A novel technique for automatic seismic data processing using both integral and local feature of seismograms

A novel technique for automatic seismic data processing using both integral and local feature of seismograms was presented in this paper. Here, the term integral feature of seismograms refers to feature which may depict the shape of the whole seismograms. However, unlike some previous efforts which completely abandon the DIAL approach, i.e., signal detection, phase identifi- cation, association, and event localization, and seek to use envelope cross-correlation to detect seismic events directly, our technique keeps following the DIAL approach, but in addition to detect signals corresponding to individual seismic phases, it also detects continuous wave-trains and explores their feature for phase-type identification and signal association. More concrete ideas about how to define wave-trains and combine them with various detections, as well as how to measure and utilize their feature in the seismic data processing were expatiated in the paper. This approach has been applied to the routine data processing by us for years, and test results for a 16 days＇ period using data from the Xinjiang seismic station network were presented. The automatic processing results have fairly low false and missed event rate simultaneously, showing that the new technique has good application prospects for improvement of the automatic seismic data processing.

Automatic signal detection based on support vector machine

Algorithm of STA/LTA is frequently used in automatic signal detection, in which the range of detection threshold is （0, ∞）, the optimal threshold should be determined by experiment to make a balance between false detection and missing detection. By using the theory of pattern recognition, a new algorithm for automatic signal detection based on support vector machine was proposed and the method of preprocess and pattern feature extraction were dis- cussed as well as the selection of kernel function for support vector machine. The detection performance of the new algorithm was analyzed by means of real seismic data. The experiments showed that the new method could simplify the selection of threshold and detect signal accurately. In addition to the better performance of anti-noise, the ratio of false detection could decrease 85% in comparison with that of STA/LTA.

Machine Learning of Weather Forecasting Rules from Large Meteorological Data Bases

Discovery of useful forecasting rules from observational weather data is an outstanding interesting topic.The traditional methods of acquiring forecasting knowledge are manual analysis and investigation performed by human scientists.This paper presents the experimental results of an automatic machine learning system which derives forecasting rules from real observational data.We tested the system on the two large real data sets from the areas of centra! China and Victoria of Australia.The experimental results show that the forecasting rules discovered by the system are very competitive to human experts.The forecasting accuracy rates are 86.4% and 78% of the two data sets respectively

Freely Available Tool(FAT)for automated quantification of lipid droplets in stained cells

In this study,we propose an automatic procedure for digital image processing.We describe a method that can efficiently quantify and characterize lipid droplets distributions in different cell types in culture.Prospectively,the lipid droplets detection method described in this work could be applied to static or time-lapse data,collected with a simple visible light or fluorescence microscopy equipment.Fully automated algorithms were implemented in Octave,a freely available scientific package.

A Method for Detecting Non-Mask Wearers Based on Regression Analysis

A novel practical and universal method of mask-wearing detection has been proposed to prevent viral respiratory infections.The proposed method quickly and accurately detects mask and facial regions using welltrained You Only Look Once(YOLO)detector,then applies image coordinates of the detected bounding box(bbox).First,the data that is used to train our model is collected under various circumstances such as light disturbances,distances,time variations,and different climate conditions.It also contains various mask types to detect in general and universal application of the model.To detect mask-wearing status,it is important to detect facial and mask region accurately and we created our own dataset by taking picture of images.Furthermore,the Convolutional Neural Network(CNN)model is trained with both our own dataset and open dataset to detect under heavy foot-traffic(Indoors).To make the model robust and reliable in various environment and situations,we collected various sample data in different distances.And through the experiment,we found out that there is a particular gradient according to the mask-wearing status.The proposed method searches the point where the distance between the gradient for each state and the coordinate information of the detected object is the minimum.Then it carry out the classification of mask-wearing status of detected object.Lastly,we defined and classified three different mask-wearing states according to the mask’s position(With mask,Wear a mask around chin and Without mask).The gradient according to the mask-wearing status,is analyzed through linear regression.The regression interpretation is based on coordinate information of mask-wearing status and the sample data collected in simulated environment that considering distances between objects and the camera in the World Coordinate System.Through the experiments,we found out that linear regression analysis is more suitable than logistic regression analysis for classification of people wearing masks in general-purpose environments.And the proposed method,through linear regression analysis,classifies in a very concise way than the others.

Near-infrared-reflection spectroscopy as measuring method to determine the state of the process for automatic control of anaerobic digestion

A recently developed control strategy for the anaerobic digestion process requires secure knowledge about the state of the process.The near infrared reflection spectroscopy(NIRS),provides the possibility to determine process parameters of the anaerobic digestion process online and directly at the digester.To investigate if the NIRS measurements can successfully be used for the characterization of the state of the process within the control strategy the control was operated on two experimental digesters.The NIR spectra were recorded during the experiments.The values of the process parameters(mainly concentrations of organic acids)obtained by NIRS differ from the values of the chemical analyses during the experiment.Nevertheless the state of the process is categorized equally on the basis of both measurement methods.It can consequently be stated that NIRS is expected to meet the requirements of the control strategy.

A Prototype Expert System for Automatic Generation of Image Processing Programs

A prototype expert system for generating image processing programs using the subroutine pack- age SPIDER is described in this paper.Based on an interactive dialog,the system can generate a complete application program using SPIDER routines.

Automatic Process Control and Management in Laohutai Coal Preparation Plant

ＡｕｔｏｍａｔｉｃＰｒｏｃｅｓｓＣｏｎｔｒｏｌａｎｄＭａｎａｇｅｍｅｎｔｉｎＬａｏｈｕｔａｉＣｏａｌＰｒｅｐａｒａｔｉｏｎＰｌａｎｔ￥ＷａｎｇＺｈｏｎｇｋｕｉａｎｄＺｈａｎｇＺｈｉｌｉａｎＴｈｅＬａｏｈｕｔａｉＣｏａｌＰｒｅｐａｒａｔｉｏｎＰｌａｎｔ...

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability.Some of these environmental issues can be monitored using remotely sensed Earth Observations(EO)data that are increasingly available from a number of freely and openly accessible repositories.However,the full information potential of EO data has not been yet realized.They remain still underutilized mainly because of their complexity,increasing volume,and the lack of efficient processing capabilities.EO Data Cubes(DC)are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form.Systematic and regular provision of Analysis Ready Data(ARD)will significantly reduce the burden on EO data users.Nevertheless,ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task.This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains.The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube.

An automated medium scale prototype for anaerobic co-digestion of olive mill wastewater

Olive oil production constitutes one of the most important agro-industrial business for Mediterranean countries,where 97% of the international production is focused.Such an activity,mainly carried out through three phase olive oil mill plants,generates huge amounts of solid and liquid by-products further than olive oil.Physico-chemical features of these by-products depend on various factors such as soil and climatic conditions,agricultural practices and processing.As currently carried out,the disposal of these byproducts may lead to numerous problems taking into account management,economic and particularly environmental aspects.Indeed,olive mill wastewater is not easily biodegradable due to its high chemical and biochemical oxygen demand,its high content in phenolic compounds,high ratio C/N and low pH,leading consequently to soil and water source pollution.Considering,the above-mentioned statements,olive mill waste disposal constitutes nowadays a challenge for oil industry stakeholders.It becomes necessary to look for alternative solutions in order to overcome environmental problems and ensure the sustainability of oil industry.Anaerobic co-digestion of olive mill wastewater with other agro-industrial matrices could be one of these solutions;since it offers the possibility to produce green energy and break down toxicological compounds contained in these wastewater for a better disposal of the digested matrices as soil conditioner.In this contest,this note reports the functioning principle of an automated medium scale plant for anaerobic co-digestion of olive mill wastewater.