On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis

In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and deter- mining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components~ we introctuce-anoveiconcept of-system-cleviation, which is ab^e＇io＇evalu[ ate the reconstructed observations with different independent components. The monitored statistics arc transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-hatch penicillin fermentation simulator, and the ex- _perimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.

Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

On-line Batch Process Monitoring and Diagnosing Based on Fisher Discriminant Analysis

A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis （FDA） was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the resuits were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.

Stylistic Analysis on English On-line Chat

Based on the fact that on-line chat has become the most developing language form in the information age, this article point out the stylistic features of on-line English chat. Though in written language form, such language is spoken language in nature, thus it is worthwhile to analyze this special phaenomenon in lexical and grammatical level.

Improved Responses with Multitaper Spectral Analysis for Magnetotelluric Time Series Data Processing:Examples from Field Data

In order to attain good quality transfer function estimates from magnetotelluric field data(i.e.,smooth behavior and small uncertainties across all frequencies),we compare time series data processing with and without a multitaper approach for spectral estimation.There are several common ways to increase the reliability of the Fourier spectral estimation from experimental(noisy)data;for example to subdivide the experimental time series into segments,taper these segments(using single taper),perform the Fourier transform of the individual segments,and average the resulting spectra.

Study Progress Analysis of Effluent Quality Prediction in Activated Sludge Process Based on CiteSpace

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

Cookie Baking Process Optimization and Quality Analysis Based on Food 3D Printing

In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.

Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.

ON USING NON-LINEAR CANONICAL CORRELATION ANALYSIS FOR VOICE CONVERSION BASED ON GAUSSIAN MIXTURE MODEL

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.

Mechanics Analysis of Overhead Transmission Lines Based On-line Monitoring

At present, the on-line monitoring is widely applied to the power line monitoring. In this paper, a new mechanical calculation model is established according to the on-line monitoring. And this model is based on the parameters that tension sensors and angle sensors on suspended points detect, and combines with the parameters of the wire itself, and also considers the deflection angel of wires due to wind. In this model, mechanics parameters of wires are turned into the new coordinate plane after deflection angel of wires due to wind, or windage yaw plane. A statics tension balance equation is built in the vertical direction of the new windage yaw plane. According to the theoretical analysis and algorithm, we verify the accuracy of this newly developed mechanical calculation model.

Analysis of Genetic Diversity of Processing Apple Varieties

Genetic diversity of 18 processing apple varieties and two fresh varieties were evaluated using 12 simple sequence repeats （SSR） primer pairs previously identified in Malus domestica Borkh. A total of 87 alleles in 10 loci were detected using 10 polymorphic SSR markers selected within the range of 5-14 alleles per locus. All the 20 varieties could be distinguished using two primer pairs and they were divided into four groups using cluster analysis. The genetic similarity （GS） of groups analyzed using cluster analysis varied from 0.14 to 0.83. High acid variety Avrolles separated from other varieties with GS less than 0.42. The second group contained Longfeng and Dolgo from Northeast of China, the inherited genes of Chinese crab apple. The five cider varieties with high tannin contents, namely, Dabinette, Frequin rouge, Kermerrien, M.Menard, and D.Coetligne were clustered into the third group. The fourth group was mainly composed of 12 juice and fresh varieties. Principal coordinate analysis （PCO） also divided all the varieties into four groups. Juice and fresh apple varieties, Longfeng and Dolgo were clustered together, respectively, using both the analyses. Both the analyses showed there was much difference between cider and juice varieties, cider and fresh varieties, as well as Chinese crab apple and western European crab apple, whereas juice varieties and fresh varieties had a similar genetic background. The genetic diversity and differentiation could be sufficiently reflected by combining the two analytical methods.

Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis(LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis(PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.

The Analysis of Chapter One of Pride and Prejudice——Via Material Processes and Relational Processes

This paper, based on the material processes and relational processes, aims to analysis the deep meaning of chapter one of Pride and Prejudice. The relevant theories will come first in this paper. I will then analyze this extract from three aspects: the analysis of the objective plane of narration, the analysis of Mrs. Bennet' s discourse and the analysis of Mr. Bennet' s discourse.

Multi-parameters uncertainty analysis of logistic support process based on GERT

The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic support systems with multiple decision-making factors. The multiple transfer parameters graphical evaluation and review technique（MTP-GERT） is used to model the logistic support process in consideration of two important factors, support activity time and support activity resources, which are two primary causes for the logistic support process uncertainty. On this basis,a global sensitivity analysis（GSA） method based on covariance is designed to analyze the logistic support process uncertainty. The aircraft support process is selected as a case application which illustrates the validity of the proposed method to analyze the support process uncertainty, and some feasible recommendations are proposed for aircraft support decision making on carrier.

Machining Process Classification Based on Carbon Footprint Analysis

Despite spending considerable effort on the development of manufacturing technology during the production process,manufacturing companies experience resources waste and worse ecological influences. To overcome the inconsistencies between energy-saving and environmental conservation,a uniform way of reporting the information and classification was presented. Based on the establishment of carbon footprint( CFP) for machine tools operation,carbon footprint per kilogram( CFK) was proposed as the normalized index to evaluate the machining process.Furthermore,a classification approach was developed as a tracking and analyzing system for the machining process. In addition,a case study was also used to illustrate the validity of the methodology. The results show that the approach is reasonable and feasible for machining process evaluation,which provides a reliable reference to the optimization measures for low carbon manufacturing.

Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

Analysis of a Cold Wave Weather Process in Chengdu in March 2010

[Objective] The aim was to analyze one cold wave weather process in Chengdu in March in 2010.[Method] Based on the NCEP 1°×1° 6 h interval reanalysis data and daily observation data,using synoptic analysis and diagnosis methods,and combining with the cold wave forecast index in spring of Sichuan,a cold wave event covering the whole region between March 21 and 24,2010 was analyzed from the aspects of circulation background,influencing weather systems and weather causation.[Result] Results showed that the 500 high-altitude cold vortex,700-850 hPa low layer shear,and ground cold front were the main systems that influenced this cold wave;there was a ridge from Lake Balkhash across Lake Baikal at 500 hPa.The early stage of the process was controlled by the high pressure ridge and the temperature was increasing obviously.The daily mean temperature was high.The range of cold high pressure was large and the central intensity was 1 043.0 hPa;the cold air was strong and deep which was in accordance with the strong surface temperature reduction center.The strong north airstream of Lake Balkhash to Lake Baikal,ground cold high pressure center intensity changes,north and south ocean pressure and temperature differences,850 hPa temperature changes,cold advection movement route and intensity were considered as reference factors for the forecast of cold wave intensity.[Conclusion] The study provided theoretical basis for improving the forecast ability of cold wave weather.

Advanced Data Collection and Analysis in Data‑Driven Manufacturing Process

The rapidly increasing demand and complexity of manufacturing process potentiates the usage of manufacturing data with the highest priority to achieve precise analyze and control,rather than using simplified physical models and human expertise.In the era of data-driven manufacturing,the explosion of data amount revolutionized how data is collected and analyzed.This paper overviews the advance of technologies developed for in-process manufacturing data collection and analysis.It can be concluded that groundbreaking sensoring technology to facilitate direct measurement is one important leading trend for advanced data collection,due to the complexity and uncertainty during indirect measurement.On the other hand,physical model-based data analysis contains inevitable simplifications and sometimes ill-posed solutions due to the limited capacity of describing complex manufacturing process.Machine learning,especially deep learning approach has great potential for making better decisions to automate the process when fed with abundant data,while trending data-driven manufacturing approaches succeeded by using limited data to achieve similar or even better decisions.And these trends can demonstrated be by analyzing some typical applications of manufacturing process.

Damage smear method for rock failure process analysis

Damage smear method(DSM)is adopted to study trans-scale progressive rock failure process,based on statistical meso-damage model and finite element solver.The statistical approach is utilized to reflect the mesoscopic rock heterogeneity.The constitutive law of representative volume element(RVE)is established according to continuum damage mechanics in which double-damage criterion is considered.The damage evolution and accumulation of RVEs are used to reveal the macroscopic rock failure characteristics.Each single RVE will be represented by one unique element.The initiation,propagation and coalescence of meso-to macro-cracks are captured by smearing failed elements.The above ideas are formulated into the framework of the DSM and programed into self-developed rock failure process analysis(RFPA)software.Two laboratory-scale examples are conducted and the well-known engineering-scale tests,i.e.Atomic Energy of Canada Limited’s(AECL’s)Underground Research Laboratory(URL)tests,are used for verification.It shows that the simulation results match with other experimental results and field observations.

Wavelet analysis and its application to signal processing

The construction of basic wavelet was discussed and many basic analyzing wavelets was compared. Acomplex analyzing wavelet which is continuous, smoothing, orthogonal and exponential decreasing was presented, andit was used to decompose two blasting seismic signals with the continuous wavelet transforms (CWT). The resultshows that wavelet analysis is the better method to help us determine the essential factors which create damage effectsthan Fourier analysis.