A Physical Security Technology Based upon Doubly Multiple Parameters Weighted Fractional Fourier Transform

Enhancing the security of the wireless communication is necessary to guarantee the reliable of the data transmission, due to the broadcast nature of wireless channels. In this paper, we provide a novel technology referred to as doubly multiple parameters weighted fractional Fourier transform(DMWFRFT), which can strengthen the physical layer security of wireless communication. This paper introduces the concept of DM-WFRFT based on multiple parameters WFRFT(MP-WFRFT), and then presents its four properties. Based on these properties, the parameters decryption probability is analyzed in terms of the number of parameters. The number of parameters for DM-WFRFT is more than that of the MP-WFRFT,which indicates that the proposed scheme can further strengthen the the physical layer security. Lastly, some numerical simulations are carried out to illustrate that the efficiency of proposed DM-WFRFT is related to preventing eavesdropping, and the effect of parameters variety on the system performance is associated with the bit error ratio(BER).

The safety parameters monitoring system for the coal mine based on CAN bus communication and intelligent data acquisition

In this paper,a monitoring and controlling system for the safety in production and environmental parameters of a small and medium-sized coal mine has been developed after analyzing the current domestic coal production and security conditions. The client computer can convert the analog signal about the safety in production and environmental parameters detected from the monitoring terminal into digital signal,and then,send the signal to the coal mine safety monitoring centre. This information can be analyzed,judged,and diagnosed by the monitoring-management-controlling software for helping the manager and technical workers to control the actual underground production and security situations. The system has many advantages including high reliability,better performance of real-time monitoring,faster data communicating and good practicability,and it can effectively prevent the occurrence of safety incidents in coal mines.

The integrated monitoring system for running parameters of key mining equipment based on condition monitoring technology

An integrated monitoring system for running parameters of key mining equipmenton the basis of condition monitoring technology and modern communication networktechnology was developed.The system consists of a client computer with functions ofsignal acquisition and processing, and a host computer in the central control room.Thesignal acquisition module of the client computer can collect the running parameters fromvarious monitoring terminals in real-time.The DSP high-speed data processing system ofthe main control module can quickly achieve the numerical calculation for the collectedsignal.The signal modulation and signal demodulation are completed by the frequencyshift keying circuit and phase-locked loop frequency circuit, respectively.Finally, the signalis sent to the host computer for logic estimation and diagnostic analysis using the networkcommunication technology, which is helpful for technicians and managers to control therunning state of equipment.

An ultra-robust fingerprinting method for quality assessment of traditional Chinese medicine using multiple reaction monitoring mass spectrometry

Chromatographic fingerprinting has been perceived as an essential tool for assessing quality and chemical equivalence of traditional Chinese medicine.However,this pattern-oriented approach still has some weak points in terms of chemical coverage and robustness.In this work,we proposed a multiple reaction monitoring(MRM)-based fingerprinting method in which approximately 100 constituents were simultaneously detected for quality assessment.The derivative MRM approach was employed to rapidly design MRM transitions independent of chemical standards,based on which the large-scale fingerprinting method was efficiently established.This approach was exemplified on QiShenYiQi Pill(QSYQ),a traditional Chinese medicine-derived drug product,and its robustness was systematically evaluated by four indices:clustering analysis by principal component analysis,similarity analysis by the congruence coefficient,the number of separated peaks,and the peak area proportion of separated peaks.Compared with conventional ultraviolet-based fingerprints,the MRM fingerprints provided not only better discriminatory capacity for the tested normal/abnormal QSYQ samples,but also higher robustness under different chromatographic conditions(i.e.,flow rate,apparent pH,column temperature,and column).The result also showed for such large-scale fingerprints including a large number of peaks,the angle cosine measure after min-max normalization was more suitable for setting a decision criterion than the unnormalized algorithm.This proof-of-concept application gives evidence that combining MRM technique with proper similarity analysis metrices can provide a highly sensitive,robust and comprehensive analytical approach for quality assessment of traditional Chinese medicine.

Multiple Respiratory Gas Monitoring Causes Changes of Inspired oxygen Concentration in Closed Anesthesia System

Effect of multiple respiratory gas monitoring (MRGM ) on inspiredconcentration of oxygen in circult system during closed anesthesia was studied in 5l adult patients scheduled for abdominal surgery. Required flow rate of fresh oxygen (OFR), inspired oxygen concentration (FiO2 ) and oxygen saturation of pulse oximeter (SpO2) were measured continu0usly. Patients were equally divided into three groups at randorn, group C (no MRGN used ), group M, (using MRGM with its tail gas returned to circuit system), group M2 (using MRGM without tail gas returned ). The results revealed that during 180 min of closed anesthesia, OFR required in group C and M, were about 200-230 ml/min, and in group M, it was ab0ut 400 ml/min. In group C FiO2 decreased by about 10 % after 60 min of closed anesthesia (P<0. 01, 60 min vs 0 min ) and then stayed stable at this level. In group M,, FiO2 decreased by 16% at 60 min and 34 % at 180 min and the decrease was significantly greater than that in gr0up C (P<0. 01). In group Me, FiO2 remained c0nstant during closed anesthesia, which was much high(Jr than those in group C and M,. The tail gas of Capnomac Ultima MRGM contained less oxygen than its sample gas drawn fr0m circuit system simuItaneously.

Multiple reaction monitoring for the detection of disease-related synaptic proteins

Synaptic dysfunction occurs early in Alzheimer＇s disease （AD） and is acknowledged as a primary pathologic target for treatment. Synaptic degeneration is the pathological feature most strongly correlated with loss of cognitive function ante mortern （Terry et al., 1991）. Synapses are heavily damaged in hippocampal and neocortical regions of AD brain, whereas motor and occipital cortices are relatively spared （Honer et al., 1992）. Despite extensive work, the molecular mechanisms underlying synaptic degeneration are largely unknown.

Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems

Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.

Tool Wear Monitoring in Drilling Using Multiple Feature Fusion of the Cutting Force

This paper presents a tool wear monitoring method in drilling process using cutting force signal. The kurtosis coefficient and the energy of a special frequency band of cutting force signals were taken as the signal features of tool wear as well as the mean value and the standard deviation from the time and frequency domain. The relationships between the signal feature and tool wear were discussed; then the vectors constituted of the signal features were input to the artificial neural network for fusion in order to realize intelligent identification of tool wear. The experimental results show that the artificial neural network can realize fusion of multiple features effectively, but the identification precision and the extending ability are not ideal owing to the relationship between the features and the tool wear being fuzzy and not certain.

Managing crawling-type gastric adenocarcinoma with endoscopic techniques and postoperative monitoring

Crawling-type gastric adenocarcinoma is a rare subtype of gastric cancer with diagnostic and therapeutic challenges due to its flat,ill-defined lesions.Advanced diagnostic techniques,such as narrow-band imaging and linear endoscopic ultrasonography,improve detection,but endoscopic submucosal dissection poses a risk of incomplete resection.Despite negative resection margins,vigilant postoperative monitoring is crucial due to the potential for recurrence.This letter highlights the importance of refined diagnostic criteria,individualized treatment approaches,and continuous follow-up to optimize management of this unique gastric cancer subtype.

Sensitivity analysis for parameters of a monitoring system for steep slopes of open-pit mines

Monitoring the stability of steep slopes of open-pit mines is a major issue relating to production safety in mines.In order to determine the technical parameters of a new type of supervising system applied in monitoring steep slopes of open-pit mines,the MSARMA method was used to establish analytical models for the monitoring system,given various parameter settings based on the description of mechanical monitoring principles.We used this sensitivity analysis to conclude that the setting of the most sensitive location of a mechanical monitoring system should be within a range of 1/5～1/2 of the lower part in a vertical direction of steep slopes,with a rational and feasible range of the dip angle setting between 0°～20°.Given the analytical results of our on-site experiments,we have shown that the parameters determined reflect the stability of steep slopes accurately and effectively.These conclusions provide a basis for the application of a new type of steep slope stability monitoring technology in open-pit mines.

CONTROLLING AND MONITORING THERMOTE NICAL PARAMETERS OF GLASS MELTING FURNACE

According to the characteristics of the ther-motechnical parameters such as temperature pressure and glass level for glass melting furnace, the design method for monitoring and controlling these parameters is introduced in this paper based on MACROMAX-2 concentrated and dis-tributedcontrol system. The configuration of management information and control loop is described, and research tests were performed to check the functions of system.

Real-time parameters monitoring system for underground equipment based on panoramic images

Complex terrain and working equipment in coal mine underground need a way to ensure coal mine safety. In this paper, the way to monitor the real-time status of underground equipment was put forward, and it was proved to be effective as commanding and dispatching system. Monitoring system for underground equipment based on panoramic images was effectively combined with real-time sensor data and static panoramic images of underground surrounding, which not only realizes real-time status monitoring for underground equipment, but also gets a direct scene for underground surrounding. B/S mode was applied in the monitoring system and this is convenient for users to monitor the equipment. Meantime, it can reduce the waste of the data resource.

Investigate Weld Quality of Gas Metal Arc Welding Process Through Online Monitoring of Input Parameters

Gas metal arc welding(GMAW)is also referred as the metal inert gas(MIG)welding which is a process of welding done by the formation of an electric arc between the consumable wire electrode and the workpiece.Through the welding process,a continuous flow of inert gas is supplied,and it avoids the weld being subjected to react with atmospheric air.The process can be automatic or semi-automatic where the main input parameters like current and the voltage can be direct and constant,respectively.Not only the current and voltage the welding quality depends on some more input parameters such as arc gap,velocity,and temperature.In this paper,we explain about a setup which is capable of real-time monitoring of input parameters mentioned above and selecting the best MIG welding parameters for the mild steel.The setup is composed of several sensors and microcontrollers for the collection and the measurement of the input parameters.The samples were categorized according to the federate and the voltage adjustment of the selected welding machine.Then the final objective was to identify the samples of the weld with different parameter changes which are monitored through the system.For the analysis,the samples were subjected to tensile and hardness tests,and microstructure tests to find the dependence of the input parameters which effect for the weld quality.Finally,the experimental results verified the effectiveness of the system for the selection of the quality weld.

Working Condition Real-Time Monitoring Model of Lithium Ion Batteries Based on Distributed Parameter System and Single Particle Model

Lithium ion batteries are complicated distributed parameter systems that can be described preferably by partial differential equations and a field theory. To reduce the solution difficulty and the calculation amount, if a distributed parameter system is described by ordinary differential equations （ODE） during the analysis and the design of distributed parameter system, the reliability of the system description will be reduced, and the systemic errors will be introduced. Studies on working condition real-time monitoring can improve the security because the rechargeable LIBs are widely used in many electronic systems and electromechanical equipment. Single particle model （SPM） is the simplification of LIB under some approximations, and can estimate the working parameters of a LIB at the faster simulation speed. A LIB modelling algorithm based on PDEs and SPM is proposed to monitor the working condition of LIBs in real time. Although the lithium ion concentration is an unmeasurable distributed parameter in the anode of LIB, the working condition monitoring model can track the real time lithium ion concentration in the anode of LIB, and calculate the residual which is the difference between the ideal data and the measured data. A fault alarm can be triggered when the residual is beyond the preset threshold. A simulation example verifies that the effectiveness and the accuracy of the working condition real-time monitoring model of LIB based on PDEs and SPM.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Disaster reduction stick equipment: A method for monitoring and early warning of pipeline-landslide hazards

Oil and gas pipelines are of great importance in China,and pipeline security problems pose a serious threat to society and the environment.Pipeline safety has therefore become an integral part of the entire national economy.Landslides are the most harmful type of pipeline accident,and have directed increasing public attention to safety issues.Although some useful results have been obtained in the investigation and prevention of pipeline-landslide hazards,there remains a need for effective monitoring and early warning methods,especially when the complexity of pipeline-landslides is considered.Because oil and gas pipeline-landslides typically occur in the superficial soil layers,monitoring instruments must be easy to install and must cause minimal disturbance to the surrounding soil and pipeline.To address the particular characteristics of pipelinelandslides,we developed a multi-parameter integrated monitoring system called disaster reduction stick equipment.In this paper,we detail this monitoring and early warning system for pipeline-landslide hazards based on an on-site monitoring network and early warning algorithms.The functionality of our system was verified by its successful application to the Chongqing Loujiazhuang pipeline-landslide in China.The results presented here provide guidelines for the monitoring,early warning,and prevention of pipeline geological hazards.

Deformation warning index for reinforced concrete dam based on structural health monitoring data and numerical simulation

The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process.This causes significant changes in the structural state of the project and makes it difficult to ensure its structural safety.In this study,a new deformation warning index for reinforced concrete dams was developed according to the prototype monitoring data,statistical models,three-dimensional finite element model(FEM)numerical simulation,and the critical conditions of the dam structure.A statistical model was established to separate the water pressure component.Then,a three-dimensional FEM of the reinforced concrete dam was constructed to simulate the water pressure component.Furthermore,the deformation components that affected the mechanical parameters of the dam under the same amount of reservoir water level change were separated and quantified accurately.In addition,the method for inversion of comprehensive mechanical parameters after dam reinforcement was used.The influence mechanisms of the deformation behavior of concrete dams under the reservoir water level and temperature changes were investigated.A new deformation warning index was developed by combining the forward-simulated critical water pressure component and temperature component in the period of extreme temperature decrease with the aging component separated by the statistical model.The new deformation warning index considers the structural state of the dam before and after reinforcement and links the structural strength criterion and the deformation evolution mechanisms.It provides a theoretical foundation and decision support for long-term service and operation management of reinforced dams.

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.

Application on drilling parameter monitor in drilling engineering monitoring

The drilling parameter monitor is an important tool in drilling engineering applied to monitoring drilling process,carrying out scientific analysis and decision--making.Based on discussing the present development situation of the domestic and foreign drilling machine parameter monitors,the metering scheme for vehicle--loaded drilling parameter monitor was designed.By using detection system for MSP430 single--chip microcomputer(SCM) in combination with peripheral circuit such as sensors,the drilling--rig control system was obtained to detect,and for every parameter in real--time display in order to keep operating the drilling rig status.The experiment shows that the drilling parameter monitor reaches design requirements and can be applied to drilling engineering monitoring,which has characters such as simple structure,high credibility and low cost.

Intensity Retrieved from Public Monitoring Video——Cases from the May 12,2008,Wenchuan Earthquake

Macroseismic investigation is presented with a new potential means of investigation in the "digital age". In this paper we studied several cases in the May 12, 2008, Wenchuan M_S8.0 earthquake with a view to exploring the feasibility of retrieving intensity, or even strong ground motion parameters, from public monitoring video which was originally deployed for security purposes. We used public monitoring video records from 44 sites across the meizoseismal region and its surroundings to estimate the intensity. By using the video from a bank in Dujiangyan, Sichuan Province and making the simple assumption that ground vibration is in the form of a propagating harmonic wave, we estimated that the local ground motion acceleration was 0.14g.