. LANDSAT 8 image (April-2015) is employed to generate land use/cover maps using ENVI (v 5.1) software. The soil map of the watershed has been digitized using Arc GIS software. Prioritization of the 43 sub-watersheds was performed using ten linear and shape parameters, and three parameters which are highly correlated with components 1 and 2. Subsequently, different sub-watersheds were prioritized by ascribing ranks based on the calculated compound parameters (Cp) using the two approaches. Comparison of the results revealed that prioritization of watersheds based on morphometric analysis is more consistent and serves for better decision making in conservation planning as compared with the PCA approach. The recommended soil conservation measures are prescribed in accordance with the specified priority, in order to avoid undesirable effects on land and environment. Sub-watersheds classified under high priority class are subjected to high erosion risk, thus, creating an urgent need for applying soil and water conservation measures. It is expected that decision makers will pay sufficient attention to the present results/information, activate programs encouraging soil conservation, integrated watershed management, and will continue working on the afforestation of the government-owned sloping lands. Such a viable approach can be applied at different parts of the rainfed highland areas to minimize soil erosion loss, and to increase infiltration and soil moisture in the soil profile, thus, reducing the impact of recurrent droughts and the possibility of flooding hazards.

A novel method for chemistry tabulation of strained premixed/stratified flames based on principal component analysis

The principal component analysis （PCA） is used to analyze the high dimen- sional chemistry data of laminar premixed/stratified flames under strain effects. The first few principal components （PCs） with larger contribution ratios axe chosen as the tabu- lated scalars to build the look-up chemistry table. Prior tests show that strained premixed flame structure can be well reconstructed. To highlight the physical meanings of the tabu- lated scalars in stratified flames, a modified PCA method is developed, where the mixture fraction is used to replace one of the PCs with the highest correlation coefficient. The other two tabulated scalars are then modified with the Schmidt orthogonalization. The modified tabulated scalars not only have clear physical meanings, but also contain passive scalars. The PCA method has good commonality, and can be extended for building the thermo-chemistry table including strain rate effects when different fuels are used.

Ecological Safety Evaluation of Land Use in Ji'an City Based on the Principal Component Analysis

According to the ecological safety evaluation index data of land-use change in Ji'an City from 1999 to 2008,positive treatment on selected reverse indices is conducted by Reciprocal Method.Meanwhile,Index Method is used to standardize the selected indices,and Principal Component Analysis is applied by using year as a unit.FB is obtained,which is related with the ecological safety of land-use change from 1999 to 2008.According to the scientific,integrative,hierarchical,practical and dynamic principles,ecological safety evaluation index system of land-use change in Ji'an City is established.Principal Component Analysis and evaluation model are used to calculate four parameters,including the natural resources safety index of land use,the socio-economic safety indicators of land use,the eco-environmental safety index of land use,and the ecological safety degree of land use in Ji'an City.Result indicates that the ecological safety degree of land use in Ji'an City shows a slow upward trend as a whole.At the same time,ecological safety degree of land-use change is relatively low in Ji'an City with the safety value of 0.645,which is at a weak safety zone and needs further monitoring and maintenance.

Application of Principal Component Analysis(PCA)to the Evaluation and Screening of Multiactivity Fungi

Continued innovation in screening methodologies remains important for the discovery of high-quality multiactive fungi,which have been of great significance to the development of new drugs.Mangrove-derived fungi,which are well recognized as prolific sources of natural products,are worth sustained attention and further study.In this study,118 fungi,which mainly included Aspergillus spp.(34.62%)and Penicillium spp.(15.38%),were isolated from the mangrove ecosystem of the Maowei Sea,and 83.1%of the cultured fungi showed at least one bioactivity in four antibacterial and three antioxidant assays.To accurately evaluate the fungal bioactivities,the fungi with multiple bioactivities were successfully evaluated and screened by principal component analysis(PCA),and this analysis provided a dataset for comparing and selecting multibioactive fungi.Among the 118 mangrove-derived fungi tested in this study,Aspergillus spp.showed the best comprehensive activity.Fungi such as A.clavatonanicus,A.flavipes and A.citrinoterreus,which exhibited high comprehensive bioactivity as determined by the PCA,have great potential in the exploitation of natural products and the development of new drugs.This study demonstrated the first use of PCA as a time-saving,scientific method with a strong ability to evaluate and screen multiactive fungi,which indicated that this method can affect the discovery and development of new drugs.

Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal Component Analysis (PCA) and Long Short Term Memory (LSTM)

The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right time.In order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this paper.The various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)method.The preprocessed data extracted by PCA is transmitted to the LSTM model for recognition.Compared with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.

A Study of the Comprehensive Urban Competitiveness Based On Principal Component Analysis

This paper studies the comprehensive urban competitiveness and performs the principal component analysis. The results show that the comprehensive evaluation of urban competitiveness is not entirely dependent on the city's economic strength or GDP,and it is necessary to consider from resource allocation capacity,openness and public service capacity. By selecting various data concerning 11 prefecture-level cities in Jiangxi Province in 2006,2009 and 2012,this paper gets the ranking results and analyzes trends,to provide a basis for making future economic policy.