The Experts below are selected from a list of 480 Experts worldwide ranked by ideXlab platform
Daniel T. Gottuk - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection
2015Co-Authors: Susan L. Rose-pehrsson, Sean J. Hart, Thomas T. Street, Frederick W. Williams, Mark H. Hammond, Daniel T. Gottuk, Mark T. Wright, Patricia A. Tatem, Jennifer T. WongAbstract:on enhancing automation of shipboard fire and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desirable to increase detection sensitivity, decrease the detection time and increase the reliability of the detection system through improved Nuisance Alarm immunity. The use of multi-criteria based detection technology offers the most promising means to achieve both improved sensitivity to real fires and reduced susceptibility to Nuisance Alarm sources. An early warning fire detection system is being developed by properly processing the output from sensors that measure different parameters of a developing fire or from analyzing multiple aspects of a given sensor output (e.g., rate of change as well as absolute value). The classification performance and speed of the probabilistic neural network deployed in real-time during recent field tests have been evaluated aboard the ex-USS SHADWELL, the Advanced Damage Control fire research platform of the Naval Research Laboratory. The real-time performance is documented, and, as a result of optimization efforts, improvements in performance have been recognized. Early fire detection, while maintaining Nuisance source immunity, has been demonstrated. A detailed examination of the PNN during fire testing has been undertaken. Using real data and simulated data, a variety of scenarios (taken from recent field experiences) have been used or recreated for the purpose of understanding potential failure modes of the PNN in this application
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Early Warning Fire Detection System Using a Probabilistic Neural Network
Fire Technology, 2003Co-Authors: Susan L. Rose-pehrsson, Sean J. Hart, Thomas T. Street, Frederick W. Williams, Mark H. Hammond, Daniel T. Gottuk, Mark T. Wright, Jennifer T. WongAbstract:The Navy program, Damage Control-Automation for Reduced Manning is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desired to increase detection sensitivity and,more importantly increase the reliability of the detection system through improved Nuisance Alarm immunity. Improved reliability is needed, such that fire detection systems can automatically control fire suppression systems. The use of multi-criteria based detection technology continues to offer the most promising means to achieve both improved sensitivity to real fires,and reduced susceptibility to Nuisance Alarm sources. A multi-criteria early warning fire detection system, has been developed to provide reliable warning of actual fire conditions, in less time, with fewer Nuisance Alarms,than can be achieved with commercially available smoke detection systems. In this study a four-sensor array and a Probabilistic Neural Network have been used to produce an early warning fire detection system. A prototype early warning fire detector was built and tested in a shipboard environment. The current Alarm algorithm resulted in better overall performance than the commercial smoke detectors, by providing both improved Nuisance source immunity with generally equivalent or faster response times.
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advanced fire detection using multi signature Alarm algorithms
Fire Safety Journal, 2002Co-Authors: Daniel T. Gottuk, Michelle J Peatross, Richard J Roby, Craig L BeylerAbstract:Abstract The objective of this work was to assess the feasibility of reducing false Alarms while increasing sensitivity through the use of combined conventional smoke detectors with carbon monoxide (CO) sensors. This was accomplished through an experimental program using both real (fire) and Nuisance Alarm sources. A broad selection of sources was used ranging from smoldering wood and flaming fabric to cooking fumes. Individual sensor outputs and various signal-conditioning schemes involving multiple sensors were explored. The results show that improved fire-detection capabilities can be achieved over standard smoke detectors by combining smoke measurements with CO measurements in specific algorithms. False Alarms can be reduced while increasing sensitivity (i.e., decreasing the detection time for real fires). Patented Alarm criteria were established using algorithms consisting of the product of smoke obscuration and the change in CO concentration. Alarm algorithms utilizing ionization detector smoke measurements proved to be more effective than measurements from photoelectric detectors.
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multi criteria fire detection systems using a probabilistic neural network
Sensors and Actuators B-chemical, 2000Co-Authors: Susan L Rosepehrsson, Sean J. Hart, Frederick W. Williams, Daniel T. Gottuk, Ronald E Shaffer, Brooke D Strehlen, Scott A HillAbstract:Abstract The Navy program, Damage Control Automation for Reduced Manning (DC-ARM), is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desired to increase detection sensitivity and, more importantly, increase the reliability of the detection system through improved Nuisance Alarm immunity. Improved reliability is needed such that fire detection systems can automatically control fire suppression systems. The use of multi-criteria-based detection technology continues to offer the most promising means to achieve both improved sensitivity to real fires and reduced susceptibility to Nuisance Alarm sources. A multi-signature early warning fire detection system is being developed to provide reliable warning of actual fire conditions in less time with fewer Nuisance Alarms than can be achieved with commercially available smoke detection systems. In this study, a large database consisting of the responses of 20 sensors to several different types of fires and Nuisance sources was generated and analyzed using a variety of multivariate methods. Three data matrices were developed at discrete times corresponding to the different Alarm levels of a conventional photoelectric smoke detector. The Alarm times represent 0.82%, 1.63% and 11% obscurations per meter. The datasets were organized into three classes representing the sensor responses for baseline (nonfire), fires and Nuisance sources. A robust data analysis strategy for use with a sensor array consisting of four to five sensors for early fire detection and Nuisance source rejection was developed using a probabilistic neural network (PNN) that was developed at the Naval Research Laboratory for chemical sensor arrays. The analysis algorithms described in this paper evaluate discrete samples and develop classification models that examine individual chemical signatures at discrete points.
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real time probabilistic neural network performance and optimization for fire detection and Nuisance Alarm rejection test series 2 results
2000Co-Authors: Susan L Rosepehrsson, Sean J. Hart, Mark H. Hammond, Daniel T. Gottuk, Mark T. WrightAbstract:Abstract : A second series of tests was conducted to evaluate and improve the multivariate data analysis notebooks and candidate sensor suites used for the Early Warning Fire Detection (EWFD) system under development. The EWFD system is to provide reliable warning of actual fire conditions in less time with fewer Nuisance Alarms than commercially available smoke detection systems. Tests were conducted from 25 April to 5 May 2000 onboard the ex-USS SHADWELL. This report documents the performance of the probabilistic neural network achieved in real-time during this test series. Further optimization of the algorithm has yielded performance gains over the real-time results. Modifications have been made that improve the real-time data acquisition and the ion sensor calibration. Background subtraction was investigated and will be used in future tests. The best performance was provided by a four sensor array consisting of ionization, photoelectric carbon monoxide and carbon dioxide sensors.
Susan L Rosepehrsson - One of the best experts on this subject based on the ideXlab platform.
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multi criteria fire detection systems using a probabilistic neural network
Sensors and Actuators B-chemical, 2000Co-Authors: Susan L Rosepehrsson, Sean J. Hart, Frederick W. Williams, Daniel T. Gottuk, Ronald E Shaffer, Brooke D Strehlen, Scott A HillAbstract:Abstract The Navy program, Damage Control Automation for Reduced Manning (DC-ARM), is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desired to increase detection sensitivity and, more importantly, increase the reliability of the detection system through improved Nuisance Alarm immunity. Improved reliability is needed such that fire detection systems can automatically control fire suppression systems. The use of multi-criteria-based detection technology continues to offer the most promising means to achieve both improved sensitivity to real fires and reduced susceptibility to Nuisance Alarm sources. A multi-signature early warning fire detection system is being developed to provide reliable warning of actual fire conditions in less time with fewer Nuisance Alarms than can be achieved with commercially available smoke detection systems. In this study, a large database consisting of the responses of 20 sensors to several different types of fires and Nuisance sources was generated and analyzed using a variety of multivariate methods. Three data matrices were developed at discrete times corresponding to the different Alarm levels of a conventional photoelectric smoke detector. The Alarm times represent 0.82%, 1.63% and 11% obscurations per meter. The datasets were organized into three classes representing the sensor responses for baseline (nonfire), fires and Nuisance sources. A robust data analysis strategy for use with a sensor array consisting of four to five sensors for early fire detection and Nuisance source rejection was developed using a probabilistic neural network (PNN) that was developed at the Naval Research Laboratory for chemical sensor arrays. The analysis algorithms described in this paper evaluate discrete samples and develop classification models that examine individual chemical signatures at discrete points.
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real time probabilistic neural network performance and optimization for fire detection and Nuisance Alarm rejection test series 2 results
2000Co-Authors: Susan L Rosepehrsson, Sean J. Hart, Mark H. Hammond, Daniel T. Gottuk, Mark T. WrightAbstract:Abstract : A second series of tests was conducted to evaluate and improve the multivariate data analysis notebooks and candidate sensor suites used for the Early Warning Fire Detection (EWFD) system under development. The EWFD system is to provide reliable warning of actual fire conditions in less time with fewer Nuisance Alarms than commercially available smoke detection systems. Tests were conducted from 25 April to 5 May 2000 onboard the ex-USS SHADWELL. This report documents the performance of the probabilistic neural network achieved in real-time during this test series. Further optimization of the algorithm has yielded performance gains over the real-time results. Modifications have been made that improve the real-time data acquisition and the ion sensor calibration. Background subtraction was investigated and will be used in future tests. The best performance was provided by a four sensor array consisting of ionization, photoelectric carbon monoxide and carbon dioxide sensors.
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real time probabilistic neural network performance and optimization for fire detection and Nuisance Alarm rejection test series 1 results
2000Co-Authors: Sean J. Hart, Mark H. Hammond, Susan L Rosepehrsson, Ronald E Shaffer, Daniel T. GottukAbstract:Abstract : A series of tests were conducted to evaluate and improve the multivariate data analysis methods and candidate sensor suites used for the Early Warning Fire Detection (EWFD) system under development. The EWFD system is to provide reliable warning of actual fire conditions in less time with fewer Nuisance Alarms than commercially available smoke detection systems. Tests were conducted from 7-18 February 2000, onboard the ex-USS Sizadwell. This report documents the performance of the probabilistic neural network achieved in real-time during this test series. Further optimization of the algorithm yielded performance gains over the real-time results. Simulation studies have been done to examine the effects of sensor drop-out, excessive noise, and erroneous sensor values.
Sean J. Hart - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection
2015Co-Authors: Susan L. Rose-pehrsson, Sean J. Hart, Thomas T. Street, Frederick W. Williams, Mark H. Hammond, Daniel T. Gottuk, Mark T. Wright, Patricia A. Tatem, Jennifer T. WongAbstract:on enhancing automation of shipboard fire and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desirable to increase detection sensitivity, decrease the detection time and increase the reliability of the detection system through improved Nuisance Alarm immunity. The use of multi-criteria based detection technology offers the most promising means to achieve both improved sensitivity to real fires and reduced susceptibility to Nuisance Alarm sources. An early warning fire detection system is being developed by properly processing the output from sensors that measure different parameters of a developing fire or from analyzing multiple aspects of a given sensor output (e.g., rate of change as well as absolute value). The classification performance and speed of the probabilistic neural network deployed in real-time during recent field tests have been evaluated aboard the ex-USS SHADWELL, the Advanced Damage Control fire research platform of the Naval Research Laboratory. The real-time performance is documented, and, as a result of optimization efforts, improvements in performance have been recognized. Early fire detection, while maintaining Nuisance source immunity, has been demonstrated. A detailed examination of the PNN during fire testing has been undertaken. Using real data and simulated data, a variety of scenarios (taken from recent field experiences) have been used or recreated for the purpose of understanding potential failure modes of the PNN in this application
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Early Warning Fire Detection System Using a Probabilistic Neural Network
Fire Technology, 2003Co-Authors: Susan L. Rose-pehrsson, Sean J. Hart, Thomas T. Street, Frederick W. Williams, Mark H. Hammond, Daniel T. Gottuk, Mark T. Wright, Jennifer T. WongAbstract:The Navy program, Damage Control-Automation for Reduced Manning is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desired to increase detection sensitivity and,more importantly increase the reliability of the detection system through improved Nuisance Alarm immunity. Improved reliability is needed, such that fire detection systems can automatically control fire suppression systems. The use of multi-criteria based detection technology continues to offer the most promising means to achieve both improved sensitivity to real fires,and reduced susceptibility to Nuisance Alarm sources. A multi-criteria early warning fire detection system, has been developed to provide reliable warning of actual fire conditions, in less time, with fewer Nuisance Alarms,than can be achieved with commercially available smoke detection systems. In this study a four-sensor array and a Probabilistic Neural Network have been used to produce an early warning fire detection system. A prototype early warning fire detector was built and tested in a shipboard environment. The current Alarm algorithm resulted in better overall performance than the commercial smoke detectors, by providing both improved Nuisance source immunity with generally equivalent or faster response times.
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multi criteria fire detection systems using a probabilistic neural network
Sensors and Actuators B-chemical, 2000Co-Authors: Susan L Rosepehrsson, Sean J. Hart, Frederick W. Williams, Daniel T. Gottuk, Ronald E Shaffer, Brooke D Strehlen, Scott A HillAbstract:Abstract The Navy program, Damage Control Automation for Reduced Manning (DC-ARM), is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is the improvement of current fire detection systems. As in many applications, it is desired to increase detection sensitivity and, more importantly, increase the reliability of the detection system through improved Nuisance Alarm immunity. Improved reliability is needed such that fire detection systems can automatically control fire suppression systems. The use of multi-criteria-based detection technology continues to offer the most promising means to achieve both improved sensitivity to real fires and reduced susceptibility to Nuisance Alarm sources. A multi-signature early warning fire detection system is being developed to provide reliable warning of actual fire conditions in less time with fewer Nuisance Alarms than can be achieved with commercially available smoke detection systems. In this study, a large database consisting of the responses of 20 sensors to several different types of fires and Nuisance sources was generated and analyzed using a variety of multivariate methods. Three data matrices were developed at discrete times corresponding to the different Alarm levels of a conventional photoelectric smoke detector. The Alarm times represent 0.82%, 1.63% and 11% obscurations per meter. The datasets were organized into three classes representing the sensor responses for baseline (nonfire), fires and Nuisance sources. A robust data analysis strategy for use with a sensor array consisting of four to five sensors for early fire detection and Nuisance source rejection was developed using a probabilistic neural network (PNN) that was developed at the Naval Research Laboratory for chemical sensor arrays. The analysis algorithms described in this paper evaluate discrete samples and develop classification models that examine individual chemical signatures at discrete points.
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real time probabilistic neural network performance and optimization for fire detection and Nuisance Alarm rejection test series 2 results
2000Co-Authors: Susan L Rosepehrsson, Sean J. Hart, Mark H. Hammond, Daniel T. Gottuk, Mark T. WrightAbstract:Abstract : A second series of tests was conducted to evaluate and improve the multivariate data analysis notebooks and candidate sensor suites used for the Early Warning Fire Detection (EWFD) system under development. The EWFD system is to provide reliable warning of actual fire conditions in less time with fewer Nuisance Alarms than commercially available smoke detection systems. Tests were conducted from 25 April to 5 May 2000 onboard the ex-USS SHADWELL. This report documents the performance of the probabilistic neural network achieved in real-time during this test series. Further optimization of the algorithm has yielded performance gains over the real-time results. Modifications have been made that improve the real-time data acquisition and the ion sensor calibration. Background subtraction was investigated and will be used in future tests. The best performance was provided by a four sensor array consisting of ionization, photoelectric carbon monoxide and carbon dioxide sensors.
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real time probabilistic neural network performance and optimization for fire detection and Nuisance Alarm rejection test series 1 results
2000Co-Authors: Sean J. Hart, Mark H. Hammond, Susan L Rosepehrsson, Ronald E Shaffer, Daniel T. GottukAbstract:Abstract : A series of tests were conducted to evaluate and improve the multivariate data analysis methods and candidate sensor suites used for the Early Warning Fire Detection (EWFD) system under development. The EWFD system is to provide reliable warning of actual fire conditions in less time with fewer Nuisance Alarms than commercially available smoke detection systems. Tests were conducted from 7-18 February 2000, onboard the ex-USS Sizadwell. This report documents the performance of the probabilistic neural network achieved in real-time during this test series. Further optimization of the algorithm yielded performance gains over the real-time results. Simulation studies have been done to examine the effects of sensor drop-out, excessive noise, and erroneous sensor values.
Jin Zhang - One of the best experts on this subject based on the ideXlab platform.
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Nuisance Alarm rate reduction using pulse width multiplexing φ otdr with optimized positioning accuracy
Optics Communications, 2020Co-Authors: Xiang Zhong, Huaxia Deng, Shisong Zhao, Dongliang Gui, Jin ZhangAbstract:Abstract High Nuisance Alarm rates and missing Alarm rates in phase-sensitive optical time-domain reflectometer ( ϕ -OTDR) greatly restrict their practical application. In this paper, an asynchronous sampling pulse width multiplexing ϕ -OTDR is proposed to reduce both of these rates. Intersection and inclusion relationships among multiple positioning intervals are used to optimize the positioning accuracy. We construct an eight-pulse-width multiplexed ϕ -OTDR system and adopt a multisensor information fusion algorithm to verify the feasibility of the method. The experimental results show that this method can reduce the missing Alarm rate by 93% and the number of Nuisance Alarms by 97%. The average positioning accuracy of the system is 21.4 m, which is equivalent to the spatial resolution level corresponding to the minimum pulse width in the optical pulse sequence. The positioning accuracy of some groups was as low as 6.4 m, far less than 20m. The proposed method provides a simple and feasible new approach for reducing Nuisance Alarm and missing Alarm rates and optimizing the positioning accuracy of ϕ -OTDR.
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Pulse-Width Multiplexing ϕ-OTDR for Nuisance-Alarm Rate Reduction.
Sensors, 2018Co-Authors: Xiang Zhong, Xicheng Gao, Huaxia Deng, Shisong Zhao, Jin ZhangAbstract:A pulse-width multiplexing method for reducing the Nuisance-Alarm rate of a phase-sensitive optical time-domain reflectometer ( ϕ -OTDR) is described. In this method, light pulses of different pulse-widths are injected into the sensing fiber; the data acquired at different pulse-widths are regarded as the outputs of different sensors; and these data are then processed by a multisensor data fusion algorithm. In laboratory tests with a sensing fiber on a vibrating table, the effects of pulse-width on the signal-to-noise ratio (SNR) of the ϕ -OTDR data are observed. Furthermore, by utilizing the SNR as the feature in a feature-layer algorithm based on Dempster⁻Shafer evidential theory, a four-pulse-width multiplexing ϕ -OTDR system is constructed, and the Nuisance-Alarm rate is reduced by about 70%. These experimental results show that the proposed method has great potential for perimeter protection, since the Nuisance-Alarm rate is significantly reduced by using a simple configuration.
Jim Katsifolis - One of the best experts on this subject based on the ideXlab platform.
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Real-time Distributed Fiber Optic Sensor for Security Systems: Performance, Event Classification and Nuisance Mitigation
Photonic Sensors, 2012Co-Authors: Seedahmed S Mahmoud, Yuvaraja Visagathilagar, Jim KatsifolisAbstract:The success of any perimeter intrusion detection system depends on three important performance parameters: the probability of detection (POD), the Nuisance Alarm rate (NAR), and the false Alarm rate (FAR). The most fundamental parameter, POD, is normally related to a number of factors such as the event of interest, the sensitivity of the sensor, the installation quality of the system, and the reliability of the sensing equipment. The suppression of Nuisance Alarms without degrading sensitivity in fiber optic intrusion detection systems is key to maintaining acceptable performance. Signal processing algorithms that maintain the POD and eliminate Nuisance Alarms are crucial for achieving this. In this paper, a robust event classification system using supervised neural networks together with a level crossings (LCs) based feature extraction algorithm is presented for the detection and recognition of intrusion and non-intrusion events in a fence-based fiber-optic intrusion detection system. A level crossings algorithm is also used with a dynamic threshold to suppress torrential rain-induced Nuisance Alarms in a fence system. Results show that rain-induced Nuisance Alarms can be suppressed for rainfall rates in excess of 100 mm/hr with the simultaneous detection of intrusion events. The use of a level crossing based detection and novel classification algorithm is also presented for a buried pipeline fiber optic intrusion detection system for the suppression of Nuisance events and discrimination of intrusion events. The sensor employed for both types of systems is a distributed bidirectional fiber-optic Mach-Zehnder (MZ) interferometer.
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Nuisance Alarm suppression techniques for fibre optic intrusion detection systems
Third Asia Pacific Optical Sensors Conference, 2012Co-Authors: Seedahmed S Mahmoud, Yuvaraja Visagathilagar, Jim KatsifolisAbstract:The suppression of Nuisance Alarms without degrading sensitivity in fibre-optic intrusion detection systems is important for maintaining acceptable performance. Signal processing algorithms that maintain the POD and minimize Nuisance Alarms are crucial for achieving this. A level crossings algorithm is presented for suppressing torrential rain-induced Nuisance Alarms in a fibre-optic fence-based perimeter intrusion detection system. Results show that rain-induced Nuisance Alarms can be suppressed for rainfall rates in excess of 100 mm/hr, and intrusion events can be detected simultaneously during rain periods. The use of a level crossing based detection and novel classification algorithm is also presented demonstrating the suppression of Nuisance events and discrimination of Nuisance and intrusion events in a buried pipeline fibre-optic intrusion detection system. The sensor employed for both types of systems is a distributed bidirectional fibre-optic Mach Zehnder interferometer.
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performance investigation of real time fiber optic perimeter intrusion detection systems using event classification
International Carnahan Conference on Security Technology, 2010Co-Authors: Seedahmed S Mahmoud, Jim KatsifolisAbstract:The success of any perimeter intrusion detection system depends on three important performance parameters: the probability of detection (POD), the Nuisance Alarm rate (NAR), and the false Alarm rate (FAR). The most fundamental parameter, POD, is normally related to a number of factors such as the event of interest, the sensitivity of the sensor, the installation quality of the system and the reliability of the sensing equipment. A critical performance parameter of any outdoor perimeter intrusion detection system is its capability of discriminating between intrusion and Nuisance events without compromising its sensitivity or POD. In this paper, the performance criteria of a real-time fence-mounted distributed fiber-optic intrusion detection system are discussed. The use of event recognition and classification techniques to maintain high detection rates and minimize Alarms caused by Nuisance events is presented with a performance analysis for different event classification algorithms. Practical results for the detection of intrusion events and suppression of Nuisances are also shown.
