Probabilities associated with a built-in-test system, focus on false alarms

A system combined the function of Industrial Computer, PLC, virtual instruments, sensors and data acquisition was designed and developed for testing and controlling of hydraulic steering control unit, which can achieve automated data acquisition, data processing, test results judgment, false alarms and etc. with the development of computer and science. The software of test system was designed through LabVIEW, and by using module design method, error detection, modify and maintenance of the software turn to be simple and the software become more robust. The system also adopted shielding and filtering with combination of hardware and software to improve test accuracy which reduces test error caused by operators and improve test efficiency.

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Machine Learning to Ensure Data Integrity in Power System Topological Network Database

Electronics ◽

10.3390/electronics9040693 ◽

2020 ◽

Vol 9 (4) ◽

pp. 693 ◽

Cited By ~ 2

Author(s):

Adnan Anwar ◽

Abdun Mahmood ◽

Biplob Ray ◽

Md Apel Mahmud ◽

Zahir Tari

Keyword(s):

Machine Learning ◽

Power System ◽

Optimal Power Flow ◽

Energy Systems ◽

Data Integrity ◽

Test System ◽

False Alarms ◽

High Detection Rate ◽

Database Model ◽

The Impact

Operational and planning modules of energy systems heavily depend on the information of the underlying topological and electric parameters, which are often kept in database within the operation centre. Therefore, these operational and planning modules are vulnerable to cyber anomalies due to accidental or deliberate changes in the power system database model. To validate, we have demonstrated the impact of cyber-anomalies on the database model used for operation of energy systems. To counter these cyber-anomalies, we have proposed a defence mechanism based on widely accepted classification techniques to identify the abnormal class of anomalies. In this study, we find that our proposed method based on multilayer perceptron (MLP), which is a special class of feedforward artificial neural network (ANN), outperforms other exiting techniques. The proposed method is validated using IEEE 33-bus and 24-bus reliability test system and analysed using ten different datasets to show the effectiveness of the proposed method in securing the Optimal Power Flow (OPF) module against data integrity anomalies. This paper highlights that the proposed machine learning-based anomaly detection technique successfully identifies the energy database manipulation at a high detection rate allowing only few false alarms.

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Immunogold labeling of CMP-KDO synthetase produced by recombinant E. Coli cells

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100128900 ◽

1987 ◽

Vol 45 ◽

pp. 924-925

Author(s):

F. A. Durum ◽

R. G. Goldman ◽

T. J. Bolling ◽

M. F. Miller

Keyword(s):

Inclusion Bodies ◽

Large Scale ◽

Recombinant Dna ◽

Test System ◽

Cell Protein ◽

Gram Negative Bacteria ◽

Cytoplasmic Inclusions ◽

Isolation And Purification ◽

E Coli ◽

Low Concentrations

CMP-KDO synthetase (CKS) is an enzyme which plays a key role in the synthesis of LPS, an outer membrane component unique to gram negative bacteria. CKS activates KDO to CMP-KDO for incorporation into LPS. The enzyme is normally present in low concentrations (0.02% of total cell protein) which makes it difficult to perform large scale isolation and purification. Recently, the gene for CKS from E. coli was cloned and various recombinant DNA constructs overproducing CKS several thousandfold (unpublished data) were derived. Interestingly, no cytoplasmic inclusions of overproduced CKS were observed by EM (Fig. 1) which is in contrast to other reports of large proteinaceous inclusion bodies in various overproducing recombinant strains. The present immunocytochemical study was undertaken to localize CKS in these cells.Immune labeling conditions were first optimized using a previously described cell-free test system. Briefly, this involves soaking small blocks of polymerized bovine serum albumin in purified CKS antigen and subjecting them to various fixation, embedding and immunochemical conditions.

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Real Ear Sound Pressure Levels Developed by Three Portable Stereo System Earphones

American Journal of Audiology ◽

10.1044/1059-0889.0104.52 ◽

1992 ◽

Vol 1 (4) ◽

pp. 52-55 ◽

Cited By ~ 4

Author(s):

Gail L. MacLean ◽

Andrew Stuart ◽

Robert Stenstrom

Keyword(s):

High Frequency ◽

Sound Pressure ◽

Low Frequency ◽

Test System ◽

Output Frequency ◽

Frequency Effects ◽

Adult Men ◽

Frequency Responses ◽

Significant Difference ◽

Sound Pressure Levels

Differences in real ear sound pressure levels (SPLs) with three portable stereo system (PSS) earphones (supraaural [Sony Model MDR-44], semiaural [Sony Model MDR-A15L], and insert [Sony Model MDR-E225]) were investigated. Twelve adult men served as subjects. Frequency response, high frequency average (HFA) output, peak output, peak output frequency, and overall RMS output for each PSS earphone were obtained with a probe tube microphone system (Fonix 6500 Hearing Aid Test System). Results indicated a significant difference in mean RMS outputs with nonsignificant differences in mean HFA outputs, peak outputs, and peak output frequencies among PSS earphones. Differences in mean overall RMS outputs were attributed to differences in low-frequency effects that were observed among the frequency responses of the three PSS earphones. It is suggested that one cannot assume equivalent real ear SPLs, with equivalent inputs, among different styles of PSS earphones.

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Picture Recognition Errors in the Differential Diagnosis of Alzheimer’s Disease

Zeitschrift für Neuropsychologie ◽

10.1024/1016-264x/a000263 ◽

2019 ◽

Vol 30 (3) ◽

pp. 157-168

Author(s):

Helmut Hildebrandt ◽

Jana Schill ◽

Jana Bördgen ◽

Andreas Kastrup ◽

Paul Eling

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Differential Diagnosis ◽

Clinical Diagnosis ◽

Response Bias ◽

Recognition Task ◽

Discriminatory Power ◽

False Alarms ◽

Picture Recognition ◽

Recognition Errors

Abstract. This article explores the possibility of differentiating between patients suffering from Alzheimer’s disease (AD) and patients with other kinds of dementia by focusing on false alarms (FAs) on a picture recognition task (PRT). In Study 1, we compared AD and non-AD patients on the PRT and found that FAs discriminate well between these groups. Study 2 served to improve the discriminatory power of the FA score on the picture recognition task by adding associated pairs. Here, too, the FA score differentiated well between AD and non-AD patients, though the discriminatory power did not improve. The findings suggest that AD patients show a liberal response bias. Taken together, these studies suggest that FAs in picture recognition are of major importance for the clinical diagnosis of AD.

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