Innovations in the Data Processing Algorithm for Chinese FY Meteorological Satellites

This study introduces some innovations in the data processing algorithm for Chinese FY meteorological satellites. Issues about satellite image navigation, radiation calibration, and data assimilation are discussed. A time series of the earth＇s disk center-line count provides information on the orientation of the satellite spin axis. With this information, the altitude parameters of the satellite and then the earth disk location in the south-north direction may be solved. In each spin cycle, the satellite views the sun and the earth. Given the satellite position and altitude, the angle （β） subtended at the satellite by the sun and the earth can be calculated and predicted. Thus, the earth＇s disk location in the east-west direction is fixed. Based on this principle, we derived an automatic image navigation algorithm for FY2 geosynchronous meteorological satellites with an accuracy approaching pixel level. The FY2 meteorological satellite traveling in a geostationary orbit suffers a large amount of radiation from the sun. The radiation varies on both diurnal and annual scales, which causes radiation responses in the thermal infrared （IR） bands wherein the wavelengths greater than 3.5 μm vibrate periodically on scales of hours to years. These vibrations must be precisely calibrated. First, based on the accurate estimation of the radiant contribution from the front-optics, the variation characteristics of the calibration parameters are obtained on a temporal scale of hours from the space-borne inner-blackbody （IBB） measurement results. Second, the in-orbit measured radiation of the lunar surface is referenced and utilized to correct the sys- tematic bias of the IBB calibration from daily to annual scales. By using such algorithms, we achieved a calibration accuracy of the FY2 satellite＇s IR imagery of less than 1 K. The on-orbit satellite instrument parameters play an important role in data quality; however, they may be mis-measured due to limitations in the measurement conditions or may be changed due to the space environment after launch. A satellite instrument parameters on-orbit optimizer （SIPOn-Opt） for a polar orbit meteorological satellite was developed to optimize the true state of the instrument parameters on-orbit with regard to the observation constraints. When applying the SIPOn-Opt to FY3 sounding instruments, the FY3 data quality was much improved, compared to its European and the U.S. polar orbit meteorological satellite counterparts, leading to improved forecast skill of numerical weather prediction.

Image processing of weld pool and keyhole in Nd:YAG laser welding of stainless steel based on visual sensing

In order to obtain good welding quality, it is necessary to apply quality control because there are many influencing factors in laser welding process. The key to realize welding quality control is to obtain the quality information. Abundant weld quality information is contained in weld pool and keyhole. Aiming at Nd：YAG laser welding of stainless steel, a coaxial visual sensing system was constructed. The images of weld pool and keyhole were obtained. Based on the gray character of weld pool and keyhole in images, an image processing algorithm was designed. The search start point and search criteria of weld pool and keyhole edge were determined respectively.

Image Processing Technology of Flaws within Infrared Transmitting Glasses

We investigated image processing algorithms of the original infrared glass flaw image. Using the Laplacian edge enhancement following LSD （Line Segment Detector） algorithm, we can get a good flaw image very consistent with the original one. This study is very helpful to further enhance the infrared glass flaw inspection technique.

Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.

Algorithm to identify circulating tumor cell clusters using in vivo flow cytometer

Recent studies in oncology have addressed the importance of detecting circulating tumor cell clusters because circulating tumor cell clusters might survive and metastasize more easily than single circulating tumor cells.Signals with larger peak widths detected by in vivo flow cytometer(IVFC)have been used to identify cell clusters in previous studies.However,the accuracy of this criterion might be greatly degraded by variance in blood°ow and the rolling behaviors of circulating tumor cells.Here,we propose a criterion and algorithm to distinguish cell clusters from single cells.In this work,we first used area-based and volume-based models for single°uorescent cells.Simulating each model,we analyzed the corresponding morphology of IVFC signals from cell clusters.According to the Rayleigh criterion,the valley between two adjacent peak signals from two distinguishable cells should be lower than 73.5%of the peak values.A novel signal processing algorithm for IVFC was developed based on this criterion.The results showed that cell clusters can be reliably identied using our proposed algorithm.Intravital imaging was also performed to further support our algorithm.With enhanced accuracy,IVFC is a powerful tool to study circulating cell clusters.

Image processing of weld pool and keyhole in Nd：YAG laser welding based on edge predicting

Laser welding is one of high efficiency, high energy density welding methods. Quality control should be applied to ensure good welding quality. Weld pool and keyhole contains abundant information of welding quality. Good image processing algorithm is necessary in quality control system based on visual sensing. Aiming at the image captured by a coaxial visual sensing system for laser welding, an image processing algorithm is designed. An edge predicting method is proposed in image processing algorithm which is based on the fact that the local shape of weld pool can be fitted to a circle. The results show that the algorithm works well. It lays solid foundation for further quality control in laser welding.

Automated Angle Detection for Industrial Production Lines Using Combined Image Processing Techniques

Angle detection is a crucial aspect of industrial automation,ensuring precise alignment and orientation ofcomponents in manufacturing processes.Despite the widespread application of computer vision in industrialsettings,angle detection remains an underexplored domain,with limited integration into production lines.Thispaper addresses the need for automated angle detection in industrial environments by presenting a methodologythat eliminates training time and higher computation cost on Graphics Processing Unit(GPU)from machinelearning in computer vision(e.g.,Convolutional Neural Networks(CNN)).Our approach leverages advanced imageprocessing techniques and a strategic combination of algorithms,including contour selection,circle regression,polar warp transformation,and outlier detection,to provide an adaptive solution for angle detection.By configuringthe algorithm with a diverse dataset and evaluating its performance across various objects,we demonstrate itsefficacy in achieving reliable results,with an average error of only 0.5 degrees.Notably,this error margin is 3.274times lower than the acceptable threshold.Our study highlights the importance of accurate angle detection inindustrial settings and showcases the reliability of our algorithm in accurately determining angles,thus contributingto improved manufacturing processes.

Vision and Geolocation Data Combination for Precise Human Detection and Tracking in Search and Rescue Operations

In this paper, a study and evaluation of the combination of GPS/GNSS techniques and advanced image processing algorithms for distressed human detection, positioning and tracking, from a fully autonomous Unmanned Aerial Vehicle (UAV)-based rescue support system, are presented. In particular, the issue of human detection both on terrestrial and marine environment under several illumination and background conditions, as the human silhouette in water differs significantly from a terrestrial one, is addressed. A robust approach, including an adaptive distressed human detection algorithm running every N input image frames combined with a much faster tracking algorithm, is proposed. Real time or near-real-time distressed human detection rates achieved, using a single, low cost day/night NIR camera mounted onboard a fully autonomous UAV for Search and Rescue (SAR) operations. Moreover, the generation of our own dataset, for the image processing algorithms training is also presented. Details about both hardware and software configuration as well as the assessment of the proposed approach performance are fully discussed. Last, a comparison of the proposed approach to other human detection methods used in the literature is presented.

Scheduling optimization problem considering time-of-use tariffs and piece-rate machine maintenance in EAF steelmaking

An operating schedule of the parallel electric arc furnaces(EAFs)considering both productivity and energy related criteria is investigated.A mathematical model is established to minimize the total completion time and the total electricity cost.This problem is proved to be an NP-hard problem,and an effective solution algorithm,longest processing time-genetic(LPT-gene)algorithm,is proposed.The impacts of varied processing energy consumption and electricity price on the optimal schedules are analyzed.The integrated influence of the different weight values and the variation between the peak price and the trough price on the optimal solution is studied.Computational experiments illustrate that considering the energy consumption costs in production has little influence on makespan;the computational performance of the proposed longest processing time-genetic algorithm is better than the genetic algorithm(GA)in the issue to be studied;considerable reductions in the energy consumption costs can be achieved by avoiding producing during high-energy price periods and reducing the machining energy consumption difference.The results can be a guidance for managers to improve productivity and to save energy costs under the time-of-use tariffs.

An Efficient Algorithm for Processing Multi-Relation Queries in Relational Databases

After a relation scheme R is decomposed into the set of schemes ρ={R_1,...,R_n},we may pose queries as if R existed in the database,taking a join of R_i's,when it is necessary to implement the query.Suppose a query involves a set of attributes S(?)R,we want to find the smallest subset of ρ whose union includes S.We prove that the problem is NP-complete and present a polynomial-bounded approximation algorithm.A subset of ρ whose union includes S and has a decomposition into 3NF with a lossless join and preservation of dependencies is given in the paper.

Generation of Interactive Questionnaires Using YAWL-based Workflow Models

A concept is introduced in this article which has strong practical impact for computer aided system configuration. System configuration is a cumbersome and fault sensitive task while setting up systems in a broad range of business applications like ERP （enterprise resource planning） and other workflow-systems. Given a generic process or workflow model in YAWL-notation （yet another workflow language） or any other process modeling language like business process model and notation or WFMC （workflow management coalition）, it could be stated that, by using a set of reduction rules as introduced, it is possible to generate a hierarchically structured tree of sub graphs of the workflow graph-representation. According to the notation used, authors call these sub graphs facts. The tree structure of the graph-representation on one hand and the logical relation between the branches and leafs of the tree on the other can be utilized to create a set of constraints and dependencies among the single facts. Some researchers showed that the nested branches can be associated to （predefined） questions with respect to the configuration of a workflow management system, for instance an ERP-application. They presented an algorithm which dynamically sorts the questions and answers in a maximum efficient configuration path, while working through the corresponding questionnaire. By combining the different elements as facts, constraints on questions, and configuration space, it is thus possible to algorithmically generate the efficient structured and interactive questionnaire for the configuration of workflow systems and algorithmically check the consistency （dead lock free, free of synchronization structural conflict） of the underlying workflow model. The concept was tested in the prototype of the interactive questionnaire for configuration of the web-service based ERP-Application Posity.

Application of wavelet scale correlation filtering and its improved algorithm in signal processing with a spark sound source

It is seriously interfered by ship noise when analyzing and extracting broadband spark sound source signal. In the energy concentrated domain which is below 5 kHz, the traditional scale correlation filtering algorithm, which is based on adjacent-scale correlation, has limited anti-interference ability due to the low signal-to-noise ratio （SNR） and similar Lipschitz exponent characteristic of each other. However, because different frequency bands of the broadband electric spark signal have different noise interferences, the filtering algorithm based on adjacent-scale correlation is adapted to high SNR and small-scale high-frequency wavelet coefficients filtering; the filtering algorithm based on cross-scale correlation is adapted to low SNR and large-scale low-frequency wavelet coefficients filtering, and the threshold coefficient selection method had been corrected in the algorithm. It is shown that the filtering algorithm has a good filtering effect and extracts the broadband spark sound source signal effectively; it is applicable to broadband underwater acoustic signM processing in the presence of narrow-band strong interference background noise.

Operational Modal Analysis of a Ship Model in the Presence of Harmonic Excitation

A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.

Direction-of-arrival estimation for a uniform circular acoustic vector-sensor array mounted around a cylindrical baffle

This work investigates the direction-of-arrival(DOA) estimation for a uniform circular acoustic Vector-Sensor Array(UCAVSA) mounted around a cylindrical baffle.The total pressure field and the total particle velocity field near the surface of the cylindrical baffle are analyzed theoretically by applying the method of spatial Fourier transform.Then the so-called modal vector-sensor array signal processing algorithm,which is based on the decomposed wavefield representations,for the UCAVSA mounted around the cylindrical baffle is proposed.Simulation and experimental results show that the UCAVSA mounted around the cylindrical baffle has distinct advantages over the same manifold of traditional uniform circular pressure-sensor array(UCPSA).It is pointed out that the acoustic Vector-Sensor(AVS) could be used under the condition of the cylindrical baffle and that the UCAVSA mounted around the cylindrical baffle could also combine the anti-noise performance of the AVS with spatial resolution performance of array system by means of modal vector-sensor array signal processing algorithms.

Molecular signature of soil organic matter under different land uses in the Lake Chaohu Basin

The concentration and molecular composition of soil organic matter(SOM)are important factors in mitigation against climate change as well as providing other ecosystem services.Our quantitative understanding of how land use influences SOM molecular composition and associated turnover dynamics is limited,which underscores the need for high-throughput analytical approaches and molecular marker signatures to clarify this etiology.Combining a high-throughput untargeted mass spectrometry screening and molecular markers,we show that forest,farmland and urban land uses result in distinct molecular signatures of SOM in the Lake Chaohu Basin.Molecular markers indicate that forest SOM has abundant carbon contents from vegetation and condensed organic carbon,leading to high soil organic carbon(SOC)concentration.Farmland SOM has moderate carbon contents from vegetation,and limited content of condensed organic carbon,with SOC significantly lower than that of forest soils.Urban SOM has high abundance of condensed organic carbon markers due to anthropogenic activities but relatively low in markers from vegetation.Consistently,urban soils have the highest black carbon/SOC ratio among these land uses.Overall,our results suggested that the molecular signature of SOM varies significantly with land use in the Lake Chaohu Basin,influencing carbon dynamics.Our strategy of molecular fingerprinting and marker discovery is expected to enlighten further research on SOM molecular signatures and cycling dynamics.

Simultaneous characterization of multiple properties of solid and liquid phases in crystallization processes using NIR

Near infrared spectroscopy （NIR） is now probably the most popular process analytical technology （PAT） for pharmaceutical and some other industries. However, unlike mid-IR, NIR is known to have difficulties in monitoring crystallization or precipitation processes because the existence of solids could cause distortion of the spectra. This phenomenon, seen as unfavorable previously, is however an indication that NIR spectra contain rich information about both solids and liquids, giving the possibility of using the same instrument for multiple property characterization. In this study, transflectance NIR calibration data was obtained using solutions and slurries of varied solution concentration, particle size, solid concentration and temperature. The data was used to build calibration models for prediction of the multiple properties of both phases. Predictive models were developed for this challenging application using an approach that combines genetic algorithm （GA） and support vector machine （SVM）. GA is used for wavelength selection and SVM for mode building. The new GA-SVM approach is shown to outperform other methods including GA-PLS （partial least squares） and traditional SVM. NIR is thus successfully applied to monitoring seeded and unseeded cooling crystallization processes of L-glutamic acid.