GHz photon-number resolving detection with high detection efficiency and low noise by ultra-narrowband interference circuits

We demonstrate the photon-number resolution(PNR)capability of a 1.25 GHz gated InGaAs single-photon avalanche photodiode(APD)that is equipped with a simple,low-distortion ultra-narrowband interference circuit for the rejection of its background capacitive response.Through discriminating the avalanche current amplitude,we are able to resolve up to four detected photons in a single detection gate with a detection efficiency as high as 45%.The PNR capability is limited by the avalanche current saturation,and can be increased to five photons at a lower detection efficiency of 34%.The PNR capability,combined with high efficiency and low noise,will find applications in quantum information processing technique based on photonic qubits.

Improved metrics for evaluating fault detection efficiency of test suite

By analyzing the average percent of faults detected （APFD） metric and its variant versions, which are widely utilized as metrics to evaluate the fault detection efficiency of the test suite, this paper points out some limitations of the APFD series metrics. These limitations include APFD series metrics having inaccurate physical explanations and being unable to precisely describe the process of fault detection. To avoid the limitations of existing metrics, this paper proposes two improved metrics for evaluating fault detection efficiency of a test suite, including relative-APFD and relative-APFDc. The proposed metrics refer to both the speed of fault detection and the constraint of the testing source. The case study shows that the two proposed metrics can provide much more precise descriptions of the fault detection process and the fault detection efficiency of the test suite.

Efficiency-determined method for thermal neutron detection with inorganic scintillator

Because of 3He shortage,sintillator is a promising alternative choice for neutron detection in the field of thermal neutron scattering and imaging.Also,the neutron detection efficiency is difficult to be determined.In this paper,the efficiency for thermal neutron detection is presented by inorganic scintillator using probability principles,supposed that the material of scintillator is uniform in element distribution,and that attenuation length of scintillation light is longer than that of its thickness in the scintillator.The efficiencies for two pieces of lithium glass are determined by this method,indicating the method is useful for determining efficiency of thermal neutron detections.

Neutron response functions and detection efficiency of a spherical proton recoil proportional counter

The neutron response function and detection efficiency of a spherical proton recoil proportional counter (SP) play key roles in precise measurement of neutron spectra of the interior materials.In this paper,the response functions and detection efficiency of three SPs developed at CAEP are simulated by Geant4.The simulated spectra are compared with pulse-height spectra measured at 0.165,0.575,1.4,and 14.1 MeV of incident neutrons.And the calculated detector efficiencies agree within 5%with the data obtained by neutron activation.

Quantum Cat Swarm Optimization Based Clustering with Intrusion Detection Technique for Future Internet of Things Environment

The Internet of Things(IoT)is one of the emergent technologies with advanced developments in several applications like creating smart environments,enabling Industry 4.0,etc.As IoT devices operate via an inbuilt and limited power supply,the effective utilization of available energy plays a vital role in designing the IoT environment.At the same time,the communication of IoT devices in wireless mediums poses security as a challenging issue.Recently,intrusion detection systems(IDS)have paved the way to detect the presence of intrusions in the IoT environment.With this motivation,this article introduces a novel QuantumCat SwarmOptimization based Clustering with Intrusion Detection Technique(QCSOBC-IDT)for IoT environment.The QCSOBC-IDT model aims to achieve energy efficiency by clustering the nodes and security by intrusion detection.Primarily,the QCSOBC-IDT model presents a new QCSO algorithm for effectively choosing cluster heads(CHs)and organizing a set of clusters in the IoT environment.Besides,the QCSO algorithm computes a fitness function involving four parameters,namely energy efficiency,inter-cluster distance,intra-cluster distance,and node density.A harmony search algorithm(HSA)with a cascaded recurrent neural network(CRNN)model can be used for an effective intrusion detection process.The design of HSA assists in the optimal selection of hyperparameters related to the CRNN model.A detailed experimental analysis of the QCSOBC-IDT model ensured its promising efficiency compared to existing models.

Verification of modified sum-peak method in Monte Carlo simulation of full energy peak and sum-peak efficiencies of a HPGe detector

The modified sum-peak method estimates radioactivity by using only the peak and the sum-peak count rates.To verify the modified sum-peak method,the dependence of the full energy peak efficiency on the source-to-detector distance in a high-purity germanium detector system was studied using a Geant4 Monte Carlo simulation.The effect of the deadlayer in the germanium crystal was estimated by reference to experiments on 241 Am and the relative efficiency of the detector.The peak efficiency dependence on the source-to-detector distance was compared between the simulation and measurements.The modified sum-peak method is discussed with respect to these peak efficiencies.

A Novel Low-Complexity Low-Latency Power Efficient Collision Detection Algorithm for Wireless Sensor Networks

Collision detection mechanisms in Wireless Sensor Networks (WSNs) have largely been revolving around direct demodulation and decoding of received packets and deciding on a collision based on some form of a frame error detection mechanism, such as a CRC check. The obvious drawback of full detection of a received packet is the need to expend a significant amount of energy and processing complexity in order to fully decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a suite of novel, yet simple and power-efficient algorithms to detect a collision without the need for full-decoding of the received packet. Our novel algorithms aim at detecting collision through fast examination of the signal statistics of a short snippet of the received packet via a relatively small number of computations over a small number of received IQ samples. Hence, the proposed algorithms operate directly at the output of the receiver's analog-to-digital converter and eliminate the need to pass the signal through the entire. In addition, we present a complexity and power-saving comparison between our novel algorithms and conventional full-decoding (for select coding schemes) to demonstrate the significant power and complexity saving advantage of our algorithms.

Determination of Gamma point source efficiency based on a backpropagation neural network

Efficiency is an important factor in quantitative and qualitative analysis of radionuclides, and the gamma point source efficiency is related to the radial angle,detection distance, and gamma-ray energy. In this work, on the basis of a back-propagation(BP) neural network model,a method to determine the gamma point source efficiency is developed and validated. The efficiency of the point sources ^(137)Cs and ^(60)Co at discrete radial angles, detection distances, and gamma-ray energies is measured, and the BP neural network prediction model is constructed using MATLAB. The gamma point source efficiencies at different radial angles, detection distances, and gamma-ray energies are predicted quickly and accurately using this nonlinear prediction model. The results show that the maximum error between the predicted and experimental values is 3.732% at 661.661 keV, 11π/24, and 35 cm, and those under other conditions are less than 3%. The gamma point source efficiencies obtained using the BP neural network model are in good agreement with experimental data.

Geant4 analysis and optimization of a double crystal phoswich detector for beta–gamma coincidence detection

In this study, a novel phoswich detector for beta–gamma coincidence detection is designed. Unlike the triple crystal phoswich detector designed by researchers at the University of Missouri, Columbia, this phoswich detector is of the semi-well type, so it has a higher detection efficiency. The detector consists of BC-400 and NaI:Tl with decay time constants of 2.4 and 230 ns, respectively.The BC-400 scintillator detects beta particles, and the Na I:Tl cell is used for gamma detection. Geant4 simulations of this phoswich detector find that a 2-mm-thick BC-400 scintillator can absorb nearly all of the beta particles whose energies are below 700 keV. Further, for a 2.00-cmthick NaI:Tl crystal, the gamma source peak efficiency for photons ranges from a maximum of nearly 90% at 30 keV to 10% at 1 MeV. The self-absorption effect is also discussed in this paper in order to determine the carrier gas' s influence.

Monte Carlo Simulation Study of Hot-Particle Detection in Voluminous Samples by Gamma Spectrometry

In this work, we addressed the inhomogeneity problem in gamma spectrometry caused by hot particles, which are dispersed into environment from large nuclear reactor accidents such as at Chernobyl and Fukushima. Using Monte Carlo simulation, we have determined the response of a gamma spectrometer to individual and grouped hot particles randomly distributed in a soil matrix of 1-L and 0.6-L sample containers. By exploring the fact that the peak-to-total ratio of efficiencies in gamma spectrometry is an empirical parameter, we derived and verified a power-law relationship between the peak efficiency and peak-to-total ratio. This enabled creation of a novel calibration model which was demonstrated to reduce the bias range and bias standard deviation, caused by measuring hot particles, by several times, as compared with the homogeneous calibration. The new model is independent of the number, location, and distribution of hot particles in the samples. In this work, we demonstrated successful performance of the model for a single-peak 137Cs radionuclide. An extension to multi-peak radionuclide was also derived.

100 Gbit/s Nyquist-WDM PDM 16-QAM Transmission over 1200 km SMF-28 with Ultrahigh Spectrum Efficiency

Nyquist wavelength-division multiplexing （N-WDM） allows high spectral efficiency （SE） in long-haul transmission systems. Compared to polarization-division multiplexing quadrature phase-shift keying （PDM-QPSK）, multilevel modulation, such as PDM 16 quadrature-amplitude modulation （16-QAM）, is much more sensitive to intrachannel noise and interchannel linear crosstalk caused by N-WDM. We experimentally generate and transmit a 6 x 128 Gbit/s N-WDM PDM 16-QAM signal over 1200 km single-mode fiber （SMF）-28 with amplification provided by an erbium-doped fiber amplifier （EDFA） only. The net SE is 7.47 bit/s/Hz, which to the best of our knowledge is the highest SE for a signal with a bit rate beyond 100 Gbit/s using the PDM 16-QAM. Such SE was achieved by DSP pre-equalization of transmitter-side impairments and DSP post-equalization of channel and receiver-side impairments. Nyquist-band can be used in pre-equalization to enhance the tolerance of PDM 16-QAM to aggressive spectral shaping. The bit-error ratio （BER） for each of the 6 channels is smaller than the forward error correction （FEC） limit of 3.8 × 10-3 after 1200 km SMF-28 transmission.

Study of Annealing the Damaged HPGe Detector

High purity germanium detectors have important applications in many fields. Detector’s performance deteriorated significantly due to radiation of neutron. The annealing of damaged HPGe detector is expounded in this monograph. The experiment results indicate that raising the temperature to 70°C for five days, the restoration efficiency can reach 90%.

Comparison and Adaptation of Two Strategies for Anomaly Detection in Load Profiles Based on Methods from the Fields of Machine Learning and Statistics

The Federal Office for Economic Affairs and Export Control (BAFA) of Germany promotes digital concepts for increasing energy efficiency as part of the “Pilotprogramm Einsparzähler”. Within this program, Limón GmbH is developing software solutions in cooperation with the University of Kassel to identify efficiency potentials in load profiles by means of automated anomaly detection. Therefore, in this study two strategies for anomaly detection in load profiles are evaluated. To estimate the monthly load profile, strategy 1 uses the artificial neural network LSTM (Long Short-Term Memory), with a data period of one month (1 M) or three months (3 M), and strategy 2 uses the smoothing method PEWMA (Probalistic Exponential Weighted Moving Average). By comparing with original load profile data, residuals or summed residuals of the sequence lengths of two, four, six and eight hours are identified as an anomaly by exceeding a predefined threshold. The thresholds are defined by the Z-Score test, i.e., residuals greater than 2, 2.5 or 3 standard deviations are considered anomalous. Furthermore, the ESD (Extreme Studentized Deviate) test is used to set thresholds by means of three significance level values of 0.05, 0.10 and 0.15, with a maximum of k = 40 iterations. Five load profiles are examined, which were obtained by the cluster method k-Means as a representative sample from all available data sets of the Limón GmbH. The evaluation shows that for strategy 1 a maximum F1-value of 0.4 (1 M) and for all examined companies an average F1-value of maximum 0.24 and standard deviation of 0.09 (1 M) could be achieved for the investigation on single residuals. In variant 3 M the highest F1-value could be achieved with an average F1-value of 0.21 and standard deviation of 0.06 (3 M) for summed residuals of the partial sequence length of four hours. The PEWMA-based strategy 2 did not show a higher anomaly detection efficacy compared to strategy 1 in any of the investigated companies.

Detection Situation and Improvement of Lightning Monitoring Network in Guizhou Province

Based on lightning location data of lightning monitoring network in Guizhou Province in recent eight years,the effective detection radius of a station and the effective detection range of lightning monitoring network in Guizhou Province were analyzed. The results show that the effective detection radius of a lightning monitoring sub-station in Guizhou Province is 160 km; some counties in the southwest,northwest and northeast of Guizhou were not detected. To improve the detector efficiency of lightning monitoring network in Guizhou Province,it is suggested that nine sub-stations should be built in Weining,Shuicheng,Qinglong,Pingtang,Rongjiang,Yuping,Songtao,Tongren and Renhuai,so that the effective detection efficiency will reach more than 95%.

Performance optimization of scintillator neutron detectors for EMD in CSNS

Chinese Spallation Neutron Source(CSNS) has successfully produced its first neutron beam in 28th August 2017. It has been running steadily from March, 2018. According to the construction plan, the engineering materials diffractometer(EMD) will be installed between 2019–2023. This instrument requires the neutron detectors with the cover area near3 m2in two 90° neutron diffraction angle positions, the neutron detecting efficiency is better than 40%@1A, and the spatial resolution is better than 4 mm×200 mm in horizontal and vertical directions respectively. We have developed a onedimensional position-sensitive neutron detector based on the oblique6Li F/Zn S(Ag) scintillators, wavelength shifting fibers,and Si PMs(silicon photomultipliers) readout. The inhomogeneity of the neutron detection efficiency between each pixel and each detector module, which caused by the inconsistency of the wave-length shifting fibers in collecting scintillation photons, needs to be mitigated before the installation. A performance optimization experiment of the detector modules was carried out on the BL20(beam line 20) of CSNS. Using water sample, the neutron beam with Φ5 mm exit hole was dispersed related evenly into the forward space. According to the neutron counts of each pixel of the detector module, the readout electronics threshold of each pixel is adjusted. Compared with the unadjusted detector module, the inhomogeneity of the detection efficiency for the adjusted one has been improved from 69% to 90%. The test result of the diffraction peak of the standard sample Si showed that the adjusted detector module works well.

MsFireD-Net:A lightweight and efficient convolutional neural network for flame and smoke segmentation

With the rising frequency and severity of wildfires across the globe,researchers have been actively searching for a reliable solution for early-stage forest fire detection.In recent years,Convolutional Neural Networks(CNNs)have demonstrated outstanding performances in computer vision-based object detection tasks,including forest fire detection.Using CNNs to detect forest fires by segmenting both flame and smoke pixels not only can provide early and accurate detection but also additional information such as the size,spread,location,and movement of the fire.However,CNN-based segmentation networks are computationally demanding and can be difficult to incorporate onboard lightweight mobile platforms,such as an Uncrewed Aerial Vehicle(UAV).To address this issue,this paper has proposed a new efficient upsampling technique based on transposed convolution to make segmentation CNNs lighter.This proposed technique,named Reversed Depthwise Separable Transposed Convolution(RDSTC),achieved F1-scores of 0.78 for smoke and 0.74 for flame,outperforming U-Net networks with bilinear upsampling,transposed convolution,and CARAFE upsampling.Additionally,a Multi-signature Fire Detection Network(MsFireD-Net)has been proposed in this paper,having 93%fewer parameters and 94%fewer computations than the RDSTC U-Net.Despite being such a lightweight and efficient network,MsFireD-Net has demonstrated strong results against the other U-Net-based networks.

Explainable deep transfer learning for energy efficiency prediction based on uncertainty detection and identification

Energy efficiency is an important aspect of increasing production capacity, minimizing environmental impact, and reducing energy usage in the petrochemical industries. However, in practice, data quality can be degraded by measurement malfunction throughout the operation, leading to unreliable and inaccurate prediction results. Therefore, this paper presents a transfer learning fault detection and identification-energy efficiency predictor (TFDI-EEP) model formulated using long short-term memory. The model aims to predict the energy efficiency of the petrochemical process under uncertainty by using the knowledge gained from the uncertainty detection task to improve prediction performance. The transfer procedure resolves weight initialization by applying partial layer freezing before fine-tuning the additional part of the model. The performance of the proposed model is verified on a wide range of fault variations to thoroughly examine the maximum contribution of faults that the model can tolerate. The results indicate that the TFDI-EEP achieved the highest r-squared and lowest error in the testing step for both the 10% and 20% fault variation datasets compared to other conventional methods. Furthermore, the revelation of interconnection between domains shows that the proposed model can also identify strong fault-correlated features, enhancing monitoring ability and strengthening the robustness and reliability of the model observed by the number of outliers. The transfer parameter improves the prediction performance by 9.86% based on detection accuracy and achieves an r-squared greater than 0.95 on the 40% testing fault variation.

炼化企业挥发性有机化合物减排的探索与实践

炼化企业加工过程有组织、无组织排放的挥发性有机化合物(VOCs)量较大,对大气环境中的颗粒物、臭氧浓度等产生重大影响。A公司自“十三五”以来对VOCs减排进行了探索与实践,通过采取油气回收、储罐浮盘高效密封改造、呼吸阀改型升级、无组织排放改为有组织回收、VOCs焚烧、常态化泄漏检测与修复工作等多种减排措施,VOCs排放量已从2015年的4230 t下降到2022年的2157 t,降低比例达到49%,取得了显著的成效。

影响相干检测灵敏度的因素

【目的】相干光通信是一种高速、高质量和高保密的通信方式,而相干检测在相干光通信中有着极其重要的作用。高灵敏度的相干检测系统可以大幅提升相干光通信的距离,通过研究影响相干检测灵敏度的因素可以针对性地做出校正,从而提高检测的灵敏度。【方法】文章简要阐述了相干检测的原理,总结了该领域的国内外研究进展,分析了无线光相干检测系统产生的热噪声、散粒噪声、波前畸变、偏振失配角、光斑尺寸偏差和光轴偏转、耦合效率、探测器的性能和前置光放大器等方面对相干检测灵敏度的影响。【结果】结果表明,系统的热噪声和散粒噪声对检测灵敏度的影响可以通过控制本振光功率使其达到最小;信号光的波前畸变可以通过增加自适应校正系统来抑制;信号光与本振光的偏振失配角可以采用偏振控制补偿装置来补偿;光斑尺寸偏差和光轴偏转需要提高硬件的加工水平来减小这种误差;使用新型的探测器可以在一定程度上提升探测器的性能;前置光放大器能够对微弱的信号光进行放大,对提升检测灵敏度十分重要。【结论】文章分析了影响无线光相干检测灵敏度的因素,总结了近年来国内外学者提高检测灵敏度的方法,为实际应用中检测系统灵敏度的提升提供了理论参考,对相干光通信系统性能的提升有着重要的意义。

硅像素探测器X射线探测效率实验研究

X射线探测器经历了气体探测器和闪烁体探测器时代,进入了半导体探测器时代。作为一种新型的半导体探测器,硅像素探测器具有分辨率好、探测效率高、时间响应快、功耗低等特点。为了研究硅像素探测器X射线探测效率,设计了一套X射线探测系统,并进行了实验研究。实验分别测试了4.51、5.41、6.40、8.05 keV共4种不同能量的X射线的探测效率。后期进行了误差分析和数据处理,得到4种不同能量X射线正面入射时探测效率分别为53.00%、51.56%、40.65%和29.91%。该实验研究为寻找高探测效率的X射线探测器提供了一种新的思路。