Industrial shape detecting system of cold rolling strip

A high-precision shape detecting system of cold rolling strip is developed to meet industrial application, which mainly consists of the shape detecting roller, the collecting ring, the digital signal processing （DSP） shape signal processing board and the shape control model. Based on the shape detecting principle, the shape detecting roller is designed with a new integral structure for improving the precision of shape detecting and avoiding scratching strip surface. Based on the DSP technology, the DSP shape signal processing circuit board is designed and embedded in the shape detecting system for the reliability and stability of shape signal processing. The shape detecting system was successfully used in Angang 1 250 mm HC 6-high reversible cold rolling mill. The precision of shape detecting is 0.2 I and the shape deviation is controlled within 6 1 after the close loop shape control is input.

Spectral Target-Detecting System Using Sine-Wave Modulation

Target detection is one of the key technology of precision chemical application.Previously the digital coding modulation technique was commonly used to emit and receive the optical signal in the target detection systems previously in China.It was difficult to adjust the output power,and the anti-interference ability was weak in these systems.In order to resolve these problems,the target detection method based on analog sine-wave modulation was studied.The spectral detecting system was set up in the aspects of working principle,electric circuit,and optical path.Lab testing was performed.The results showed that the reflected signal from the target varied inversely with detection distances.It indicated that it was feasible to establish the target detection system using analog sine-wave modulation technology.Furthermore,quantitative measurement of the reflected optical signal for near-infrared and visible light could be achieved by using this system.The research laid the foundation for the future development of the corresponding instrument.

Optoelectronic Detecting System for Inner Walls of Pipes

This paper is concerned with a high characteristic image processing and recognition system that is used for inspecting real-time blemishes, streaks and cracks on the inner walls of high accuracy pipes. As a regular detector, the BP neural network is used for extracting features of the image inspected and classifying these images, it takes fully advantage of the function of artificial neural network, such as the information distributed memory, large scale self-adapting parallel processing, high fault-tolerant ability and so forth. Besides, an improved BP algorithm is used in the system for training the network, and making the learning procedure of the net converges to the minimum of overall situation at high rate.

INDUCTION OF CYTOCHROME P450 ISOZYMES IN FL CELLS AND ITS USE IN BIOLOGICAL DETECTING SYSTEMS FOR MUTAGENS

Using arylhydrocarbon hydroxylase (AHH),ethoxyre-sorufin-O-deethylase,ethoxycoumarin-O-deethylase andaminopyrine-N-demethylase as marker enzymes and 3-methylcholanthrene (3-MC),-naphthof1avon,norepine-phrine (NE) and phenobarbita1 as inducers,it is con-firmed that there are inducib1e Cyt P450 IA and

THE PARALLEL CONFOCAL DETECTING SYSTEM USING OPTICAL FIBER PLATE

Objective Focusing on the problem such as slow scanning speed, complex system design and low light efficiency, a new parallel confocal 3D profile detecting method based on optical fiber technology, which realizes whole-field confocal detecting, is proposed. Methods The optical fiber plate generates an 2D point light source array, which splits one light beam into N2 subbeams and act the role of pinholes as point source and point detecting to filter the stray light and reflect light. By introducing the construction and working principle of the multi-beam 3D detecting system, the feasibility is investigated. Results Experiment result indicates that the optical fiber technology is applicable in parallel confocal detecting. Conclusion The equipment needn't mechanical rotation. The measuring parameters that influence the detecting can easily be adapted to satisfy different requirments of measurement. Compared with the conventional confocal method, the parallel confocal detecting system using optical fiber plate is simple in the mechanism, the measuring field is larger and the speed is faster.

Research of handwriting detecting system for space pen

A handwriting detecting system based on Micro- accelerometer and Micro-gyros is proposed. And the algorithm of the detecting system is also described in detail. And the error analysis of the detecting system is also described in de-tail. The motion contrail of the handwriting de-tecting in the 3-D space can be recognized through compute the matrix of attitude angles and the dynamic information of the handwriting detecting which is mapped on the 2-D plane. Then the information of contrail can be recurred on the writing plane by integral. There were good results in the actual experiment.

ERROR ANALYSIS OF 3D DETECTING SYSTEM BASED ON WHOLE-FIELD PARALLEL CONFOCAL MICROSCOPE

Compared with the traditional scanning confocal microscopy, the effect of various factors on characteristic in multi-beam parallel confocal system is discussed, the error factors in multi-beam parallel confocal system are analyzed. The factors influencing the characteristics of the multi-beam parallel confocal system are discussed. The construction and working principle of the non-scanning 3D detecting system is introduced, and some experiment results prove the effect of various factors on the detecting system.

A NON-INVASIVE AUTOMATIC DETECTING SYSTEM FOR BLOOD FLOW PARAMETERS OF CARDIOVASCULAR SYSTEM

In this paper,a non-invasive detecting system for measuring blood flow parame-ters of cardiovascular system is described.The device employs a new unique methodwhich is based on the theory of hemodynamics,ordinary measurement of blood pres-sure and pulse information of variation of pulse contour parameter Ko The sphygmo-gram is picked up from radial artery via sensor.As the blood pressure changes。

Direction Detecting System of Indoor Smartphone Users Using BLE in IoT

Indoor organization user activity’s (UA) direction detection monitoring system and also emergency prediction are major challenging tasks in the field of the typical body sensor and indoor fixed sensor networks. In this paper, indoor UA based direction detection monitoring system is achieved by the combination of both the orientation sensor and Bluetooth Low Energy (BLE) in user’s smartphones belonging to the Internet of Things (IoT). The orientation sensor senses the actual orientation of the user and BLE transmits the sensed BLE signals to monitoring system using star topology in IoT. In monitoring system, classification algorithm is used to identify the directions of the smartphone users. The emergency situation of the user is also predicted based on signal variation instantly in real time. The user activity’s signals are captured using LabVIEW toolkit then applied to various classification algorithms such asRF—91.42%, Ibk—90.55%, j48— 85.61%, K*—73.54% are the results obtained. An average of 85% was obtained in all the classifi- cation algorithims indicating the consistency and accuracy in detecting the directions of the users. RF was found to be the best among all the classification algorithms. IoT enabled devices have high demand in near coming future, moreover smartphones users increase day by day, hence implementing and maintaining the above said system would be much easier and cheaper compared to other conventional networks.

Procalcitonin and presepsin for detecting bacterial infection and spontaneous bacterial peritonitis in cirrhosis:A systematic review and meta-analysis

BACKGROUND Diagnosing bacterial infections(BI)in patients with cirrhosis can be challenging because of unclear symptoms,low diagnostic accuracy,and lengthy culture testing times.Various biomarkers have been studied,including serum procal-citonin(PCT)and presepsin.However,the diagnostic performance of these markers remains unclear,requiring further informative studies to ascertain their diagnostic value.AIM To evaluate the pooled diagnostic performance of PCT and presepsin in detecting BI among patients with cirrhosis.INTRODUCTION Bacterial infections(BI)commonly occur in patients with cirrhosis,resulting in poor outcomes,including the development of cirrhotic complications,septic shock,acute-on-chronic liver failure(ACLF),multiple organ failures,and mortality[1,2].BI is observed in 20%-30%of hospitalized patients,with and without ACLF[3].Patients with cirrhosis are susceptible to BI because of internal and external factors.The major internal factors are changes in gut microbial composition and function,bacterial translocation,and cirrhosis-associated immune dysfunction syndrome[4,5].External factors include alcohol use,proton-pump inhibitor use,frailty,readmission,and invasive procedures.Spontaneous bacterial peritonitis(SBP),urinary tract infection,pneumonia,and primary bacteremia are the common BIs in hospit-alized patients with cirrhosis[6].Early diagnosis and adequate empirical antibiotic therapy are two critical factors that improve the prognosis of BI in patients with cirrhosis.However,early detection of BI in cirrhosis is challenging due to subtle clinical signs and symptoms,low sensitivity and specificity of systemic inflammatory response syndrome criteria,and low sensitivity of bacterial cultures.Thus,effective biomarkers need to be identified for the early detection of BI.Several biomarkers have been evaluated,but their efficacy in detecting BI is unclear.Procalcitonin(PCT)is a precursor of the hormone calcitonin,which is secreted by parafollicular cells of the thyroid gland[7].In the presence of BI,PCT gene expression increases in extrathyroidal tissues,causing a subsequent increase in serum PCT level[8].Changes in serum PCT are detectable as early as 4 hours after infection onset and peaks between 8 and 24 hours,making it a valuable diagnostic biomarker for BI.Several studies have demonstrated the favorable diagnostic accuracy of PCT in the diagnosis of BI in individuals with cirrhosis[9-13]and without cirrhosis[14-16].Since 2014,two meta-analyses have been published on the diagnostic value of PCT for SBP and BI in patients with cirrhosis[17,18].Other related studies have been conducted since then[10-12,19-33].Serum presepsin has recently emerged as a promising biomarker for diagnosing BI.This biomarker is the N-terminal fraction protein of the soluble CD14 g-negative bacterial lipopolysaccharide–lipopolysaccharide binding protein(sCD14-LPS-LBP)complex,which is cleaved by inflammatory serum protease in response to BI[34].Presepsin levels increase within 2 hours and peaks in 3 hours[35].This is useful for detecting BI since presepsin levels increase earlier than serum Our systematic review and meta-analysis was performed with adherence to PRISMA guidelines[37].

IoT-CDS:Internet of Things Cyberattack Detecting System Based on Deep Learning Models

The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT security.Deep learning(DL)-based intrusion detection(ID)has emerged as a vital method for protecting IoT environments.To rectify the deficiencies of current detection methodologies,we proposed and developed an IoT cyberattacks detection system(IoT-CDS)based on DL models for detecting bot attacks in IoT networks.The DL models—long short-term memory(LSTM),gated recurrent units(GRUs),and convolutional neural network-LSTM(CNN-LSTM)were suggested to detect and classify IoT attacks.The BoT-IoT dataset was used to examine the proposed IoT-CDS system,and the dataset includes six attacks with normal packets.The experiments conducted on the BoT-IoT network dataset reveal that the LSTM model attained an impressive accuracy rate of 99.99%.Compared with other internal and external methods using the same dataset,it is observed that the LSTM model achieved higher accuracy rates.LSTMs are more efficient than GRUs and CNN-LSTMs in real-time performance and resource efficiency for cyberattack detection.This method,without feature selection,demonstrates advantages in training time and detection accuracy.Consequently,the proposed approach can be extended to improve the security of various IoT applications,representing a significant contribution to IoT security.

Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky board images and an improved Cascade R-CNN

Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.

Establishment of a Multiplex PCR System for Detecting Transgenic Ingredients from Citrus

[Objective] This study aimed to establish a multiplex PCR system for de- tecting transgenic ingredients from Citrus. [Method] Based on the pBI121 plasmid sequences published in GenBank and actin gene sequence of Citrus, the primers specific to CaMV35S promoter, NOS promoter, NOS terminator and actin gene were designed, to establish a multiple PCR system which could detect four types of sequences. In addition, orthogonal tests were performed to determine the optimal concentrations of all the components in PCR reaction system, as well as the optimal PCR cycle parameters. [Result] The optimal PCR reaction system should contain 2.5μl of 10xPCR buffer, 2.0μl of MgCI2 （25 mmol/L）, 2.0 μl of dNTP mixture （2.5 mmol/L of each dNTP）, 1.0 μl of actin gene primers （10μmol/L）, 1.0μl of 35S promoter primers （10 μmol/L）, 1.5 μl of NOS promoter primers （10 μmol/L） and 0.5 μl of NOS terminator primers （10μmol/L）, 0.1 μg of template DNA, 1.25 U of Taq DNA polymerase; ddH20 was added to the total reaction system of 25μl. The PCR reaction program consisted of pre-denaturing at 94℃ for 5 min; 31 cycles of denaturing at 94℃ for 30 s, annealing at 64.1℃ for 45 s and extension at 72℃ for 50 s; final extension at 72℃ for 10 min. The reaction system optimized with the orthogonal tests could detect as less as 0.1% transgenic component in the tested samples. [Conclusion] The MPCR detection system established in this study can meet the requirements in theory for detecting the genetically modified ingredients in Citrus or the deep-processed products.

DC LOOP-CURRENT DETECTING AND RESTRAINING METHODS FOR PARALLEL INVERTER SYSTEM

DC component is contained in inverter output voltage due to many reasons such as the zero-point deviation of operational amplifiers and the differences between power switching transistors′ characteristics. For the parallel inverter system without output isolation transformers, the difference of DC components of the output voltage can cause large DC loop-current among modular inverters. Aiming at this problem, this paper studies several DC loop-current detecting and restraining methods. By digital adjustment with high precision on the DC components of reference sine wave, the DC components of inverter′s output voltage can be adjusted to restrain DC loop-current. Experimental results prove that the DC loop-current detecting and restraining methods have a good performance.

Detecting abnormalities for empty nest elder in smart monitoring system

In order to implement the real-time detection of abnormality of elder and devices in an empty nest home,multi-modal joint sensors are used to collect discrete action sequences of behavior,and the improved hierarchical hidden Markov model is adopted to Abstract these discrete action sequences captured by multi-modal joint sensors into an occupant’s high-level behavior—event,then structure representation models of occupant normality are modeled from large amounts of spatio-temporal data. These models are used as classifiers of normality to detect an occupant’s abnormal behavior.In order to express context information needed by reasoning and detection,multi-media ontology （MMO） is designed to annotate and reason about the media information in the smart monitoring system.A pessimistic emotion model （PEM） is improved to analyze multi-interleaving events of multi-active devices in the home.Experiments demonstrate that the PEM can enhance the accuracy and reliability for detecting active devices when these devices are in blind regions or are occlusive. The above approach has good performance in detecting abnormalities involving occupants and devices in a real-time way.

Optimization design of the angle detecting system used in the fast steering mirror

In this paper, in order to design a fast steering mirror(FSM) with large deflection angle and high linearity, a deflection angle detecting system(DADS) using quadrant detector(QD) is developed. And the mathematical model describing DADS is established by analyzing the principle of position detecting and error characteristics of QD. Based on this mathematical model, the variation tendencies of deflection angle and linearity of FSM are simulated. Then, by changing the parameters of the DADS, the optimization of deflection angle and linearity of FSM is demonstrated. Finally, a QD-based FSM is designed based on this method, which achieves ±2° deflection angle and 0.72% and 0.68% linearity along x and y axis, respectively. Moreover, this method will be beneficial to the design of large deflection angle and high linearity FSM.

Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging

BACKGROUND Non-magnifying endoscopy with narrow-band imaging(NM-NBI)has been frequently used in routine screening of esophagus squamous cell carcinoma(ESCC).The performance of NBI for screening of early ESCC is,however,significantly affected by operator experience.Artificial intelligence may be a unique approach to compensate for the lack of operator experience.AIM To construct a computer-aided detection(CAD)system for application in NMNBI to identify early ESCC and to compare it with our previously reported CAD system with endoscopic white-light imaging(WLI).METHODS A total of 2167 abnormal NM-NBI images of early ESCC and 2568 normal images were collected from three institutions(Zhongshan Hospital of Fudan University,Xuhui Hospital,and Kiang Wu Hospital)as the training dataset,and 316 pairs of images,each pair including images obtained by WLI and NBI(same part),were collected for validation.Twenty endoscopists participated in this study to review the validation images with or without the assistance of the CAD systems.The diagnostic results of the two CAD systems and improvement in diagnostic efficacy of endoscopists were compared in terms of sensitivity,specificity,accuracy,positive predictive value,and negative predictive value.RESULTS The area under receiver operating characteristic curve for CAD-NBI was 0.9761.For the validation dataset,the sensitivity,specificity,accuracy,positive predictive value,and negative predictive value of CAD-NBI were 91.0%,96.7%,94.3%,95.3%,and 93.6%,respectively,while those of CAD-WLI were 98.5%,83.1%,89.5%,80.8%,and 98.7%,respectively.CAD-NBI showed superior accuracy and specificity than CAD-WLI(P=0.028 and P≤0.001,respectively),while CAD-WLI had higher sensitivity than CAD-NBI(P=0.006).By using both CAD-WLI and CAD-NBI,the endoscopists could improve their diagnostic efficacy to the highest level,with accuracy,sensitivity,and specificity of 94.9%,92.4%,and 96.7%,respectively.CONCLUSION The CAD-NBI system for screening early ESCC has higher accuracy and specificity than CAD-WLI.Endoscopists can achieve the best diagnostic efficacy using both CAD-WLI and CAD-NBI.

A new approach to detecting weak signal in strong noise based on chaos system control

In this paper, a chaos system and proportional differential control are both used to detect the frequency of an unknown signal. In traditional methods the useful signal is obtained through the Duffing equation or other chaotic oscillators. But these methods are too complex because of using a lot of chaos oscillators. In this paper a new method is presented that uses the Rossler equation and proportional differential control to detect a weak signal frequency. Substituting the detected signal frequency into the RSssler equation leads the Rossler phase state to be considerably changed. The chaos state can be controlled through the proportional differential method. Through its phase diagram and spectrum analysis, the unknown frequency is obtained. The simulation results verify that the presented method is feasible and that the detection accuracy is higher than those of other methods.

Application of the new pattern recognition system in the new e-nose to detecting Chinese spirits

We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits.

Towards an Artificial Immune System for Detecting Anomalies in Wireless Mesh Networks

This paper focuses on investigating immunological principles in designing a multi-agent security architecture for intrusion detection and response in wireless mesh networks.In this approach,the immunity-based agents monitor the situation in the network.These agents can take appropriate actions according to the underlying security policies.Specifically,their activities are coordinated in a hierarchical fashion while sensing,communicating,determining and generating responses.Such an agent can learn about and adapt to its environment dynamically and can detect both known and unknown intrusions.The proposed intrusion detection architecture is designed to be flexible,extendible,and adaptable so that it can perform real-time monitoring.This paper provides the conceptual view and a general framework of the proposed system.In the end,the architecture is illustrated by an example and by simulation to show it can prevent attacks efficiently.