scholarly journals High-Resolution Distributed Radiation Detector System Assisted by Intelligent Image Recognition

2021 ◽  
Vol 9 ◽  
Author(s):  
Hong Shao ◽  
Chenyue Wang ◽  
Zhixin Fu ◽  
Zhen Liu
Keyword(s):  
Image Recognition ◽  
Detection System ◽  
Power Grid ◽  
Radiation Detector ◽  
Radiation Detectors ◽  
Detector System ◽  
Stable Operation ◽  
Efficient Manner ◽  
Large Numbers ◽  
Sufficient Energy

With the development of machine learning and image recognition technology, the detector system tends to be standardized and intelligent. However, large numbers of distributed radiation detectors connected to the power grid will bring huge uncertainty to the operation of the power grid and even cause certain interference. The monitoring system of the distributed radiation detectors can control the running status of the distributed photoelectric detection system in real-time and guarantee the safe and stable operation of the detector system. This article proposes an improved genetic detector system to avoid “blind spots” in the radiation detector monitoring based on the characteristics of photovoltaic (PV) arrays, which are considered as individual pixels, and then the reliability of the monitoring is ensured by the monitoring coverage of these pixels by the detector nodes. The performance of the radiation detector monitoring is restored by activating those spare nodes with sufficient energy to replace those that fail, ensuring that the distributed detection system can be monitored in a timely and efficient manner at all times. The simulation results confirm the reasonable validity of the algorithm.

Acquisition of EELS spectra for high-resolution spectroscopy

1993 ◽  
Vol 51 ◽  
pp. 578-579
Author(s):  
Maoxu Qian ◽  
Mehmet Sarikaya ◽  
Edward A. Stern
Keyword(s):  
Energy Loss ◽  
Detection System ◽  
High Resolution Spectroscopy ◽  
High Signal ◽  
Short Report ◽  
Edge Structure ◽  
High Background ◽  
Gain Variation ◽  
Sufficient Energy ◽  
On Line

It is difficult, in general, to perform quantitative EELS to determine, for example, relative or absolute compositions of elements with relatively high atomic numbers (using, e.g., K edge energies from 500 eV to 2000 eV), to study ELNES (energy loss near edge structure) signal using the white lines to determine oxidation states, and to analyze EXELFS (extended energy loss fine structure) to study short range ordering. In all these cases, it is essential to have high signal-to-noise (S/N) ratio (low systematical error) with high overall counts, and sufficient energy resolution (∽ 1 eV), requirements which are, in general, difficult to attain. The reason is mainly due to three important inherent limitations in spectrum acquisition with EELS in the TEM. These are (i) large intrinsic background in EELS spectra, (ii) channel-to-channel gain variation (CCGV) in the parallel detection system, and (iii) difficulties in obtaining statistically high total counts (∽106) per channel (CH). Except the high background in the EELS spectrum, the last two limitations may be circumvented, and the S/N ratio may be attained by the improvement in the on-line acquisition procedures. This short report addresses such procedures.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

Self-Efficacy Regarding Social Work Competencies

2015 ◽  
Vol 27 (5) ◽  
pp. 594-606 ◽  
Author(s):  
Gary Holden ◽  
Kathleen Barker ◽  
Sofie Kuppens ◽  
Gary Rosenberg
Keyword(s):  
Social Work ◽  
Educational Outcomes ◽  
Self Efficacy ◽  
Empirical Work ◽  
Preliminary Evidence ◽  
Efficient Manner ◽  
Replication Studies ◽  
Large Numbers ◽  
Social Work Programs ◽  
Work Education

Purpose: The need for psychometrically sound measurement approaches to social work educational outcomes assessment is increasing. Method: The research reported here describes an original and two replication studies of a new scale ( N = 550) designed to assess an individual’s self-efficacy regarding social work competencies specified by the Council on Social Work Education as part of the accreditation of social work programs. Results: This new measure, the Self-Efficacy Regarding Social Work Competencies Scale (SERSWCS), generally performed in line with our expectations. Discussion: The SERSWCS is a measure that is based on substantial theoretical and empirical work, has preliminary evidence regarding the psychometric properties of the data it produces, can be used with large numbers of students in an efficient manner, is neither expensive or subject to user restrictions, and provides views of outcomes that have utility for pedagogical considerations at multiple curricular levels.

Mid-Term Stability Assessment of Jiangxi Power Grid

2013 ◽  
Vol 732-733 ◽  
pp. 882-887
Author(s):  
Yong Chun Su ◽  
Hao Wei Jia
Keyword(s):  
Power System ◽  
Power Grid ◽  
Assessment Method ◽  
Control Measures ◽  
Assessment Process ◽  
Jiangxi Province ◽  
Stable Operation ◽  
Stability Assessment ◽  
Term Stability ◽  
The Stability

Mid-term stability assessment is an important work to support power system operation in a province power grid of China every year. The stability assessment method and process was introduced in this paper. As an example, the stability of Jiangxi province power system was evaluated in the following two years. Weak area and weak transmission line were found out in each power supply area. Prevention and control measures were proposed. According to problems among the assessment process and using the state monitoring data, an approach was discussed to increase the assessment result accuracy. The analysis conclusion provides the reference to the safe and stable operation of Jiangxi power system.

Hybridization of Machine Learning Algorithm in Intrusion Detection System

2020 ◽  
pp. 150-175
Author(s):  
Amudha P. ◽  
Sivakumari S.
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Classification Accuracy ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Feature Selection Technique ◽  
Efficient Manner

In recent years, the field of machine learning grows very fast both on the development of techniques and its application in intrusion detection. The computational complexity of the machine learning algorithms increases rapidly as the number of features in the datasets increases. By choosing the significant features, the number of features in the dataset can be reduced, which is critical to progress the classification accuracy and speed of algorithms. Also, achieving high accuracy and detection rate and lowering false alarm rates are the major challenges in designing an intrusion detection system. The major motivation of this work is to address these issues by hybridizing machine learning and swarm intelligence algorithms for enhancing the performance of intrusion detection system. It also emphasizes applying principal component analysis as feature selection technique on intrusion detection dataset for identifying the most suitable feature subsets which may provide high-quality results in a fast and efficient manner.

scholarly journals Control of MMC-Based STATCOM as an Effective Interface between Energy Sources and the Power Grid

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1264 ◽  
Author(s):  
Fatemeh Shahnazian ◽  
Ebrahim Adabi ◽  
Jafar Adabi ◽  
Edris Pouresmaeil ◽  
Kumars Rouzbehi ◽  
...  
Keyword(s):  
Reactive Power ◽  
Control Method ◽  
Second Harmonic ◽  
Power Grid ◽  
Harmonic Component ◽  
Current Control ◽  
Energy Sources ◽  
Stable Operation ◽  
Multilevel Converters ◽  
Steady State Operation

This paper presents a dynamic model of modular multilevel converters (MMCs), which are considered as an effective interface between energy sources and the power grid. By improving the converter performance, appropriate reactive power compensation is guaranteed. Modulation indices are calculated based on detailed harmonic evaluations of both dynamic and steady-state operation modes, which is considered as the main contribution of this paper in comparison with other methods. As another novelty of this paper, circulating current control is accomplished by embedding an additional second harmonic component in the modulation process. The proposed control method leads to an effective reduction in capacitor voltage fluctuation and losses. Finally, converter’s maximum stable operation range is modified, which provides efficiency enhancements and also stability assurance. The proficiency and functionality of the proposed controller are demonstrated through detailed theoretical analysis and simulations with MATLAB/Simulink.

A duplex ddPCR assay for simultaneously detecting Ips sexdentatus and Ips typographus (Coleoptera: Curculionidae) in bulk trap samples

2019 ◽  
Vol 49 (8) ◽  
pp. 903-914 ◽  
Author(s):  
Frida A. Zink ◽  
Luke R. Tembrock ◽  
Alicia E. Timm ◽  
Todd M. Gilligan
Keyword(s):  
Dna Extraction ◽  
Bark Beetles ◽  
Digital Pcr ◽  
The United States ◽  
Ips Typographus ◽  
Morphological Identification ◽  
Efficient Manner ◽  
Large Numbers ◽  
The Family ◽  
Ips Sexdentatus

Bark beetles in the family Curculionidae present a growing hazard to forests worldwide. Like native bark beetles, introduced exotic species can pose a serious threat to North American forests. Ips typographus (Boerner) and Ips sexdentatus (Linnaeus), both native to Europe, are two such pests that have caused widespread forest loss in their native ranges. International trade has led to increased interceptions of Scolytine beetles at ports of entry to the United States. Most intercepted individuals are not identified to species due to lack of expert identifiers, poor specimen quality, or incomplete taxonomy. These same problems affect identification for domestic surveys. Therefore, development of molecular methods for identification of potentially invasive Ips species is essential. Because of the need to scrutinize large numbers of beetles in an efficient manner, we describe a duplex droplet digital PCR (ddPCR) assay to identify I. typographus and I. sexdentatus simultaneously in bulk trap samples containing 500 Scolytinae specimens using a scalable, two-step DNA extraction. This ddPCR method is highly effective for processing the entire contents of beetle traps and identifying these potentially invasive species in a timely and definitive manner. We also describe a nondestructive DNA extraction technique that preserves specimens for morphological identification.

scholarly journals Evaluation of Various Scintillator Materials in Radiation Detector Design for Positron Emission Tomography (PET)

Crystals ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 869
Author(s):  
Siwei Xie ◽  
Xi Zhang ◽  
Yibin Zhang ◽  
Gaoyang Ying ◽  
Qiu Huang ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
High Performance ◽  
Radiation Detector ◽  
Experimental Studies ◽  
Light Output ◽  
Radiation Detectors ◽  
Emission Tomography ◽  
Timing Resolution ◽  
Positron Emission ◽  
Co Doped

The performance of radiation detectors used in positron-emission tomography (PET) is determined by the intrinsic properties of the scintillators, the geometry and surface treatment of the scintillator crystals and the electrical and optical characteristics of the photosensors. Experimental studies were performed to assess the timing resolution and energy resolution of detectors constructed with samples of different scintillator materials (LaBr3, CeBr3, LFS, LSO, LYSO: Ce, Ca and GAGG) that were fabricated into different shapes with various surface treatments. The saturation correction of SiPMs was applied for tested detectors based on a Tracepro simulation. Overall, we tested 28 pairs of different forms of scintillators to determine the one with the best CTR and light output. Two common high-performance silicon photomultipliers (SiPMs) provided by SensL (J-series, 6 mm) or AdvanSiD (NUV, 6 mm) were used for photodetectors. The PET detector constructed with 6 mm CeBr3 cubes achieved the best CTR with a FWHM of 74 ps. The 4 mm co-doped LYSO: Ce, Ca pyramid crystals achieved 88.1 ps FWHM CTR. The 2 mm, 4 mm and 6 mm 0.2% Ce, 0.1% Ca co-doped LYSO cubes achieved 95.6 ps, 106 ps and 129 ps FWHM CTR, respectively. The scintillator crystals with unpolished surfaces had better timing than those with polished surfaces. The timing resolution was also improved by using certain geometric factors, such as a pyramid shape, to improve light transportation in the scintillator crystals.

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