Improve Detection Efficiency Research Articles

Automatic Detection System of ECG Performance Based on Single Channel ECG Recorder

Aiming at the ECG performance indicators in the YY 0885-2013 "Medical electrical equipment Part 2: Particular requirements for the safety including essential performance of ambulatory electrocardiographic systems", the traditional detection methods are time-consuming and the test results are greatly affected by human factors. In this paper, an automated detection method is proposed. A set of automatic detection software for ECG performance is designed and developed based on a single-channel ECG recorder, and an automated detection system is set up in combination with standard testing equipment. And then, the MSA tool is used to analyze the repeatability and stability of the detection system, and the results show that the detection system is acceptable, and it can improve detection efficiency.

Rapid Detection of Single Viable Escherichia coli O157 Cells in Fresh Lettuce and Strawberry by Immunomagnetic Flow Cytometry in Combination with Pre-Enrichment.

Enterohemorrhagic Escherichia coli are an important pathogen causing food poisoning. The rapid detection of viable E. coli O157 in vegetables and fruits at single-cell level is critical because of the low infective dose of this pathogen. In this study, an immunomagnetic flow cytometry (IMFC)-based method was developed to detect E. coli O157 in lettuce and strawberries inoculated with 1 CFU/25 g. This method developed immunomagnetic (IM)-beads to capture E. coli O157 cells. The pre-enrichment of E. coli O157 and IM-bead separation rapidly increased the concentration of cells to a detectable range for flow cytometry. Compared with the plate-based method, the diagnostic sensitivity and specificity of the IMFC-based method were 100% in 166 samples, including 100 artificially contaminated samples, 60 retail samples, and six O157-positive samples for proficiency testing. The developed IMFC-based method was found to be effective in detecting E. coli O157 at single-cell level in 25 g of lettuce or strawberry with relatively shorter associated time to results of 5.7 h. Therefore, the IMFC-based method could improve detection efficiency and also make early warnings in a short time.

A New Method of Different Neural Network Depth and Feature Map Size on Remote Sensing Small Target Detection

Convolutional neural networks (CNNs) have shown strong learning capabilities in computer vision tasks such as classification and detection. Especially with the introduction of excellent detection models such as YOLO (V1, V2 and V3) and Faster R-CNN, CNNs have greatly improved detection efficiency and accuracy. However, due to the special angle of view, small size, few features, and complicated background, CNNs that performs well in the ground perspective dataset, fails to reach a good detection accuracy in the remote sensing image dataset. To this end, based on the YOLO V3 model, we used feature maps of different depths as detection outputs to explore the reasons for the poor detection rate of small targets in remote sensing images by deep neural networks. We also analyzed the effect of neural network depth on small target detection, and found that the excessive deep semantic information of neural network has little effect on small target detection. Finally, the verification on the VEDAI dataset shows, that the fusion of shallow feature maps with precise location information and deep feature maps with rich semantics in the CNNs can effectively improve the accuracy of small target detection in remote sensing images.

Infrared and ultrasonic intelligent damage recognition of composite materials based on deep learning.

With the large-scale application of composite materials in military aircraft, various composite material detection technologies with infrared nondestructive and ultrasonic nondestructive testing as the core have played an important role in detecting composite material component damage in military aircraft. At present, the damage of composite materials is mostly recognized manually, which is time-consuming, laborious, and inefficient. It can effectively improve detection efficiency and accuracy by using intelligent detection methods to detect and recognize damage. Moreover, the effect of infrared detection is significantly reduced with increasing detection depth, while ultrasonic detection has shallow-blind areas. A cascade fusion R-CNN network is proposed in order to comprehensively identify composite material damage. This network realizes the intelligent fusion recognition of infrared and ultrasonic damage images of composite materials. The network is based on a cascade R-CNN network, using fusion modules and BiFPN for improvement. For the infrared image and ultrasonic C-scan image data set established in this paper, the algorithm can identify the type and location of damage detected by infrared and ultrasonic testing. Its recognition accuracy is 99.3% and mean average precision (mAP) is 90.4%. In the detection process, the characteristics of infrared and ultrasonic images are used to realize the recognition of damage depth. Compared to SSD, YOLOv4, faster R-CNN and cascade R-CNN, the network proposed in this paper is better and more effective.

Intelligent recognition of composite material damage based on deep learning and infrared testing.

Composite materials are commonly used in aircraft, and the integrity of these materials affects both flight and safety performance. Damage detection technology involving infrared nondestructive testing has played an important role in damage detection in aircraft composite materials. Traditional manual detection methods are inefficient, and the use of intelligent detection methods can effectively improve detection efficiency. Due to the diverse types of damage that can occur in composite materials, this damage is difficult to distinguish solely from infrared images. The introduction of infrared signals, which is temporal signals, provides the possibility of judging the type of damage. In this paper, a 1D-YOLOv4 network is established. The network is based on the YOLOv4 network and adds a changing neck and a 1D-CNN for improvement. Testing shows that the algorithm can identify infrared images and infrared signals in composite materials. Its recognition accuracy is 98.3%, with an AP of 91.9%, and a kappa of 0.997. Comparing the network in this paper with networks such as YOLOv3, YOLOv4 and YOLOv4+Neck, the results show that the proposed network is more effective. At the same time, the detection effects of the original data, the fitted data, the first derivative data and the second derivative data are studied, and the detection effect of the first derivative data has the best outcome.

Study of Čerenkov Light Emission in the Semiconductors TlBr and TlCl for TOF-PET.

Thallium bromide (TlBr) and thallium chloride (TlCl) are semiconductor materials with high transparency to visible light, high index of refraction, and high detection efficiency for gamma rays and annihilation photons. This manuscript reports on measurements of the light intensity and timing response of Čerenkov light emitted in one 3 mm × 3 mm × 5 mm slab of each of these materials operated in coincidence with a lutetium fine silicate (LFS) crystal with dimensions of 3 mm × 3 mm × 20 mm. A 22Na radioactive source was used. The measured average number of detected photons per event was 1.5 photons for TlBr and 2.8 photons for TlCl when these materials were coupled to a silicon photomultiplier. Simulation predicts these results with an overestimation of 12%. The best coincidence time resolution (CTR) for events in TlBr and TlCl were 329 ± 9 ps and 316 ± 9 ps, respectively, when events with 4 photons and >7 photons were selected. Simulation showed the CTR degraded from 120 ps to 405 ps in TlCl, and from 160 ps to 700 ps in TlBr when the first or second Čerenkov photon were selected. Results of this work show TlCl has a stronger Čerenkov light emission compared to TlBr and a greater potential to obtain the best timing measurements. Results also stress the importance of improving detection efficiency and transport of light to capture the first Čerenkov photon in timing measurements.

A new ionic liquid bridged periodic mesoporous organosilicas stationary phase for per aqueous liquid chromatography and its application in the detection of biogenic amines

In order to solve the problems of using a large proportion of acetonitrile on the hydrophilic interaction liquid chromatography (HILIC) columns that was not environmentally friendly, and the poor acid and base resistance of traditional bonded silica columns, we reported a novel stationary phase of Au nanoparticles (Au NPs) covalently bonded to ionic liquid (ILs) bridged periodic mesoporous organosilicas (PMO) hydrophilic microspheres (PMO-ILs-Au NPs) for per aqueous liquid chromatography (PALC). The PMO hydrophilic microspheres were prepared by condensation of 1,3-bis(trimethoxysilylpropyl)imidazoliumchloride and 1, 2-Bis (triethoxysilyl) ethane and then modified with Au NPs the surface. The obtained materials were characterized by elemental analysis, FT-IR spectra, scanning electron microscope and transmission electron microscopy. The retention behavior was evaluated by investigating the effect of various chromatographic factors on the retention of different types of solutes. The retention mechanism of the stationary phases in PALC was a mixed type of anion-exchange and hydrophobic interaction. Compared with C18–SiO2 column, the acid and base resistance of the stationary phase were greatly improved. Compared with the HILIC column and C18 column, some hydrophilic compounds such as six organic acids and eight biogenic amines were baseline separated with the enhanced resolution of the PMO-ILs-Au NPs column under the PALC mode. The efficiency of the new column was significantly higher than that of the HILIC column. Furthermore, the analysis of PALC-triple quadrupole mass spectrometry was developed for simultaneous detection of eight biogenic amines. This method could improve detection efficiency, save reagent and reduce environmental pollution. PALC as a green chromatography analytical method was suitable for the replacement of HILIC.

Research on insulator defect detection algorithm of transmission line based on CenterNet.

The reliability of the insulator has directly affected the stable operation of electric power system. The detection of defective insulators has always been an important issue in smart grid systems. However, the traditional transmission line detection method has low accuracy and poor real-time performance. We present an insulator defect detection method based on CenterNet. In order to improve detection efficiency, we simplified the backbone network. In addition, an attention mechanism is utilized to suppress useless information and improve the accuracy of network detection. In image preprocessing, the blurring of some detected images results in the samples being discarded, so we use super-resolution reconstruction algorithm to reconstruct the blurred images to enhance the dataset. The results show that the AP of the proposed method reaches 96.16% and the reasoning speed reaches 30FPS under the test condition of NVIDIA GTX 1080 test conditions. Compared with Faster R-CNN, YOLOV3, RetinaNet and FSAF, the detection accuracy of proposed method is greatly improved, which fully proves the effectiveness of the proposed method.

Multiple Leak Detection in Water Distribution Networks Following Seismic Damage

Water pipe leaks due to seismic damage are more difficult to detect than bursts, and such leaks, if not repaired in a timely manner, can eventually reduce supply pressure and generate both pollutant penetration risks and economic losses. Therefore, leaks must be promptly identified, and damaged pipes must be replaced or repaired. Leak-detection using equipment in the field is accurate; however, it is a considerably labor-intensive process that necessitates expensive equipment. Therefore, indirect leak detection methods applicable before fieldwork are necessary. In this study, a computer-based, multiple-leak-detection model is developed. The proposed technique uses observational data, such as the pressure and flow rate, in conjunction with an optimization method and hydraulic analysis simulations, to improve detection efficiency (DE) for multiple leaks in the field. A novel approach is proposed, i.e., use of a cascade and iteration search algorithms to effectively detect multiple leaks (with the unknown locations, quantities, and sizes encountered in real-world situations) due to large-scale disasters, such as earthquakes. This method is verified through application to small block-scale water distribution networks (WDNs), and the DE is analyzed. The proposed detection model can be used for efficient leak detection and the repair of WDNs following earthquakes.

Simulation-based optimization of a thermal neutron detector with SERS method

A Monte Carlo simulation-based optimization of a SERS-based thermal neutron detector is aimed at improving detection efficiency for thermal neutrons. The moderator and collimator have been designed for a 241Am-9Be source to maximize the thermal neutrons flux in the irradiation area. Simulated thermal neutron detection efficiency can be increased to approximately 50.5% when polyethylene is added as neutron reflector and 10B enriched 4-MPBA is used. With Monte Carlo software GEANT4, the SERS-based thermal neutron detector has been proven to have almost no effect on original thermal neutron flux as well as the distribution and could be used for thermal neutron detection accurately.

Novel poly(β-cyclodextrin) porous material as solid phase extraction sorbent for aniline derivatives in rubber samples

Aniline derivates are widely used in rubber products as anti-aging agents, but their biological toxicity causes harm in the use and recycling of rubber products. Therefore, in this study, a high-performance poly(3,4,5,6-tetra-fluorophthalonitrile-β-cyclodextrin)(P-TFPN-β-CD) solid-phase extraction (SPE) adsorbent was synthesized and applied to extract and detect aniline derivatives in rubber specimens. After crosslinking with 3,4,5,6-tetrafluorophthalonitrile (TFPN), the polymer exhibited mesopores (7.88 nm) and a large specific area (55.2 m2/g). The porous structure significantly improved the extraction efficiency (recovery was between 90.1% and 110.5%) and rate (60% in 10 s) of 4 aniline derivatives (aniline, N-ethyl aniline, 1,2-phenylenediamine, and p-phenylenediamine). With a combined gas chromatographic-flame ionization detector (GC-FID), the extracted aniline derivatives were accurately detected (RSD = 1.60% to 9.90%). Due to the weak interaction between P-TFPN-β-CD and analyte, the prepared poly-TFPN-β-CD SPE adsorbent was regenerated via mild washing with methanol. The high absorption capacities remained after 4 extracting-washing cycles. The novel porous SPE adsorbent showed good extracting and recycling performance for the analysis and detection of aniline compounds in rubber. Thus, it has good prospects for application to improve detection efficiency and reduce cost.

Integrated and Portable Probe Based on Functional Plastic Scintillator for Detection of Radioactive Cesium

The highly reliable and direct detection of radioactive cesium has gained potential interest due to in-situ detection and monitoring in environments. In this study, we elucidated an integrated and portable probe based on functional plastic scintillator for detection of radioactive cesium. A functional plastic scintillator with improved detection efficiency was fabricated including CdTe (cadmium telluride) material. Monolith-typed functional plastic scintillator having a diameter of 50 mm and a thickness of 30 mm was manufactured by adding 2,5-diphenyloxazole (PPO, 0.4 wt%), 1,4 di[2-(5phenyloxazolyl)]benzene (POPOP, 0.01 wt%), and CdTe (0.2 wt%) materials in a styrene-based matrix. To evaluate the applicability of the plastic scintillator manufactured to in-situ radiological measurement, an integrated plastic detection system was created, and the measurement experiment was performed using the Cs-137 radiation source. Additionally, detection efficiency was compared with a commercial plastic scintillator. As results, the efficiency and light yield of a functional plastic scintillator including CdTe were higher than a commercial plastic scintillator. Furthermore, the remarkable performance of the functional plastic scintillator was confirmed through comparative analysis with Monte Carlo simulation.

Recent Progress on Electrical and Optical Manipulations of Perovskite Photodetectors.

Photodetectors built from conventional bulk materials such as silicon, III–V or II–VI compound semiconductors are one of the most ubiquitous types of technology in use today. The past decade has witnessed a dramatic increase in interest in emerging photodetectors based on perovskite materials driven by the growing demands for uncooled, low‐cost, lightweight, and even flexible photodetection technology. Though perovskite has good electrical and optical properties, perovskite‐based photodetectors always suffer from nonideal quantum efficiency and high‐power consumption. Joint manipulation of electrons and photons in perovskite photodetectors is a promising strategy to improve detection efficiency. In this review, electrical and optical characteristics of typical types of perovskite photodetectors are first summarized. Electrical manipulations of electrons in perovskite photodetectors are discussed. Then, artificial photonic nanostructures for photon manipulations are detailed to improve light absorption efficiency. By reviewing the manipulation of electrons and photons in perovskite photodetectors, this review aims to provide strategies to achieve high‐performance photodetectors.

Efficacy of an improved method to screen semiochemicals of insect.

BackgroundA combination of gas chromatography-electroantennographic detection (GC-EAD) and gas chromatography-mass spectrometry (GC-MS) is typically used to screen active compounds that play a role in the regulation of insect behavior. This method uses two kinds of gas chromatography (GC) equipment and needs to compare compounds between the two chromatograms, and it is tedious and costly. To improve detection efficiency, as well as reduce costs and the rate of missed detection, we designed a system connecting gas chromatography (GC), electroantennography (EAG), and mass spectrometry (MS), with MS used instead of the flame ionization detector (FID) as the GC-EAD detector. To verify the feasibility of the improved method, we compare two methods—GC-EAG-MS and GC-EAD—through a series of experiments. Some researchers made similar improvements, but these were not compared with GC-EAD, and their method needed to be improved in the synchronization and split ratio. Our method has been optimized and improved in these aspects.MethodsHelicoverpa armigera was the test organism; the improved method and conventional method were used to detect known and unknown compounds, as well as screen out active compounds that could generate responses in H. armigera antennae.ResultsScreening known single compounds using the two methods, the active compound benzaldehyde was detected in all seven concentrations of solution. By using the two methods, the five same active compounds of Helicoverpa armigera were detected in high concentration solution of the mixed compounds (100 mg L−1, 50 mg L−1); the four same active compounds were detected at 20 mg L−1 concentration; two identical same compounds were detected in low concentration solution (concentrations of 10 mg L−1 and below). By using the two methods, six identical active compounds of Helicoverpa armigera were detected in unknown compounds.ConclusionThe improved method was consistent with the conventional method in terms of accuracy and sensitivity. However, compared with the traditional methods, Gas chromatography-electroantennographic-mass spectrometry (GC-EAG-MS) saved the cost of GC and FID equipment, thereby greatly lowering the experimental cost. In the experiment, GC-EAG-MS combined the two experimental operations of screening active substances by GC-EAD and identifying active substances by GC-MS into one, which not only reduced the experimental steps, but also avoided the false positive caused by the comparison of the two chromatograms, and it greatly reduced the difficulty level of the overall experimental analysis. GC-EAG-MS is more convenient, efficient, economical, and practical, and could confidently replace traditional methods. With further optimization, it could be widely applied in the study of plant and insect chemical ecology.

Study of two-layer tapered depth of interaction PET detector

Improving detection efficiency in small animal PET scanners without degrading spatial resolution is one of the main problems of these scanners. Commercial small animal PET scanners use different methods to achieve desirable levels of sensitivity and spatial resolution. GE Healthcare eXplore VISTA PET scanner uses double layer (LYSO-GSO) depth-of-interaction (DOI) capable cuboid detector modules. In this work, the design of GE Healthcare eXplore VISTA PET scanner is improved using tapered detector geometry instead of cuboid geometry. Using tapered detector geometry, the gaps between adjacent modules are filled and the sensitive volume has increased about 11.5%. The new designed PET scanner sensitivity and spatial resolution are studied for different crystal layer configurations (LYSO-GSO and GSO-LYSO with different thicknesses). As expected, average sensitivity over FOV is improved. Spatial resolution is slightly degraded but it is still uniform over FOV.

An aggregate gradation detection method based on multi-view information fusion

In this study, the morphologies of the aggregate in multiple views were analysed during the falling of particles to calculate aggregate gradation. Four types of characterisation parameters were selected to extract the multi-view information of aggregate particles in five views. Based on the multi-view information, the aggregate particles were classified using principal component analysis and a probabilistic neural network. An aggregate equivalent volume characterisation method was formulated to calculate the aggregate mass, whereby the aggregate volume was converted into the aggregate mass by the least-squares method. The experimental results show that the proposed aggregate sieving method can effectively realise the gradation classification of aggregates. Considering the product of the maximum area and the minimum equivalent Feret ellipse minor axis as the equivalent volume, the calculated aggregate mass yielded a good correlation with the actual aggregate mass. Compared with single-view information, multi-view information can improve the accuracy and repeatability of gradation calculations. The use of multi-view information to calculate aggregate gradation can reduce manpower and improve detection efficiency, which is important for applications in the construction industry.

Modified PMA-qPCR Method for Rapid Quantification of Viable Lactobacillus spp. in Fermented Dairy Products

Quantification of viable lactic acid bacteria is an important quality index of fermented dairy products, but the current detection method, which is plate counting, is time-consuming. This study describes a modified PMA-qPCR assay that allows the detection and enumeration of total viable Lactobacillus spp. in commercial fermented milk products. Here, a new pair of genus-specific primers and minor groove binder probes was designed based on the 16S rRNA gene sequences of 24 reference strains, 13 of which were from different Lactobacillus species commonly used in commercial fermented dairy products. Subsequently, compared with the heat killing method, the 20-cycle homogenization method was chosen for initial PMA-qPCR optimization. Standard curves were prepared, and the efficiency obtained was 97% (R2 = 0.992), demonstrating that this PMA-qPCR method was feasible and effective. Additionally, the viable Lactobacillus spp. detection range was 103–109 CFU/mL, which meets the detection requirements in fermented dairy products. Furthermore, total number of viable Lactobacillus spp. in six different commercial fermented dairy products was estimated by three individual methods (plate count in MRS agar, qPCR, and PMA-qPCR). Comparison of results obtained by PMA-qPCR and plate counts was similar, and both decreased accordingly, showing a good correlation with the decline of strain vitality, 30 days after the expiration date. The PMA-qPCR procedure established in this paper allows the quantification of viable Lactobacillus spp. in approximately 3 h, greatly improving detection efficiency, and has broad application prospects in the testing of fermented dairy products.

Optimizing Experimental Conditions for Accurate Quantitative Energy-Dispersive X-ray Analysis of Interfaces at the Atomic Scale.

The invention of silicon drift detectors has resulted in an unprecedented improvement in detection efficiency for energy-dispersive X-ray (EDX) spectroscopy in the scanning transmission electron microscope. The result is numerous beautiful atomic-scale maps, which provide insights into the internal structure of a variety of materials. However, the task still remains to understand exactly where the X-ray signal comes from and how accurately it can be quantified. Unfortunately, when crystals are aligned with a low-order zone axis parallel to the incident beam direction, as is necessary for atomic-resolution imaging, the electron beam channels. When the beam becomes localized in this way, the relationship between the concentration of a particular element and its spectroscopic X-ray signal is generally nonlinear. Here, we discuss the combined effect of both spatial integration and sample tilt for ameliorating the effects of channeling and improving the accuracy of EDX quantification. Both simulations and experimental results will be presented for a perovskite-based oxide interface. We examine how the scattering and spreading of the electron beam can lead to erroneous interpretation of interface compositions, and what approaches can be made to improve our understanding of the underlying atomic structure.

Identification of grouting compactness in bridge bellows based on the BP neural network

Prestressed concrete beams are widely used in bridge engineering due to their long span, lightweight, and good integrity. However, the grouting quality in the bellows of the beams will directly affect the safety and durability of the entire structure. Therefore, it is fairly important to detect and evaluate the quality of the grouting in bellows. The current mainstream detection methods are mainly horizontal detection methods that can only identify local defects and that have low detection efficiency. This paper proposes a longitudinal transmission detection method based on the BP neural network algorithm. This method could quickly identify the grouting compactness of an entire pipeline. The main process was as follows: First, a signal at one end of the beam was transmitted and the signals at both ends of the beam were collected. Second, the features of the processed signal were extracted. Through the qualitative analysis of wave propagation theory, the extracted characteristic parameters including the wave speed, energy attenuation, wavelet high-frequency energy attenuation, and frequency shift value were all closely related to the compactness of the grouting, which confirmed their rationality as the network input vector. Third, advantage was taken of the close relationship between the characteristic parameters and the grouting saturation to establish a BP neural network regression model. Moreover, the network parameters were adjusted to make the model prediction effect the best. After adjusting the network parameters, the network performance was significantly changed and improved, in that the training set error dropped by 0.0081 and the test set error dropped by 0.0011. The end of the paper describes how a full-scale experimental model was designed to verify the reliability of the method. The result showed that the error between the predicted grouting compactness based on the proposed method and the actual compactness in the test was within 3%, which met the actual engineering requirements and verified the reliability of the proposed method. The significance of this method could significantly improve detection efficiency and prepare for defect location and quantification.

Multichannel Paper Chip-Based Gas Pressure Bioassay for Simultaneous Detection of Multiple MicroRNAs.

Simultaneous detection of multi-biomarkers not only enhances the accuracy of disease diagnosis but also improves detection efficiency and reduces cost. It is vital to achieve portable, simple, low-cost, and simultaneous detection of biomarkers for point-of-care (POC) diagnostics in a low-resource setting. Herein, a multichannel paper chip-based gas pressure bioassay was developed for the simultaneous detection of multiple biomarkers by combining multichannel paper chips with a portable gas pressure meter. Four DNA tetrahedral probes (DTPs) were used as capture probes and were immobilized in different detection zones of the paper chips to improve hybridization efficiency and reduce nonspecific adsorption. The formation of a sandwich structure between target microRNAs (miRNAs), the capture probe, and platinum nanoparticles (PtNPs)-modified complementary DNA (PtNPs-cDNA) transformed biomolecular recognition into quantitative detection of gas pressure. Four lung cancer-related miRNAs were detected simultaneously by a portable gas pressure meter. There is a good linear relationship between gas pressure and the logarithm of miRNA concentration in the range of 10 pM to 100 nM. Compared with single-stranded DNA capture probe, the signal-to-noise (S/N) of DNA tetrahedral probes improved more than 3 times. Using ring-oven washing, the unbound reagents in all channels of the paper chip were simultaneously and continuously washed away, leading to a more cheap, simple, and fast separation than magnetic separation. Therefore, it offers a promising multichannel paper chip-based gas pressure bioassay for portable and simultaneous detection of multiple biomarkers.