Detection and identification of green vitriol in autopsy material- A case study

The development of Fault Detection and Identification (FDI) systems for complex mechatronic systems is a challenging process. Many quantitative and qualitative fault detection methods have been proposed in past literature. Few methods address multiple faults, instead an emphasis is placed on accurately proving a single fault exists. The omission of multiple faults regulates the capability of most fault detection methods. The Functional Failure Identification and Propagation (FFIP) framework has been utilized in past research for various applications related to fault propagation in complex systems. In this paper a Hierarchical Functional Fault Detection and Identification (HFFDI) system is proposed. The development of the HFFDI system is based on machine learning techniques, commonly used as a basis for FDI systems, and the functional system decomposition of the FFIP framework. The HFFDI is composed of a plant-wide FDI system and function-specific FDI systems. The HFFDI aims at fault identification in multiple fault scenarios using single fault data sets, when faults happen in different system functions. The methodology is applied to a case study of a generic nuclear power plant with 17 system functions. Compared with a plant-wide FDI system, in multiple fault scenarios the HFFDI gave better results for identifying one fault and also was able to identify more than one faults. The case study results show that in two fault scenarios the HFFDI was able to identify one of the faults with 79% accuracy and both faults with 13% accuracy. In three fault scenarios the HFFDI was able to identify one of the faults with 69% accuracy, two faults with 22% accuracy and all three faults with 1% accuracy.

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Detection, Recovery, and Destruction System for Sea-Disposed Chemical Munitions: Port Kanda, Japan

Marine Technology Society Journal ◽

10.4031/mtsj.43.4.8 ◽

2009 ◽

Vol 43 (4) ◽

pp. 116-126 ◽

Cited By ~ 1

Author(s):

Joseph K. Asahina ◽

Hisamitsu Shimoyama ◽

Koichi Hayashi ◽

Atsushi Shinkai

Keyword(s):

Deep Sea ◽

High Efficiency ◽

Chemical Agent ◽

Critical Depth ◽

Operation System ◽

The Public ◽

Potential Source ◽

Detection And Identification

AbstractAt Port Kanda, Japan, a systematic project for detection, recovery, and destruction of sea-disposed chemical munitions is ongoing. This project is unique in its size, scope, and the complexity of operations, and, therefore, provides an excellent case study. The systems used include a high efficiency magnetometer system for detection and identification of chemical munitions, a chemical agent resistant diving gear, a double-walled container for chemical munitions recovery and transport, and a DAVINCH® float detonation system. The development of the systems is described together with the testing results and operating record.The surveyed and cleaned-up area is 650 ha, and over 2,700 chemical munitions have been recovered and destroyed. The pre- and post-recovery sampling and analysis results are disclosed to the public to gain their acceptance and assure them that no contamination remains. The potential impacts to seafood from arsenic-bearing compounds were a concern and are briefly discussed.A deep sea operation system involving a remote recovery sealing-up container, and DAVINCH®, a floating detonation system on the recovery site, are proposed. The authors introduce the concept of “Critical Depth” for the munitions clean-up. This is the reasonable depth to which recovery of a potential source of contamination should be considered and beyond which it should not.

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A Functional Modelling Based Methodology for Testing the Predictions of Fault Detection and Identification Systems

Volume 1B: 36th Computers and Information in Engineering Conference ◽

10.1115/detc2016-59916 ◽

2016 ◽

Cited By ~ 1

Author(s):

Nikolaos Papakonstantinou ◽

Scott Proper ◽

Douglas L. Van Bossuyt ◽

Bryan O’Halloran ◽

Irem Y. Tumer

Keyword(s):

Fault Detection ◽

Nuclear Power ◽

Past Research ◽

System A ◽

Detection And Identification ◽

Incorrect Prediction ◽

Fault Detection And Identification ◽

Analytical Proof ◽

Critical Process

Fault detection and identification (FDI) systems, which are based on data mining and artificial intelligence techniques, cannot guarantee a perfect success rate or provide analytical proof for their predictions. This characteristic is problematic when such an FDI system is monitoring a safety-critical process. In these cases, the predictions of the FDI system need to be verified by other means, such as tests on the process, to increase trust in the diagnosis. This paper contributes an extension of the Hierarchical Functional Fault Detection and Identification (HFFDI) system, a combination of a plant-wide and multiple function-specific FDI modules, developed in past research. A test preparation and test-based verification phase is added to the HFFDI methodology. The functional decomposition of the process and the type of the faulty components guides the preparation of specific tests for every fault to be identifiable by the HFFDI system. These tests have the potential to confirm or disprove the existence of the fault(s) in the target process. The target is minor automation faults in redundant systems of the monitored process. The proposed extension of the HFFDI system is applied to a case study of a generic Nuclear Power Plant model. Two HFFDI predictions are tested (a successful and an incorrect prediction) in single fault scenarios and one prediction is tested in a in a two fault scenario. The results of the case study show that the testing phase introduced in this paper is able to confirm correct fault predictions and reject incorrect fault predictions, thus the HFFDI extension presented here improves the confidence of the HFFDI output.

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A Data-Challenge Case Study of Analyte Detection and Identification with Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry (GC×GC-MS)

Separations ◽

10.3390/separations6030038 ◽

2019 ◽

Vol 6 (3) ◽

pp. 38

Author(s):

Stephen E. Reichenbach ◽

Qingping Tao ◽

Chiara Cordero ◽

Carlo Bicchi

Keyword(s):

Mass Spectrometry ◽

New Method ◽

Two Dimensional ◽

Spectral Library ◽

Mass Spectral ◽

Detection And Identification ◽

Percent Response ◽

Analyte Detection ◽

Detector Saturation

This case study describes data analysis of a chromatogram distributed for the 2019 GC×GC Data Challenge for the Tenth Multidimensional Chromatography Workshop (Liege, Belgium). The chromatogram resulted from chemical analysis of a terpene-standards sample by comprehensive two-dimensional chromatography with mass spectrometry (GC×GC-MS). First, several aspects of the data quality are assessed, including detector saturation and oscillation, and operations to prepare the data for analyte detection and identification are described, including phase roll for modulation-cycle alignment and baseline correction to account for the non-zero detector baseline. Then, the case study presents operations for analyte detection with filtering, a new method to flag false detections, interactive review to confirm detected peaks, and ion-peaks detection to reveal peaks that are obscured by noise or coelution. Finally, the case study describes analyte identification including mass-spectral library search with a new method for optimizing spectra extraction, retention-index calibration from preliminary identifications, and expression-based identification checks. Processing of the first 40 min of data detected 144 analytes, 21 of which have at least one percent response, plus an additional 20 trace and/or coeluted analytes.

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Investigating on OLI and TIRS data fusion methods in detection and identification of ancient hills (case study: Farahan area)

Applied Geomatics ◽

10.1007/s12518-021-00366-3 ◽

2021 ◽

Author(s):

Ali GholamReza ◽

Abbass Malian

Keyword(s):

Data Fusion ◽

Detection And Identification ◽

Fusion Methods

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OPCW Proficiency Test: A Practical Approach Also for Interlaboratory Test on Detection and Identification of Pesticides in Environmental Matrices

The Scientific World JOURNAL ◽

10.1155/2014/542357 ◽

2014 ◽

Vol 2014 ◽

pp. 1-25 ◽

Cited By ~ 3

Author(s):

Leszek Konopski ◽

Pingfeng Liu ◽

Wuri Wuryani ◽

Maciej Śliwakowski

Keyword(s):

Proficiency Test ◽

Organophosphorus Compounds ◽

General Strategy ◽

Chemical Weapon ◽

Environmental Matrices ◽

Chemical Weapon Convention ◽

Interlaboratory Test ◽

Detection And Identification ◽

Standard Procedures

An overview of general strategy, standard procedures, and critical points, which may be found during carrying out an OPCW Proficiency Test concerning detection and identification of scheduled compounds relevant to Chemical Weapon Convention, has been presented. The observations have been illustrated following the case of the Eight OPCW Designated Laboratories Proficiency Test, which was performed in the OPCW Laboratory in Rijswijk in November and December 2000. Various useful hints, comments, and practical observations concerning the case study have been included as well. The same methodology and procedures may be also applied for detection, identification, and environmental analyses of pesticides and biocides, especially organophosphorus compounds.

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DETECTION OF GREEN OPEN SPACE USING COMBINATION INDEX OF LANDSAT 8 DATA (CASE STUDY: DKI JAKARTA)

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2016.v13.a2712 ◽

2017 ◽

Vol 13 (1) ◽

pp. 1

Author(s):

Sayidah Sulma ◽

Jalu Tejo Nugroho ◽

Any Zubaidah ◽

Hana Listi Fitriana ◽

Nanik Suryo Haryani

Keyword(s):

Urban Areas ◽

Open Space ◽

Spatial Information ◽

Landsat 8 ◽

Open Spaces ◽

Water Index ◽

Detection And Identification ◽

Index Approach ◽

Normalized Difference Water Index

Spatial information about the availability and presence of green open space in urban areas to be up to date and transparent was a necessity. This study explained the technique to get the green open spaces of spatial information quickly using an index approach of Landsat 8. The purpose of this study was to evaluate the ability of the method to detect the green open spaces, especially using Landsat 8 with a combination of several indices, namely Normalized Difference Build-up Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Build-up Index (NDBI) and Normalized Difference Bareness Index (NDBaI) with a study area of Jakarta. This study found that the detection and identification of green open space classes used a combination of index and band gave good results with an accuracy of 81%.

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