49 CFR 236.562 - Minimum rail current required.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.562 Minimum... continuous inductive automatic train stop or train control device to normal condition or to obtain a proceed...

49 CFR 236.586 - Daily or after trip test.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.586 Daily or after trip test..., each locomotive equipped with an automatic cab signal or train stop or train control device operating...

49 CFR 236.588 - Periodic test.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.588 Periodic test. Except as provided in § 236.586, periodic test of the automatic train stop, train control, or cab signal apparatus...

49 CFR 236.509 - Two or more locomotives coupled.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.509 Two or more locomotives coupled. The automatic train stop, train control or cab signal apparatus shall be arranged so that when two or...

49 CFR 236.557 - Receiver; location with respect to rail.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.557 Receiver... automatic cab signal, train stop, or train control device of locomotive equipped with onboard test equipment...

49 CFR 236.586 - Daily or after trip test.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.586 Daily or after trip test..., each locomotive equipped with an automatic cab signal or train stop or train control device operating...

49 CFR 236.509 - Two or more locomotives coupled.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.509 Two or more locomotives coupled. The automatic train stop, train control or cab signal apparatus shall be arranged so that when two or...

49 CFR 236.564 - Acknowledging time.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.564 Acknowledging time. Acknowledging time of intermittent automatic train-stop device shall be not more than 30 seconds. ...

49 CFR 236.508 - Interference with application of brakes by means of brake valve.

Code of Federal Regulations, 2011 CFR

2011-10-01

... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.508 Interference with application of brakes by means of brake valve. The automatic train stop, train control, or...

49 CFR 236.508 - Interference with application of brakes by means of brake valve.

Code of Federal Regulations, 2010 CFR

2010-10-01

... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.508 Interference with application of brakes by means of brake valve. The automatic train stop, train control, or...

49 CFR 236.513 - Audible indicator.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.513 Audible indicator. (a) The automatic cab signal... control system shall have a distinctive sound and be clearly audible throughout the cab under all...

49 CFR 236.513 - Audible indicator.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.513 Audible indicator. (a) The automatic cab signal... control system shall have a distinctive sound and be clearly audible throughout the cab under all...

49 CFR 236.528 - Restrictive condition resulting from open hand-operated switch; requirement.

Code of Federal Regulations, 2010 CFR

2010-10-01

... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and... with circuit controller is used, the resultant restrictive condition of an automatic train stop or...

49 CFR 236.528 - Restrictive condition resulting from open hand-operated switch; requirement.

Code of Federal Regulations, 2011 CFR

2011-10-01

... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and... with circuit controller is used, the resultant restrictive condition of an automatic train stop or...

Train Control and Operations

DOT National Transportation Integrated Search

1971-06-01

ATO (automatic train operation) and ATC (automatic train control) systems are evaluated relative to available technology and cost-benefit. The technological evaluation shows that suitable mathematical models of the dynamics of long trains are require...

49 CFR 236.511 - Cab signals controlled in accordance with block conditions stopping distance in advance.

Code of Federal Regulations, 2011 CFR

2011-10-01

... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... automatic cab signal system shall be arranged so that cab signals will be continuously controlled in...

49 CFR 236.739 - Device, acknowledging.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.739... locomotive equipped with an automatic train stop or train control device, an automatic brake application can be forestalled, or by means of which, on a locomotive equipped with an automatic cab signal device...

49 CFR 236.560 - Contact element, mechanical trip type; location with respect to rail.

Code of Federal Regulations, 2011 CFR

2011-10-01

... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and.... Contact element of automatic train stop device of the mechanical trip type shall be maintained at a height...

49 CFR 236.567 - Restrictions imposed when device fails and/or is cut out en route.

Code of Federal Regulations, 2011 CFR

2011-10-01

... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions... restricted speed or if an automatic block signal system is in operation according to signal indication but...

49 CFR 236.512 - Cab signal indication when locomotive enters block where restrictive conditions obtain.

Code of Federal Regulations, 2011 CFR

2011-10-01

... TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.512 Cab signal indication when locomotive enters block where restrictive conditions obtain. The automatic cab signal system shall be arranged so that when a locomotive enters or is...

49 CFR 236.566 - Locomotive of each train operating in train stop, train control or cab signal territory; equipped.

Code of Federal Regulations, 2010 CFR

2010-10-01

... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and... controlled, of each train operating in automatic train stop, train control, or cab signal territory shall be..., train control or cab signal territory; equipped. 236.566 Section 236.566 Transportation Other...

49 CFR 236.566 - Locomotive of each train operating in train stop, train control or cab signal territory; equipped.

Code of Federal Regulations, 2011 CFR

2011-10-01

... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and... controlled, of each train operating in automatic train stop, train control, or cab signal territory shall be..., train control or cab signal territory; equipped. 236.566 Section 236.566 Transportation Other...

49 CFR 236.554 - Rate of pressure reduction; equalizing reservoir or brake pipe.

Code of Federal Regulations, 2011 CFR

2011-10-01

... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions... pressure or brake-pipe pressure reduction during an automatic brake application shall be at a rate not less...

49 CFR 236.554 - Rate of pressure reduction; equalizing reservoir or brake pipe.

Code of Federal Regulations, 2010 CFR

2010-10-01

... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions... pressure or brake-pipe pressure reduction during an automatic brake application shall be at a rate not less...

49 CFR 236.747 - Forestall.

Code of Federal Regulations, 2011 CFR

2011-10-01

... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.747 Forestall. As applied to an automatic train stop or train control device, to prevent an automatic brake application by operation of an acknowledging device or by manual control of the speed of the train. ...

49 CFR 236.747 - Forestall.

Code of Federal Regulations, 2010 CFR

2010-10-01

... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.747 Forestall. As applied to an automatic train stop or train control device, to prevent an automatic brake application by operation of an acknowledging device or by manual control of the speed of the train. ...

The research of automatic speed control algorithm based on Green CBTC

NASA Astrophysics Data System (ADS)

Lin, Ying; Xiong, Hui; Wang, Xiaoliang; Wu, Youyou; Zhang, Chuanqi

2017-06-01

Automatic speed control algorithm is one of the core technologies of train operation control system. It’s a typical multi-objective optimization control algorithm, which achieve the train speed control for timing, comfort, energy-saving and precise parking. At present, the train speed automatic control technology is widely used in metro and inter-city railways. It has been found that the automatic speed control technology can effectively reduce the driver’s intensity, and improve the operation quality. However, the current used algorithm is poor at energy-saving, even not as good as manual driving. In order to solve the problem of energy-saving, this paper proposes an automatic speed control algorithm based on Green CBTC system. Based on the Green CBTC system, the algorithm can adjust the operation status of the train to improve the efficient using rate of regenerative braking feedback energy while ensuring the timing, comfort and precise parking targets. Due to the reason, the energy-using of Green CBTC system is lower than traditional CBTC system. The simulation results show that the algorithm based on Green CBTC system can effectively reduce the energy-using due to the improvement of the using rate of regenerative braking feedback energy.

49 CFR 236.830 - Time, acknowledging.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.830 Time, acknowledging. As applied to an intermittent automatic train stop system, a predetermined time within which an automatic brake application may be forestalled by means of the acknowledging device. ...

49 CFR 236.824 - System, automatic block signal.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false System, automatic block signal. 236.824 Section..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.824 System, automatic block signal. A block signal system wherein the use of each block is...

49 CFR 236.824 - System, automatic block signal.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false System, automatic block signal. 236.824 Section..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.824 System, automatic block signal. A block signal system wherein the use of each block is...

49 CFR 236.824 - System, automatic block signal.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 4 2013-10-01 2013-10-01 false System, automatic block signal. 236.824 Section..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.824 System, automatic block signal. A block signal system wherein the use of each block is...

Design and development of an automatic data acquisition system for a balance study using a smartcard system.

PubMed

Ambrozy, C; Kolar, N A; Rattay, F

2010-01-01

For measurement value logging of board angle values during balance training, it is necessary to develop a measurement system. This study will provide data for a balance study using the smartcard. The data acquisition comes automatically. An individually training plan for each proband is necessary. To store the proband identification a smartcard with an I2C data bus protocol and an E2PROM memory system is used. For reading the smartcard data a smartcard reader is connected via universal serial bus (USB) to a notebook. The data acquisition and smartcard read programme is designed with Microsoft® Visual C#. A training plan file contains the individual training plan for each proband. The data of the test persons are saved in a proband directory. Each event is automatically saved as a log-file for the exact documentation. This system makes study development easy and time-saving.

49 CFR 236.401 - Automatic block signal system and interlocking standards applicable to traffic control systems.

Code of Federal Regulations, 2011 CFR

2011-10-01

... TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Traffic Control Systems Standards § 236.401 Automatic... 49 Transportation 4 2011-10-01 2011-10-01 false Automatic block signal system and interlocking standards applicable to traffic control systems. 236.401 Section 236.401 Transportation Other Regulations...

49 CFR 233.1 - Scope.

Code of Federal Regulations, 2011 CFR

2011-10-01

... TRANSPORTATION SIGNAL SYSTEMS REPORTING REQUIREMENTS § 233.1 Scope. This part prescribed reporting requirements with respect to methods of train operation, block signal systems, interlockings, traffic control systems, automatic train stop, train control, and cab signal systems, or other similar appliances, methods...

49 CFR 233.1 - Scope.

Code of Federal Regulations, 2010 CFR

2010-10-01

... TRANSPORTATION SIGNAL SYSTEMS REPORTING REQUIREMENTS § 233.1 Scope. This part prescribed reporting requirements with respect to methods of train operation, block signal systems, interlockings, traffic control systems, automatic train stop, train control, and cab signal systems, or other similar appliances, methods...

49 CFR 236.576 - Roadway element.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Roadway § 236.576 Roadway element. Roadway...

49 CFR 236.576 - Roadway element.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Roadway § 236.576 Roadway element. Roadway...

49 CFR 236.589 - Relays.

Code of Federal Regulations, 2010 CFR

2010-10-01

... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.589 Relays. (a) Each relay shall be removed... train stop or train control system, at least once every two years; and (2) All other relays, at least...

49 CFR 236.589 - Relays.

Code of Federal Regulations, 2011 CFR

2011-10-01

... OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.589 Relays. (a) Each relay shall be removed... train stop or train control system, at least once every two years; and (2) All other relays, at least...

49 CFR 236.556 - Adjustment of relay.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.556 Adjustment of relay...

49 CFR 236.527 - Roadway element insulation resistance.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.527 Roadway...

49 CFR 236.527 - Roadway element insulation resistance.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.527 Roadway...

49 CFR 236.556 - Adjustment of relay.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.556 Adjustment of relay...

49 CFR 236.555 - Repaired or rewound receiver coil.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.555 Repaired...

49 CFR 236.558-236.559 - [Reserved

Code of Federal Regulations, 2010 CFR

2010-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives §§ 236.558-236.559 [Reserved] ...

49 CFR 236.529 - Roadway element inductor; height and distance from rail.

Code of Federal Regulations, 2011 CFR

2011-10-01

... THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions...

49 CFR 236.529 - Roadway element inductor; height and distance from rail.

Code of Federal Regulations, 2010 CFR

2010-10-01

... THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions...

49 CFR 236.558-236.559 - [Reserved

Code of Federal Regulations, 2011 CFR

2011-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives §§ 236.558-236.559 [Reserved] ...

49 CFR 236.555 - Repaired or rewound receiver coil.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.555 Repaired...

Automatic Training of Rat Cyborgs for Navigation.

PubMed

Yu, Yipeng; Wu, Zhaohui; Xu, Kedi; Gong, Yongyue; Zheng, Nenggan; Zheng, Xiaoxiang; Pan, Gang

2016-01-01

A rat cyborg system refers to a biological rat implanted with microelectrodes in its brain, via which the outer electrical stimuli can be delivered into the brain in vivo to control its behaviors. Rat cyborgs have various applications in emergency, such as search and rescue in disasters. Prior to a rat cyborg becoming controllable, a lot of effort is required to train it to adapt to the electrical stimuli. In this paper, we build a vision-based automatic training system for rat cyborgs to replace the time-consuming manual training procedure. A hierarchical framework is proposed to facilitate the colearning between rats and machines. In the framework, the behavioral states of a rat cyborg are visually sensed by a camera, a parameterized state machine is employed to model the training action transitions triggered by rat's behavioral states, and an adaptive adjustment policy is developed to adaptively adjust the stimulation intensity. The experimental results of three rat cyborgs prove the effectiveness of our system. To the best of our knowledge, this study is the first to tackle automatic training of animal cyborgs.

Automatic Training of Rat Cyborgs for Navigation

PubMed Central

Yu, Yipeng; Wu, Zhaohui; Xu, Kedi; Gong, Yongyue; Zheng, Nenggan; Zheng, Xiaoxiang; Pan, Gang

2016-01-01

A rat cyborg system refers to a biological rat implanted with microelectrodes in its brain, via which the outer electrical stimuli can be delivered into the brain in vivo to control its behaviors. Rat cyborgs have various applications in emergency, such as search and rescue in disasters. Prior to a rat cyborg becoming controllable, a lot of effort is required to train it to adapt to the electrical stimuli. In this paper, we build a vision-based automatic training system for rat cyborgs to replace the time-consuming manual training procedure. A hierarchical framework is proposed to facilitate the colearning between rats and machines. In the framework, the behavioral states of a rat cyborg are visually sensed by a camera, a parameterized state machine is employed to model the training action transitions triggered by rat's behavioral states, and an adaptive adjustment policy is developed to adaptively adjust the stimulation intensity. The experimental results of three rat cyborgs prove the effectiveness of our system. To the best of our knowledge, this study is the first to tackle automatic training of animal cyborgs. PMID:27436999

49 CFR 236.532 - Strap iron inductor; use restricted.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.532 Strap iron...

49 CFR 236.532 - Strap iron inductor; use restricted.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.532 Strap iron...

49 CFR 236.577 - Test, acknowledgement, and cut-in circuits.

Code of Federal Regulations, 2011 CFR

2011-10-01

... INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Inspection and Tests; Roadway § 236.577 Test...

49 CFR 236.526 - Roadway element not functioning properly.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.526 Roadway..., train control or cab signal system is not functioning as intended, the signal associated with such...

49 CFR 236.526 - Roadway element not functioning properly.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.526 Roadway..., train control or cab signal system is not functioning as intended, the signal associated with such...

49 CFR 236.503 - Automatic brake application; initiation when predetermined rate of speed exceeded.

Code of Federal Regulations, 2011 CFR

2011-10-01

... predetermined rate of speed exceeded. 236.503 Section 236.503 Transportation Other Regulations Relating to... § 236.503 Automatic brake application; initiation when predetermined rate of speed exceeded. An automatic train control system shall operate to initiate an automatic brake application when the speed of...

49 CFR 236.503 - Automatic brake application; initiation when predetermined rate of speed exceeded.

Code of Federal Regulations, 2010 CFR

2010-10-01

... predetermined rate of speed exceeded. 236.503 Section 236.503 Transportation Other Regulations Relating to... § 236.503 Automatic brake application; initiation when predetermined rate of speed exceeded. An automatic train control system shall operate to initiate an automatic brake application when the speed of...

49 CFR 236.552 - Insulation resistance; requirement.

Code of Federal Regulations, 2010 CFR

2010-10-01

... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RULES, STANDARDS, AND INSTRUCTIONS GOVERNING THE INSTALLATION... resistance between wiring and ground of continuous inductive automatic cab signal system, automatic train...

49 CFR 236.15 - Timetable instructions.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Rules and Instructions: All Systems General § 236.15 Timetable instructions. Automatic block, traffic control, train stop, train control and cab signal territory shall be designated in timetable instructions. ...

49 CFR 236.15 - Timetable instructions.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Rules and Instructions: All Systems General § 236.15 Timetable instructions. Automatic block, traffic control, train stop, train control and cab signal territory shall be designated in timetable instructions. ...

An automatically-shifted two-speed transaxle system for an electric vehicle

NASA Technical Reports Server (NTRS)

Gordon, H. S.; Hassman, G. V.

1980-01-01

An automatic shifting scheme for a two speed transaxle for use with an electric vehicle propulsion system is described. The transaxle system was to be installed in an instrumented laboratory propulsion system of an ac electric vehicle drive train. The transaxle which had been fabricated is also described.

Relationship of Automatic Data Processing Training Curriculum and Methodology in the Federal Government.

ERIC Educational Resources Information Center

Office of Education (DHEW), Washington, DC.

A conference, held in Washington, D. C., in 1967 by the Association for Educational Data Systems and the U.S. Office of Education, attempted to lay the groundwork for an efficient automatic data processing training program for the Federal Government utilizing new instructional methodologies. The rapid growth of computer applications and computer…

49 CFR 236.505 - Proper operative relation between parts along roadway and parts on locomotive.

Code of Federal Regulations, 2012 CFR

2012-10-01

... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... all conditions of speed, weather, wear, oscillation, and shock. ...

49 CFR 236.505 - Proper operative relation between parts along roadway and parts on locomotive.

Code of Federal Regulations, 2011 CFR

2011-10-01

... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... all conditions of speed, weather, wear, oscillation, and shock. ...

49 CFR 236.505 - Proper operative relation between parts along roadway and parts on locomotive.

Code of Federal Regulations, 2014 CFR

2014-10-01

... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... all conditions of speed, weather, wear, oscillation, and shock. ...

49 CFR 236.505 - Proper operative relation between parts along roadway and parts on locomotive.

Code of Federal Regulations, 2010 CFR

2010-10-01

... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... all conditions of speed, weather, wear, oscillation, and shock. ...

49 CFR 236.505 - Proper operative relation between parts along roadway and parts on locomotive.

Code of Federal Regulations, 2013 CFR

2013-10-01

... INSTRUCTIONS GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards... all conditions of speed, weather, wear, oscillation, and shock. ...

49 CFR 235.1 - Scope.

Code of Federal Regulations, 2010 CFR

2010-10-01

... SIGNAL SYSTEM OR RELIEF FROM THE REQUIREMENTS OF PART 236 § 235.1 Scope. This part prescribes application for approval to discontinue or materially modify block signal systems, interlockings, traffic control systems, automatic train stop, train control, or cab signal systems, or other similar appliances, devices...

An Automated Motion Detection and Reward System for Animal Training.

PubMed

Miller, Brad; Lim, Audrey N; Heidbreder, Arnold F; Black, Kevin J

2015-12-04

A variety of approaches has been used to minimize head movement during functional brain imaging studies in awake laboratory animals. Many laboratories expend substantial effort and time training animals to remain essentially motionless during such studies. We could not locate an "off-the-shelf" automated training system that suited our needs.  We developed a time- and labor-saving automated system to train animals to hold still for extended periods of time. The system uses a personal computer and modest external hardware to provide stimulus cues, monitor movement using commercial video surveillance components, and dispense rewards. A custom computer program automatically increases the motionless duration required for rewards based on performance during the training session but allows changes during sessions. This system was used to train cynomolgus monkeys (Macaca fascicularis) for awake neuroimaging studies using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). The automated system saved the trainer substantial time, presented stimuli and rewards in a highly consistent manner, and automatically documented training sessions. We have limited data to prove the training system's success, drawn from the automated records during training sessions, but we believe others may find it useful. The system can be adapted to a range of behavioral training/recording activities for research or commercial applications, and the software is freely available for non-commercial use.

49 CFR 236.553 - Seal, where required.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.553 Seal, where required. Seal shall be maintained on any device other than brake-pipe cut-out cock (double-heading cock), by...

49 CFR 236.553 - Seal, where required.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.553 Seal, where required. Seal shall be maintained on any device other than brake-pipe cut-out cock (double-heading cock), by...

49 CFR 236.567 - Restrictions imposed when device fails and/or is cut out en route.

Code of Federal Regulations, 2013 CFR

2013-10-01

... cut out en route. 236.567 Section 236.567 Transportation Other Regulations Relating to Transportation... GOVERNING THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Rules and Instructions...

49 CFR 236.515 - Visibility of cab signals.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Visibility of cab signals. 236.515 Section 236.515..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.515 Visibility of cab signals. The cab signals...

49 CFR 236.515 - Visibility of cab signals.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Visibility of cab signals. 236.515 Section 236.515..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.515 Visibility of cab signals. The cab signals...

49 CFR 236.515 - Visibility of cab signals.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false Visibility of cab signals. 236.515 Section 236.515..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.515 Visibility of cab signals. The cab signals...

49 CFR 236.515 - Visibility of cab signals.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 4 2012-10-01 2012-10-01 false Visibility of cab signals. 236.515 Section 236.515..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.515 Visibility of cab signals. The cab signals...

49 CFR 236.515 - Visibility of cab signals.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 4 2013-10-01 2013-10-01 false Visibility of cab signals. 236.515 Section 236.515..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Train Stop, Train Control and Cab Signal Systems Standards § 236.515 Visibility of cab signals. The cab signals...

Development of ATC for High Speed and High Density Commuter Line

NASA Astrophysics Data System (ADS)

Okutani, Tamio; Nakamura, Nobuyuki; Araki, Hisato; Irie, Shouji; Osa, Hiroki; Sano, Minoru; Ikeda, Keigo; Ozawa, Hiroyuki

A new ATC (Automatic Train Control) system has been developed with solutions to realize short train headway by assured braking utilizing digital data transmission via rails; the digital data for the ATP (Automatic Train Protection) function; and to achieve EMC features for both AC and DC sections. The DC section is of the unprecedented DC traction power supply system utilizing IGBT PWM converter at all DC substations. Within the AC section, train traction force is controlled by PWM converter/inverters. The carrier frequencies of the digital data signals and chopping frequency of PWM traction power converters on-board are decided via spectral analysis of noise up to degraded mode cases of equipment. Developed system was equipped to the Tukuba Express Line, new commuter line of Tokyo metropolitan area, and opened since Aug. 2005.

Coordinated train control and energy management control strategies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gordon, S.P.; Lehrer, D.G.

1998-05-01

The Bay Area Rapid Transit (BART) system, in collaboration with Hughes Aircraft Company and Harmon Industries, as in the process of developing an Advanced Automatic Train Control (AATC) system to replace the current fixed-block automatic system. In the long run, the AATC system is expected to not only allow for safe short headway operation, but also to facilitate coordinated train control and energy management. This new system will employ spread spectrum radios, installed onboard trains, at wayside locations, and at control stations, to determine train locations and reliably transfer control information. Sandia National Laboratories has worked cooperatively with BART tomore » develop a simulator of the train control and the power consumption of the AATC system. The authors are now in the process of developing enhanced train control algorithms to supplement the safety critical controller in order to smooth out train trajectories through coordinated control of multiple trains, and to reduce energy consumption and power infrastructure requirements. The control algorithms so far considered include (1) reducing peak power consumption to avoid voltage sags, especially during an outage or while clearing a backup, (2) rapid and smooth recovery from a backup, (3) avoiding oscillations due to train interference, (4) limiting needle peaks in power demand at substations to some specified level, (5) coasting, and (6) coordinating train movement, e.g., starts/stops and hills.« less

Advanced Simulation in Undergraduate Pilot Training: Automatic Instructional System. Final Report for the Period March 1971-January 1975.

ERIC Educational Resources Information Center

Faconti, Victor; Epps, Robert

The Advanced Simulator for Undergraduate Pilot Training (ASUPT) was designed to investigate the role of simulation in the future Undergraduate Pilot Training (UPT) program. The Automated Instructional System designed for the ASUPT simulator was described in this report. The development of the Automated Instructional System for ASUPT was based upon…

A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease: A prospective pilot study.

PubMed

Chomiak, Taylor; Watts, Alexander; Meyer, Nicole; Pereira, Fernando V; Hu, Bin

2017-02-01

Deficits in motor movement automaticity in Parkinson's disease (PD), especially during multitasking, are early and consistent hallmarks of cognitive function decline, which increases fall risk and reduces quality of life. This study aimed to test the feasibility and potential efficacy of a wearable sensor-enabled technological platform designed for an in-home music-contingent stepping-in-place (SIP) training program to improve step automaticity during dual-tasking (DT). This was a 4-week prospective intervention pilot study. The intervention uses a sensor system and algorithm that runs off the iPod Touch which calculates step height (SH) in real-time. These measurements were then used to trigger auditory (treatment group, music; control group, radio podcast) playback in real-time through wireless headphones upon maintenance of repeated large amplitude stepping. With small steps or shuffling, auditory playback stops, thus allowing participants to use anticipatory motor control to regain positive feedback. Eleven participants were recruited from an ongoing trial (Trial Number: ISRCTN06023392). Fear of falling (FES-I), general cognitive functioning (MoCA), self-reported freezing of gait (FOG-Q), and DT step automaticity were evaluated. While we found no significant effect of training on FES-I, MoCA, or FOG-Q, we did observe a significant group (music vs podcast) by training interaction in DT step automaticity (P<0.01). Wearable device technology can be used to enable musically-contingent SIP training to increase motor automaticity for people living with PD. The training approach described here can be implemented at home to meet the growing demand for self-management of symptoms by patients.

76 FR 8699 - Reporting Requirements for Positive Train Control Expenses and Investments

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-15

... DEPARTMENT OF TRANSPORTATION Surface Transportation Board 49 CFR Part 1201 [Docket No. EP 706] Reporting Requirements for Positive Train Control Expenses and Investments AGENCY: Surface Transportation... Train Control, a federally mandated safety system that will automatically stop or slow a train before an...

A dual-systems perspective on addiction: contributions from neuroimaging and cognitive training.

PubMed

McClure, Samuel M; Bickel, Warren K

2014-10-01

Dual-systems theories explain lapses in self-control in terms of a conflict between automatic and deliberative modes of behavioral control. Numerous studies have now tested whether the brain areas that control behavior are organized in a manner consistent with dual-systems models. Brain regions directly associated with the mesolimbic dopamine system, the nucleus accumbens and ventromedial prefrontal cortex in particular, capture some of the features assumed by automatic processing. Regions in the lateral prefrontal cortex are more closely linked to deliberative processing and the exertion of self-control in the suppression of impulses. While identifying these regions crudely supports dual-systems theories, important modifications to what constitutes automatic and deliberative behavioral control are also suggested. Experiments have identified various means by which automatic processes may be sculpted. Additional work decomposes deliberative processes into component functions such as generalized working memory, reappraisal of emotional stimuli, and prospection. The importance of deconstructing dual-systems models into specific cognitive processes is clear for understanding and treating addiction. We discuss intervention possibilities suggested by recent research, and focus in particular on cognitive training approaches to bolster deliberative control processes that may aid quit attempts. © 2014 New York Academy of Sciences.

A dual-systems perspective on addiction: contributions from neuroimaging and cognitive training

PubMed Central

McClure, Samuel M.; Bickel, Warren K.

2014-01-01

Dual-systems theories explain lapses in self-control in terms of a conflict between automatic and deliberative modes of behavioral control. Numerous studies have now tested whether the brain areas that control behavior are organized in a manner consistent with dual-systems models. Brain regions directly associated with the mesolimbic dopamine system, the nucleus accumbens (NAcc) and ventromedial prefrontal cortex (vmPFC) in particular, capture some of the features assumed by automatic processing. Regions in the lateral prefrontal cortex (lPFC) are more closely linked to deliberative processing and the exertion of self-control in the suppression of impulses. While identifying these regions crudely supports dual-system theories, important modifications to what constitutes automatic and deliberative behavioral control are also suggested. Experiments have identified various means by which automatic processes may be sculpted. Additional work decomposes deliberative processes into component functions such as generalized working memory, reappraisal of emotional stimuli, and prospection. The importance of deconstructing dual-systems models into specific cognitive processes is clear for understanding and treating addiction. We discuss intervention possibilities suggested by recent research, and focus in particular on cognitive training approaches to bolster deliberative control processes that may aid quit attempts. PMID:25336389

Representing System Behaviors and Expert Behaviors for Intelligent Tutoring. Technical Report No. 108.

ERIC Educational Resources Information Center

Towne, Douglas M.; And Others

Simulation-based software tools that can infer system behaviors from a deep model of the system have the potential for automatically building the semantic representations required to support intelligent tutoring in fault diagnosis. The Intelligent Maintenance Training System (IMTS) is such a resource, designed for use in training troubleshooting…

49 CFR 236.730 - Coil, receiver.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.730 Coil, receiver. Concentric layers of insulated wire wound around the core of a receiver of an automatic train stop, train control or cab signal device on a locomotive. ...

A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease

PubMed Central

Chomiak, Taylor; Watts, Alexander; Meyer, Nicole; Pereira, Fernando V.; Hu, Bin

2017-01-01

Abstract Background: Deficits in motor movement automaticity in Parkinson's disease (PD), especially during multitasking, are early and consistent hallmarks of cognitive function decline, which increases fall risk and reduces quality of life. This study aimed to test the feasibility and potential efficacy of a wearable sensor-enabled technological platform designed for an in-home music-contingent stepping-in-place (SIP) training program to improve step automaticity during dual-tasking (DT). Methods: This was a 4-week prospective intervention pilot study. The intervention uses a sensor system and algorithm that runs off the iPod Touch which calculates step height (SH) in real-time. These measurements were then used to trigger auditory (treatment group, music; control group, radio podcast) playback in real-time through wireless headphones upon maintenance of repeated large amplitude stepping. With small steps or shuffling, auditory playback stops, thus allowing participants to use anticipatory motor control to regain positive feedback. Eleven participants were recruited from an ongoing trial (Trial Number: ISRCTN06023392). Fear of falling (FES-I), general cognitive functioning (MoCA), self-reported freezing of gait (FOG-Q), and DT step automaticity were evaluated. Results: While we found no significant effect of training on FES-I, MoCA, or FOG-Q, we did observe a significant group (music vs podcast) by training interaction in DT step automaticity (P<0.01). Conclusion: Wearable device technology can be used to enable musically-contingent SIP training to increase motor automaticity for people living with PD. The training approach described here can be implemented at home to meet the growing demand for self-management of symptoms by patients. PMID:28151878

Automatic ground control point recognition with parallel associative memory

NASA Technical Reports Server (NTRS)

Al-Tahir, Raid; Toth, Charles K.; Schenck, Anton F.

1990-01-01

The basic principle of the associative memory is to match the unknown input pattern against a stored training set, and responding with the 'closest match' and the corresponding label. Generally, an associative memory system requires two preparatory steps: selecting attributes of the pattern class, and training the system by associating patterns with labels. Experimental results gained from using Parallel Associative Memory are presented. The primary concern is an automatic search for ground control points in aerial photographs. Synthetic patterns are tested followed by real data. The results are encouraging as a relatively high level of correct matches is reached.

Design and implementation of online automatic judging system

NASA Astrophysics Data System (ADS)

Liang, Haohui; Chen, Chaojie; Zhong, Xiuyu; Chen, Yuefeng

2017-06-01

For lower efficiency and poorer reliability in programming training and competition by currently artificial judgment, design an Online Automatic Judging (referred to as OAJ) System. The OAJ system including the sandbox judging side and Web side, realizes functions of automatically compiling and running the tested codes, and generating evaluation scores and corresponding reports. To prevent malicious codes from damaging system, the OAJ system utilizes sandbox, ensuring the safety of the system. The OAJ system uses thread pools to achieve parallel test, and adopt database optimization mechanism, such as horizontal split table, to improve the system performance and resources utilization rate. The test results show that the system has high performance, high reliability, high stability and excellent extensibility.

A dual-user teleoperation system with Online Authority Adjustment for haptic training.

PubMed

Fei Liu; Leleve, Arnaud; Eberard, Damien; Redarce, Tanneguy

2015-08-01

This paper introduces a dual-user teleoperation system for hands-on medical training. A shared control based architecture is presented for authority management. In this structure, the combination of control signals is obtained using a dominance factor. Its main improvement is Online Authority Adjustment (OAA): the authority can be adjusted manually/automatically during the training progress. Experimental results are provided to validate the performances of the system.

In-Flight Simulator for IFR Training

NASA Technical Reports Server (NTRS)

Parker, L. C.

1986-01-01

Computer-controlled unit feeds navigation signals to airplane instruments. Electronic training system allows students to learn to fly according to instrument flight rules (IFR) in uncrowded airspace. New system self-contained IFR simulator carried aboard training plane. Generates signals and commands for standard instruments on airplane, including navigational receiver, distance-measuring equipment, automatic direction finder, a marker-beacon receiver, altimeter, airspeed indicator, and heading indicator.

78 FR 75442 - Emergency Order Under 49 U.S.C. 20104 Establishing Requirements for Controlling Passenger Train...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-11

...- North Commuter Railroad Company (Metro-North) take certain actions to control passenger train speed at...-approved action plan that institutes modifications to its existing Automatic Train Control System or other... qualified railroad employees be present in the control compartment of Metro-North's passenger trains when...

49 CFR 236.516 - Power supply.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Power supply. 236.516 Section 236.516..., Train Control and Cab Signal Systems Standards § 236.516 Power supply. Automatic cab signal, train stop, or train control device hereafter installed shall operate from a separate or isolated power supply...

49 CFR 236.516 - Power supply.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Power supply. 236.516 Section 236.516..., Train Control and Cab Signal Systems Standards § 236.516 Power supply. Automatic cab signal, train stop, or train control device hereafter installed shall operate from a separate or isolated power supply...

49 CFR 236.590 - Pneumatic apparatus.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Pneumatic apparatus. 236.590 Section 236.590..., Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.590 Pneumatic apparatus. Automatic train stop, train control, or cab signal pneumatic apparatus shall be inspected, cleaned, and the...

49 CFR 236.590 - Pneumatic apparatus.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Pneumatic apparatus. 236.590 Section 236.590..., Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.590 Pneumatic apparatus. Automatic train stop, train control, or cab signal pneumatic apparatus shall be inspected, cleaned, and the...

Acquisition of automatic imitation is sensitive to sensorimotor contingency.

PubMed

Cook, Richard; Press, Clare; Dickinson, Anthony; Heyes, Cecilia

2010-08-01

The associative sequence learning model proposes that the development of the mirror system depends on the same mechanisms of associative learning that mediate Pavlovian and instrumental conditioning. To test this model, two experiments used the reduction of automatic imitation through incompatible sensorimotor training to assess whether mirror system plasticity is sensitive to contingency (i.e., the extent to which activation of one representation predicts activation of another). In Experiment 1, residual automatic imitation was measured following incompatible training in which the action stimulus was a perfect predictor of the response (contingent) or not at all predictive of the response (noncontingent). A contingency effect was observed: There was less automatic imitation indicative of more learning in the contingent group. Experiment 2 replicated this contingency effect and showed that, as predicted by associative learning theory, it can be abolished by signaling trials in which the response occurs in the absence of an action stimulus. These findings support the view that mirror system development depends on associative learning and indicate that this learning is not purely Hebbian. If this is correct, associative learning theory could be used to explain, predict, and intervene in mirror system development.

Ubiquitous Stereo Vision for Controlling Safety on Platforms in Railroad Station

NASA Astrophysics Data System (ADS)

Yoda, Ikushi; Hosotani, Daisuke; Sakaue, Katushiko

Dozens of people are killed every year when they fall off of train platforms, making this an urgent issue to be addressed by the railroads, especially in the major cities. This concern prompted the present work that is now in progress to develop a Ubiquitous Stereo Vision based system for safety management at the edge of rail station platforms. In this approach, a series of stereo cameras are installed in a row on the ceiling that are pointed downward at the edge of the platform to monitor the disposition of people waiting for the train. The purpose of the system is to determine automatically and in real-time whether anyone or anything is in the danger zone at the very edge of the platform, whether anyone has actually fallen off the platform, or whether there is any sign of these things happening. The system could be configured to automatically switch over to a surveillance monitor or automatically connect to an emergency brake system in the event of trouble.

Creating a medical dictionary using word alignment: the influence of sources and resources.

PubMed

Nyström, Mikael; Merkel, Magnus; Petersson, Håkan; Ahlfeldt, Hans

2007-11-23

Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English-Swedish dictionary contains 24,000 term pairs in base forms. More resources give better results in the automatic word alignment, but some resources only give small improvements. The most important type of resource is training and the most general resources were generated from ICD-10.

Creating a medical dictionary using word alignment: The influence of sources and resources

PubMed Central

Nyström, Mikael; Merkel, Magnus; Petersson, Håkan; Åhlfeldt, Hans

2007-01-01

Background Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. Methods We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. Results The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English-Swedish dictionary contains 24,000 term pairs in base forms. Conclusion More resources give better results in the automatic word alignment, but some resources only give small improvements. The most important type of resource is training and the most general resources were generated from ICD-10. PMID:18036221

Automatic delineation and 3D visualization of the human ventricular system using probabilistic neural networks

NASA Astrophysics Data System (ADS)

Hatfield, Fraser N.; Dehmeshki, Jamshid

1998-09-01

Neurosurgery is an extremely specialized area of medical practice, requiring many years of training. It has been suggested that virtual reality models of the complex structures within the brain may aid in the training of neurosurgeons as well as playing an important role in the preparation for surgery. This paper focuses on the application of a probabilistic neural network to the automatic segmentation of the ventricles from magnetic resonance images of the brain, and their three dimensional visualization.

Karst show caves - how DTN technology as used in space assists automatic environmental monitoring and tourist protection - experiment in Postojna cave

NASA Astrophysics Data System (ADS)

Gabrovšek, F.; Grašič, B.; Božnar, M. Z.; Mlakar, P.; Udén, M.; Davies, E.

2013-10-01

The paper presents an experiment demonstrating a novel and successful application of Delay- and Disruption-Tolerant Networking (DTN) technology for automatic data transfer in a karst cave Early Warning and Measuring System. The experiment took place inside the Postojna Cave in Slovenia, which is open to tourists. Several automatic meteorological measuring stations are set up inside the cave, as an adjunct to the surveillance infrastructure; the regular data transfer provided by the DTN technology allows the surveillance system to take on the role of an Early Warning System (EWS). One of the stations is set up alongside the railway tracks, which allows the tourist to travel inside the cave by train. The experiment was carried out by placing a DTN "data mule" (a DTN-enabled computer with WiFi connection) on the train and by upgrading the meteorological station with a DTN-enabled WiFi transmission system. When the data mule is in the wireless drive-by mode, it collects measurement data from the station over a period of several seconds as the train passes the stationary equipment, and delivers data at the final train station by the cave entrance. This paper describes an overview of the experimental equipment and organisation allowing the use of a DTN system for data collection and an EWS inside karst caves where there is a regular traffic of tourists and researchers.

Karst show caves - how DTN technology as used in space assists automatic environmental monitoring and tourist protection - experiment in Postojna Cave

NASA Astrophysics Data System (ADS)

Gabrovšek, F.; Grašič, B.; Božnar, M. Z.; Mlakar, P.; Udén, M.; Davies, E.

2014-02-01

The paper presents an experiment demonstrating a novel and successful application of delay- and disruption-tolerant networking (DTN) technology for automatic data transfer in a karst cave early warning and measuring system. The experiment took place inside the Postojna Cave in Slovenia, which is open to tourists. Several automatic meteorological measuring stations are set up inside the cave, as an adjunct to the surveillance infrastructure; the regular data transfer provided by the DTN technology allows the surveillance system to take on the role of an early warning system (EWS). One of the stations is set up alongside the railway tracks, which allows the tourist to travel inside the cave by train. The experiment was carried out by placing a DTN "data mule" (a DTN-enabled computer with WiFi connection) on the train and by upgrading the meteorological station with a DTN-enabled WiFi transmission system. When the data mule is in the wireless drive-by mode, it collects measurement data from the station over a period of several seconds as the train without stopping passes the stationary equipment, and delivers data at the final train station by the cave entrance. This paper describes an overview of the experimental equipment and organization allowing the use of a DTN system for data collection and an EWS inside karst caves where there is regular traffic of tourists and researchers.

Dynamic simulation of train derailments

DOT National Transportation Integrated Search

2006-11-05

This paper describes a planar rigid-body model to examine the gross motions of rail cars in a train derailment. The model is implemented using a commercial software package called ADAMS (Automatic Dynamic Analysis of Mechanical Systems). The results ...

49 CFR 236.588 - Periodic test.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false Periodic test. 236.588 Section 236.588..., Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.588 Periodic test. Except as provided in § 236.586, periodic test of the automatic train stop, train control, or cab signal apparatus...

49 CFR 236.588 - Periodic test.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Periodic test. 236.588 Section 236.588..., Train Control and Cab Signal Systems Inspection and Tests; Locomotive § 236.588 Periodic test. Except as provided in § 236.586, periodic test of the automatic train stop, train control, or cab signal apparatus...

A System Approach to Navy Medical Education and Training. Appendix 40. Competency Curricula for Pharmacy Assistant and Pharmacy Technician.

DTIC Science & Technology

1974-08-31

Procedures and techniques for compounding syrups, collodion, waters, spirits, liniments Use and maintenance of automatic liquid prepacker IIi [ o [ I... liniments , glycerites, elixirs Use and maintenance of automatic liquid prepacker 31 J ] Competency: PHARMACY TECHNICIAN (PHT) Unit II: Compounding

Contextual Modulation of Mirror and Countermirror Sensorimotor Associations

ERIC Educational Resources Information Center

Cook, Richard; Dickinson, Anthony; Heyes, Cecilia

2012-01-01

Automatic imitation--the unintended copying of observed actions--is thought to be a behavioral product of the mirror neuron system (MNS). Evidence that the MNS develops through associative learning comes from previous research showing that automatic imitation is attenuated by countermirror training, in which the observation of one action is paired…

Acquisition of Automatic Imitation Is Sensitive to Sensorimotor Contingency

ERIC Educational Resources Information Center

Cook, Richard; Press, Clare; Dickinson, Anthony; Heyes, Cecilia

2010-01-01

The associative sequence learning model proposes that the development of the mirror system depends on the same mechanisms of associative learning that mediate Pavlovian and instrumental conditioning. To test this model, two experiments used the reduction of automatic imitation through incompatible sensorimotor training to assess whether mirror…

Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision

PubMed Central

Reina, Giulio; Milella, Annalisa

2012-01-01

Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively.

Simulator training to automaticity leads to improved skill transfer compared with traditional proficiency-based training: a randomized controlled trial.

PubMed

Stefanidis, Dimitrios; Scerbo, Mark W; Montero, Paul N; Acker, Christina E; Smith, Warren D

2012-01-01

We hypothesized that novices will perform better in the operating room after simulator training to automaticity compared with traditional proficiency based training (current standard training paradigm). Simulator-acquired skill translates to the operating room, but the skill transfer is incomplete. Secondary task metrics reflect the ability of trainees to multitask (automaticity) and may improve performance assessment on simulators and skill transfer by indicating when learning is complete. Novices (N = 30) were enrolled in an IRB-approved, blinded, randomized, controlled trial. Participants were randomized into an intervention (n = 20) and a control (n = 10) group. The intervention group practiced on the FLS suturing task until they achieved expert levels of time and errors (proficiency), were tested on a live porcine fundoplication model, continued simulator training until they achieved expert levels on a visual spatial secondary task (automaticity) and were retested on the operating room (OR) model. The control group participated only during testing sessions. Performance scores were compared within and between groups during testing sessions. : Intervention group participants achieved proficiency after 54 ± 14 and automaticity after additional 109 ± 57 repetitions. Participants achieved better scores in the OR after automaticity training [345 (range, 0-537)] compared with after proficiency-based training [220 (range, 0-452; P < 0.001]. Simulator training to automaticity takes more time but is superior to proficiency-based training, as it leads to improved skill acquisition and transfer. Secondary task metrics that reflect trainee automaticity should be implemented during simulator training to improve learning and skill transfer.

Experience of automation failures in training: effects on trust, automation bias, complacency and performance.

PubMed

Sauer, Juergen; Chavaillaz, Alain; Wastell, David

2016-06-01

This work examined the effects of operators' exposure to various types of automation failures in training. Forty-five participants were trained for 3.5 h on a simulated process control environment. During training, participants either experienced a fully reliable, automatic fault repair facility (i.e. faults detected and correctly diagnosed), a misdiagnosis-prone one (i.e. faults detected but not correctly diagnosed) or a miss-prone one (i.e. faults not detected). One week after training, participants were tested for 3 h, experiencing two types of automation failures (misdiagnosis, miss). The results showed that automation bias was very high when operators trained on miss-prone automation encountered a failure of the diagnostic system. Operator errors resulting from automation bias were much higher when automation misdiagnosed a fault than when it missed one. Differences in trust levels that were instilled by the different training experiences disappeared during the testing session. Practitioner Summary: The experience of automation failures during training has some consequences. A greater potential for operator errors may be expected when an automatic system failed to diagnose a fault than when it failed to detect one.

40 CFR 265.16 - Personnel training.

Code of Federal Regulations, 2013 CFR

2013-07-01

... automatic waste feed cut-off systems; (iii) Communications or alarm -systems; (iv) Response to fires or... bargaining unit, but must include the requisite skill, education, or other qualifications, and duties of...

40 CFR 265.16 - Personnel training.

Code of Federal Regulations, 2012 CFR

2012-07-01

... automatic waste feed cut-off systems; (iii) Communications or alarm -systems; (iv) Response to fires or... bargaining unit, but must include the requisite skill, education, or other qualifications, and duties of...

40 CFR 265.16 - Personnel training.

Code of Federal Regulations, 2014 CFR

2014-07-01

... automatic waste feed cut-off systems; (iii) Communications or alarm -systems; (iv) Response to fires or... bargaining unit, but must include the requisite skill, education, or other qualifications, and duties of...

49 CFR 236.831 - Time, delay.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Time, delay. 236.831 Section 236.831 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Time, delay. As applied to an automatic train stop or train control system, the time which elapses...

Container-code recognition system based on computer vision and deep neural networks

NASA Astrophysics Data System (ADS)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

49 CFR 236.202 - Signal governing movements over hand-operated switch.

Code of Federal Regulations, 2011 CFR

2011-10-01

... THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Block Signal Systems Standards § 236.202 Signal governing movements over hand...

49 CFR 236.531 - Trip arm; height and distance from rail.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false Trip arm; height and distance from rail. 236.531... Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.531 Trip arm; height and distance from rail. Trip arm of automatic train stop device when in the stop position shall be...

49 CFR 236.531 - Trip arm; height and distance from rail.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Trip arm; height and distance from rail. 236.531... Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.531 Trip arm; height and distance from rail. Trip arm of automatic train stop device when in the stop position shall be...

49 CFR 236.531 - Trip arm; height and distance from rail.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 4 2013-10-01 2013-10-01 false Trip arm; height and distance from rail. 236.531... Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.531 Trip arm; height and distance from rail. Trip arm of automatic train stop device when in the stop position shall be...

49 CFR 236.531 - Trip arm; height and distance from rail.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Trip arm; height and distance from rail. 236.531... Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.531 Trip arm; height and distance from rail. Trip arm of automatic train stop device when in the stop position shall be...

49 CFR 236.531 - Trip arm; height and distance from rail.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 4 2012-10-01 2012-10-01 false Trip arm; height and distance from rail. 236.531... Train Stop, Train Control and Cab Signal Systems Rules and Instructions; Roadway § 236.531 Trip arm; height and distance from rail. Trip arm of automatic train stop device when in the stop position shall be...

Perceptual Units Training for Improving Word Analysis Skills. Technical Report No. 1.

ERIC Educational Resources Information Center

Weaver, Phyllis A.; And Others

A training program was devised to develop automaticity of one subcomponent of reading--locating and disembedding multiletter units within words. The system involved the use of a training task that was implemented in a microcomputer-based game that required students to detect whether a target unit was presented within words that were shown in rapid…

Automatic query formulations in information retrieval.

PubMed

Salton, G; Buckley, C; Fox, E A

1983-07-01

Modern information retrieval systems are designed to supply relevant information in response to requests received from the user population. In most retrieval environments the search requests consist of keywords, or index terms, interrelated by appropriate Boolean operators. Since it is difficult for untrained users to generate effective Boolean search requests, trained search intermediaries are normally used to translate original statements of user need into useful Boolean search formulations. Methods are introduced in this study which reduce the role of the search intermediaries by making it possible to generate Boolean search formulations completely automatically from natural language statements provided by the system patrons. Frequency considerations are used automatically to generate appropriate term combinations as well as Boolean connectives relating the terms. Methods are covered to produce automatic query formulations both in a standard Boolean logic system, as well as in an extended Boolean system in which the strict interpretation of the connectives is relaxed. Experimental results are supplied to evaluate the effectiveness of the automatic query formulation process, and methods are described for applying the automatic query formulation process in practice.

Popular song and lyrics synchronization and its application to music information retrieval

NASA Astrophysics Data System (ADS)

Chen, Kai; Gao, Sheng; Zhu, Yongwei; Sun, Qibin

2006-01-01

An automatic synchronization system of the popular song and its lyrics is presented in the paper. The system includes two main components: a) automatically detecting vocal/non-vocal in the audio signal and b) automatically aligning the acoustic signal of the song with its lyric using speech recognition techniques and positioning the boundaries of the lyrics in its acoustic realization at the multiple levels simultaneously (e.g. the word / syllable level and phrase level). The GMM models and a set of HMM-based acoustic model units are carefully designed and trained for the detection and alignment. To eliminate the severe mismatch due to the diversity of musical signal and sparse training data available, the unsupervised adaptation technique such as maximum likelihood linear regression (MLLR) is exploited for tailoring the models to the real environment, which improves robustness of the synchronization system. To further reduce the effect of the missed non-vocal music on alignment, a novel grammar net is build to direct the alignment. As we know, this is the first automatic synchronization system only based on the low-level acoustic feature such as MFCC. We evaluate the system on a Chinese song dataset collecting from 3 popular singers. We obtain 76.1% for the boundary accuracy at the syllable level (BAS) and 81.5% for the boundary accuracy at the phrase level (BAP) using fully automatic vocal/non-vocal detection and alignment. The synchronization system has many applications such as multi-modality (audio and textual) content-based popular song browsing and retrieval. Through the study, we would like to open up the discussion of some challenging problems when developing a robust synchronization system for largescale database.

49 CFR 236.564 - Acknowledging time.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Acknowledging time. 236.564 Section 236.564..., Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.564 Acknowledging time. Acknowledging time of intermittent automatic train-stop device shall be not more than 30 seconds. ...

49 CFR 236.564 - Acknowledging time.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false Acknowledging time. 236.564 Section 236.564..., Train Control and Cab Signal Systems Rules and Instructions; Locomotives § 236.564 Acknowledging time. Acknowledging time of intermittent automatic train-stop device shall be not more than 30 seconds. ...

49 CFR 236.204 - Track signaled for movements in both directions, requirements.

Code of Federal Regulations, 2010 CFR

2010-10-01

... THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Block Signal Systems Standards § 236.204 Track signaled for movements in both...

49 CFR 236.204 - Track signaled for movements in both directions, requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Block Signal Systems Standards § 236.204 Track signaled for movements in both...

Automated Management of Exercise Intervention at the Point of Care: Application of a Web-Based Leg Training System

PubMed Central

2015-01-01

Background Recent advances in information and communication technology have prompted development of Web-based health tools to promote physical activity, the key component of cardiac rehabilitation and chronic disease management. Mobile apps can facilitate behavioral changes and help in exercise monitoring, although actual training usually takes place away from the point of care in specialized gyms or outdoors. Daily participation in conventional physical activities is expensive, time consuming, and mostly relies on self-management abilities of patients who are typically aged, overweight, and unfit. Facilitation of sustained exercise training at the point of care might improve patient engagement in cardiac rehabilitation. Objective In this study we aimed to test the feasibility of execution and automatic monitoring of several exercise regimens on-site using a Web-enabled leg training system. Methods The MedExercise leg rehabilitation machine was equipped with wireless temperature sensors in order to monitor its usage by the rise of temperature in the resistance unit (Δt°). Personal electronic devices such as laptop computers were fitted with wireless gateways and relevant software was installed to monitor the usage of training machines. Cloud-based software allowed monitoring of participant training over the Internet. Seven healthy participants applied the system at various locations with training protocols typically used in cardiac rehabilitation. The heart rates were measured by fingertip pulse oximeters. Results Exercising in home chairs, in bed, and under an office desk was made feasible and resulted in an intensity-dependent increase of participants’ heart rates and Δt° in training machine temperatures. Participants self-controlled their activities on smart devices, while a supervisor monitored them over the Internet. Individual Δt° reached during 30 minutes of moderate-intensity continuous training averaged 7.8°C (SD 1.6). These Δt° were used as personalized daily doses of exercise with automatic email alerts sent upon achieving them. During 1-week training at home, automatic notifications were received on 4.4 days (SD 1.8). Although the high intensity interval training regimen was feasible on-site, it was difficult for self- and remote management. Opportunistic leg exercise under the desk, while working with a computer, and training in bed while viewing television were less intensive than dosed exercise bouts, but allowed prolonged leg mobilization of 73.7 minutes/day (SD 29.7). Conclusions This study demonstrated the feasibility of self-control exercise training on-site, which was accompanied by online monitoring, electronic recording, personalization of exercise doses, and automatic reporting of adherence. The results suggest that this technology and its applications are useful for the delivery of Web-based exercise rehabilitation and cardiac training programs at the point of care. PMID:28582243

An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

PubMed

Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

2017-08-01

Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

Towards the Development of a Comprehensive Pedagogical Framework for Pronunciation Training Based on Adapted Automatic Speech Recognition Systems

ERIC Educational Resources Information Center

Ali, Saandia

2016-01-01

This paper reports on the early stages of a locally funded research and development project taking place at Rennes 2 university. It aims at developing a comprehensive pedagogical framework for pronunciation training for adult learners of English. This framework will combine a direct approach to pronunciation training (face-to-face teaching) with…

Development of an automated ultrasonic testing system

NASA Astrophysics Data System (ADS)

Shuxiang, Jiao; Wong, Brian Stephen

2005-04-01

Non-Destructive Testing is necessary in areas where defects in structures emerge over time due to wear and tear and structural integrity is necessary to maintain its usability. However, manual testing results in many limitations: high training cost, long training procedure, and worse, the inconsistent test results. A prime objective of this project is to develop an automatic Non-Destructive testing system for a shaft of the wheel axle of a railway carriage. Various methods, such as the neural network, pattern recognition methods and knowledge-based system are used for the artificial intelligence problem. In this paper, a statistical pattern recognition approach, Classification Tree is applied. Before feature selection, a thorough study on the ultrasonic signals produced was carried out. Based on the analysis of the ultrasonic signals, three signal processing methods were developed to enhance the ultrasonic signals: Cross-Correlation, Zero-Phase filter and Averaging. The target of this step is to reduce the noise and make the signal character more distinguishable. Four features: 1. The Auto Regressive Model Coefficients. 2. Standard Deviation. 3. Pearson Correlation 4. Dispersion Uniformity Degree are selected. And then a Classification Tree is created and applied to recognize the peak positions and amplitudes. Searching local maximum is carried out before feature computing. This procedure reduces much computation time in the real-time testing. Based on this algorithm, a software package called SOFRA was developed to recognize the peaks, calibrate automatically and test a simulated shaft automatically. The automatic calibration procedure and the automatic shaft testing procedure are developed.

49 CFR 238.431 - Brake system.

Code of Federal Regulations, 2011 CFR

2011-10-01

... train is operating under worst-case adhesion conditions. (b) The brake system shall be designed to allow... a brake rate consistent with prevailing adhesion, passenger safety, and brake system thermal... adhesion control system designed to automatically adjust the braking force on each wheel to prevent sliding...

Automatic rule generation for high-level vision

NASA Technical Reports Server (NTRS)

Rhee, Frank Chung-Hoon; Krishnapuram, Raghu

1992-01-01

Many high-level vision systems use rule-based approaches to solving problems such as autonomous navigation and image understanding. The rules are usually elaborated by experts. However, this procedure may be rather tedious. In this paper, we propose a method to generate such rules automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

Training and subjective workload in a category search task

NASA Technical Reports Server (NTRS)

Vidulich, Michael A.; Pandit, Parimal

1986-01-01

This study examined automaticity as a means by which training influences mental workload. Two groups were trained in a category search task. One group received a training paradigm designed to promote the development of automaticity; the other group received a training paradigm designed to prohibit it. Resultant performance data showed the expected improvement as a result of the development of automaticity. Subjective workload assessments mirrored the performance results in most respects. The results supported the position that subjective mental workload assessments may be sensitive to the effect of training when it produces a lower level of cognitive load.

49 CFR 238.433 - Draft system.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Draft system. 238.433 Section 238.433 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Equipment § 238.433 Draft system. (a) Leading and trailing automatic couplers of trains shall be compatible...

49 CFR 238.433 - Draft system.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 4 2012-10-01 2012-10-01 false Draft system. 238.433 Section 238.433 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Equipment § 238.433 Draft system. (a) Leading and trailing automatic couplers of trains shall be compatible...

49 CFR 238.433 - Draft system.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false Draft system. 238.433 Section 238.433 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Equipment § 238.433 Draft system. (a) Leading and trailing automatic couplers of trains shall be compatible...

49 CFR 238.433 - Draft system.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Draft system. 238.433 Section 238.433 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Equipment § 238.433 Draft system. (a) Leading and trailing automatic couplers of trains shall be compatible...

49 CFR 238.433 - Draft system.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 4 2013-10-01 2013-10-01 false Draft system. 238.433 Section 238.433 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Equipment § 238.433 Draft system. (a) Leading and trailing automatic couplers of trains shall be compatible...

Automated Management of Exercise Intervention at the Point of Care: Application of a Web-Based Leg Training System.

PubMed

Dedov, Vadim N; Dedova, Irina V

2015-11-23

Recent advances in information and communication technology have prompted development of Web-based health tools to promote physical activity, the key component of cardiac rehabilitation and chronic disease management. Mobile apps can facilitate behavioral changes and help in exercise monitoring, although actual training usually takes place away from the point of care in specialized gyms or outdoors. Daily participation in conventional physical activities is expensive, time consuming, and mostly relies on self-management abilities of patients who are typically aged, overweight, and unfit. Facilitation of sustained exercise training at the point of care might improve patient engagement in cardiac rehabilitation. In this study we aimed to test the feasibility of execution and automatic monitoring of several exercise regimens on-site using a Web-enabled leg training system. The MedExercise leg rehabilitation machine was equipped with wireless temperature sensors in order to monitor its usage by the rise of temperature in the resistance unit (Δt°). Personal electronic devices such as laptop computers were fitted with wireless gateways and relevant software was installed to monitor the usage of training machines. Cloud-based software allowed monitoring of participant training over the Internet. Seven healthy participants applied the system at various locations with training protocols typically used in cardiac rehabilitation. The heart rates were measured by fingertip pulse oximeters. Exercising in home chairs, in bed, and under an office desk was made feasible and resulted in an intensity-dependent increase of participants' heart rates and Δt° in training machine temperatures. Participants self-controlled their activities on smart devices, while a supervisor monitored them over the Internet. Individual Δt° reached during 30 minutes of moderate-intensity continuous training averaged 7.8°C (SD 1.6). These Δt° were used as personalized daily doses of exercise with automatic email alerts sent upon achieving them. During 1-week training at home, automatic notifications were received on 4.4 days (SD 1.8). Although the high intensity interval training regimen was feasible on-site, it was difficult for self- and remote management. Opportunistic leg exercise under the desk, while working with a computer, and training in bed while viewing television were less intensive than dosed exercise bouts, but allowed prolonged leg mobilization of 73.7 minutes/day (SD 29.7). This study demonstrated the feasibility of self-control exercise training on-site, which was accompanied by online monitoring, electronic recording, personalization of exercise doses, and automatic reporting of adherence. The results suggest that this technology and its applications are useful for the delivery of Web-based exercise rehabilitation and cardiac training programs at the point of care. ©Vadim N Dedov, Irina V Dedova. Originally published in JMIR Rehabilitation and Assistive Technology (http://rehab.jmir.org), 23.11.2015.

Image simulation for automatic license plate recognition

NASA Astrophysics Data System (ADS)

Bala, Raja; Zhao, Yonghui; Burry, Aaron; Kozitsky, Vladimir; Fillion, Claude; Saunders, Craig; Rodríguez-Serrano, José

2012-01-01

Automatic license plate recognition (ALPR) is an important capability for traffic surveillance applications, including toll monitoring and detection of different types of traffic violations. ALPR is a multi-stage process comprising plate localization, character segmentation, optical character recognition (OCR), and identification of originating jurisdiction (i.e. state or province). Training of an ALPR system for a new jurisdiction typically involves gathering vast amounts of license plate images and associated ground truth data, followed by iterative tuning and optimization of the ALPR algorithms. The substantial time and effort required to train and optimize the ALPR system can result in excessive operational cost and overhead. In this paper we propose a framework to create an artificial set of license plate images for accelerated training and optimization of ALPR algorithms. The framework comprises two steps: the synthesis of license plate images according to the design and layout for a jurisdiction of interest; and the modeling of imaging transformations and distortions typically encountered in the image capture process. Distortion parameters are estimated by measurements of real plate images. The simulation methodology is successfully demonstrated for training of OCR.

AUTOMATIC COUNTING APPARATUS

DOEpatents

Howell, W.D.

1957-08-20

An apparatus for automatically recording the results of counting operations on trains of electrical pulses is described. The disadvantages of prior devices utilizing the two common methods of obtaining the count rate are overcome by this apparatus; in the case of time controlled operation, the disclosed system automatically records amy information stored by the scaler but not transferred to the printer at the end of the predetermined time controlled operations and, in the case of count controlled operation, provision is made to prevent a weak sample from occupying the apparatus for an excessively long period of time.

A forced running wheel system with a microcontroller that provides high-intensity exercise training in an animal ischemic stroke model.

PubMed

Chen, C C; Chang, M W; Chang, C P; Chan, S C; Chang, W Y; Yang, C L; Lin, M T

2014-10-01

We developed a forced non-electric-shock running wheel (FNESRW) system that provides rats with high-intensity exercise training using automatic exercise training patterns that are controlled by a microcontroller. The proposed system successfully makes a breakthrough in the traditional motorized running wheel to allow rats to perform high-intensity training and to enable comparisons with the treadmill at the same exercise intensity without any electric shock. A polyvinyl chloride runway with a rough rubber surface was coated on the periphery of the wheel so as to permit automatic acceleration training, and which allowed the rats to run consistently at high speeds (30 m/min for 1 h). An animal ischemic stroke model was used to validate the proposed system. FNESRW, treadmill, control, and sham groups were studied. The FNESRW and treadmill groups underwent 3 weeks of endurance running training. After 3 weeks, the experiments of middle cerebral artery occlusion, the modified neurological severity score (mNSS), an inclined plane test, and triphenyltetrazolium chloride were performed to evaluate the effectiveness of the proposed platform. The proposed platform showed that enhancement of motor function, mNSS, and infarct volumes was significantly stronger in the FNESRW group than the control group (P<0.05) and similar to the treadmill group. The experimental data demonstrated that the proposed platform can be applied to test the benefit of exercise-preconditioning-induced neuroprotection using the animal stroke model. Additional advantages of the FNESRW system include stand-alone capability, independence of subjective human adjustment, and ease of use.

A forced running wheel system with a microcontroller that provides high-intensity exercise training in an animal ischemic stroke model

PubMed Central

Chen, C.C.; Chang, M.W.; Chang, C.P.; Chan, S.C.; Chang, W.Y.; Yang, C.L.; Lin, M.T.

2014-01-01

We developed a forced non-electric-shock running wheel (FNESRW) system that provides rats with high-intensity exercise training using automatic exercise training patterns that are controlled by a microcontroller. The proposed system successfully makes a breakthrough in the traditional motorized running wheel to allow rats to perform high-intensity training and to enable comparisons with the treadmill at the same exercise intensity without any electric shock. A polyvinyl chloride runway with a rough rubber surface was coated on the periphery of the wheel so as to permit automatic acceleration training, and which allowed the rats to run consistently at high speeds (30 m/min for 1 h). An animal ischemic stroke model was used to validate the proposed system. FNESRW, treadmill, control, and sham groups were studied. The FNESRW and treadmill groups underwent 3 weeks of endurance running training. After 3 weeks, the experiments of middle cerebral artery occlusion, the modified neurological severity score (mNSS), an inclined plane test, and triphenyltetrazolium chloride were performed to evaluate the effectiveness of the proposed platform. The proposed platform showed that enhancement of motor function, mNSS, and infarct volumes was significantly stronger in the FNESRW group than the control group (P<0.05) and similar to the treadmill group. The experimental data demonstrated that the proposed platform can be applied to test the benefit of exercise-preconditioning-induced neuroprotection using the animal stroke model. Additional advantages of the FNESRW system include stand-alone capability, independence of subjective human adjustment, and ease of use. PMID:25140816

49 CFR Appendix B to Part 232 - Part 232 Prior to May 31, 2001 as Clarified Effective April 10, 2002

Code of Federal Regulations, 2010 CFR

2010-10-01

... coupled to train, after which, an automatic brake application and release test of airbrakes on rear car... closing angle cocks for cutting off one or more cars from the rear end of train, automatic air brake must... automatic air brake must not be depended upon to hold a locomotive, cars or train, when standing on a grade...

49 CFR Appendix B to Part 232 - Part 232 Prior to May 31, 2001 as Clarified Effective April 10, 2002

Code of Federal Regulations, 2011 CFR

2011-10-01

... coupled to train, after which, an automatic brake application and release test of airbrakes on rear car... closing angle cocks for cutting off one or more cars from the rear end of train, automatic air brake must... automatic air brake must not be depended upon to hold a locomotive, cars or train, when standing on a grade...

Dynamic user data analysis and web composition technique using big data

NASA Astrophysics Data System (ADS)

Soundarya, P.; Vanitha, M.; Sumaiya Thaseen, I.

2017-11-01

In the existing system, a reliable service oriented system is built which is more important when compared with the traditional standalone system in the unpredictable internet service and it also a challenging task to build reliable web service. In the proposed system, the fault tolerance is determined by using the proposed heuristic algorithm. There are two kinds of strategies active and passive strategies. The user requirement is also formulated as local and global constraints. Different services are deployed in the modification process. Two bus reservation and two train reservation services are deployed along with hotel reservation service. User can choose any one of the bus reservation and specify their destination location. If corresponding destination is not available then automatic backup service to another bus reservation system is carried. If same, the service is not available then parallel service of train reservation is initiated. Automatic hotel reservation is also initiated based on the mode and type of travel of the user.

General collaboration offer of Johnson Controls regarding the performance of air conditioning automatic control systems and other buildings` automatic control systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gniazdowski, J.

1995-12-31

JOHNSON CONTROLS manufactures measuring and control equipment (800 types) and is as well a {open_quotes}turn-key{close_quotes} supplier of complete automatic controls systems for heating, air conditioning, ventilation and refrigerating engineering branches. The Company also supplies Buildings` Computer-Based Supervision and Monitoring Systems that may be applied in both small and large structures. Since 1990 the company has been performing full-range trade and contracting activities on the Polish market. We have our own well-trained technical staff and we collaborate with a series of designing and contracting enterprises that enable us to have our projects carried out all over Poland. The prices of ourmore » supplies and services correspond with the level of the Polish market.« less

Design and implementation of a general and automatic test platform base on NI PXI system

NASA Astrophysics Data System (ADS)

Shi, Long

2018-05-01

Aiming at some difficulties of test equipment such as the short product life, poor generality and high development cost, a general and automatic test platform base on NI PXI system is designed in this paper, which is able to meet most test requirements of circuit boards. The test platform is devided into 5 layers, every layer is introduced in detail except for the "Equipment Under Test" layer. An output board of a track-side equipment, which is an important part of high speed train control system, is taken as an example to make the functional circuit test by the test platform. The results show that the test platform is easy to realize add-on functions development, automatic test, wide compatibility and strong generality.

Error Rates in Users of Automatic Face Recognition Software

PubMed Central

White, David; Dunn, James D.; Schmid, Alexandra C.; Kemp, Richard I.

2015-01-01

In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated ‘candidate lists’ selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers–who use the system in their daily work–and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced “facial examiners” outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems–potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems. PMID:26465631

An Interactive Decision Support System for Scheduling Fighter Pilot Training

DTIC Science & Technology

2002-03-26

Deitel , H.M. and Deitel , P.J. C: How to Program , 2nd ed., Prentice Hall, 1994. 8. Deitel , H.M. and Deitel , P.J. How to Program Java...Visual Basic Programming language, the Excel tool is modified in several ways. Scheduling Dispatch rules are implemented to automatically generate... programming language, the Excel tool was modified in several ways. Scheduling dispatch rules are implemented to automatically generate

Associative (not Hebbian) learning and the mirror neuron system.

PubMed

Cooper, Richard P; Cook, Richard; Dickinson, Anthony; Heyes, Cecilia M

2013-04-12

The associative sequence learning (ASL) hypothesis suggests that sensorimotor experience plays an inductive role in the development of the mirror neuron system, and that it can play this crucial role because its effects are mediated by learning that is sensitive to both contingency and contiguity. The Hebbian hypothesis proposes that sensorimotor experience plays a facilitative role, and that its effects are mediated by learning that is sensitive only to contiguity. We tested the associative and Hebbian accounts by computational modelling of automatic imitation data indicating that MNS responsivity is reduced more by contingent and signalled than by non-contingent sensorimotor training (Cook et al. [7]). Supporting the associative account, we found that the reduction in automatic imitation could be reproduced by an existing interactive activation model of imitative compatibility when augmented with Rescorla-Wagner learning, but not with Hebbian or quasi-Hebbian learning. The work argues for an associative, but against a Hebbian, account of the effect of sensorimotor training on automatic imitation. We argue, by extension, that associative learning is potentially sufficient for MNS development. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

NASA Astrophysics Data System (ADS)

Jiménez del Toro, Oscar; Atzori, Manfredo; Otálora, Sebastian; Andersson, Mats; Eurén, Kristian; Hedlund, Martin; Rönnquist, Peter; Müller, Henning

2017-03-01

The Gleason grading system was developed for assessing prostate histopathology slides. It is correlated to the outcome and incidence of relapse in prostate cancer. Although this grading is part of a standard protocol performed by pathologists, visual inspection of whole slide images (WSIs) has an inherent subjectivity when evaluated by different pathologists. Computer aided pathology has been proposed to generate an objective and reproducible assessment that can help pathologists in their evaluation of new tissue samples. Deep convolutional neural networks are a promising approach for the automatic classification of histopathology images and can hierarchically learn subtle visual features from the data. However, a large number of manual annotations from pathologists are commonly required to obtain sufficient statistical generalization when training new models that can evaluate the daily generated large amounts of pathology data. A fully automatic approach that detects prostatectomy WSIs with high-grade Gleason score is proposed. We evaluate the performance of various deep learning architectures training them with patches extracted from automatically generated regions-of-interest rather than from manually segmented ones. Relevant parameters for training the deep learning model such as size and number of patches as well as the inclusion or not of data augmentation are compared between the tested deep learning architectures. 235 prostate tissue WSIs with their pathology report from the publicly available TCGA data set were used. An accuracy of 78% was obtained in a balanced set of 46 unseen test images with different Gleason grades in a 2-class decision: high vs. low Gleason grade. Grades 7-8, which represent the boundary decision of the proposed task, were particularly well classified. The method is scalable to larger data sets with straightforward re-training of the model to include data from multiple sources, scanners and acquisition techniques. Automatically generated heatmaps for theWSIs could be useful for improving the selection of patches when training networks for big data sets and to guide the visual inspection of these images.

Automatic Train Operation Using Autonomic Prediction of Train Runs

NASA Astrophysics Data System (ADS)

Asuka, Masashi; Kataoka, Kenji; Komaya, Kiyotoshi; Nishida, Syogo

In this paper, we present an automatic train control method adaptable to disturbed train traffic conditions. The proposed method presumes transmission of detected time of a home track clearance to trains approaching to the station by employing equipment of Digital ATC (Automatic Train Control). Using the information, each train controls its acceleration by the method that consists of two approaches. First, by setting a designated restricted speed, the train controls its running time to arrive at the next station in accordance with predicted delay. Second, the train predicts the time at which it will reach the current braking pattern generated by Digital ATC, along with the time when the braking pattern transits ahead. By comparing them, the train correctly chooses the coasting drive mode in advance to avoid deceleration due to the current braking pattern. We evaluated the effectiveness of the proposed method regarding driving conditions, energy consumption and reduction of delays by simulation.

A real-time monitoring system for the facial nerve.

PubMed

Prell, Julian; Rachinger, Jens; Scheller, Christian; Alfieri, Alex; Strauss, Christian; Rampp, Stefan

2010-06-01

Damage to the facial nerve during surgery in the cerebellopontine angle is indicated by A-trains, a specific electromyogram pattern. These A-trains can be quantified by the parameter "traintime," which is reliably correlated with postoperative functional outcome. The system presented was designed to monitor traintime in real-time. A dedicated hardware and software platform for automated continuous analysis of the intraoperative facial nerve electromyogram was specifically designed. The automatic detection of A-trains is performed by a software algorithm for real-time analysis of nonstationary biosignals. The system was evaluated in a series of 30 patients operated on for vestibular schwannoma. A-trains can be detected and measured automatically by the described method for real-time analysis. Traintime is monitored continuously via a graphic display and is shown as an absolute numeric value during the operation. It is an expression of overall, cumulated length of A-trains in a given channel; a high correlation between traintime as measured by real-time analysis and functional outcome immediately after the operation (Spearman correlation coefficient [rho] = 0.664, P < .001) and in long-term outcome (rho = 0.631, P < .001) was observed. Automated real-time analysis of the intraoperative facial nerve electromyogram is the first technique capable of reliable continuous real-time monitoring. It can critically contribute to the estimation of functional outcome during the course of the operative procedure.

Variably Transmittive, Electronically-Controlled Eyewear

NASA Technical Reports Server (NTRS)

Chapman, John J. (Inventor); Glaab, Louis J. (Inventor); Schott, Timothy D. (Inventor); Howell, Charles T. (Inventor); Fleck, Vincent J. (Inventor)

2013-01-01

A system and method for flight training and evaluation of pilots comprises electronically activated vision restriction glasses that detect the pilot's head position and automatically darken and restrict the pilot's ability to see through the front and side windscreens when the pilot-in-training attempts to see out the windscreen. Thus, the pilot-in-training sees only within the aircraft cockpit, forcing him or her to fly by instruments in the most restricted operational mode.

An air brake model for longitudinal train dynamics studies

NASA Astrophysics Data System (ADS)

Wei, Wei; Hu, Yang; Wu, Qing; Zhao, Xubao; Zhang, Jun; Zhang, Yuan

2017-04-01

Experience of heavy haul train operation shows that heavy haul train fatigue fracture of coupler and its related components, even the accidents are caused by excessive coupler force. The most economical and effective method to study on train longitudinal impulse by reducing the coupler force is simulation method. The characteristics of train air brake system is an important excitation source for the study of longitudinal impulse. It is very difficult to obtain the braking characteristic by the test method, a better way to get the input parameters of the excitation source in the train longitudinal dynamics is modelling the train air brake system. In this paper, the air brake system model of integrated system of air brake and longitudinal dynamics is introduced. This introduce is focus on the locomotive automatic brake valve and vehicle distribution valve model, and the comparative analysis of the simulation and test results of the braking system is given. It is proved that the model can predict the characteristics of train braking system. This method provides a good solution for the excitation source of longitudinal dynamic analysis system.

Automatic recognition of falls in gait-slip training: Harness load cell based criteria.

PubMed

Yang, Feng; Pai, Yi-Chung

2011-08-11

Over-head-harness systems, equipped with load cell sensors, are essential to the participants' safety and to the outcome assessment in perturbation training. The purpose of this study was to first develop an automatic outcome recognition criterion among young adults for gait-slip training and then verify such criterion among older adults. Each of 39 young and 71 older subjects, all protected by safety harness, experienced 8 unannounced, repeated slips, while walking on a 7m walkway. Each trial was monitored with a motion capture system, bilateral ground reaction force (GRF), harness force, and video recording. The fall trials were first unambiguously indentified with careful visual inspection of all video records. The recoveries without balance loss (in which subjects' trailing foot landed anteriorly to the slipping foot) were also first fully recognized from motion and GRF analyses. These analyses then set the gold standard for the outcome recognition with load cell measurements. Logistic regression analyses based on young subjects' data revealed that the peak load cell force was the best predictor of falls (with 100% accuracy) at the threshold of 30% body weight. On the other hand, the peak moving average force of load cell across 1s period, was the best predictor (with 100% accuracy) separating recoveries with backward balance loss (in which the recovery step landed posterior to slipping foot) from harness assistance at the threshold of 4.5% body weight. These threshold values were fully verified using the data from older adults (100% accuracy in recognizing falls). Because of the increasing popularity in the perturbation training coupling with the protective over-head-harness system, this new criterion could have far reaching implications in automatic outcome recognition during the movement therapy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Automated knowledge-base refinement

NASA Technical Reports Server (NTRS)

Mooney, Raymond J.

1994-01-01

Over the last several years, we have developed several systems for automatically refining incomplete and incorrect knowledge bases. These systems are given an imperfect rule base and a set of training examples and minimally modify the knowledge base to make it consistent with the examples. One of our most recent systems, FORTE, revises first-order Horn-clause knowledge bases. This system can be viewed as automatically debugging Prolog programs based on examples of correct and incorrect I/O pairs. In fact, we have already used the system to debug simple Prolog programs written by students in a programming language course. FORTE has also been used to automatically induce and revise qualitative models of several continuous dynamic devices from qualitative behavior traces. For example, it has been used to induce and revise a qualitative model of a portion of the Reaction Control System (RCS) of the NASA Space Shuttle. By fitting a correct model of this portion of the RCS to simulated qualitative data from a faulty system, FORTE was also able to correctly diagnose simple faults in this system.

Automatic mine detection based on multiple features

NASA Astrophysics Data System (ADS)

Yu, Ssu-Hsin; Gandhe, Avinash; Witten, Thomas R.; Mehra, Raman K.

2000-08-01

Recent research sponsored by the Army, Navy and DARPA has significantly advanced the sensor technologies for mine detection. Several innovative sensor systems have been developed and prototypes were built to investigate their performance in practice. Most of the research has been focused on hardware design. However, in order for the systems to be in wide use instead of in limited use by a small group of well-trained experts, an automatic process for mine detection is needed to make the final decision process on mine vs. no mine easier and more straightforward. In this paper, we describe an automatic mine detection process consisting of three stage, (1) signal enhancement, (2) pixel-level mine detection, and (3) object-level mine detection. The final output of the system is a confidence measure that quantifies the presence of a mine. The resulting system was applied to real data collected using radar and acoustic technologies.

Energy Monitoring and Control Systems Operator Training - Recommended Qualifications, Staffing, Job Description, and Training Requirements for EMCS Operators.

DTIC Science & Technology

1982-06-01

start/stop chiller optimization , and demand limiting were added. The system monitors a 7,000 ton chiller plant and controls 74 air handlers. The EMCS does...Modify analog limits. g. Adjust setpoints of selected controllers. h. Select manual or automatic control modes. i. Enable and disable individual points...or event schedules and controller setpoints ; make nonscheduled starts and stops of equipment or disable field panels when required for routine

High-speed railway signal trackside equipment patrol inspection system

NASA Astrophysics Data System (ADS)

Wu, Nan

2018-03-01

High-speed railway signal trackside equipment patrol inspection system comprehensively applies TDI (time delay integration), high-speed and highly responsive CMOS architecture, low illumination photosensitive technique, image data compression technique, machine vision technique and so on, installed on high-speed railway inspection train, and achieves the collection, management and analysis of the images of signal trackside equipment appearance while the train is running. The system will automatically filter out the signal trackside equipment images from a large number of the background image, and identify of the equipment changes by comparing the original image data. Combining with ledger data and train location information, the system accurately locate the trackside equipment, conscientiously guiding maintenance.

77 FR 5294 - Petition for Waiver of Compliance

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-02

... automatic train supervision controls. This work initially includes certain tracks within PATH's Harrison... tracks, other yard tracks, and terminals as the Automatic Train Control (ATC, which is a type of PTC... the requirements of 49 CFR 235.5 to expedite successful installation of Positive Train Control (PTC...

Training effectiveness of an intelligent tutoring system for a propulsion console trainer

NASA Technical Reports Server (NTRS)

Johnson, Debra Steele

1990-01-01

A formative evaluation was conducted on an Intelligent Tutoring System (ITS) developed for tasks performed on the Propulsion Console. The ITS, which was developed primarily as a research tool, provides training on use of the Manual Select Keyboard (MSK). Three subjects completed three phases of training using the ITS: declarative, speed, and automaticity training. Data were collected on several performance dimensions, including training time, number of trials performed in each training phase, and number of errors. Information was also collected regarding the user interface and content of training. Suggestions for refining the ITS are discussed. Further, future potential uses and limitations of the ITS are discussed. The results provide an initial demonstration of the effectiveness of the Propulsion Console ITS and indicate the potential benefits of this form of training tool for related tasks.

49 CFR 236.205 - Signal control circuits; requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Signal control circuits; requirements. 236.205..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Block Signal Systems Standards § 236.205 Signal control circuits; requirements. The circuits shall be so...

49 CFR 236.205 - Signal control circuits; requirements.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Signal control circuits; requirements. 236.205..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Block Signal Systems Standards § 236.205 Signal control circuits; requirements. The circuits shall be so...

49 CFR 236.205 - Signal control circuits; requirements.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false Signal control circuits; requirements. 236.205..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Block Signal Systems Standards § 236.205 Signal control circuits; requirements. The circuits shall be so...

49 CFR 236.205 - Signal control circuits; requirements.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 4 2012-10-01 2012-10-01 false Signal control circuits; requirements. 236.205..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Block Signal Systems Standards § 236.205 Signal control circuits; requirements. The circuits shall be so...

49 CFR 236.205 - Signal control circuits; requirements.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 4 2013-10-01 2013-10-01 false Signal control circuits; requirements. 236.205..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Block Signal Systems Standards § 236.205 Signal control circuits; requirements. The circuits shall be so...

Voice Interactive Analysis System Study. Final Report, August 28, 1978 through March 23, 1979.

ERIC Educational Resources Information Center

Harry, D. P.; And Others

The Voice Interactive Analysis System study continued research and development of the LISTEN real-time, minicomputer based connected speech recognition system, within NAVTRAEQUIPCEN'S program of developing automatic speech technology in support of training. An attempt was made to identify the most effective features detected by the TTI-500 model…

Fully automatic lesion segmentation in breast MRI using mean-shift and graph-cuts on a region adjacency graph.

PubMed

McClymont, Darryl; Mehnert, Andrew; Trakic, Adnan; Kennedy, Dominic; Crozier, Stuart

2014-04-01

To present and evaluate a fully automatic method for segmentation (i.e., detection and delineation) of suspicious tissue in breast MRI. The method, based on mean-shift clustering and graph-cuts on a region adjacency graph, was developed and its parameters tuned using multimodal (T1, T2, DCE-MRI) clinical breast MRI data from 35 subjects (training data). It was then tested using two data sets. Test set 1 comprises data for 85 subjects (93 lesions) acquired using the same protocol and scanner system used to acquire the training data. Test set 2 comprises data for eight subjects (nine lesions) acquired using a similar protocol but a different vendor's scanner system. Each lesion was manually delineated in three-dimensions by an experienced breast radiographer to establish segmentation ground truth. The regions of interest identified by the method were compared with the ground truth and the detection and delineation accuracies quantitatively evaluated. One hundred percent of the lesions were detected with a mean of 4.5 ± 1.2 false positives per subject. This false-positive rate is nearly 50% better than previously reported for a fully automatic breast lesion detection system. The median Dice coefficient for Test set 1 was 0.76 (interquartile range, 0.17), and 0.75 (interquartile range, 0.16) for Test set 2. The results demonstrate the efficacy and accuracy of the proposed method as well as its potential for direct application across different MRI systems. It is (to the authors' knowledge) the first fully automatic method for breast lesion detection and delineation in breast MRI.

49 CFR 236.201 - Track-circuit control of signals.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Block Signal Systems Standards § 236.201 Track-circuit control of signals. The control circuits for home... 49 Transportation 4 2011-10-01 2011-10-01 false Track-circuit control of signals. 236.201 Section...

49 CFR 236.207 - Electric lock on hand-operated switch; control.

Code of Federal Regulations, 2011 CFR

2011-10-01

... THE INSTALLATION, INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Block Signal Systems Standards § 236.207 Electric lock on hand-operated... 49 Transportation 4 2011-10-01 2011-10-01 false Electric lock on hand-operated switch; control...

49 CFR 236.201 - Track-circuit control of signals.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Automatic Block Signal Systems Standards § 236.201 Track-circuit control of signals. The control circuits for home... 49 Transportation 4 2010-10-01 2010-10-01 false Track-circuit control of signals. 236.201 Section...

Potential fault region detection in TFDS images based on convolutional neural network

NASA Astrophysics Data System (ADS)

Sun, Junhua; Xiao, Zhongwen

2016-10-01

In recent years, more than 300 sets of Trouble of Running Freight Train Detection System (TFDS) have been installed on railway to monitor the safety of running freight trains in China. However, TFDS is simply responsible for capturing, transmitting, and storing images, and fails to recognize faults automatically due to some difficulties such as such as the diversity and complexity of faults and some low quality images. To improve the performance of automatic fault recognition, it is of great importance to locate the potential fault areas. In this paper, we first introduce a convolutional neural network (CNN) model to TFDS and propose a potential fault region detection system (PFRDS) for simultaneously detecting four typical types of potential fault regions (PFRs). The experimental results show that this system has a higher performance of image detection to PFRs in TFDS. An average detection recall of 98.95% and precision of 100% are obtained, demonstrating the high detection ability and robustness against various poor imaging situations.

Learning without labeling: domain adaptation for ultrasound transducer localization.

PubMed

Heimann, Tobias; Mountney, Peter; John, Matthias; Ionasec, Razvan

2013-01-01

The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transform between both imaging systems, we employ a discriminative learning based approach to localize the TEE transducer in X-ray images. Instead of time-consuming manual labeling, we generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. An evaluation on more than 1900 images reveals that our approach reduces detection failures by 95% compared to cross validation on the test set and improves the localization error from 1.5 to 0.8 mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts.

Chocolate equals stop. Chocolate-specific inhibition training reduces chocolate intake and go associations with chocolate.

PubMed

Houben, Katrijn; Jansen, Anita

2015-04-01

Earlier research has demonstrated that food-specific inhibition training wherein food cues are repeatedly and consistently mapped onto stop signals decreases food intake and bodyweight. The mechanisms underlying these training effects, however, remain unclear. It has been suggested that consistently pairing stimuli with stop signals induces automatic stop associations with those stimuli, thereby facilitating automatic, bottom-up inhibition. This study examined this hypothesis with respect to food-inhibition training. Participants performed a training that consistently paired chocolate with no go cues (chocolate/no-go) or with go cues (chocolate/go). Following training, we measured automatic associations between chocolate and stop versus go, as well as food intake and desire to eat. As expected, food that was consistently mapped onto stopping was indeed more associated with stopping versus going afterwards. In replication of previous results, participants in the no-go condition also showed less desire to eat and reduced food intake relative to the go condition. Together these findings support the idea that food-specific inhibition training prompts the development of automatic inhibition associations, which subsequently facilitate inhibitory control over unwanted food-related urges. Copyright © 2015 Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hentschke, Clemens M., E-mail: clemens.hentschke@gmail.com; Tönnies, Klaus D.; Beuing, Oliver

Purpose: The early detection of cerebral aneurysms plays a major role in preventing subarachnoid hemorrhage. The authors present a system to automatically detect cerebral aneurysms in multimodal 3D angiographic data sets. The authors’ system is parametrizable for contrast-enhanced magnetic resonance angiography (CE-MRA), time-of-flight magnetic resonance angiography (TOF-MRA), and computed tomography angiography (CTA). Methods: Initial volumes of interest are found by applying a multiscale sphere-enhancing filter. Several features are combined in a linear discriminant function (LDF) to distinguish between true aneurysms and false positives. The features include shape information, spatial information, and probability information. The LDF can either be parametrized bymore » domain experts or automatically by training. Vessel segmentation is avoided as it could heavily influence the detection algorithm. Results: The authors tested their method with 151 clinical angiographic data sets containing 112 aneurysms. The authors reach a sensitivity of 95% with CE-MRA data sets at an average false positive rate per data set (FP{sub DS}) of 8.2. For TOF-MRA, we achieve 95% sensitivity at 11.3 FP{sub DS}. For CTA, we reach a sensitivity of 95% at 22.8 FP{sub DS}. For all modalities, the expert parametrization led to similar or better results than the trained parametrization eliminating the need for training. 93% of aneurysms that were smaller than 5 mm were found. The authors also showed that their algorithm is capable of detecting aneurysms that were previously overlooked by radiologists. Conclusions: The authors present an automatic system to detect cerebral aneurysms in multimodal angiographic data sets. The system proved as a suitable computer-aided detection tool to help radiologists find cerebral aneurysms.« less

Differential Arc expression in the hippocampus and striatum during the transition from attentive to automatic navigation on a plus maze

PubMed Central

Gardner, Robert S.; Suarez, Daniel F.; Robinson-Burton, Nadira K.; Rudnicky, Christopher J.; Gulati, Asish; Ascoli, Giorgio A.; Dumas, Theodore C.

2016-01-01

The strategies utilized to effectively perform a given task change with practice and experience. During a spatial navigation task, with relatively little training, performance is typically attentive enabling an individual to locate the position of a goal by relying on spatial landmarks. These (place) strategies require an intact hippocampus. With task repetition, performance becomes automatic; the same goal is reached using a fixed response or sequence of actions. These (response) strategies require an intact striatum. The current work aims to understand the activation patterns across these neural structures during this experience-dependent strategy transition. This was accomplished by region-specific measurement of activity-dependent immediate early gene expression among rats trained to different degrees on a dual-solution task (i.e., a task that can be solved using either place or response navigation). As expected, rats increased their reliance on response navigation with extended task experience. In addition, dorsal hippocampal expression of the immediate early gene Arc was considerably reduced in rats that used a response strategy late in training (as compared with hippocampal expression in rats that used a place strategy early in training). In line with these data, vicarious trial and error, a behavior linked to hippocampal function, also decreased with task repetition. Although Arc mRNA expression in dorsal medial or lateral striatum alone did not correlate with training stage, the ratio of expression in the medial striatum to that in the lateral striatum was relatively high among rats that used a place strategy early in training as compared with the ratio among over-trained response rats. Altogether, these results identify specific changes in the activation of dissociated neural systems that may underlie the experience-dependent emergence of response-based automatic navigation. PMID:26976088

49 CFR 236.830 - Time, acknowledging.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Time, acknowledging. 236.830 Section 236.830 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Time, acknowledging. As applied to an intermittent automatic train stop system, a predetermined time...

Does training under consistent mapping conditions lead to automatic attention attraction to targets in search tasks?

PubMed

Lefebvre, Christine; Cousineau, Denis; Larochelle, Serge

2008-11-01

Schneider and Shiffrin (1977) proposed that training under consistent stimulus-response mapping (CM) leads to automatic target detection in search tasks. Other theories, such as Treisman and Gelade's (1980) feature integration theory, consider target-distractor discriminability as the main determinant of search performance. The first two experiments pit these two principles against each other. The results show that CM training is neither necessary nor sufficient to achieve optimal search performance. Two other experiments examine whether CM trained targets, presented as distractors in unattended display locations, attract attention away from current targets. The results are again found to vary with target-distractor similarity. Overall, the present study strongly suggests that CM training does not invariably lead to automatic attention attraction in search tasks.

A method for automatically abstracting visual documents

NASA Technical Reports Server (NTRS)

Rorvig, Mark E.

1994-01-01

Visual documents--motion sequences on film, videotape, and digital recording--constitute a major source of information for the Space Agency, as well as all other government and private sector entities. This article describes a method for automatically selecting key frames from visual documents. These frames may in turn be used to represent the total image sequence of visual documents in visual libraries, hypermedia systems, and training algorithm reduces 51 minutes of video sequences to 134 frames; a reduction of information in the range of 700:1.

A study on ship automatic berthing with assistance of auxiliary devices

NASA Astrophysics Data System (ADS)

Tran, Van Luong; Im, Namkyun

2012-09-01

The recent researches on the automatic berthing control problems have used various kinds of tools as a control method such as expert system, fuzzy logic controllers and artificial neural network (ANN). Among them, ANN has proved to be one of the most effective and attractive options. In a marine context, the berthing maneuver is a complicated procedure in which both human experience and intensive control operations are involved. Nowadays, in most cases of berthing operation, auxiliary devices are used to make the schedule safer and faster but none of above researches has taken into account. In this study, ANN is applied to design the controllers for automatic ship berthing using assistant devices such as bow thruster and tug. Using back-propagation algorithm, we trained ANN with set of teaching data to get a minimal error between output values and desired values of four control outputs including rudder, propeller revolution, bow thruster and tug. Then, computer simulations of automatic berthing were carried out to verify the effecttiveness of the system. The results of the simulations showed good performance for the proposed berthing control system.

45 CFR 1159.15 - Who has the responsibility for maintaining adequate technical, physical, and security safeguards...

Code of Federal Regulations, 2010 CFR

2010-10-01

... disclosure or destruction of manual and automatic record systems. These security safeguards shall apply to... use of records contained in a system of records are adequately trained to protect the security and... adequate technical, physical, and security safeguards to prevent unauthorized disclosure or destruction of...

Automatic sleep stage classification using two-channel electro-oculography.

PubMed

Virkkala, Jussi; Hasan, Joel; Värri, Alpo; Himanen, Sari-Leena; Müller, Kiti

2007-10-15

An automatic method for the classification of wakefulness and sleep stages SREM, S1, S2 and SWS was developed based on our two previous studies. The method is based on a two-channel electro-oculography (EOG) referenced to the left mastoid (M1). Synchronous electroencephalographic (EEG) activity in S2 and SWS was detected by calculating cross-correlation and peak-to-peak amplitude difference in the 0.5-6 Hz band between the two EOG channels. An automatic slow eye-movement (SEM) estimation was used to indicate wakefulness, SREM and S1. Beta power 18-30 Hz and alpha power 8-12 Hz was also used for wakefulness detection. Synchronous 1.5-6 Hz EEG activity and absence of large eye movements was used for S1 separation from SREM. Simple smoothing rules were also applied. Sleep EEG, EOG and EMG were recorded from 265 subjects. The system was tuned using data from 132 training subjects and then applied to data from 131 validation subjects that were different to the training subjects. Cohen's Kappa between the visual and the developed new automatic scoring in separating 30s wakefulness, SREM, S1, S2 and SWS epochs was substantial 0.62 with epoch by epoch agreement of 72%. With automatic subject specific alpha thresholds for offline applications results improved to 0.63 and 73%. The automatic method can be further developed and applied for ambulatory sleep recordings by using only four disposable, self-adhesive and self-applicable electrodes.

Specific acoustic models for spontaneous and dictated style in indonesian speech recognition

NASA Astrophysics Data System (ADS)

Vista, C. B.; Satriawan, C. H.; Lestari, D. P.; Widyantoro, D. H.

2018-03-01

The performance of an automatic speech recognition system is affected by differences in speech style between the data the model is originally trained upon and incoming speech to be recognized. In this paper, the usage of GMM-HMM acoustic models for specific speech styles is investigated. We develop two systems for the experiments; the first employs a speech style classifier to predict the speech style of incoming speech, either spontaneous or dictated, then decodes this speech using an acoustic model specifically trained for that speech style. The second system uses both acoustic models to recognise incoming speech and decides upon a final result by calculating a confidence score of decoding. Results show that training specific acoustic models for spontaneous and dictated speech styles confers a slight recognition advantage as compared to a baseline model trained on a mixture of spontaneous and dictated training data. In addition, the speech style classifier approach of the first system produced slightly more accurate results than the confidence scoring employed in the second system.

System transfer modelling for automatic target recognizer evaluations

NASA Astrophysics Data System (ADS)

Clark, Lloyd G.

1991-11-01

Image processing to accomplish automatic recognition of military vehicles has promised increased weapons systems effectiveness and reduced timelines for a number of Department of Defense missions. Automatic Target Recognizers (ATR) are often claimed to be able to recognize many different ground vehicles as possible targets in military air-to- surface targeting applications. The targeting scenario conditions include different vehicle poses and histories as well as a variety of imaging geometries, intervening atmospheres, and background environments. Testing these ATR subsystems in most cases has been limited to a handful of the scenario conditions of interest, as is represented by imagery collected with the desired imaging sensor. The question naturally arises as to how robust the performance of the ATR is for all scenario conditions of interest, not just for the set of imagery upon which an algorithm was trained.

Predicting Correctness of Problem Solving from Low-Level Log Data in Intelligent Tutoring Systems

ERIC Educational Resources Information Center

Cetintas, Suleyman; Si, Luo; Xin, Yan Ping; Hord, Casey

2009-01-01

This paper proposes a learning based method that can automatically determine how likely a student is to give a correct answer to a problem in an intelligent tutoring system. Only log files that record students' actions with the system are used to train the model, therefore the modeling process doesn't require expert knowledge for identifying…

An automatic speech recognition system with speaker-independent identification support

NASA Astrophysics Data System (ADS)

Caranica, Alexandru; Burileanu, Corneliu

2015-02-01

The novelty of this work relies on the application of an open source research software toolkit (CMU Sphinx) to train, build and evaluate a speech recognition system, with speaker-independent support, for voice-controlled hardware applications. Moreover, we propose to use the trained acoustic model to successfully decode offline voice commands on embedded hardware, such as an ARMv6 low-cost SoC, Raspberry PI. This type of single-board computer, mainly used for educational and research activities, can serve as a proof-of-concept software and hardware stack for low cost voice automation systems.

A new fast scanning system for the measurement of large angle tracks in nuclear emulsions

NASA Astrophysics Data System (ADS)

Alexandrov, A.; Buonaura, A.; Consiglio, L.; D'Ambrosio, N.; De Lellis, G.; Di Crescenzo, A.; Di Marco, N.; Galati, G.; Lauria, A.; Montesi, M. C.; Pupilli, F.; Shchedrina, T.; Tioukov, V.; Vladymyrov, M.

2015-11-01

Nuclear emulsions have been widely used in particle physics to identify new particles through the observation of their decays thanks to their unique spatial resolution. Nevertheless, before the advent of automatic scanning systems, the emulsion analysis was very demanding in terms of well trained manpower. Due to this reason, they were gradually replaced by electronic detectors, until the '90s, when automatic microscopes started to be developed in Japan and in Europe. Automatic scanning was essential to conceive large scale emulsion-based neutrino experiments like CHORUS, DONUT and OPERA. Standard scanning systems have been initially designed to recognize tracks within a limited angular acceptance (θ lesssim 30°) where θ is the track angle with respect to a line perpendicular to the emulsion plane. In this paper we describe the implementation of a novel fast automatic scanning system aimed at extending the track recognition to the full angular range and improving the present scanning speed. Indeed, nuclear emulsions do not have any intrinsic limit to detect particle direction. Such improvement opens new perspectives to use nuclear emulsions in several fields in addition to large scale neutrino experiments, like muon radiography, medical applications and dark matter directional detection.

Second-order sliding mode controller with model reference adaptation for automatic train operation

NASA Astrophysics Data System (ADS)

Ganesan, M.; Ezhilarasi, D.; Benni, Jijo

2017-11-01

In this paper, a new approach to model reference based adaptive second-order sliding mode control together with adaptive state feedback is presented to control the longitudinal dynamic motion of a high speed train for automatic train operation with the objective of minimal jerk travel by the passengers. The nonlinear dynamic model for the longitudinal motion of the train comprises of a locomotive and coach subsystems is constructed using multiple point-mass model by considering the forces acting on the vehicle. An adaptation scheme using Lyapunov criterion is derived to tune the controller gains by considering a linear, stable reference model that ensures the stability of the system in closed loop. The effectiveness of the controller tracking performance is tested under uncertain passenger load, coupler-draft gear parameters, propulsion resistance coefficients variations and environmental disturbances due to side wind and wet rail conditions. The results demonstrate improved tracking performance of the proposed control scheme with a least jerk under maximum parameter uncertainties when compared to constant gain second-order sliding mode control.

Evaluation of odometry algorithm performances using a railway vehicle dynamic model

NASA Astrophysics Data System (ADS)

Allotta, B.; Pugi, L.; Ridolfi, A.; Malvezzi, M.; Vettori, G.; Rindi, A.

2012-05-01

In modern railway Automatic Train Protection and Automatic Train Control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. Simplified two-dimensional models of railway vehicles have been usually used for Hardware in the Loop test rig testing of conventional odometry algorithms and of on-board safety relevant subsystems (like the Wheel Slide Protection braking system) in which the train speed is estimated from the measures of the wheel angular speed. Two-dimensional models are not suitable to develop solutions like the inertial type localisation algorithms (using 3D accelerometers and 3D gyroscopes) and the introduction of Global Positioning System (or similar) or the magnetometer. In order to test these algorithms correctly and increase odometry performances, a three-dimensional multibody model of a railway vehicle has been developed, using Matlab-Simulink™, including an efficient contact model which can simulate degraded adhesion conditions (the development and prototyping of odometry algorithms involve the simulation of realistic environmental conditions). In this paper, the authors show how a 3D railway vehicle model, able to simulate the complex interactions arising between different on-board subsystems, can be useful to evaluate the odometry algorithm and safety relevant to on-board subsystem performances.

Public knowledge of how to use an automatic external defibrillator in out-of-hospital cardiac arrest in Hong Kong.

PubMed

Fan, K L; Leung, L P; Poon, H T; Chiu, H Y; Liu, H L; Tang, W Y

2016-12-01

The survival rate of out-of-hospital cardiac arrest in Hong Kong is low. A long delay between collapse and defibrillation is a contributing factor. Public access to defibrillation may shorten this delay. It is unknown, however, whether Hong Kong's public is willing or able to use an automatic external defibrillator. This study aimed to evaluate public knowledge of how to use an automatic external defibrillator in out-of-hospital cardiac arrest. A face-to-face semi-structured questionnaire survey of the public was conducted in six locations with a high pedestrian flow in Hong Kong. In this study, 401 members of the public were interviewed. Most had no training in first aid (65.8%) or in use of an automatic external defibrillator (85.3%). Nearly all (96.5%) would call for help for a victim of out-of-hospital cardiac arrest but only 18.0% would use an automatic external defibrillator. Public knowledge of automatic external defibrillator use was low: 77.6% did not know the location of an automatic external defibrillator in the vicinity of their home or workplace. People who had ever been trained in both first aid and use of an automatic external defibrillator were more likely to respond to and help a victim of cardiac arrest, and to use an automatic external defibrillator. Public knowledge of automatic external defibrillator use is low in Hong Kong. A combination of training in first aid and in the use of an automatic external defibrillator is better than either one alone.

AUTOMATIC RECOGNITION OF FALLS IN GAIT-SLIP: A HARNESS LOAD CELL BASED CRITERION

PubMed Central

Yang, Feng; Pai, Yi-Chung

2012-01-01

Over-head-harness systems, equipped with load cell sensors, are essential to the participants’ safety and to the outcome assessment in perturbation training. The purpose of this study was to first develop an automatic outcome recognition criterion among young adults for gait-slip training and then verify such criterion among older adults. Each of 39 young and 71 older subjects, all protected by safety harness, experienced 8 unannounced, repeated slips, while walking on a 7-m walkway. Each trial was monitored with a motion capture system, bilateral ground reaction force (GRF), harness force and video recording. The fall trials were first unambiguously indentified with careful visual inspection of all video records. The recoveries without balance loss (in which subjects’ trailing foot landed anteriorly to the slipping foot) were also first fully recognized from motion and GRF analyses. These analyses then set the gold standard for the outcome recognition with load cell measurements. Logistic regression analyses based on young subjects’ data revealed that peak load cell force was the best predictor of falls (with 100% accuracy) at the threshold of 30% body weight. On the other hand, the peak moving average force of load cell across 1-s period, was the best predictor (with 100% accuracy) separating recoveries with backward balance loss (in which the recovery step landed posterior to slipping foot) from harness assistance at the threshold of 4.5% body weight. These threshold values were fully verified using the data from older adults (100% accuracy in recognizing falls). Because of the increasing popularity in the perturbation training coupling with the protective over-head-harness system, this new criterion could have far reaching implications in automatic outcome recognition during the movement therapy. PMID:21696744

Using Statistical Techniques and Web Search to Correct ESL Errors

ERIC Educational Resources Information Center

Gamon, Michael; Leacock, Claudia; Brockett, Chris; Dolan, William B.; Gao, Jianfeng; Belenko, Dmitriy; Klementiev, Alexandre

2009-01-01

In this paper we present a system for automatic correction of errors made by learners of English. The system has two novel aspects. First, machine-learned classifiers trained on large amounts of native data and a very large language model are combined to optimize the precision of suggested corrections. Second, the user can access real-life web…

DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real-Time Recognition and Localization of Marine Mammals

DTIC Science & Technology

2013-09-30

method has been successfully implemented to automatically detect and recognize pulse trains from minke whales ( songs ) and sperm whales (Physeter...workshops, conferences and data challenges 2. Enhancements of the ASR algorithm for frequency-modulated sounds: Right Whale Study 3...Enhancements of the ASR algorithm for pulse trains: Minke Whale Study 4. Mining Big Data Sound Archives using High Performance Computing software and hardware

Changing the Army’s Weapon Training Strategies to Meet Operational Requirements More Efficiently and Effectively

DTIC Science & Technology

2014-01-01

System Maneuver COe M4/16 Rifle M9 pistol M2 , MK19, and M240B Machine Guns , M249 Squad Automatic Rifle Bradley Fighting Vehicle Abrams Tank Fires COe 155mm...27 Rifle, Machine Gun , and SAW Training...are called desig- nated weapons. For example, a maintenance company may have some machine guns authorized for self-protection that are manned by

Classification and Quality Evaluation of Tobacco Leaves Based on Image Processing and Fuzzy Comprehensive Evaluation

PubMed Central

Zhang, Fan; Zhang, Xinhong

2011-01-01

Most of classification, quality evaluation or grading of the flue-cured tobacco leaves are manually operated, which relies on the judgmental experience of experts, and inevitably limited by personal, physical and environmental factors. The classification and the quality evaluation are therefore subjective and experientially based. In this paper, an automatic classification method of tobacco leaves based on the digital image processing and the fuzzy sets theory is presented. A grading system based on image processing techniques was developed for automatically inspecting and grading flue-cured tobacco leaves. This system uses machine vision for the extraction and analysis of color, size, shape and surface texture. Fuzzy comprehensive evaluation provides a high level of confidence in decision making based on the fuzzy logic. The neural network is used to estimate and forecast the membership function of the features of tobacco leaves in the fuzzy sets. The experimental results of the two-level fuzzy comprehensive evaluation (FCE) show that the accuracy rate of classification is about 94% for the trained tobacco leaves, and the accuracy rate of the non-trained tobacco leaves is about 72%. We believe that the fuzzy comprehensive evaluation is a viable way for the automatic classification and quality evaluation of the tobacco leaves. PMID:22163744

Leveraging Paraphrase Labels to Extract Synonyms from Twitter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Antoniak, Maria A.; Bell, Eric B.; Xia, Fei

2015-05-18

We present an approach for automatically learning synonyms from a paraphrase corpus of tweets. This work shows improvement on the task of paraphrase detection when we substitute our extracted synonyms into the training set. The synonyms are learned by using chunks from a shallow parse to create candidate synonyms and their context windows, and the synonyms are incorporated into a paraphrase detection system that uses machine translation metrics as features for a classifier. We demonstrate a 2.29% improvement in F1 when we train and test on the paraphrase training set, providing better coverage than previous systems, which shows the potentialmore » power of synonyms that are representative of a specific topic.« less

Experimental teaching and training system based on volume holographic storage

NASA Astrophysics Data System (ADS)

Jiang, Zhuqing; Wang, Zhe; Sun, Chan; Cui, Yutong; Wan, Yuhong; Zou, Rufei

2017-08-01

The experiment of volume holographic storage for teaching and training the practical ability of senior students in Applied Physics is introduced. The students can learn to use advanced optoelectronic devices and the automatic control means via this experiment, and further understand the theoretical knowledge of optical information processing and photonics disciplines that have been studied in some courses. In the experiment, multiplexing holographic recording and readout is based on Bragg selectivity of volume holographic grating, in which Bragg diffraction angle is dependent on grating-recording angel. By using different interference angle between reference and object beams, the holograms can be recorded into photorefractive crystal, and then the object images can be read out from these holograms via angular addressing by using the original reference beam. In this system, the experimental data acquisition and the control of the optoelectronic devices, such as the shutter on-off, image loaded in SLM and image acquisition of a CCD sensor, are automatically realized by using LabVIEW programming.

Automatic classification of radiological reports for clinical care.

PubMed

Gerevini, Alfonso Emilio; Lavelli, Alberto; Maffi, Alessandro; Maroldi, Roberto; Minard, Anne-Lyse; Serina, Ivan; Squassina, Guido

2018-06-07

Radiological reporting generates a large amount of free-text clinical narratives, a potentially valuable source of information for improving clinical care and supporting research. The use of automatic techniques to analyze such reports is necessary to make their content effectively available to radiologists in an aggregated form. In this paper we focus on the classification of chest computed tomography reports according to a classification schema proposed for this task by radiologists of the Italian hospital ASST Spedali Civili di Brescia. The proposed system is built exploiting a training data set containing reports annotated by radiologists. Each report is classified according to the schema developed by radiologists and textual evidences are marked in the report. The annotations are then used to train different machine learning based classifiers. We present in this paper a method based on a cascade of classifiers which make use of a set of syntactic and semantic features. The resulting system is a novel hierarchical classification system for the given task, that we have experimentally evaluated. Copyright © 2018 Elsevier B.V. All rights reserved.

Key features for ATA / ATR database design in missile systems

NASA Astrophysics Data System (ADS)

Özertem, Kemal Arda

2017-05-01

Automatic target acquisition (ATA) and automatic target recognition (ATR) are two vital tasks for missile systems, and having a robust detection and recognition algorithm is crucial for overall system performance. In order to have a robust target detection and recognition algorithm, an extensive image database is required. Automatic target recognition algorithms use the database of images in training and testing steps of algorithm. This directly affects the recognition performance, since the training accuracy is driven by the quality of the image database. In addition, the performance of an automatic target detection algorithm can be measured effectively by using an image database. There are two main ways for designing an ATA / ATR database. The first and easy way is by using a scene generator. A scene generator can model the objects by considering its material information, the atmospheric conditions, detector type and the territory. Designing image database by using a scene generator is inexpensive and it allows creating many different scenarios quickly and easily. However the major drawback of using a scene generator is its low fidelity, since the images are created virtually. The second and difficult way is designing it using real-world images. Designing image database with real-world images is a lot more costly and time consuming; however it offers high fidelity, which is critical for missile algorithms. In this paper, critical concepts in ATA / ATR database design with real-world images are discussed. Each concept is discussed in the perspective of ATA and ATR separately. For the implementation stage, some possible solutions and trade-offs for creating the database are proposed, and all proposed approaches are compared to each other with regards to their pros and cons.

Space Weather Effects on Mid-Latitude Railways: a Statistical Study of Anomalies observed in the Operation of Signaling and Train Control Equipment on the East-Siberian Railway

NASA Astrophysics Data System (ADS)

Kasinskii, V. V.; Ptitsyna, N. G.; Lyahov, N. N.; Dorman, L. I.; Villoresi, G.; Iucci, N.

The end result of a long chain of space weather events beginning on the Sun is the induction of currents in ground-based long conductors as power lines pipelines and railways Intense geomagnetically induced currents GIC can hamper rail traffic by disturbing signaling and train control systems In few cases induced voltages were believed to have affected signaling equipment in Sweden Jansen et al 2000 and in the North of Russia Belov et al 2005 GIC threats have been a concern for technological systems at high-latitude locations due to disturbances driven by electrojet intensifications However other geomagnetic storm processes such as SSC and ring current enhancement can also cause GIC concerns for the technological systems Objective of this report is to continue our research Ptitsyna et al 2005 on possible influence of geomagnetic storms on mid-latitude railways and to perform a statistical research in addition to case studies This will help in providing a basis for railway companies to evaluate the risk of disruption to signaling and train control equipment and devise engineering solutions In the present report we analyzed anomalies in operation of automatic signaling and train control equipment occurred in 2004-2005 on the East-Siberian Railway located at mid-latitudes latitudes 51N-56N longitudes 96E-114E The anomalies consist mainly in unstable functioning and false operations in traffic automatic control systems rail chain switches locomotive control devices etc often resulting in false engagement of railway

POPCORN: a Supervisory Control Simulation for Workload and Performance Research

NASA Technical Reports Server (NTRS)

Hart, S. G.; Battiste, V.; Lester, P. T.

1984-01-01

A multi-task simulation of a semi-automatic supervisory control system was developed to provide an environment in which training, operator strategy development, failure detection and resolution, levels of automation, and operator workload can be investigated. The goal was to develop a well-defined, but realistically complex, task that would lend itself to model-based analysis. The name of the task (POPCORN) reflects the visual display that depicts different task elements milling around waiting to be released and pop out to be performed. The operator's task was to complete each of 100 task elements that ere represented by different symbols, by selecting a target task and entering the desired a command. The simulated automatic system then completed the selected function automatically. Highly significant differences in performance, strategy, and rated workload were found as a function of all experimental manipulations (except reward/penalty).

Automatically classifying sentences in full-text biomedical articles into Introduction, Methods, Results and Discussion.

PubMed

Agarwal, Shashank; Yu, Hong

2009-12-01

Biomedical texts can be typically represented by four rhetorical categories: Introduction, Methods, Results and Discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied different approaches for automatically classifying sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We first evaluated whether sentences in full-text biomedical articles could be reliably annotated into the IMRAD format and then explored different approaches for automatically classifying these sentences into the IMRAD categories. Our results show an overall annotation agreement of 82.14% with a Kappa score of 0.756. The best classification system is a multinomial naïve Bayes classifier trained on manually annotated data that achieved 91.95% accuracy and an average F-score of 91.55%, which is significantly higher than baseline systems. A web version of this system is available online at-http://wood.ims.uwm.edu/full_text_classifier/.

End-to-End ASR-Free Keyword Search From Speech

NASA Astrophysics Data System (ADS)

Audhkhasi, Kartik; Rosenberg, Andrew; Sethy, Abhinav; Ramabhadran, Bhuvana; Kingsbury, Brian

2017-12-01

End-to-end (E2E) systems have achieved competitive results compared to conventional hybrid hidden Markov model (HMM)-deep neural network based automatic speech recognition (ASR) systems. Such E2E systems are attractive due to the lack of dependence on alignments between input acoustic and output grapheme or HMM state sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation. The second sub-system is a character-level RNN language model using embeddings learned from a convolutional neural network. Since the acoustic and text query embeddings occupy different representation spaces, they are input to a third feed-forward neural network that predicts whether the query occurs in the acoustic utterance or not. This E2E ASR-free KWS system performs respectably despite lacking a conventional ASR system and trains much faster.

The MOD-OA 200 kilowatt wind turbine generator design and analysis report

NASA Astrophysics Data System (ADS)

Andersen, T. S.; Bodenschatz, C. A.; Eggers, A. G.; Hughes, P. S.; Lampe, R. F.; Lipner, M. H.; Schornhorst, J. R.

1980-08-01

The project requirements, approach, system description, design requirements, design, analysis, system tests, installation safety considerations, failure modes and effects analysis, data acquisition, and initial performance for the MOD-OA 200 kw wind turbine generator are discussed. The components, the rotor, driven train, nacelle equipment, yaw drive mechanism and brake, tower, foundation, electrical system, and control systems are presented. The rotor includes the blades, hub and pitch change mechanism. The drive train includes the low speed shaft, speed increaser, high speed shaft, and rotor brake. The electrical system includes the generator, switchgear, transformer, and utility connection. The control systems are the blade pitch, yaw, and generator control, and the safety system. Manual, automatic, and remote control and Dynamic loads and fatigue are analyzed.

The MOD-OA 200 kilowatt wind turbine generator design and analysis report

NASA Technical Reports Server (NTRS)

Andersen, T. S.; Bodenschatz, C. A.; Eggers, A. G.; Hughes, P. S.; Lampe, R. F.; Lipner, M. H.; Schornhorst, J. R.

1980-01-01

The project requirements, approach, system description, design requirements, design, analysis, system tests, installation safety considerations, failure modes and effects analysis, data acquisition, and initial performance for the MOD-OA 200 kw wind turbine generator are discussed. The components, the rotor, driven train, nacelle equipment, yaw drive mechanism and brake, tower, foundation, electrical system, and control systems are presented. The rotor includes the blades, hub and pitch change mechanism. The drive train includes the low speed shaft, speed increaser, high speed shaft, and rotor brake. The electrical system includes the generator, switchgear, transformer, and utility connection. The control systems are the blade pitch, yaw, and generator control, and the safety system. Manual, automatic, and remote control and Dynamic loads and fatigue are analyzed.

Audio-visual imposture

NASA Astrophysics Data System (ADS)

Karam, Walid; Mokbel, Chafic; Greige, Hanna; Chollet, Gerard

2006-05-01

A GMM based audio visual speaker verification system is described and an Active Appearance Model with a linear speaker transformation system is used to evaluate the robustness of the verification. An Active Appearance Model (AAM) is used to automatically locate and track a speaker's face in a video recording. A Gaussian Mixture Model (GMM) based classifier (BECARS) is used for face verification. GMM training and testing is accomplished on DCT based extracted features of the detected faces. On the audio side, speech features are extracted and used for speaker verification with the GMM based classifier. Fusion of both audio and video modalities for audio visual speaker verification is compared with face verification and speaker verification systems. To improve the robustness of the multimodal biometric identity verification system, an audio visual imposture system is envisioned. It consists of an automatic voice transformation technique that an impostor may use to assume the identity of an authorized client. Features of the transformed voice are then combined with the corresponding appearance features and fed into the GMM based system BECARS for training. An attempt is made to increase the acceptance rate of the impostor and to analyzing the robustness of the verification system. Experiments are being conducted on the BANCA database, with a prospect of experimenting on the newly developed PDAtabase developed within the scope of the SecurePhone project.

49 CFR 236.804 - Signal, block.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Signal, block. 236.804 Section 236.804..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.804 Signal, block. A roadway signal operated either automatically or manually at the entrance to a block. ...

Research Directory for Manpower, Personnel, Training, and Human Factors.

DTIC Science & Technology

1991-01-01

Enhance Automatic Recognition of Speech in Noisy, Highly Stressful Environments Cofod R* Lica Systems Inc 703-359-0996 Smart Contract Preparation...Lab 301-278-2946 Smart Contract Preparation Expediter Frezell T LTCOL Human Engineering Lab 301-278-5998 Impulse Noise Hazard Information Processing R&D

49 CFR 236.804 - Signal, block.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false Signal, block. 236.804 Section 236.804..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.804 Signal, block. A roadway signal operated either automatically or manually at the entrance to a block. ...

49 CFR 236.804 - Signal, block.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 4 2013-10-01 2013-10-01 false Signal, block. 236.804 Section 236.804..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.804 Signal, block. A roadway signal operated either automatically or manually at the entrance to a block. ...

49 CFR 236.804 - Signal, block.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 4 2012-10-01 2012-10-01 false Signal, block. 236.804 Section 236.804..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.804 Signal, block. A roadway signal operated either automatically or manually at the entrance to a block. ...

Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems.

PubMed

Zerrouki, Taha; Balla, Amar

2017-04-01

Arabic diacritics are often missed in Arabic scripts. This feature is a handicap for new learner to read َArabic, text to speech conversion systems, reading and semantic analysis of Arabic texts. The automatic diacritization systems are the best solution to handle this issue. But such automation needs resources as diactritized texts to train and evaluate such systems. In this paper, we describe our corpus of Arabic diacritized texts. This corpus is called Tashkeela. It can be used as a linguistic resource tool for natural language processing such as automatic diacritics systems, dis-ambiguity mechanism, features and data extraction. The corpus is freely available, it contains 75 million of fully vocalized words mainly 97 books from classical and modern Arabic language. The corpus is collected from manually vocalized texts using web crawling process.

Development of use of an Operational Procedure Information System (OPIS) for future space missions

NASA Technical Reports Server (NTRS)

Illmer, N.; Mies, L.; Schoen, A.; Jain, A.

1994-01-01

A MS-Windows based electronic procedure system, called OPIS (Operational Procedure Information System), was developed. The system consists of two parts, the editor, for 'writing' the procedure and the notepad application, for the usage of the procedures by the crew during training and flight. The system is based on standardized, structured procedure format and language. It allows the embedding of sketches, photos, animated graphics and video sequences and the access to off-nominal procedures by linkage to an appropriate database. The system facilitates the work with procedures of different degrees of detail, depending on the training status of the crew. The development of a 'language module' for the automatic translation of the procedures, for example into Russian, is planned.

Adaptive Self-Tuning Networks

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Lawry, B.; Chael, E. P.; Faust, A.; Peterson, M. G.

2015-12-01

The quality of automatic detections from seismic sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. Yet, achieving superior automatic detection of seismic events is closely related to these parameters. We present an automated sensor tuning (AST) system that learns near-optimal parameter settings for each event type using neuro-dynamic programming (reinforcement learning) trained with historic data. AST learns to test the raw signal against all event-settings and automatically self-tunes to an emerging event in real-time. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Reducing false alarms early in the seismic pipeline processing will have a significant impact on this goal. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections in seismic waveforms from a network of stations, we show that AST increases the probability of detection while decreasing false alarms.

Auto identification technology and its impact on patient safety in the Operating Room of the Future.

PubMed

Egan, Marie T; Sandberg, Warren S

2007-03-01

Automatic identification technologies, such as bar coding and radio frequency identification, are ubiquitous in everyday life but virtually nonexistent in the operating room. User expectations, based on everyday experience with automatic identification technologies, have generated much anticipation that these systems will improve readiness, workflow, and safety in the operating room, with minimal training requirements. We report, in narrative form, a multi-year experience with various automatic identification technologies in the Operating Room of the Future Project at Massachusetts General Hospital. In each case, the additional human labor required to make these ;labor-saving' technologies function in the medical environment has proved to be their undoing. We conclude that while automatic identification technologies show promise, significant barriers to realizing their potential still exist. Nevertheless, overcoming these obstacles is necessary if the vision of an operating room of the future in which all processes are monitored, controlled, and optimized is to be achieved.

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

PubMed

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Design of the liquefied natural gas (LNG) vehicle gas cylinder filling semi-physical simulation training and assessment system

NASA Astrophysics Data System (ADS)

Gao, Jie; Zheng, Jianrong; Zhao, Yinghui

2017-08-01

With the rapid development of LNG vehicle in China, the operator's training and assessment of the operating skills cannot operate on material objects, because of Vehicle Gas Cylinder's high pressure, flammable and explosive characteristics. LNG Vehicle Gas Cylinder's filling simulation system with semi-physical simulation technology presents the overall design and procedures of the simulation system, and elaborates the realization of the practical analog machine, data acquisition and control system and the computer software, and introduces the design process of equipment simulation model in detail. According to the designed assessment system of the Vehicle Gas Cylinder, it can obtain the operation on the actual cylinder filling and visual effects for the operator, and automatically record operation, the results of real operation with its software, and achieve the operators' training and assessment of operating skills on mobile special equipment.

Computerized training management system

DOEpatents

Rice, H.B.; McNair, R.C.; White, K.; Maugeri, T.

1998-08-04

A Computerized Training Management System (CTMS) is disclosed for providing a procedurally defined process that is employed to develop accreditable performance based training programs for job classifications that are sensitive to documented regulations and technical information. CTMS is a database that links information needed to maintain a five-phase approach to training-analysis, design, development, implementation, and evaluation independent of training program design. CTMS is designed using R-Base{trademark}, an-SQL compliant software platform. Information is logically entered and linked in CTMS. Each task is linked directly to a performance objective, which, in turn, is linked directly to a learning objective; then, each enabling objective is linked to its respective test items. In addition, tasks, performance objectives, enabling objectives, and test items are linked to their associated reference documents. CTMS keeps all information up to date since it automatically sorts, files and links all data; CTMS includes key word and reference document searches. 18 figs.

Computerized training management system

DOEpatents

Rice, Harold B.; McNair, Robert C.; White, Kenneth; Maugeri, Terry

1998-08-04

A Computerized Training Management System (CTMS) for providing a procedurally defined process that is employed to develop accreditable performance based training programs for job classifications that are sensitive to documented regulations and technical information. CTMS is a database that links information needed to maintain a five-phase approach to training-analysis, design, development, implementation, and evaluation independent of training program design. CTMS is designed using R-Base.RTM., an-SQL compliant software platform. Information is logically entered and linked in CTMS. Each task is linked directly to a performance objective, which, in turn, is linked directly to a learning objective; then, each enabling objective is linked to its respective test items. In addition, tasks, performance objectives, enabling objectives, and test items are linked to their associated reference documents. CTMS keeps all information up to date since it automatically sorts, files and links all data; CTMS includes key word and reference document searches.

Automatic segmentation of right ventricle on ultrasound images using sparse matrix transform and level set

NASA Astrophysics Data System (ADS)

Qin, Xulei; Cong, Zhibin; Halig, Luma V.; Fei, Baowei

2013-03-01

An automatic framework is proposed to segment right ventricle on ultrasound images. This method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform (SMT), a training model, and a localized region based level set. First, the sparse matrix transform extracts main motion regions of myocardium as eigenimages by analyzing statistical information of these images. Second, a training model of right ventricle is registered to the extracted eigenimages in order to automatically detect the main location of the right ventricle and the corresponding transform relationship between the training model and the SMT-extracted results in the series. Third, the training model is then adjusted as an adapted initialization for the segmentation of each image in the series. Finally, based on the adapted initializations, a localized region based level set algorithm is applied to segment both epicardial and endocardial boundaries of the right ventricle from the whole series. Experimental results from real subject data validated the performance of the proposed framework in segmenting right ventricle from echocardiography. The mean Dice scores for both epicardial and endocardial boundaries are 89.1%+/-2.3% and 83.6+/-7.3%, respectively. The automatic segmentation method based on sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.

Development of automatic body condition scoring using a low-cost 3-dimensional Kinect camera.

PubMed

Spoliansky, Roii; Edan, Yael; Parmet, Yisrael; Halachmi, Ilan

2016-09-01

Body condition scoring (BCS) is a farm-management tool for estimating dairy cows' energy reserves. Today, BCS is performed manually by experts. This paper presents a 3-dimensional algorithm that provides a topographical understanding of the cow's body to estimate BCS. An automatic BCS system consisting of a Kinect camera (Microsoft Corp., Redmond, WA) triggered by a passive infrared motion detector was designed and implemented. Image processing and regression algorithms were developed and included the following steps: (1) image restoration, the removal of noise; (2) object recognition and separation, identification and separation of the cows; (3) movie and image selection, selection of movies and frames that include the relevant data; (4) image rotation, alignment of the cow parallel to the x-axis; and (5) image cropping and normalization, removal of irrelevant data, setting the image size to 150×200 pixels, and normalizing image values. All steps were performed automatically, including image selection and classification. Fourteen individual features per cow, derived from the cows' topography, were automatically extracted from the movies and from the farm's herd-management records. These features appear to be measurable in a commercial farm. Manual BCS was performed by a trained expert and compared with the output of the training set. A regression model was developed, correlating the features with the manual BCS references. Data were acquired for 4 d, resulting in a database of 422 movies of 101 cows. Movies containing cows' back ends were automatically selected (389 movies). The data were divided into a training set of 81 cows and a test set of 20 cows; both sets included the identical full range of BCS classes. Accuracy tests gave a mean absolute error of 0.26, median absolute error of 0.19, and coefficient of determination of 0.75, with 100% correct classification within 1 step and 91% correct classification within a half step for BCS classes. Results indicated good repeatability, with all standard deviations under 0.33. The algorithm is independent of the background and requires 10 cows for training with approximately 30 movies of 4 s each. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

System and Method for Outlier Detection via Estimating Clusters

NASA Technical Reports Server (NTRS)

Iverson, David J. (Inventor)

2016-01-01

An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.

Automated Instructional Monitors for Complex Operational Tasks. Final Report.

ERIC Educational Resources Information Center

Feurzeig, Wallace

A computer-based instructional system is described which incorporates diagnosis of students difficulties in acquiring complex concepts and skills. A computer automatically generated a simulated display. It then monitored and analyzed a student's work in the performance of assigned training tasks. Two major tasks were studied. The first,…

Color in Computer-Assisted Instruction.

ERIC Educational Resources Information Center

Steinberg, Esther R.

Color monitors are in wide use in computer systems. Thus, it is important to understand how to apply color effectively in computer assisted instruction (CAI) and computer based training (CBT). Color can enhance learning, but it does not automatically do so. Indiscriminate application of color can mislead a student and thereby even interfere with…

Combined Ozone Retrieval From METOP Sensors Using META-Training Of Deep Neural Networks

NASA Astrophysics Data System (ADS)

Felder, Martin; Sehnke, Frank; Kaifel, Anton

2013-12-01

The newest installment of our well-proven Neural Net- work Ozone Retrieval System (NNORSY) combines the METOP sensors GOME-2 and IASI with cloud information from AVHRR. Through the use of advanced meta- learning techniques like automatic feature selection and automatic architecture search applied to a set of deep neural networks, having at least two or three hidden layers, we have been able to avoid many technical issues normally encountered during the construction of such a joint retrieval system. This has been made possible by harnessing the processing power of modern consumer graphics cards with high performance graphic processors (GPU), which decreases training times by about two orders of magnitude. The system was trained on data from 2009 and 2010, including target ozone profiles from ozone sondes, ACE- FTS and MLS-AURA. To make maximum use of tropospheric information in the spectra, the data were partitioned into several sets of different cloud fraction ranges with the GOME-2 FOV, on which specialized retrieval networks are being trained. For the final ozone retrieval processing the different specialized networks are combined. The resulting retrieval system is very stable and does not show any systematic dependence on solar zenith angle, scan angle or sensor degradation. We present several sensitivity studies with regard to cloud fraction and target sensor type, as well as the performance in several latitude bands and with respect to independent validation stations. A visual cross-comparison against high-resolution ozone profiles from the KNMI EUMETSAT Ozone SAF product has also been performed and shows some distinctive features which we will briefly discuss. Overall, we demonstrate that a complex retrieval system can now be constructed with a minimum of ma- chine learning knowledge, using automated algorithms for many design decisions previously requiring expert knowledge. Provided sufficient training data and computation power of GPUs is available, the method can be applied to almost any kind of retrieval or, more generally, regression problem.

Computer Recognition of Facial Profiles

DTIC Science & Technology

1974-08-01

facial recognition 20. ABSTRACT (Continue on reverse side It necessary and Identify by block number) A system for the recognition of human faces from...21 2.6 Classification Algorithms ........... ... 32 III FACIAL RECOGNITION AND AUTOMATIC TRAINING . . . 37 3.1 Facial Profile Recognition...provide a fair test of the classification system. The work of Goldstein, Harmon, and Lesk [81 indicates, however, that for facial recognition , a ten class

Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data.

PubMed

Heimann, Tobias; Mountney, Peter; John, Matthias; Ionasec, Razvan

2014-12-01

The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transformation between both imaging systems, we employ a discriminative learning (DL) based approach to localize the TEE transducer in X-ray images. The successful application of DL methods is strongly dependent on the available training data, which entails three challenges: (1) the transducer can move with six degrees of freedom meaning it requires a large number of images to represent its appearance, (2) manual labeling is time consuming, and (3) manual labeling has inherent errors. This paper proposes to generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. Two approaches for instance weighting, probabilistic classification and Kullback-Leibler importance estimation (KLIEP), are evaluated for different stages of the proposed DL pipeline. An analysis on more than 1900 images reveals that our approach reduces detection failures from 7.3% in cross validation on the test set to zero and improves the localization error from 1.5 to 0.8mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts. Copyright © 2014 Elsevier B.V. All rights reserved.

Acoustic emission source location in complex structures using full automatic delta T mapping technique

NASA Astrophysics Data System (ADS)

Al-Jumaili, Safaa Kh.; Pearson, Matthew R.; Holford, Karen M.; Eaton, Mark J.; Pullin, Rhys

2016-05-01

An easy to use, fast to apply, cost-effective, and very accurate non-destructive testing (NDT) technique for damage localisation in complex structures is key for the uptake of structural health monitoring systems (SHM). Acoustic emission (AE) is a viable technique that can be used for SHM and one of the most attractive features is the ability to locate AE sources. The time of arrival (TOA) technique is traditionally used to locate AE sources, and relies on the assumption of constant wave speed within the material and uninterrupted propagation path between the source and the sensor. In complex structural geometries and complex materials such as composites, this assumption is no longer valid. Delta T mapping was developed in Cardiff in order to overcome these limitations; this technique uses artificial sources on an area of interest to create training maps. These are used to locate subsequent AE sources. However operator expertise is required to select the best data from the training maps and to choose the correct parameter to locate the sources, which can be a time consuming process. This paper presents a new and improved fully automatic delta T mapping technique where a clustering algorithm is used to automatically identify and select the highly correlated events at each grid point whilst the "Minimum Difference" approach is used to determine the source location. This removes the requirement for operator expertise, saving time and preventing human errors. A thorough assessment is conducted to evaluate the performance and the robustness of the new technique. In the initial test, the results showed excellent reduction in running time as well as improved accuracy of locating AE sources, as a result of the automatic selection of the training data. Furthermore, because the process is performed automatically, this is now a very simple and reliable technique due to the prevention of the potential source of error related to manual manipulation.

Integrating different tracking systems in football: multiple camera semi-automatic system, local position measurement and GPS technologies.

PubMed

Buchheit, Martin; Allen, Adam; Poon, Tsz Kit; Modonutti, Mattia; Gregson, Warren; Di Salvo, Valter

2014-12-01

Abstract During the past decade substantial development of computer-aided tracking technology has occurred. Therefore, we aimed to provide calibration equations to allow the interchangeability of different tracking technologies used in soccer. Eighty-two highly trained soccer players (U14-U17) were monitored during training and one match. Player activity was collected simultaneously with a semi-automatic multiple-camera (Prozone), local position measurement (LPM) technology (Inmotio) and two global positioning systems (GPSports and VX). Data were analysed with respect to three different field dimensions (small, <30 m 2 to full-pitch, match). Variables provided by the systems were compared, and calibration equations (linear regression models) between each system were calculated for each field dimension. Most metrics differed between the 4 systems with the magnitude of the differences dependant on both pitch size and the variable of interest. Trivial-to-small between-system differences in total distance were noted. However, high-intensity running distance (>14.4 km · h -1 ) was slightly-to-moderately greater when tracked with Prozone, and accelerations, small-to-very largely greater with LPM. For most of the equations, the typical error of the estimate was of a moderate magnitude. Interchangeability of the different tracking systems is possible with the provided equations, but care is required given their moderate typical error of the estimate.

Automatic Mexican sign language and digits recognition using normalized central moments

NASA Astrophysics Data System (ADS)

Solís, Francisco; Martínez, David; Espinosa, Oscar; Toxqui, Carina

2016-09-01

This work presents a framework for automatic Mexican sign language and digits recognition based on computer vision system using normalized central moments and artificial neural networks. Images are captured by digital IP camera, four LED reflectors and a green background in order to reduce computational costs and prevent the use of special gloves. 42 normalized central moments are computed per frame and used in a Multi-Layer Perceptron to recognize each database. Four versions per sign and digit were used in training phase. 93% and 95% of recognition rates were achieved for Mexican sign language and digits respectively.

Game-powered machine learning

PubMed Central

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-01-01

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the “wisdom of the crowds.” Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., “funky jazz with saxophone,” “spooky electronica,” etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data. PMID:22460786

Game-powered machine learning.

PubMed

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-04-24

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the "wisdom of the crowds." Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., "funky jazz with saxophone," "spooky electronica," etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data.

Unconstrained face detection and recognition based on RGB-D camera for the visually impaired

NASA Astrophysics Data System (ADS)

Zhao, Xiangdong; Wang, Kaiwei; Yang, Kailun; Hu, Weijian

2017-02-01

It is highly important for visually impaired people (VIP) to be aware of human beings around themselves, so correctly recognizing people in VIP assisting apparatus provide great convenience. However, in classical face recognition technology, faces used in training and prediction procedures are usually frontal, and the procedures of acquiring face images require subjects to get close to the camera so that frontal face and illumination guaranteed. Meanwhile, labels of faces are defined manually rather than automatically. Most of the time, labels belonging to different classes need to be input one by one. It prevents assisting application for VIP with these constraints in practice. In this article, a face recognition system under unconstrained environment is proposed. Specifically, it doesn't require frontal pose or uniform illumination as required by previous algorithms. The attributes of this work lie in three aspects. First, a real time frontal-face synthesizing enhancement is implemented, and frontal faces help to increase recognition rate, which is proved with experiment results. Secondly, RGB-D camera plays a significant role in our system, from which both color and depth information are utilized to achieve real time face tracking which not only raises the detection rate but also gives an access to label faces automatically. Finally, we propose to use neural networks to train a face recognition system, and Principal Component Analysis (PCA) is applied to pre-refine the input data. This system is expected to provide convenient help for VIP to get familiar with others, and make an access for them to recognize people when the system is trained enough.

Automatic Rock Detection and Mapping from HiRISE Imagery

NASA Technical Reports Server (NTRS)

Huertas, Andres; Adams, Douglas S.; Cheng, Yang

2008-01-01

This system includes a C-code software program and a set of MATLAB software tools for statistical analysis and rock distribution mapping. The major functions include rock detection and rock detection validation. The rock detection code has been evolved into a production tool that can be used by engineers and geologists with minor training.

AUTOMOTIVE DIESEL MAINTENANCE 2. UNIT IX, AUTOMATIC TRANSMISSIONS--HYDRAULIC SYSTEM (PART I).

ERIC Educational Resources Information Center

Human Engineering Inst., Cleveland, OH.

THIS MODULE OF A 25-MODULE COURSE IS DESIGNED TO DEVELOP AN UNDERSTANDING OF THE OIL FLOW WITHIN HYDRAULIC TRANSMISSIONS USED ON DIESEL POWERED VEHICLES. TOPICS ARE GENERAL DESCRIPTION, HYDRAULIC CIRCUITS, AND BRAKE HYDRAULIC CIRCUIT AND OPERATION. THE MODULE CONSISTS OF A SELF-INSTRUCTIONAL PROGRAMED TRAINING FILM "LEARNING ABOUT THE ALLISON…

Automated Analysis of Short Responses in an Interactive Synthetic Tutoring System for Introductory Physics

ERIC Educational Resources Information Center

Nakamura, Christopher M.; Murphy, Sytil K.; Christel, Michael G.; Stevens, Scott M.; Zollman, Dean A.

2016-01-01

Computer-automated assessment of students' text responses to short-answer questions represents an important enabling technology for online learning environments. We have investigated the use of machine learning to train computer models capable of automatically classifying short-answer responses and assessed the results. Our investigations are part…

Higher-order neural network software for distortion invariant object recognition

NASA Technical Reports Server (NTRS)

Reid, Max B.; Spirkovska, Lilly

1991-01-01

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Toward comprehensive detection of sight threatening retinal disease using a multiscale AM-FM methodology

NASA Astrophysics Data System (ADS)

Agurto, C.; Barriga, S.; Murray, V.; Murillo, S.; Zamora, G.; Bauman, W.; Pattichis, M.; Soliz, P.

2011-03-01

In the United States and most of the western world, the leading causes of vision impairment and blindness are age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma. In the last decade, research in automatic detection of retinal lesions associated with eye diseases has produced several automatic systems for detection and screening of AMD, DR, and glaucoma. However. advanced, sight-threatening stages of DR and AMD can present with lesions not commonly addressed by current approaches to automatic screening. In this paper we present an automatic eye screening system based on multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions that addresses not only the early stages, but also advanced stages of retinal and optic nerve disease. Ten different experiments were performed in which abnormal features such as neovascularization, drusen, exudates, pigmentation abnormalities, geographic atrophy (GA), and glaucoma were classified. The algorithm achieved an accuracy detection range of [0.77 to 0.98] area under the ROC curve for a set of 810 images. When set to a specificity value of 0.60, the sensitivity of the algorithm to the detection of abnormal features ranged between 0.88 and 1.00. Our system demonstrates that, given an appropriate training set, it is possible to use a unique algorithm to detect a broad range of eye diseases.

Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images.

PubMed

Møllersen, Kajsa; Zortea, Maciel; Schopf, Thomas R; Kirchesch, Herbert; Godtliebsen, Fred

2017-01-01

Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.

Constructing distributed Hippocratic video databases for privacy-preserving online patient training and counseling.

PubMed

Peng, Jinye; Babaguchi, Noboru; Luo, Hangzai; Gao, Yuli; Fan, Jianping

2010-07-01

Digital video now plays an important role in supporting more profitable online patient training and counseling, and integration of patient training videos from multiple competitive organizations in the health care network will result in better offerings for patients. However, privacy concerns often prevent multiple competitive organizations from sharing and integrating their patient training videos. In addition, patients with infectious or chronic diseases may not want the online patient training organizations to identify who they are or even which video clips they are interested in. Thus, there is an urgent need to develop more effective techniques to protect both video content privacy and access privacy . In this paper, we have developed a new approach to construct a distributed Hippocratic video database system for supporting more profitable online patient training and counseling. First, a new database modeling approach is developed to support concept-oriented video database organization and assign a degree of privacy of the video content for each database level automatically. Second, a new algorithm is developed to protect the video content privacy at the level of individual video clip by filtering out the privacy-sensitive human objects automatically. In order to integrate the patient training videos from multiple competitive organizations for constructing a centralized video database indexing structure, a privacy-preserving video sharing scheme is developed to support privacy-preserving distributed classifier training and prevent the statistical inferences from the videos that are shared for cross-validation of video classifiers. Our experiments on large-scale video databases have also provided very convincing results.

Expert system applications for army vehicle diagnostics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halle, R.F.

1987-01-01

Bulky manuals, limited training procedures, and complex Automatic Test Equipment are but a few of the problems a mechanic must face when trying to repair many of the military's new and highly complex vehicle systems. Recent technological advances in Expert Systms has given the mechanic the potential to solve many of these problems and to actually enhance his maintenance proficiency. This paper describes both the history of and the future potential of the Expert System and how it could impact on the present military maintenance system.

CATOS (Computer Aided Training/Observing System): Automating animal observation and training.

PubMed

Oh, Jinook; Fitch, W Tecumseh

2017-02-01

In animal behavioral biology, an automated observing/training system may be useful for several reasons: (a) continuous observation of animals for documentation of specific, irregular events, (b) long-term intensive training of animals in preparation for behavioral experiments, (c) elimination of potential cues and biases induced by humans during training and testing. Here, we describe an open-source-based system named CATOS (Computer Aided Training/Observing System) developed for such situations. There are several notable features in this system. CATOS is flexible and low cost because it is based on free open-source software libraries, common hardware parts, and open-system electronics based on Arduino. Automated video condensation is applied, leading to significantly reduced video data storage compared to the total active hours of the system. A data-viewing utility program helps a user browse recorded data quickly and more efficiently. With these features, CATOS has the potential to be applied to many different animal species in various environments such as laboratories, zoos, or even private homes. Also, an animal's free access to the device without constraint, and a gamified learning process, enhance the animal's welfare and enriches their environment. As a proof of concept, the system was built and tested with two different species. Initially, the system was tested for approximately 10 months with a domesticated cat. The cat was successfully and fully automatically trained to discriminate three different spoken words. Then, in order to test the system's adaptability to other species and hardware components, we used it to train a laboratory rat for 3 weeks.

Automatic Data Processing Equipment (ADPE) acquisition plan for the medical sciences

NASA Technical Reports Server (NTRS)

1979-01-01

An effective mechanism for meeting the SLSD/MSD data handling/processing requirements for Shuttle is discussed. The ability to meet these requirements depends upon the availability of a general purpose high speed digital computer system. This system is expected to implement those data base management and processing functions required across all SLSD/MSD programs during training, laboratory operations/analysis, simulations, mission operations, and post mission analysis/reporting.

Advanced Simulation in Undergraduate Pilot Training: Automatic Instructional System

DTIC Science & Technology

1975-10-01

an addressable reel-to--reel audio tape recorder, a random access audio memory drum , and an interactive software package which permits the user to...audio memory drum , and an interactive software package which permits the user to develop preptogtahmed exercises. Figure 2 illustrates overall...Data Recprding System consists of two elements; an overlay program which performs the real-time sampling of specified variables and stores data to disc

Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree.

PubMed

Carneiro, Gustavo; Georgescu, Bogdan; Good, Sara; Comaniciu, Dorin

2008-09-01

We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.

Intelligent Image Analysis for Image-Guided Laser Hair Removal and Skin Therapy

NASA Technical Reports Server (NTRS)

Walker, Brian; Lu, Thomas; Chao, Tien-Hsin

2012-01-01

We present the development of advanced automatic target recognition (ATR) algorithms for the hair follicles identification in digital skin images to accurately direct the laser beam to remove the hair. The ATR system first performs a wavelet filtering to enhance the contrast of the hair features in the image. The system then extracts the unique features of the targets and sends the features to an Adaboost based classifier for training and recognition operations. The ATR system automatically classifies the hair, moles, or other skin lesion and provides the accurate coordinates of the intended hair follicle locations. The coordinates can be used to guide a scanning laser to focus energy only on the hair follicles. The intended benefit would be to protect the skin from unwanted laser exposure and to provide more effective skin therapy.

The effect of motor control training on abdominal muscle contraction during simulated weight bearing in elite cricketers.

PubMed

Hides, Julie A; Endicott, Timothy; Mendis, M Dilani; Stanton, Warren R

2016-07-01

To investigate whether motor control training alters automatic contraction of abdominal muscles in elite cricketers with low back pain (LBP) during performance of a simulated unilateral weight-bearing task. Clinical trial. 26 male elite-cricketers attended a 13-week cricket training camp. Prior to the camp, participants were allocated to a LBP or asymptomatic group. Real-time ultrasound imaging was used to assess automatic abdominal muscle response to axial loading. During the camp, the LBP group performed a staged motor control training program. Following the camp, the automatic response of the abdominal muscles was re-assessed. At pre-camp assessment, when participants were axially loaded with 25% of their own bodyweight, the LBP group showed a 15.5% thicker internal oblique (IO) muscle compared to the asymptomatic group (p = 0.009). The post-camp assessment showed that participants in the LBP group demonstrated less contraction of the IO muscle in response to axial loading compared with the asymptomatic group. A trend was found in the automatic recruitment pattern of the transversus abdominis (p = 0.08). Motor control training normalized excessive contraction of abdominal muscles in response to a low load task. This may be a useful strategy for rehabilitation of cricketers with LBP. Copyright © 2016 Elsevier Ltd. All rights reserved.

Automatic Pedestrian Crossing Detection and Impairment Analysis Based on Mobile Mapping System

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, Y.; Li, Q.

2017-09-01

Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians' lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.

A tool for developing an automatic insect identification system based on wing outlines

PubMed Central

Yang, He-Ping; Ma, Chun-Sen; Wen, Hui; Zhan, Qing-Bin; Wang, Xin-Li

2015-01-01

For some insect groups, wing outline is an important character for species identification. We have constructed a program as the integral part of an automated system to identify insects based on wing outlines (DAIIS). This program includes two main functions: (1) outline digitization and Elliptic Fourier transformation and (2) classifier model training by pattern recognition of support vector machines and model validation. To demonstrate the utility of this program, a sample of 120 owlflies (Neuroptera: Ascalaphidae) was split into training and validation sets. After training, the sample was sorted into seven species using this tool. In five repeated experiments, the mean accuracy for identification of each species ranged from 90% to 98%. The accuracy increased to 99% when the samples were first divided into two groups based on features of their compound eyes. DAIIS can therefore be a useful tool for developing a system of automated insect identification. PMID:26251292

Automated target recognition and tracking using an optical pattern recognition neural network

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin

1991-01-01

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Facial Emotions Recognition using Gabor Transform and Facial Animation Parameters with Neural Networks

NASA Astrophysics Data System (ADS)

Harit, Aditya; Joshi, J. C., Col; Gupta, K. K.

2018-03-01

The paper proposed an automatic facial emotion recognition algorithm which comprises of two main components: feature extraction and expression recognition. The algorithm uses a Gabor filter bank on fiducial points to find the facial expression features. The resulting magnitudes of Gabor transforms, along with 14 chosen FAPs (Facial Animation Parameters), compose the feature space. There are two stages: the training phase and the recognition phase. Firstly, for the present 6 different emotions, the system classifies all training expressions in 6 different classes (one for each emotion) in the training stage. In the recognition phase, it recognizes the emotion by applying the Gabor bank to a face image, then finds the fiducial points, and then feeds it to the trained neural architecture.

Training lay-people to use automatic external defibrillators: are all of their needs being met?

PubMed

Harrison-Paul, Russell; Timmons, Stephen; van Schalkwyk, Wilna Dirkse

2006-10-01

We explored the experiences of lay people who have been trained to use automatic external defibrillators. The research questions were: (1) How can training courses help prepare people for dealing with real life situations? (2) Who is ultimately responsible for providing critical incident debriefing and how should this be organised? (3) What is the best process for providing feedback to those who have used an AED? Fifty-three semi-structured, qualitative interviews were conducted, some with those who had been trained and others with trainers. Locations included airports, railway stations, private companies and first responder schemes. Geographically, we covered Nottinghamshire, Lincolnshire, Yorkshire, Staffordshire, Essex and the West Midlands in the UK. Our analysis of the data indicates that most people believe scenarios based within their place of work were most useful in preparing for 'real life'. Many people had not received critical incident debriefing after using an AED. There were a variety of systems in place to provide support after an incident, many of which were informal. Training scenarios should be conducted outside the classroom. There should be more focus on critical incident debriefing during training and a clear identification of who should provide support after an incident. Other issues which were of interest included: (1) people's views on do not attempt resuscitation (DNAR); (2) perceived boundaries of responsibility when using an AED; (3) when is someone no longer 'qualified' to use an AED?

Iterative dataset optimization in automated planning: Implementation for breast and rectal cancer radiotherapy.

PubMed

Fan, Jiawei; Wang, Jiazhou; Zhang, Zhen; Hu, Weigang

2017-06-01

To develop a new automated treatment planning solution for breast and rectal cancer radiotherapy. The automated treatment planning solution developed in this study includes selection of the iterative optimized training dataset, dose volume histogram (DVH) prediction for the organs at risk (OARs), and automatic generation of clinically acceptable treatment plans. The iterative optimized training dataset is selected by an iterative optimization from 40 treatment plans for left-breast and rectal cancer patients who received radiation therapy. A two-dimensional kernel density estimation algorithm (noted as two parameters KDE) which incorporated two predictive features was implemented to produce the predicted DVHs. Finally, 10 additional new left-breast treatment plans are re-planned using the Pinnacle 3 Auto-Planning (AP) module (version 9.10, Philips Medical Systems) with the objective functions derived from the predicted DVH curves. Automatically generated re-optimized treatment plans are compared with the original manually optimized plans. By combining the iterative optimized training dataset methodology and two parameters KDE prediction algorithm, our proposed automated planning strategy improves the accuracy of the DVH prediction. The automatically generated treatment plans using the dose derived from the predicted DVHs can achieve better dose sparing for some OARs without compromising other metrics of plan quality. The proposed new automated treatment planning solution can be used to efficiently evaluate and improve the quality and consistency of the treatment plans for intensity-modulated breast and rectal cancer radiation therapy. © 2017 American Association of Physicists in Medicine.

Automatic quality assessment of apical four-chamber echocardiograms using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Abdi, Amir H.; Luong, Christina; Tsang, Teresa; Allan, Gregory; Nouranian, Saman; Jue, John; Hawley, Dale; Fleming, Sarah; Gin, Ken; Swift, Jody; Rohling, Robert; Abolmaesumi, Purang

2017-02-01

Echocardiography (echo) is the most common test for diagnosis and management of patients with cardiac condi- tions. While most medical imaging modalities benefit from a relatively automated procedure, this is not the case for echo and the quality of the final echo view depends on the competency and experience of the sonographer. It is not uncommon that the sonographer does not have adequate experience to adjust the transducer and acquire a high quality echo, which may further affect the clinical diagnosis. In this work, we aim to aid the operator during image acquisition by automatically assessing the quality of the echo and generating the Automatic Echo Score (AES). This quality assessment method is based on a deep convolutional neural network, trained in an end-to-end fashion on a large dataset of apical four-chamber (A4C) echo images. For this project, an expert car- diologist went through 2,904 A4C images obtained from independent studies and assessed their condition based on a 6-scale grading system. The scores assigned by the expert ranged from 0 to 5. The distribution of scores among the 6 levels were almost uniform. The network was then trained on 80% of the data (2,345 samples). The average absolute error of the trained model in calculating the AES was 0.8 +/- 0:72. The computation time of the GPU implementation of the neural network was estimated at 5 ms per frame, which is sufficient for real-time deployment.

Automatic Detection of Student Mental Models Based on Natural Language Student Input during Metacognitive Skill Training

ERIC Educational Resources Information Center

Lintean, Mihai; Rus, Vasile; Azevedo, Roger

2012-01-01

This article describes the problem of detecting the student mental models, i.e. students' knowledge states, during the self-regulatory activity of prior knowledge activation in MetaTutor, an intelligent tutoring system that teaches students self-regulation skills while learning complex science topics. The article presents several approaches to…

An automatic identification procedure to promote the use of FES-cycling training for hemiparetic patients.

PubMed

Ambrosini, Emilia; Ferrante, Simona; Schauer, Thomas; Ferrigno, Giancarlo; Molteni, Franco; Pedrocchi, Alessandra

2014-01-01

Cycling induced by Functional Electrical Stimulation (FES) training currently requires a manual setting of different parameters, which is a time-consuming and scarcely repeatable procedure. We proposed an automatic procedure for setting session-specific parameters optimized for hemiparetic patients. This procedure consisted of the identification of the stimulation strategy as the angular ranges during which FES drove the motion, the comparison between the identified strategy and the physiological muscular activation strategy, and the setting of the pulse amplitude and duration of each stimulated muscle. Preliminary trials on 10 healthy volunteers helped define the procedure. Feasibility tests on 8 hemiparetic patients (5 stroke, 3 traumatic brain injury) were performed. The procedure maximized the motor output within the tolerance constraint, identified a biomimetic strategy in 6 patients, and always lasted less than 5 minutes. Its reasonable duration and automatic nature make the procedure usable at the beginning of every training session, potentially enhancing the performance of FES-cycling training.

Advanced Concepts of Naval Engineering Maintenance Training. Volume 2. Appendix F

DTIC Science & Technology

1976-05-01

maintenance instruction, the Hagan Automatic Boiler Control (ABC) course. These job requirements also included the tasks, skills, and knowledges for all...Pressure 1 3/4 NAVTRAEQÜIPCEN 74-C-0151-1 TABLE OF CONTENTS VOLUME II OF II APPENDIX F Page Hagan Automatic Boiler Controls Systems (FAS) 6...a c a ■* w ■H u 0 M « s? u ’• B n J-riH 3 o c 0 hhO a a o -i •H -I 0 -H 9J ■ a a oi « C -a u <rl « vi) •a - 8 ai >> u u

Automatic classification of seismic events within a regional seismograph network

NASA Astrophysics Data System (ADS)

Tiira, Timo; Kortström, Jari; Uski, Marja

2015-04-01

A fully automatic method for seismic event classification within a sparse regional seismograph network is presented. The tool is based on a supervised pattern recognition technique, Support Vector Machine (SVM), trained here to distinguish weak local earthquakes from a bulk of human-made or spurious seismic events. The classification rules rely on differences in signal energy distribution between natural and artificial seismic sources. Seismic records are divided into four windows, P, P coda, S, and S coda. For each signal window STA is computed in 20 narrow frequency bands between 1 and 41 Hz. The 80 discrimination parameters are used as a training data for the SVM. The SVM models are calculated for 19 on-line seismic stations in Finland. The event data are compiled mainly from fully automatic event solutions that are manually classified after automatic location process. The station-specific SVM training events include 11-302 positive (earthquake) and 227-1048 negative (non-earthquake) examples. The best voting rules for combining results from different stations are determined during an independent testing period. Finally, the network processing rules are applied to an independent evaluation period comprising 4681 fully automatic event determinations, of which 98 % have been manually identified as explosions or noise and 2 % as earthquakes. The SVM method correctly identifies 94 % of the non-earthquakes and all the earthquakes. The results imply that the SVM tool can identify and filter out blasts and spurious events from fully automatic event solutions with a high level of confidence. The tool helps to reduce work-load in manual seismic analysis by leaving only ~5 % of the automatic event determinations, i.e. the probable earthquakes for more detailed seismological analysis. The approach presented is easy to adjust to requirements of a denser or wider high-frequency network, once enough training examples for building a station-specific data set are available.

Performance analysis of distributed applications using automatic classification of communication inefficiencies

DOEpatents

Vetter, Jeffrey S.

2005-02-01

The method and system described herein presents a technique for performance analysis that helps users understand the communication behavior of their message passing applications. The method and system described herein may automatically classifies individual communication operations and reveal the cause of communication inefficiencies in the application. This classification allows the developer to quickly focus on the culprits of truly inefficient behavior, rather than manually foraging through massive amounts of performance data. Specifically, the method and system described herein trace the message operations of Message Passing Interface (MPI) applications and then classify each individual communication event using a supervised learning technique: decision tree classification. The decision tree may be trained using microbenchmarks that demonstrate both efficient and inefficient communication. Since the method and system described herein adapt to the target system's configuration through these microbenchmarks, they simultaneously automate the performance analysis process and improve classification accuracy. The method and system described herein may improve the accuracy of performance analysis and dramatically reduce the amount of data that users must encounter.

A low-cost, computer-controlled robotic flower system for behavioral experiments.

PubMed

Kuusela, Erno; Lämsä, Juho

2016-04-01

Human observations during behavioral studies are expensive, time-consuming, and error prone. For this reason, automatization of experiments is highly desirable, as it reduces the risk of human errors and workload. The robotic system we developed is simple and cheap to build and handles feeding and data collection automatically. The system was built using mostly off-the-shelf components and has a novel feeding mechanism that uses servos to perform refill operations. We used the robotic system in two separate behavioral studies with bumblebees (Bombus terrestris): The system was used both for training of the bees and for the experimental data collection. The robotic system was reliable, with no flight in our studies failing due to a technical malfunction. The data recorded were easy to apply for further analysis. The software and the hardware design are open source. The development of cheap open-source prototyping platforms during the recent years has opened up many possibilities in designing of experiments. Automatization not only reduces workload, but also potentially allows experimental designs never done before, such as dynamic experiments, where the system responds to, for example, learning of the animal. We present a complete system with hardware and software, and it can be used as such in various experiments requiring feeders and collection of visitation data. Use of the system is not limited to any particular experimental setup or even species.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

PubMed Central

Li, Kan; Príncipe, José C.

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime. PMID:29666568

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

PubMed

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

Steam plant startup and control in system restoration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mello, F.P. de; Westcott, J.C.

1994-02-01

The IEEE Working Group on Power System Restoration developed a panel session for the Summer Power Meeting on July 14, 1992 on Special Considerations in Power System Restoration. One of the contributions to this session is presented in this paper dealing with aspects of steam plant startup and control in scenarios of system restoration. The topics addressed include the complexity of a steam plant, the contrast between normal plant startups and shutdowns and those following major system blackouts including the effects of plant design, automatic controls, bypass valving and operator training.

Design a Learning-Oriented Fall Event Reporting System Based on Kirkpatrick Model.

PubMed

Zhou, Sicheng; Kang, Hong; Gong, Yang

2017-01-01

Patient fall has been a severe problem in healthcare facilities around the world due to its prevalence and cost. Routine fall prevention training programs are not as effective as expected. Using event reporting systems is the trend for reducing patient safety events such as falls, although some limitations of the systems exist at current stage. We summarized these limitations through literature review, and developed an improved web-based fall event reporting system. The Kirkpatrick model, widely used in the business area for training program evaluation, has been integrated during the design of our system. Different from traditional event reporting systems that only collect and store the reports, our system automatically annotates and analyzes the reported events, and provides users with timely knowledge support specific to the reported event. The paper illustrates the design of our system and how its features are intended to reduce patient falls by learning from previous errors.

Implicit measures of beliefs about sport ability in swimming and basketball.

PubMed

Mascret, Nicolas; Falconetti, Jean-Louis; Cury, François

2016-01-01

Sport ability may be seen as relatively stable, genetically determined and not easily modified by practice, or as increasable with training, work and effort. Using the Implicit Association Test (IAT), the purpose of the present study is to examine whether the practice of a particular sport (swimming or basketball) can influence automatic beliefs about sport ability in these two sports. The IAT scores evidence that swimmers and basketball players automatically and implicitly associate their own sport with training rather than genetics, whereas non-sportspersons have no significant automatic association. This result is strengthened when perceived competence and intrinsic motivation in swimming or basketball are high.

[The mediating role of anger in the relationship between automatic thoughts and physical aggression in adolescents].

PubMed

Yavuzer, Yasemin; Karataş, Zeynep

2013-01-01

This study aimed to examine the mediating role of anger in the relationship between automatic thoughts and physical aggression in adolescents. The study included 224 adolescents in the 9th grade of 3 different high schools in central Burdur during the 2011-2012 academic year. Participants completed the Aggression Questionnaire and Automatic Thoughts Scale in their classrooms during counseling sessions. Data were analyzed using simple and multiple linear regression analysis. There were positive correlations between the adolescents' automatic thoughts, and physical aggression, and anger. According to regression analysis, automatic thoughts effectively predicted the level of physical aggression (b= 0.233, P < 0.001)) and anger (b= 0.325, P < 0.001). Analysis of the mediating role of anger showed that anger fully mediated the relationship between automatic thoughts and physical aggression (Sobel z = 5.646, P < 0.001). Anger fully mediated the relationship between automatic thoughts and physical aggression. Providing adolescents with anger management skills training is very important for the prevention of physical aggression. Such training programs should include components related to the development of an awareness of dysfunctional and anger-triggering automatic thoughts, and how to change them. As the study group included adolescents from Burdur, the findings can only be generalized to groups with similar characteristics.

Automated aortic calcium scoring on low-dose chest computed tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Isgum, Ivana; Rutten, Annemarieke; Prokop, Mathias

Purpose: Thoracic computed tomography (CT) scans provide information about cardiovascular risk status. These scans are non-ECG synchronized, thus precise quantification of coronary calcifications is difficult. Aortic calcium scoring is less sensitive to cardiac motion, so it is an alternative to coronary calcium scoring as an indicator of cardiovascular risk. The authors developed and evaluated a computer-aided system for automatic detection and quantification of aortic calcifications in low-dose noncontrast-enhanced chest CT. Methods: The system was trained and tested on scans from participants of a lung cancer screening trial. A total of 433 low-dose, non-ECG-synchronized, noncontrast-enhanced 16 detector row examinations of themore » chest was randomly divided into 340 training and 93 test data sets. A first observer manually identified aortic calcifications on training and test scans. A second observer did the same on the test scans only. First, a multiatlas-based segmentation method was developed to delineate the aorta. Segmented volume was thresholded and potential calcifications (candidate objects) were extracted by three-dimensional connected component labeling. Due to image resolution and noise, in rare cases extracted candidate objects were connected to the spine. They were separated into a part outside and parts inside the aorta, and only the latter was further analyzed. All candidate objects were represented by 63 features describing their size, position, and texture. Subsequently, a two-stage classification with a selection of features and k-nearest neighbor classifiers was performed. Based on the detected aortic calcifications, total calcium volume score was determined for each subject. Results: The computer system correctly detected, on the average, 945 mm{sup 3} out of 965 mm{sup 3} (97.9%) calcified plaque volume in the aorta with an average of 64 mm{sup 3} of false positive volume per scan. Spearman rank correlation coefficient was {rho}=0.960 between the system and the first observer compared to {rho}=0.961 between the two observers. Conclusions: Automatic calcium scoring in the aorta thus appears feasible with good correlation between manual and automatic scoring.« less

Anatomical entity mention recognition at literature scale

PubMed Central

Pyysalo, Sampo; Ananiadou, Sophia

2014-01-01

Motivation: Anatomical entities ranging from subcellular structures to organ systems are central to biomedical science, and mentions of these entities are essential to understanding the scientific literature. Despite extensive efforts to automatically analyze various aspects of biomedical text, there have been only few studies focusing on anatomical entities, and no dedicated methods for learning to automatically recognize anatomical entity mentions in free-form text have been introduced. Results: We present AnatomyTagger, a machine learning-based system for anatomical entity mention recognition. The system incorporates a broad array of approaches proposed to benefit tagging, including the use of Unified Medical Language System (UMLS)- and Open Biomedical Ontologies (OBO)-based lexical resources, word representations induced from unlabeled text, statistical truecasing and non-local features. We train and evaluate the system on a newly introduced corpus that substantially extends on previously available resources, and apply the resulting tagger to automatically annotate the entire open access scientific domain literature. The resulting analyses have been applied to extend services provided by the Europe PubMed Central literature database. Availability and implementation: All tools and resources introduced in this work are available from http://nactem.ac.uk/anatomytagger. Contact: sophia.ananiadou@manchester.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24162468

Hybrid Approach for Automatic Evaluation of Emotion Elicitation Oriented to People with Intellectual Disabilities

NASA Astrophysics Data System (ADS)

Martínez, R.; de Ipiña, K. López; Irigoyen, E.; Asla, N.

People with intellectual disabilities and elderly need physical and intellectual support to ensuring independent living. This is one of the main issues in applying Information and Communication Technology (ICT) into Assistive Technology field. In this sense the development of appropriated Intelligent Systems (ISs) offers new perspectives to this community. In our project a new IS system (LAGUNTXO) which adds user affective information oriented to people with intellectual disabilities has been developed. The system integrates a Human Emotion Analysis System (HEAS) which attempts to solve critical situations for this community as block stages. In the development of the HEAS one of the critical issues was to create appropriated databases to train the system due to the difficulty to simulate pre-block stages in laboratory. Finally a films and real sequences based emotion elicitation database was created. The elicitation material was categorized with more actual features based on discrete emotions and dimensional terms (pleasant, unpleasant). Classically the evaluation is carried out by a specialist (psychologist). In this work we present a hybrid approach for Automatic Evaluation of Emotion Elicitation databases based on Machine Learning classifiers and K-means clustering. The new categorization and the automatic evaluation show a high level of accuracy with respect to others methodologies presented in the literature.

Sample Selection for Training Cascade Detectors.

PubMed

Vállez, Noelia; Deniz, Oscar; Bueno, Gloria

2015-01-01

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

PubMed

Song, Qi; Song, Yong-Duan

2011-12-01

This paper investigates the position and velocity tracking control problem of high-speed trains with multiple vehicles connected through couplers. A dynamic model reflecting nonlinear and elastic impacts between adjacent vehicles as well as traction/braking nonlinearities and actuation faults is derived. Neuroadaptive fault-tolerant control algorithms are developed to account for various factors such as input nonlinearities, actuator failures, and uncertain impacts of in-train forces in the system simultaneously. The resultant control scheme is essentially independent of system model and is primarily data-driven because with the appropriate input-output data, the proposed control algorithms are capable of automatically generating the intermediate control parameters, neuro-weights, and the compensation signals, literally producing the traction/braking force based upon input and response data only--the whole process does not require precise information on system model or system parameter, nor human intervention. The effectiveness of the proposed approach is also confirmed through numerical simulations.

Brain activity across the development of automatic categorization: A comparison of categorization tasks using multi-voxel pattern analysis

PubMed Central

Soto, Fabian A.; Waldschmidt, Jennifer G.; Helie, Sebastien; Ashby, F. Gregory

2013-01-01

Previous evidence suggests that relatively separate neural networks underlie initial learning of rule-based and information-integration categorization tasks. With the development of automaticity, categorization behavior in both tasks becomes increasingly similar and exclusively related to activity in cortical regions. The present study uses multi-voxel pattern analysis to directly compare the development of automaticity in different categorization tasks. Each of three groups of participants received extensive training in a different categorization task: either an information-integration task, or one of two rule-based tasks. Four training sessions were performed inside an MRI scanner. Three different analyses were performed on the imaging data from a number of regions of interest (ROIs). The common patterns analysis had the goal of revealing ROIs with similar patterns of activation across tasks. The unique patterns analysis had the goal of revealing ROIs with dissimilar patterns of activation across tasks. The representational similarity analysis aimed at exploring (1) the similarity of category representations across ROIs and (2) how those patterns of similarities compared across tasks. The results showed that common patterns of activation were present in motor areas and basal ganglia early in training, but only in the former later on. Unique patterns were found in a variety of cortical and subcortical areas early in training, but they were dramatically reduced with training. Finally, patterns of representational similarity between brain regions became increasingly similar across tasks with the development of automaticity. PMID:23333700

A Machine Learning-based Method for Question Type Classification in Biomedical Question Answering.

PubMed

Sarrouti, Mourad; Ouatik El Alaoui, Said

2017-05-18

Biomedical question type classification is one of the important components of an automatic biomedical question answering system. The performance of the latter depends directly on the performance of its biomedical question type classification system, which consists of assigning a category to each question in order to determine the appropriate answer extraction algorithm. This study aims to automatically classify biomedical questions into one of the four categories: (1) yes/no, (2) factoid, (3) list, and (4) summary. In this paper, we propose a biomedical question type classification method based on machine learning approaches to automatically assign a category to a biomedical question. First, we extract features from biomedical questions using the proposed handcrafted lexico-syntactic patterns. Then, we feed these features for machine-learning algorithms. Finally, the class label is predicted using the trained classifiers. Experimental evaluations performed on large standard annotated datasets of biomedical questions, provided by the BioASQ challenge, demonstrated that our method exhibits significant improved performance when compared to four baseline systems. The proposed method achieves a roughly 10-point increase over the best baseline in terms of accuracy. Moreover, the obtained results show that using handcrafted lexico-syntactic patterns as features' provider of support vector machine (SVM) lead to the highest accuracy of 89.40 %. The proposed method can automatically classify BioASQ questions into one of the four categories: yes/no, factoid, list, and summary. Furthermore, the results demonstrated that our method produced the best classification performance compared to four baseline systems.

Combining MEDLINE and publisher data to create parallel corpora for the automatic translation of biomedical text

PubMed Central

2013-01-01

Background Most of the institutional and research information in the biomedical domain is available in the form of English text. Even in countries where English is an official language, such as the United States, language can be a barrier for accessing biomedical information for non-native speakers. Recent progress in machine translation suggests that this technique could help make English texts accessible to speakers of other languages. However, the lack of adequate specialized corpora needed to train statistical models currently limits the quality of automatic translations in the biomedical domain. Results We show how a large-sized parallel corpus can automatically be obtained for the biomedical domain, using the MEDLINE database. The corpus generated in this work comprises article titles obtained from MEDLINE and abstract text automatically retrieved from journal websites, which substantially extends the corpora used in previous work. After assessing the quality of the corpus for two language pairs (English/French and English/Spanish) we use the Moses package to train a statistical machine translation model that outperforms previous models for automatic translation of biomedical text. Conclusions We have built translation data sets in the biomedical domain that can easily be extended to other languages available in MEDLINE. These sets can successfully be applied to train statistical machine translation models. While further progress should be made by incorporating out-of-domain corpora and domain-specific lexicons, we believe that this work improves the automatic translation of biomedical texts. PMID:23631733

Providers issue brief: automated external defibrillators.

PubMed

Rothouse, M

1999-06-25

With expanded access to automatic external defibrillators, hundreds of lives could be saved on a daily basis. By training nonphysician providers, such as emergency medical service personnel or first responders, this life-saving medical equipment could help improve the survival rates for people suffering from cardiac arrest. During the last two years, state lawmakers have begun to enact legislation that develops training standards and provides immunity from civil liability for automatic external defibrillator users.

State of the Art and Challenges of Radio Spectrum Monitoring in China

NASA Astrophysics Data System (ADS)

Lu, Q. N.; Yang, J. J.; Jin, Z. Y.; Chen, D. Z.; Huang, M.

2017-10-01

This paper provides an overview of radio spectrum monitoring in China. First, research background, the motivation is described and then train of thought, the prototype system, and the accomplishments are presented. Current radio spectrum monitoring systems are man-machine communication systems, which are unable to detect and process the radio interference automatically. In order to realize intelligent radio monitoring and spectrum management, we proposed an Internet of Things-based spectrum sensing approach using information system architecture and implemented a pilot program; then some very interesting results were obtained.

An e-Learning Collaborative Filtering Approach to Suggest Problems to Solve in Programming Online Judges

ERIC Educational Resources Information Center

Toledo, Raciel Yera; Mota, Yailé Caballero

2014-01-01

The paper proposes a recommender system approach to cover online judge's domains. Online judges are e-learning tools that support the automatic evaluation of programming tasks done by individual users, and for this reason they are usually used for training students in programming contest and for supporting basic programming teachings. The…

Clustering and classification of infrasonic events at Mount Etna using pattern recognition techniques

NASA Astrophysics Data System (ADS)

Cannata, A.; Montalto, P.; Aliotta, M.; Cassisi, C.; Pulvirenti, A.; Privitera, E.; Patanè, D.

2011-04-01

Active volcanoes generate sonic and infrasonic signals, whose investigation provides useful information for both monitoring purposes and the study of the dynamics of explosive phenomena. At Mt. Etna volcano (Italy), a pattern recognition system based on infrasonic waveform features has been developed. First, by a parametric power spectrum method, the features describing and characterizing the infrasound events were extracted: peak frequency and quality factor. Then, together with the peak-to-peak amplitude, these features constituted a 3-D ‘feature space’; by Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) three clusters were recognized inside it. After the clustering process, by using a common location method (semblance method) and additional volcanological information concerning the intensity of the explosive activity, we were able to associate each cluster to a particular source vent and/or a kind of volcanic activity. Finally, for automatic event location, clusters were used to train a model based on Support Vector Machine, calculating optimal hyperplanes able to maximize the margins of separation among the clusters. After the training phase this system automatically allows recognizing the active vent with no location algorithm and by using only a single station.

3D Convolutional Neural Network for Automatic Detection of Lung Nodules in Chest CT.

PubMed

Hamidian, Sardar; Sahiner, Berkman; Petrick, Nicholas; Pezeshk, Aria

2017-01-01

Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images. The same principles and architectures can be extended to three dimensions to obtain 3D CNNs that are suitable for volumetric data such as CT scans. In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. We then convert the 3D CNN which has a fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. Compared to the sliding window approach for applying a CNN across the entire input volume, the FCN leads to a nearly 800-fold speed-up, and thereby fast generation of output scores for a single case. This screening FCN is used to generate difficult negative examples that are used to train a new discriminant CNN. The overall system consists of the screening FCN for fast generation of candidate regions of interest, followed by the discrimination CNN.

3D convolutional neural network for automatic detection of lung nodules in chest CT

NASA Astrophysics Data System (ADS)

Hamidian, Sardar; Sahiner, Berkman; Petrick, Nicholas; Pezeshk, Aria

2017-03-01

Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images. The same principles and architectures can be extended to three dimensions to obtain 3D CNNs that are suitable for volumetric data such as CT scans. In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. We then convert the 3D CNN which has a fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. Compared to the sliding window approach for applying a CNN across the entire input volume, the FCN leads to a nearly 800-fold speed-up, and thereby fast generation of output scores for a single case. This screening FCN is used to generate difficult negative examples that are used to train a new discriminant CNN. The overall system consists of the screening FCN for fast generation of candidate regions of interest, followed by the discrimination CNN.

Text Mining to Support Gene Ontology Curation and Vice Versa.

PubMed

Ruch, Patrick

2017-01-01

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

Automatic Tortuosity-Based Retinopathy of Prematurity Screening System

NASA Astrophysics Data System (ADS)

Sukkaew, Lassada; Uyyanonvara, Bunyarit; Makhanov, Stanislav S.; Barman, Sarah; Pangputhipong, Pannet

Retinopathy of Prematurity (ROP) is an infant disease characterized by increased dilation and tortuosity of the retinal blood vessels. Automatic tortuosity evaluation from retinal digital images is very useful to facilitate an ophthalmologist in the ROP screening and to prevent childhood blindness. This paper proposes a method to automatically classify the image into tortuous and non-tortuous. The process imitates expert ophthalmologists' screening by searching for clearly tortuous vessel segments. First, a skeleton of the retinal blood vessels is extracted from the original infant retinal image using a series of morphological operators. Next, we propose to partition the blood vessels recursively using an adaptive linear interpolation scheme. Finally, the tortuosity is calculated based on the curvature of the resulting vessel segments. The retinal images are then classified into two classes using segments characterized by the highest tortuosity. For an optimal set of training parameters the prediction is as high as 100%.

Targeting Cognitive-Affective Risk Mechanisms in Stress-Precipitated Alcohol Dependence: An Integrated, Biopsychosocial Model of Automaticity, Allostasis, and Addiction

PubMed Central

Garland, Eric L.; Boettiger, Charlotte A.; Howard, Matthew O.

2011-01-01

This paper proposes a novel hypothetical model integrating formerly discrete theories of stress appraisal, neurobiological allostasis, automatic cognitive processing, and addictive behavior to elucidate how alcohol misuse and dependence are maintained and re-activated by stress. We outline a risk chain in which psychosocial stress initiates physiological arousal, perseverative cognition, and negative affect that, in turn, triggers automatized schema to compel alcohol consumption. This implicit cognitive process then leads to attentional biases toward alcohol, subjective experiences of craving, paradoxical increases in arousal and alcohol-related cognitions due to urge suppression, and palliative coping through drinking. When palliative coping relieves distress, it results in negative reinforcement conditioning that perpetuates the cycle by further sensitizing the system to future stressful encounters. This model has implications for development and implementation of innovative behavioral interventions (such as mindfulness training) that disrupt cognitive-affective mechanisms underpinning stress-precipitated dependence on alcohol. PMID:21354711

Automated Interactive Simulation Model (AISIM) VAX Version 5.0 Training Manual.

DTIC Science & Technology

1987-05-29

action, activity, decision , etc. that consumes time. The entity is automatically created by the system when an ACTION Primitive is placed. 1.3.2.4 The...MODELED SYSTEM 1.3.2.1 The Process Entity. A Process is used to represent the operations, decisions , actions or activities that can be decomposed and...is associated with the Action entity described below, is included in Process definitions to indicate the time a certain Action (or process, decision

Fuzzy logic controllers: A knowledge-based system perspective

NASA Technical Reports Server (NTRS)

Bonissone, Piero P.

1993-01-01

Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems.

FOCIH: Form-Based Ontology Creation and Information Harvesting

NASA Astrophysics Data System (ADS)

Tao, Cui; Embley, David W.; Liddle, Stephen W.

Creating an ontology and populating it with data are both labor-intensive tasks requiring a high degree of expertise. Thus, scaling ontology creation and population to the size of the web in an effort to create a web of data—which some see as Web 3.0—is prohibitive. Can we find ways to streamline these tasks and lower the barrier enough to enable Web 3.0? Toward this end we offer a form-based approach to ontology creation that provides a way to create Web 3.0 ontologies without the need for specialized training. And we offer a way to semi-automatically harvest data from the current web of pages for a Web 3.0 ontology. In addition to harvesting information with respect to an ontology, the approach also annotates web pages and links facts in web pages to ontological concepts, resulting in a web of data superimposed over the web of pages. Experience with our prototype system shows that mappings between conceptual-model-based ontologies and forms are sufficient for creating the kind of ontologies needed for Web 3.0, and experiments with our prototype system show that automatic harvesting, automatic annotation, and automatic superimposition of a web of data over a web of pages work well.

Implementation Of Fuzzy Automated Brake Controller Using TSK Algorithm

NASA Astrophysics Data System (ADS)

Mittal, Ruchi; Kaur, Magandeep

2010-11-01

In this paper an application of Fuzzy Logic for Automatic Braking system is proposed. Anti-blocking system (ABS) brake controllers pose unique challenges to the designer: a) For optimal performance, the controller must operate at an unstable equilibrium point, b) Depending on road conditions, the maximum braking torque may vary over a wide range, c) The tire slippage measurement signal, crucial for controller performance, is both highly uncertain and noisy. A digital controller design was chosen which combines a fuzzy logic element and a decision logic network. The controller identifies the current road condition and generates a command braking pressure signal Depending upon the speed and distance of train. This paper describes design criteria, and the decision and rule structure of the control system. The simulation results present the system's performance depending upon the varying speed and distance of the train.

Computer assisted analysis of auroral images obtained from high altitude polar satellites

NASA Technical Reports Server (NTRS)

Samadani, Ramin; Flynn, Michael

1993-01-01

Automatic techniques that allow the extraction of physically significant parameters from auroral images were developed. This allows the processing of a much larger number of images than is currently possible with manual techniques. Our techniques were applied to diverse auroral image datasets. These results were made available to geophysicists at NASA and at universities in the form of a software system that performs the analysis. After some feedback from users, an upgraded system was transferred to NASA and to two universities. The feasibility of user-trained search and retrieval of large amounts of data using our automatically derived parameter indices was demonstrated. Techniques based on classification and regression trees (CART) were developed and applied to broaden the types of images to which the automated search and retrieval may be applied. Our techniques were tested with DE-1 auroral images.

Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks.

PubMed

Tang, Guoyu; Ni, Yuan; Wang, Keqiang; Yong, Qin

2018-01-01

The online patient question and answering (Q&A) system attracts an increasing amount of users in China. Patient will post their questions and wait for doctors' response. To avoid the lag time involved with the waiting and to reduce the workload on the doctors, a better method is to automatically retrieve the semantically equivalent question from the archive. We present a Generative Adversarial Networks (GAN) based approach to automatically retrieve patient question. We apply supervised deep learning based approaches to determine the similarity between patient questions. Then a GAN framework is used to fine-tune the pre-trained deep learning models. The experiment results show that fine-tuning by GAN can improve the performance.

Using the Weighted Keyword Model to Improve Information Retrieval for Answering Biomedical Questions

PubMed Central

Yu, Hong; Cao, Yong-gang

2009-01-01

Physicians ask many complex questions during the patient encounter. Information retrieval systems that can provide immediate and relevant answers to these questions can be invaluable aids to the practice of evidence-based medicine. In this study, we first automatically identify topic keywords from ad hoc clinical questions with a Condition Random Field model that is trained over thousands of manually annotated clinical questions. We then report on a linear model that assigns query weights based on their automatically identified semantic roles: topic keywords, domain specific terms, and their synonyms. Our evaluation shows that this weighted keyword model improves information retrieval from the Text Retrieval Conference Genomics track data. PMID:21347188

Using the weighted keyword model to improve information retrieval for answering biomedical questions.

PubMed

Yu, Hong; Cao, Yong-Gang

2009-03-01

Physicians ask many complex questions during the patient encounter. Information retrieval systems that can provide immediate and relevant answers to these questions can be invaluable aids to the practice of evidence-based medicine. In this study, we first automatically identify topic keywords from ad hoc clinical questions with a Condition Random Field model that is trained over thousands of manually annotated clinical questions. We then report on a linear model that assigns query weights based on their automatically identified semantic roles: topic keywords, domain specific terms, and their synonyms. Our evaluation shows that this weighted keyword model improves information retrieval from the Text Retrieval Conference Genomics track data.

Changes in default mode network as automaticity develops in a categorization task.

PubMed

Shamloo, Farzin; Helie, Sebastien

2016-10-15

The default mode network (DMN) is a set of brain regions in which blood oxygen level dependent signal is suppressed during attentional focus on the external environment. Because automatic task processing requires less attention, development of automaticity in a rule-based categorization task may result in less deactivation and altered functional connectivity of the DMN when compared to the initial learning stage. We tested this hypothesis by re-analyzing functional magnetic resonance imaging data of participants trained in rule-based categorization for over 10,000 trials (Helie et al., 2010) [12,13]. The results show that some DMN regions are deactivated in initial training but not after automaticity has developed. There is also a significant decrease in DMN deactivation after extensive practice. Seed-based functional connectivity analyses with the precuneus, medial prefrontal cortex (two important DMN regions) and Brodmann area 6 (an important region in automatic categorization) were also performed. The results show increased functional connectivity with both DMN and non-DMN regions after the development of automaticity, and a decrease in functional connectivity between the medial prefrontal cortex and ventromedial orbitofrontal cortex. Together, these results further support the hypothesis of a strategy shift in automatic categorization and bridge the cognitive and neuroscientific conceptions of automaticity in showing that the reduced need for cognitive resources in automatic processing is accompanied by a disinhibition of the DMN and stronger functional connectivity between DMN and task-related brain regions. Copyright © 2016 Elsevier B.V. All rights reserved.

A novel method of language modeling for automatic captioning in TC video teleconferencing.

PubMed

Zhang, Xiaojia; Zhao, Yunxin; Schopp, Laura

2007-05-01

We are developing an automatic captioning system for teleconsultation video teleconferencing (TC-VTC) in telemedicine, based on large vocabulary conversational speech recognition. In TC-VTC, doctors' speech contains a large number of infrequently used medical terms in spontaneous styles. Due to insufficiency of data, we adopted mixture language modeling, with models trained from several datasets of medical and nonmedical domains. This paper proposes novel modeling and estimation methods for the mixture language model (LM). Component LMs are trained from individual datasets, with class n-gram LMs trained from in-domain datasets and word n-gram LMs trained from out-of-domain datasets, and they are interpolated into a mixture LM. For class LMs, semantic categories are used for class definition on medical terms, names, and digits. The interpolation weights of a mixture LM are estimated by a greedy algorithm of forward weight adjustment (FWA). The proposed mixing of in-domain class LMs and out-of-domain word LMs, the semantic definitions of word classes, as well as the weight-estimation algorithm of FWA are effective on the TC-VTC task. As compared with using mixtures of word LMs with weights estimated by the conventional expectation-maximization algorithm, the proposed methods led to a 21% reduction of perplexity on test sets of five doctors, which translated into improvements of captioning accuracy.

Designing train-speed trajectory with energy efficiency and service quality

NASA Astrophysics Data System (ADS)

Jia, Jiannan; Yang, Kai; Yang, Lixing; Gao, Yuan; Li, Shukai

2018-05-01

With the development of automatic train operations, optimal trajectory design is significant to the performance of train operations in railway transportation systems. Considering energy efficiency and service quality, this article formulates a bi-objective train-speed trajectory optimization model to minimize simultaneously the energy consumption and travel time in an inter-station section. This article is distinct from previous studies in that more sophisticated train driving strategies characterized by the acceleration/deceleration gear, the cruising speed, and the speed-shift site are specifically considered. For obtaining an optimal train-speed trajectory which has equal satisfactory degree on both objectives, a fuzzy linear programming approach is applied to reformulate the objectives. In addition, a genetic algorithm is developed to solve the proposed train-speed trajectory optimization problem. Finally, a series of numerical experiments based on a real-world instance of Beijing-Tianjin Intercity Railway are implemented to illustrate the practicability of the proposed model as well as the effectiveness of the solution methodology.

Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

NASA Astrophysics Data System (ADS)

Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

2016-09-01

It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image classification activities. Currently, the approach is used only on high resolution optical three-band remote sensing imagery. The feasibility using the approach on other kinds of remote sensing images or involving additional bands in classification will be studied in future.

Digital intelligent booster for DCC miniature train networks

NASA Astrophysics Data System (ADS)

Ursu, M. P.; Condruz, D. A.

2017-08-01

Modern miniature trains are now driven by means of the DCC (Digital Command and Control) system, which allows the human operator or a personal computer to launch commands to each individual train or even to control different features of the same train. The digital command station encodes these commands and sends them to the trains by means of electrical pulses via the rails of the railway network. Due to the development of the miniature railway network, it may happen that the power requirement of the increasing number of digital locomotives, carriages and accessories exceeds the nominal output power of the digital command station. This digital intelligent booster relieves the digital command station from powering the entire railway network all by itself, and it automatically handles the multiple powered sections of the network. This electronic device is also able to detect and process short-circuits and overload conditions, without the intervention of the digital command station.

Automatic and Controlled Response Inhibition: Associative Learning in the Go/No-Go and Stop-Signal Paradigms

ERIC Educational Resources Information Center

Verbruggen, Frederick; Logan, Gordon D.

2008-01-01

In 5 experiments, the authors examined the development of automatic response inhibition in the go/no-go paradigm and a modified version of the stop-signal paradigm. They hypothesized that automatic response inhibition may develop over practice when stimuli are consistently associated with stopping. All 5 experiments consisted of a training phase…

Development of a CCTV system for welder training and monitoring of Space Shuttle Main Engine welds

NASA Technical Reports Server (NTRS)

Gordon, S. S.; Flanigan, L. A.; Dyer, G. E.

1987-01-01

A Weld Operator's Remote Monitoring System (WORMS) for remote viewing of manual and automatic GTA welds has been developed for use in Space Shuttle Main Engine (SSME) manufacturing. This system utilizes fiberoptics to transmit images from a receiving lens to a small closed-circuit television (CCTV) camera. The camera converts the image to an electronic signal, which is sent to a videotape recorder (VTR) and a monitor. The overall intent of this system is to provide a clearer, more detailed view of welds than is available by direct observation. This system has six primary areas of application: (1) welder training; (2) viewing of joint penetration; (3) viewing visually inaccessible welds; (4) quality control and quality assurance; (5) remote joint tracking and adjustment of variables in machine welds; and (6) welding research and development. This paper describes WORMS and how it applies to each application listed.

Automatic and Controlled Response Inhibition: Associative Learning in the Go/No-Go and Stop-Signal Paradigms

PubMed Central

Verbruggen, Frederick; Logan, Gordon D.

2008-01-01

In five experiments, the authors examined the development of automatic response inhibition in the go/no-go paradigm and a modified version of the stop-signal paradigm. They hypothesized that automatic response inhibition may develop over practice when stimuli are consistently associated with stopping. All five experiments consisted of a training phase and a test phase in which the stimulus mapping was reversed for a subset of the stimuli. Consistent with the automatic-inhibition hypothesis, the authors found that responding in the test phase was slowed when the stimulus had been consistently associated with stopping in the training phase. In addition, they found that response inhibition benefited from consistent stimulus-stop associations. These findings suggest that response inhibition may rely on the retrieval of stimulus-stop associations after practice with consistent stimulus-stop mappings. Stimulus-stop mapping is typically consistent in the go/no-go paradigm, so automatic inhibition is likely to occur. However, stimulus-stop mapping is typically inconsistent in the stop-signal paradigm, so automatic inhibition is unlikely to occur. Thus, the results suggest that the two paradigms are not equivalent because they allow different kinds of response inhibition. PMID:18999358

The effectiveness of a training method using self-modeling webcam photos for reducing musculoskeletal risk among office workers using computers.

PubMed

Taieb-Maimon, Meirav; Cwikel, Julie; Shapira, Bracha; Orenstein, Ido

2012-03-01

An intervention study was conducted to examine the effectiveness of an innovative self-modeling photo-training method for reducing musculoskeletal risk among office workers using computers. Sixty workers were randomly assigned to either: 1) a control group; 2) an office training group that received personal, ergonomic training and workstation adjustments or 3) a photo-training group that received both office training and an automatic frequent-feedback system that displayed on the computer screen a photo of the worker's current sitting posture together with the correct posture photo taken earlier during office training. Musculoskeletal risk was evaluated using the Rapid Upper Limb Assessment (RULA) method before, during and after the six weeks intervention. Both training methods provided effective short-term posture improvement; however, sustained improvement was only attained with the photo-training method. Both interventions had a greater effect on older workers and on workers suffering more musculoskeletal pain. The photo-training method had a greater positive effect on women than on men. Copyright © 2011 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Automatic cataloguing and characterization of Earth science data using SE-trees

NASA Technical Reports Server (NTRS)

Rymon, Ron; Short, Nicholas M., Jr.

1994-01-01

In the future, NASA's Earth Observing System (EOS) platforms will produce enormous amounts of remote sensing image data that will be stored in the EOS Data Information System. For the past several years, the Intelligent Data Management group at Goddard's Information Science and Technology Office has been researching techniques for automatically cataloguing and characterizing image data (ADCC) from EOS into a distributed database. At the core of the approach, scientists will be able to retrieve data based upon the contents of the imagery. The ability to automatically classify imagery is key to the success of contents-based search. We report results from experiments applying a novel machine learning framework, based on Set-Enumeration (SE) trees, to the ADCC domain. We experiment with two images: one taken from the Blackhills region in South Dakota; and the other from the Washington DC area. In a classical machine learning experimentation approach, an image's pixels are randomly partitioned into training (i.e. including ground truth or survey data) and testing sets. The prediction model is built using the pixels in the training set, and its performance is estimated using the testing set. With the first Blackhills image, we perform various experiments achieving an accuracy level of 83.2 percent, compared to 72.7 percent using a Back Propagation Neural Network (BPNN) and 65.3 percent using a Gaussain Maximum Likelihood Classifier (GMLC). However, with the Washington DC image, we were only able to achieve 71.4 percent, compared with 67.7 percent reported for the BPNN model and 62.3 percent for the GMLC.

Voice technology and BBN

NASA Technical Reports Server (NTRS)

Wolf, Jared J.

1977-01-01

The following research was discussed: (1) speech signal processing; (2) automatic speech recognition; (3) continuous speech understanding; (4) speaker recognition; (5) speech compression; (6) subjective and objective evaluation of speech communication system; (7) measurement of the intelligibility and quality of speech when degraded by noise or other masking stimuli; (8) speech synthesis; (9) instructional aids for second-language learning and for training of the deaf; and (10) investigation of speech correlates of psychological stress. Experimental psychology, control systems, and human factors engineering, which are often relevant to the proper design and operation of speech systems are described.

Large Scale System Defense

DTIC Science & Technology

2008-10-01

AD); Aeolos, a distributed intrusion detection and event correlation infrastructure; STAND, a training-set sanitization technique applicable to ADs...UU 18. NUMBER OF PAGES 25 19a. NAME OF RESPONSIBLE PERSON Frank H. Born a. REPORT U b. ABSTRACT U c . THIS PAGE U 19b. TELEPHONE...Summary of findings 2 (a) Automatic Patch Generation 2 (b) Better Patch Management 2 ( c ) Artificial Diversity 3 (d) Distributed Anomaly Detection 3

Software Master Plan. Volume 2. Background (Annexes A-G)

DTIC Science & Technology

1990-02-09

AFLC is also responsible for the support of the Avionics Integration Support Facilities, the pilot training systems support and the Automatic Test ...Deputy Director of Defense Research and Engineering ( Test & Evaluation) ..... ............ A.1.1.3 Office of the Deputy Director of Defense Research and...Department of Defense .... ........ 3 A.3 Operational Test & Evaluation ........ ................. 4 A.4 Office of the Assistant Secretary of Defense

Scuba Weights

NASA Technical Reports Server (NTRS)

1995-01-01

The Attitude Adjuster is a system for weight repositioning corresponding to a SCUBA diver's changing positions. Compact tubes on the diver's air tank permit controlled movement of lead balls within the Adjuster, automatically repositioning when the diver changes position. Manufactured by Think Tank Technologies, the system is light and small, reducing drag and energy requirements and contributing to lower air consumption. The Mid-Continent Technology Transfer Center helped the company with both technical and business information and arranged for the testing at Marshall Space Flight Center's Weightlessness Environmental Training Facility for astronauts.

Fully automatic time-window selection using machine learning for global adjoint tomography

NASA Astrophysics Data System (ADS)

Chen, Y.; Hill, J.; Lei, W.; Lefebvre, M. P.; Bozdag, E.; Komatitsch, D.; Tromp, J.

2017-12-01

Selecting time windows from seismograms such that the synthetic measurements (from simulations) and measured observations are sufficiently close is indispensable in a global adjoint tomography framework. The increasing amount of seismic data collected everyday around the world demands "intelligent" algorithms for seismic window selection. While the traditional FLEXWIN algorithm can be "automatic" to some extent, it still requires both human input and human knowledge or experience, and thus is not deemed to be fully automatic. The goal of intelligent window selection is to automatically select windows based on a learnt engine that is built upon a huge number of existing windows generated through the adjoint tomography project. We have formulated the automatic window selection problem as a classification problem. All possible misfit calculation windows are classified as either usable or unusable. Given a large number of windows with a known selection mode (select or not select), we train a neural network to predict the selection mode of an arbitrary input window. Currently, the five features we extract from the windows are its cross-correlation value, cross-correlation time lag, amplitude ratio between observed and synthetic data, window length, and minimum STA/LTA value. More features can be included in the future. We use these features to characterize each window for training a multilayer perceptron neural network (MPNN). Training the MPNN is equivalent to solve a non-linear optimization problem. We use backward propagation to derive the gradient of the loss function with respect to the weighting matrices and bias vectors and use the mini-batch stochastic gradient method to iteratively optimize the MPNN. Numerical tests show that with a careful selection of the training data and a sufficient amount of training data, we are able to train a robust neural network that is capable of detecting the waveforms in an arbitrary earthquake data with negligible detection error compared to existing selection methods (e.g. FLEXWIN). We will introduce in detail the mathematical formulation of the window-selection-oriented MPNN and show very encouraging results when applying the new algorithm to real earthquake data.

A hypertext system that learns from user feedback

NASA Technical Reports Server (NTRS)

Mathe, Nathalie

1994-01-01

Retrieving specific information from large amounts of documentation is not an easy task. It could be facilitated if information relevant in the current problem solving context could be automatically supplied to the user. As a first step towards this goal, we have developed an intelligent hypertext system called CID (Computer Integrated Documentation). Besides providing an hypertext interface for browsing large documents, the CID system automatically acquires and reuses the context in which previous searches were appropriate. This mechanism utilizes on-line user information requirements and relevance feedback either to reinforce current indexing in case of success or to generate new knowledge in case of failure. Thus, the user continually augments and refines the intelligence of the retrieval system. This allows the CID system to provide helpful responses, based on previous usage of the documentation, and to improve its performance over time. We successfully tested the CID system with users of the Space Station Freedom requirements documents. We are currently extending CID to other application domains (Space Shuttle operations documents, airplane maintenance manuals, and on-line training). We are also exploring the potential commercialization of this technique.

Task-oriented rehabilitation robotics.

PubMed

Schweighofer, Nicolas; Choi, Younggeun; Winstein, Carolee; Gordon, James

2012-11-01

Task-oriented training is emerging as the dominant and most effective approach to motor rehabilitation of upper extremity function after stroke. Here, the authors propose that the task-oriented training framework provides an evidence-based blueprint for the design of task-oriented robots for the rehabilitation of upper extremity function in the form of three design principles: skill acquisition of functional tasks, active participation training, and individualized adaptive training. The previous robotic systems that incorporate elements of task-oriented trainings are then reviewed. Finally, the authors critically analyze their own attempt to design and test the feasibility of a TOR robot, ADAPT (Adaptive and Automatic Presentation of Tasks), which incorporates the three design principles. Because of its task-oriented training-based design, ADAPT departs from most other current rehabilitation robotic systems: it presents realistic functional tasks in which the task goal is constantly adapted, so that the individual actively performs doable but challenging tasks without physical assistance. To maximize efficacy for a large clinical population, the authors propose that future task-oriented robots need to incorporate yet-to-be developed adaptive task presentation algorithms that emphasize acquisition of fine motor coordination skills while minimizing compensatory movements.

A case-oriented web-based training system for breast cancer diagnosis.

PubMed

Huang, Qinghua; Huang, Xianhai; Liu, Longzhong; Lin, Yidi; Long, Xingzhang; Li, Xuelong

2018-03-01

Breast cancer is still considered as the most common form of cancer as well as the leading causes of cancer deaths among women all over the world. We aim to provide a web-based breast ultrasound database for online training inexperienced radiologists and giving computer-assisted diagnostic information for detection and classification of the breast tumor. We introduce a web database which stores breast ultrasound images from breast cancer patients as well as their diagnostic information. A web-based training system using a feature scoring scheme based on Breast Imaging Reporting and Data System (BI-RADS) US lexicon was designed. A computer-aided diagnosis (CAD) subsystem was developed to assist the radiologists to make scores on the BI-RADS features for an input case. The training system possesses 1669 scored cases, where 412 cases are benign and 1257 cases are malignant. It was tested by 31 users including 12 interns, 11 junior radiologists, and 8 experienced senior radiologists. This online training system automatically creates case-based exercises to train and guide the newly employed or resident radiologists for the diagnosis of breast cancer using breast ultrasound images based on the BI-RADS. After the trainings, the interns and junior radiologists show significant improvement in the diagnosis of the breast tumor with ultrasound imaging (p-value < .05); meanwhile the senior radiologists show little improvement (p-value > .05). The online training system can improve the capabilities of early-career radiologists in distinguishing between the benign and malignant lesions and reduce the misdiagnosis of breast cancer in a quick, convenient and effective manner. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Adaptive Personalized Training Games for Individual and Collaborative Rehabilitation of People with Multiple Sclerosis

PubMed Central

2014-01-01

Any rehabilitation involves people who are unique individuals with their own characteristics and rehabilitation needs, including patients suffering from Multiple Sclerosis (MS). The prominent variation of MS symptoms and the disease severity elevate a need to accommodate the patient diversity and support adaptive personalized training to meet every patient's rehabilitation needs. In this paper, we focus on integrating adaptivity and personalization in rehabilitation training for MS patients. We introduced the automatic adjustment of difficulty levels as an adaptation that can be provided in individual and collaborative rehabilitation training exercises for MS patients. Two user studies have been carried out with nine MS patients to investigate the outcome of this adaptation. The findings showed that adaptive personalized training trajectories have been successfully provided to MS patients according to their individual training progress, which was appreciated by the patients and the therapist. They considered the automatic adjustment of difficulty levels to provide more variety in the training and to minimize the therapists involvement in setting up the training. With regard to social interaction in the collaborative training exercise, we have observed some social behaviors between the patients and their training partner which indicated the development of social interaction during the training. PMID:24982862

Automatic rule generation for high-level vision

NASA Technical Reports Server (NTRS)

Rhee, Frank Chung-Hoon; Krishnapuram, Raghu

1992-01-01

A new fuzzy set based technique that was developed for decision making is discussed. It is a method to generate fuzzy decision rules automatically for image analysis. This paper proposes a method to generate rule-based approaches to solve problems such as autonomous navigation and image understanding automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

Quality assurance program guidelines for application to and use by manufacturers of rail/guideway vehicles, buses, automatic train control systems, and their major subsystems

NASA Technical Reports Server (NTRS)

Witkin, S. A.

1976-01-01

Guidelines are presented for a quality assurance system to be implemented by the manufacturer in support of designing, developing, fabricating, assembling, inspecting, testing, handling, and delivery of equipment being procured for use in public urban mass transit systems. The guidelines apply to this equipment when being procured for: (1) use in revenue service; (2) demonstration of systems that will be revenue producing or used by the public; (3) use as a prototype for follow-on operational/revenue producing equipment procurements; and (4) qualification tests.

MOD-0A 200 kW wind turbine generator design and analysis report

NASA Astrophysics Data System (ADS)

Anderson, T. S.; Bodenschatz, C. A.; Eggers, A. G.; Hughes, P. S.; Lampe, R. F.; Lipner, M. H.; Schornhorst, J. R.

1980-08-01

The design, analysis, and initial performance of the MOD-OA 200 kW wind turbine generator at Clayton, NM is documented. The MOD-OA was designed and built to obtain operation and performance data and experience in utility environments. The project requirements, approach, system description, design requirements, design, analysis, system tests, installation, safety considerations, failure modes and effects analysis, data acquisition, and initial performance for the wind turbine are discussed. The design and analysis of the rotor, drive train, nacelle equipment, yaw drive mechanism and brake, tower, foundation, electricl system, and control systems are presented. The rotor includes the blades, hub, and pitch change mechanism. The drive train includes the low speed shaft, speed increaser, high speed shaft, and rotor brake. The electrical system includes the generator, switchgear, transformer, and utility connection. The control systems are the blade pitch, yaw, and generator control, and the safety system. Manual, automatic, and remote control are discussed. Systems analyses on dynamic loads and fatigue are presented.

MOD-0A 200 kW wind turbine generator design and analysis report

NASA Technical Reports Server (NTRS)

Anderson, T. S.; Bodenschatz, C. A.; Eggers, A. G.; Hughes, P. S.; Lampe, R. F.; Lipner, M. H.; Schornhorst, J. R.

1980-01-01

The design, analysis, and initial performance of the MOD-OA 200 kW wind turbine generator at Clayton, NM is documented. The MOD-OA was designed and built to obtain operation and performance data and experience in utility environments. The project requirements, approach, system description, design requirements, design, analysis, system tests, installation, safety considerations, failure modes and effects analysis, data acquisition, and initial performance for the wind turbine are discussed. The design and analysis of the rotor, drive train, nacelle equipment, yaw drive mechanism and brake, tower, foundation, electricl system, and control systems are presented. The rotor includes the blades, hub, and pitch change mechanism. The drive train includes the low speed shaft, speed increaser, high speed shaft, and rotor brake. The electrical system includes the generator, switchgear, transformer, and utility connection. The control systems are the blade pitch, yaw, and generator control, and the safety system. Manual, automatic, and remote control are discussed. Systems analyses on dynamic loads and fatigue are presented.

A Dynamic Time Warping Approach to Real-Time Activity Recognition for Food Preparation

NASA Astrophysics Data System (ADS)

Pham, Cuong; Plötz, Thomas; Olivier, Patrick

We present a dynamic time warping based activity recognition system for the analysis of low-level food preparation activities. Accelerometers embedded into kitchen utensils provide continuous sensor data streams while people are using them for cooking. The recognition framework analyzes frames of contiguous sensor readings in real-time with low latency. It thereby adapts to the idiosyncrasies of utensil use by automatically maintaining a template database. We demonstrate the effectiveness of the classification approach by a number of real-world practical experiments on a publically available dataset. The adaptive system shows superior performance compared to a static recognizer. Furthermore, we demonstrate the generalization capabilities of the system by gradually reducing the amount of training samples. The system achieves excellent classification results even if only a small number of training samples is available, which is especially relevant for real-world scenarios.

Predicting human activities in sequences of actions in RGB-D videos

NASA Astrophysics Data System (ADS)

Jardim, David; Nunes, Luís.; Dias, Miguel

2017-03-01

In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.

The Automatic Integration of Folksonomies with Taxonomies Using Non-axiomatic Logic

NASA Astrophysics Data System (ADS)

Geldart, Joe; Cummins, Stephen

Cooperative tagging systems such as folksonomies are powerful tools when used to annotate information resources. The inherent power of folksonomies is in their ability to allow casual users to easily contribute ad hoc, yet meaningful, resource metadata without any specialist training. Older folksonomies have begun to degrade due to the lack of internal structure and from the use of many low quality tags. This chapter describes a remedy for some of the problems associated with folksonomies. We introduce a method of automatic integration and inference of the relationships between tags and resources in a folksonomy using non-axiomatic logic. We test this method on the CiteULike corpus of tags by comparing precision and recall between it and standard keyword search. Our results show that non-axiomatic reasoning is a promising technique for integrating tagging systems with more structured knowledge representations.

Standing-up exerciser based on functional electrical stimulation and body weight relief.

PubMed

Ferrarin, M; Pavan, E E; Spadone, R; Cardini, R; Frigo, C

2002-05-01

The goal of the present work was to develop and test an innovative system for the training of paraplegic patients when they are standing up. The system consisted of a computer-controlled stimulator, surface electrodes for quadricep muscle stimulation, two knee angle sensors, a digital proportional-integrative-derivative (PID) controller and a mechanical device to support, partially, the body weight (weight reliever (WR)). A biomechanical model of the combined WR and patient was developed to find an optimum reference trajectory for the PID controller. The system was tested on three paraplegic patients and was shown to be reliable and safe. One patient completed a 30-session training period. Initially he was able to stand up only with 62% body weight relief, whereas, after the training period, he performed a series of 30 standing-up/sitting-down cycles with 45% body weight relief. The closed-loop controller was able to keep the patient standing upright with minimum stimulation current, to compensate automatically for muscle fatigue and to smooth the sitting-down movement. The limitations of the controller in connection with a highly non-linear system are considered.

Truck circuits diagnosis for railway lines equipped with an automatic block signalling system

NASA Astrophysics Data System (ADS)

Spunei, E.; Piroi, I.; Muscai, C.; Răduca, E.; Piroi, F.

2018-01-01

This work presents a diagnosis method for detecting track circuits failures on a railway traffic line equipped with an Automatic Block Signalling installation. The diagnosis method uses the installation’s electrical schemas, based on which a series of diagnosis charts have been created. Further, the diagnosis charts were used to develop a software package, CDCBla, which substantially contributes to reducing the diagnosis time and human error during failure remedies. The proposed method can also be used as a training package for the maintenance staff. Since the diagnosis method here does not need signal or measurement inputs, using it does not necessitate additional IT knowledge and can be deployed on a mobile computing device (tablet, smart phone).

Automated planning of MRI scans of knee joints

NASA Astrophysics Data System (ADS)

Bystrov, Daniel; Pekar, Vladimir; Young, Stewart; Dries, Sebastian P. M.; Heese, Harald S.; van Muiswinkel, Arianne M.

2007-03-01

A novel and robust method for automatic scan planning of MRI examinations of knee joints is presented. Clinical knee examinations require acquisition of a 'scout' image, in which the operator manually specifies the scan volume orientations (off-centres, angulations, field-of-view) for the subsequent diagnostic scans. This planning task is time-consuming and requires skilled operators. The proposed automated planning system determines orientations for the diagnostic scan by using a set of anatomical landmarks derived by adapting active shape models of the femur, patella and tibia to the acquired scout images. The expert knowledge required to position scan geometries is learned from previous manually planned scans, allowing individual preferences to be taken into account. The system is able to automatically discriminate between left and right knees. This allows to use and merge training data from both left and right knees, and to automatically transform all learned scan geometries to the side for which a plan is required, providing a convenient integration of the automated scan planning system in the clinical routine. Assessment of the method on the basis of 88 images from 31 different individuals, exhibiting strong anatomical and positional variability demonstrates success, robustness and efficiency of all parts of the proposed approach, which thus has the potential to significantly improve the clinical workflow.

BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

PubMed

Rinaldi, Fabio; Ellendorff, Tilia Renate; Madan, Sumit; Clematide, Simon; van der Lek, Adrian; Mevissen, Theo; Fluck, Juliane

2016-01-01

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text. © The Author(s) 2016. Published by Oxford University Press.

Automatic categorization of diverse experimental information in the bioscience literature

PubMed Central

2012-01-01

Background Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. Results We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at WormBase for automatic association of newly published papers with ten data types including RNAi, antibody, phenotype, gene regulation, mutant allele sequence, gene expression, gene product interaction, overexpression phenotype, gene interaction, and gene structure correction. Conclusions Our methods are applicable to a variety of data types with training set containing several hundreds to a few thousand documents. It is completely automatic and, thus can be readily incorporated to different workflow at different literature-based databases. We believe that the work presented here can contribute greatly to the tremendous task of automating the important yet labor-intensive biocuration effort. PMID:22280404

Automatic categorization of diverse experimental information in the bioscience literature.

PubMed

Fang, Ruihua; Schindelman, Gary; Van Auken, Kimberly; Fernandes, Jolene; Chen, Wen; Wang, Xiaodong; Davis, Paul; Tuli, Mary Ann; Marygold, Steven J; Millburn, Gillian; Matthews, Beverley; Zhang, Haiyan; Brown, Nick; Gelbart, William M; Sternberg, Paul W

2012-01-26

Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at WormBase for automatic association of newly published papers with ten data types including RNAi, antibody, phenotype, gene regulation, mutant allele sequence, gene expression, gene product interaction, overexpression phenotype, gene interaction, and gene structure correction. Our methods are applicable to a variety of data types with training set containing several hundreds to a few thousand documents. It is completely automatic and, thus can be readily incorporated to different workflow at different literature-based databases. We believe that the work presented here can contribute greatly to the tremendous task of automating the important yet labor-intensive biocuration effort.

Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients.

PubMed

Melillo, P; Orrico, A; Scala, P; Crispino, F; Pecchia, L

2015-10-01

The aim of this paper is to describe the design and the preliminary validation of a platform developed to collect and automatically analyze biomedical signals for risk assessment of vascular events and falls in hypertensive patients. This m-health platform, based on cloud computing, was designed to be flexible, extensible, and transparent, and to provide proactive remote monitoring via data-mining functionalities. A retrospective study was conducted to train and test the platform. The developed system was able to predict a future vascular event within the next 12 months with an accuracy rate of 84 % and to identify fallers with an accuracy rate of 72 %. In an ongoing prospective trial, almost all the recruited patients accepted favorably the system with a limited rate of inadherences causing data losses (<20 %). The developed platform supported clinical decision by processing tele-monitored data and providing quick and accurate risk assessment of vascular events and falls.

The development of automated behavior analysis software

NASA Astrophysics Data System (ADS)

Jaana, Yuki; Prima, Oky Dicky A.; Imabuchi, Takashi; Ito, Hisayoshi; Hosogoe, Kumiko

2015-03-01

The measurement of behavior for participants in a conversation scene involves verbal and nonverbal communications. The measurement validity may vary depending on the observers caused by some aspects such as human error, poorly designed measurement systems, and inadequate observer training. Although some systems have been introduced in previous studies to automatically measure the behaviors, these systems prevent participants to talk in a natural way. In this study, we propose a software application program to automatically analyze behaviors of the participants including utterances, facial expressions (happy or neutral), head nods, and poses using only a single omnidirectional camera. The camera is small enough to be embedded into a table to allow participants to have spontaneous conversation. The proposed software utilizes facial feature tracking based on constrained local model to observe the changes of the facial features captured by the camera, and the Japanese female facial expression database to recognize expressions. Our experiment results show that there are significant correlations between measurements observed by the observers and by the software.

Semi-automatic image analysis methodology for the segmentation of bubbles and drops in complex dispersions occurring in bioreactors

NASA Astrophysics Data System (ADS)

Taboada, B.; Vega-Alvarado, L.; Córdova-Aguilar, M. S.; Galindo, E.; Corkidi, G.

2006-09-01

Characterization of multiphase systems occurring in fermentation processes is a time-consuming and tedious process when manual methods are used. This work describes a new semi-automatic methodology for the on-line assessment of diameters of oil drops and air bubbles occurring in a complex simulated fermentation broth. High-quality digital images were obtained from the interior of a mechanically stirred tank. These images were pre-processed to find segments of edges belonging to the objects of interest. The contours of air bubbles and oil drops were then reconstructed using an improved Hough transform algorithm which was tested in two, three and four-phase simulated fermentation model systems. The results were compared against those obtained manually by a trained observer, showing no significant statistical differences. The method was able to reduce the total processing time for the measurements of bubbles and drops in different systems by 21-50% and the manual intervention time for the segmentation procedure by 80-100%.

Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set

NASA Astrophysics Data System (ADS)

Qin, Xulei; Cong, Zhibin; Fei, Baowei

2013-11-01

An automatic segmentation framework is proposed to segment the right ventricle (RV) in echocardiographic images. The method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform, a training model, and a localized region-based level set. First, the sparse matrix transform extracts main motion regions of the myocardium as eigen-images by analyzing the statistical information of the images. Second, an RV training model is registered to the eigen-images in order to locate the position of the RV. Third, the training model is adjusted and then serves as an optimized initialization for the segmentation of each image. Finally, based on the initializations, a localized, region-based level set algorithm is applied to segment both epicardial and endocardial boundaries in each echocardiograph. Three evaluation methods were used to validate the performance of the segmentation framework. The Dice coefficient measures the overall agreement between the manual and automatic segmentation. The absolute distance and the Hausdorff distance between the boundaries from manual and automatic segmentation were used to measure the accuracy of the segmentation. Ultrasound images of human subjects were used for validation. For the epicardial and endocardial boundaries, the Dice coefficients were 90.8 ± 1.7% and 87.3 ± 1.9%, the absolute distances were 2.0 ± 0.42 mm and 1.79 ± 0.45 mm, and the Hausdorff distances were 6.86 ± 1.71 mm and 7.02 ± 1.17 mm, respectively. The automatic segmentation method based on a sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.

Empirical study on neural network based predictive techniques for automatic number plate recognition

NASA Astrophysics Data System (ADS)

Shashidhara, M. S.; Indrakumar, S. S.

2011-10-01

The objective of this study is to provide an easy, accurate and effective technology for the Bangalore city traffic control. This is based on the techniques of image processing and laser beam technology. The core concept chosen here is an image processing technology by the method of automatic number plate recognition system. First number plate is recognized if any vehicle breaks the traffic rules in the signals. The number is fetched from the database of the RTO office by the process of automatic database fetching. Next this sends the notice and penalty related information to the vehicle owner email-id and an SMS sent to vehicle owner. In this paper, we use of cameras with zooming options & laser beams to get accurate pictures further applied image processing techniques such as Edge detection to understand the vehicle, Identifying the location of the number plate, Identifying the number plate for further use, Plain plate number, Number plate with additional information, Number plates in the different fonts. Accessing the database of the vehicle registration office to identify the name and address and other information of the vehicle number. The updates to be made to the database for the recording of the violation and penalty issues. A feed forward artificial neural network is used for OCR. This procedure is particularly important for glyphs that are visually similar such as '8' and '9' and results in training sets of between 25,000 and 40,000 training samples. Over training of the neural network is prevented by Bayesian regularization. The neural network output value is set to 0.05 when the input is not desired glyph, and 0.95 for correct input.

Segmentation and Recognition of Continuous Human Activity

DTIC Science & Technology

2001-01-01

This paper presents a methodology for automatic segmentation and recognition of continuous human activity . We segment a continuous human activity into...commencement or termination. We use single action sequences for the training data set. The test sequences, on the other hand, are continuous sequences of human ... activity that consist of three or more actions in succession. The system has been tested on continuous activity sequences containing actions such as

Translations on USSR Military Affairs, Number 1280

DTIC Science & Technology

1977-06-17

engineer, the conclusion was automatic : he is an undisciplined person. However, this idea was totally inconsistent with the image I had developed of V...pro- jectors, trainers, all sorts of simulators, automatic devices, and so forth. As is known, the technical devices for the mass training and...in the equipment and assemblies. In possessing "feedback," within a few seconds they can record and automatically analyze the actions of the

On the automaticity of response inhibition in individuals with alcoholism.

PubMed

Noël, Xavier; Brevers, Damien; Hanak, Catherine; Kornreich, Charles; Verbanck, Paul; Verbruggen, Frederick

2016-06-01

Response inhibition is usually considered a hallmark of executive control. However, recent work indicates that stop performance can become associatively mediated ('automatic') over practice. This study investigated automatic response inhibition in sober and recently detoxified individuals with alcoholism.. We administered to forty recently detoxified alcoholics and forty healthy participants a modified stop-signal task that consisted of a training phase in which a subset of the stimuli was consistently associated with stopping or going, and a test phase in which this mapping was reversed. In the training phase, stop performance improved for the consistent stop stimuli, compared with control stimuli that were not associated with going or stopping. In the test phase, go performance tended to be impaired for old stop stimuli. Combined, these findings support the automatic inhibition hypothesis. Importantly, performance was similar in both groups, which indicates that automatic inhibitory control develops normally in individuals with alcoholism.. This finding is specific to individuals with alcoholism without other psychiatric disorders, which is rather atypical and prevents generalization. Personalized stimuli with a stronger affective content should be used in future studies. These results advance our understanding of behavioral inhibition in individuals with alcoholism. Furthermore, intact automatic inhibitory control may be an important element of successful cognitive remediation of addictive behaviors.. Copyright © 2016 Elsevier Ltd. All rights reserved.

Variability of wetland reflectance and its effect on automatic categorization of satellite imagery

NASA Technical Reports Server (NTRS)

Klemas, V. (Principal Investigator); Bartlett, D.

1977-01-01

The author has identified the following significant results. Land cover categorization of data from the same overpass in four test wetland areas was carried out using a four category classification system. The tests indicate that training data based on in situ reflectance measurements and atmospheric correction of LANDSAT data can produce comparable accuracy of categorization to that achieved using more than four wetlands cover categories (salt marsh cordgrass, salt hay, unvegetated, and water tidal flat) produced overall classification accuracies of 85% by conventional and relative radiance training and 81% by use of in situ measurements. Overall mapping accuracies were 76% and 72% respectively.

Automated threat response recommendation in environments of high data uncertainty using the Countermeasure Association Technique (CMAT)

NASA Astrophysics Data System (ADS)

Chapman, George B.; Johnson, Glenn; Burdick, Robert

1991-09-01

The CounterMeasure Association Technique (CMAT) is discussed which was developed for the Air Force, and is used to automatically recommend countermeasure and maneuver response to a pilot while he is under missile attack. The overall system is discussed, as well as several key technical components. These components include use of fuzzy sets to specify data uncertainty, use of mimic nets to train the CMAT algorithm to make the same resource optimization tradeoffs as made in a data base of library of training scenarios, and use of several data compression techniques to store the countermeasure effectiveness data base.

Automatic cerebrospinal fluid segmentation in non-contrast CT images using a 3D convolutional network

NASA Astrophysics Data System (ADS)

Patel, Ajay; van de Leemput, Sil C.; Prokop, Mathias; van Ginneken, Bram; Manniesing, Rashindra

2017-03-01

Segmentation of anatomical structures is fundamental in the development of computer aided diagnosis systems for cerebral pathologies. Manual annotations are laborious, time consuming and subject to human error and observer variability. Accurate quantification of cerebrospinal fluid (CSF) can be employed as a morphometric measure for diagnosis and patient outcome prediction. However, segmenting CSF in non-contrast CT images is complicated by low soft tissue contrast and image noise. In this paper we propose a state-of-the-art method using a multi-scale three-dimensional (3D) fully convolutional neural network (CNN) to automatically segment all CSF within the cranial cavity. The method is trained on a small dataset comprised of four manually annotated cerebral CT images. Quantitative evaluation of a separate test dataset of four images shows a mean Dice similarity coefficient of 0.87 +/- 0.01 and mean absolute volume difference of 4.77 +/- 2.70 %. The average prediction time was 68 seconds. Our method allows for fast and fully automated 3D segmentation of cerebral CSF in non-contrast CT, and shows promising results despite a limited amount of training data.

Basic forest cover mapping using digitized remote sensor data and automated data processing techniques

NASA Technical Reports Server (NTRS)

Coggeshall, M. E.; Hoffer, R. M.

1973-01-01

Remote sensing equipment and automatic data processing techniques were employed as aids in the institution of improved forest resource management methods. On the basis of automatically calculated statistics derived from manually selected training samples, the feature selection processor of LARSYS selected, upon consideration of various groups of the four available spectral regions, a series of channel combinations whose automatic classification performances (for six cover types, including both deciduous and coniferous forest) were tested, analyzed, and further compared with automatic classification results obtained from digitized color infrared photography.

An automatic multi-atlas prostate segmentation in MRI using a multiscale representation and a label fusion strategy

NASA Astrophysics Data System (ADS)

Álvarez, Charlens; Martínez, Fabio; Romero, Eduardo

2015-01-01

The pelvic magnetic Resonance images (MRI) are used in Prostate cancer radiotherapy (RT), a process which is part of the radiation planning. Modern protocols require a manual delineation, a tedious and variable activity that may take about 20 minutes per patient, even for trained experts. That considerable time is an important work ow burden in most radiological services. Automatic or semi-automatic methods might improve the efficiency by decreasing the measure times while conserving the required accuracy. This work presents a fully automatic atlas- based segmentation strategy that selects the more similar templates for a new MRI using a robust multi-scale SURF analysis. Then a new segmentation is achieved by a linear combination of the selected templates, which are previously non-rigidly registered towards the new image. The proposed method shows reliable segmentations, obtaining an average DICE Coefficient of 79%, when comparing with the expert manual segmentation, under a leave-one-out scheme with the training database.

Automatic Detection of Landslides at Stromboli Volcano

NASA Astrophysics Data System (ADS)

Giudicepietro, F.; Esposito, A. M.; D'Auria, L.; Peluso, R.; Martini, M.

2011-12-01

In this work we present an automatic system for the landslide detection at Stromboli volcano that has proved to be effective both during the 2007 effusive eruption and in the recent (2 August 2011) volcanic activity. The study of the landslides at Stromboli is important because they could be considered short-term precursors of effusive eruptions, as seen during the 2007 eruption, and in addition they allow to improve the monitoring of the gravitational instabilities of the Sciara del Fuoco flank. The proposed system uses a two-class MLP (Multi Layer Perceptron) neural network in order to discriminate the landslides from other seismic signals usually recorded at Stromboli, such as explosion-quakes and volcanic tremor. To train and test the network we used a dataset of 537 signals, including 267 landslides and 270 other events (130 explosion-quakes and 140 tremor signals). The net performance is of 98.7%, if averaged over different net configurations, and of 99.5% for the best net performance. Based on the neural network response, the automatic system calculates a Landslide Percentage Index (LPI) defined on the number of signals identified as landslides by the net on a given temporal interval in order to recognize anomalies in the landslide rate. This system was sensitive to the signals produced by the flow of lava front during a recent mild effusive episode on the "La Sciara del Fuoco" slope.

Laparoscopic training using a quantitative assessment and instructional system.

PubMed

Yamaguchi, T; Nakamura, R

2018-04-28

Laparoscopic surgery requires complex surgical skills; hence, surgeons require regular training to improve their surgical techniques. The quantitative assessment of a surgeon's skills and the provision of feedback are important processes for conducting effective training. The aim of this study was to develop an inexpensive training system that provides automatic technique evaluation and feedback. We detected the instrument using image processing of commercial web camera images and calculated the motion analysis parameters (MAPs) of the instrument to quantify performance features. Upon receiving the results, we developed a method of evaluating the surgeon's skill level. The feedback system was developed using MAPs-based radar charts and scores for determining the skill level. These methods were evaluated using the videos of 38 surgeons performing a suturing task. There were significant differences in MAPs among surgeons; therefore, MAPs can be effectively used to quantify a surgeon's performance features. The results of skill evaluation and feedback differed greatly between skilled and unskilled surgeons, and it was possible to indicate points of improvement for the procedure performed in this study. Furthermore, the results obtained for certain novice surgeons were similar to those obtained for skilled surgeons. This system can be used to assess the skill level of surgeons, independent of the years of experience, and provide an understanding of the individual's current surgical skill level effectively. We conclude that our system is useful as an inexpensive laparoscopic training system that might aid in skill improvement.

Invariant-feature-based adaptive automatic target recognition in obscured 3D point clouds

NASA Astrophysics Data System (ADS)

Khuon, Timothy; Kershner, Charles; Mattei, Enrico; Alverio, Arnel; Rand, Robert

2014-06-01

Target recognition and classification in a 3D point cloud is a non-trivial process due to the nature of the data collected from a sensor system. The signal can be corrupted by noise from the environment, electronic system, A/D converter, etc. Therefore, an adaptive system with a desired tolerance is required to perform classification and recognition optimally. The feature-based pattern recognition algorithm architecture as described below is particularly devised for solving a single-sensor classification non-parametrically. Feature set is extracted from an input point cloud, normalized, and classifier a neural network classifier. For instance, automatic target recognition in an urban area would require different feature sets from one in a dense foliage area. The figure above (see manuscript) illustrates the architecture of the feature based adaptive signature extraction of 3D point cloud including LIDAR, RADAR, and electro-optical data. This network takes a 3D cluster and classifies it into a specific class. The algorithm is a supervised and adaptive classifier with two modes: the training mode and the performing mode. For the training mode, a number of novel patterns are selected from actual or artificial data. A particular 3D cluster is input to the network as shown above for the decision class output. The network consists of three sequential functional modules. The first module is for feature extraction that extracts the input cluster into a set of singular value features or feature vector. Then the feature vector is input into the feature normalization module to normalize and balance it before being fed to the neural net classifier for the classification. The neural net can be trained by actual or artificial novel data until each trained output reaches the declared output within the defined tolerance. In case new novel data is added after the neural net has been learned, the training is then resumed until the neural net has incrementally learned with the new novel data. The associative memory capability of the neural net enables the incremental learning. The back propagation algorithm or support vector machine can be utilized for the classification and recognition.

Automatic script identification from images using cluster-based templates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hochberg, J.; Kerns, L.; Kelly, P.

We have developed a technique for automatically identifying the script used to generate a document that is stored electronically in bit image form. Our approach differs from previous work in that the distinctions among scripts are discovered by an automatic learning procedure, without any handson analysis. We first develop a set of representative symbols (templates) for each script in our database (Cyrillic, Roman, etc.). We do this by identifying all textual symbols in a set of training documents, scaling each symbol to a fixed size, clustering similar symbols, pruning minor clusters, and finding each cluster`s centroid. To identify a newmore » document`s script, we identify and scale a subset of symbols from the document and compare them to the templates for each script. We choose the script whose templates provide the best match. Our current system distinguishes among the Armenian, Burmese, Chinese, Cyrillic, Ethiopic, Greek, Hebrew, Japanese, Korean, Roman, and Thai scripts with over 90% accuracy.« less

Eye-tracking for clinical decision support: A method to capture automatically what physicians are viewing in the EMR.

PubMed

King, Andrew J; Hochheiser, Harry; Visweswaran, Shyam; Clermont, Gilles; Cooper, Gregory F

2017-01-01

Eye-tracking is a valuable research tool that is used in laboratory and limited field environments. We take steps toward developing methods that enable widespread adoption of eye-tracking and its real-time application in clinical decision support. Eye-tracking will enhance awareness and enable intelligent views, more precise alerts, and other forms of decision support in the Electronic Medical Record (EMR). We evaluated a low-cost eye-tracking device and found the device's accuracy to be non-inferior to a more expensive device. We also developed and evaluated an automatic method for mapping eye-tracking data to interface elements in the EMR (e.g., a displayed laboratory test value). Mapping was 88% accurate across the six participants in our experiment. Finally, we piloted the use of the low-cost device and the automatic mapping method to label training data for a Learning EMR (LEMR) which is a system that highlights the EMR elements a physician is predicted to use.

Eye-tracking for clinical decision support: A method to capture automatically what physicians are viewing in the EMR

PubMed Central

King, Andrew J.; Hochheiser, Harry; Visweswaran, Shyam; Clermont, Gilles; Cooper, Gregory F.

2017-01-01

Eye-tracking is a valuable research tool that is used in laboratory and limited field environments. We take steps toward developing methods that enable widespread adoption of eye-tracking and its real-time application in clinical decision support. Eye-tracking will enhance awareness and enable intelligent views, more precise alerts, and other forms of decision support in the Electronic Medical Record (EMR). We evaluated a low-cost eye-tracking device and found the device’s accuracy to be non-inferior to a more expensive device. We also developed and evaluated an automatic method for mapping eye-tracking data to interface elements in the EMR (e.g., a displayed laboratory test value). Mapping was 88% accurate across the six participants in our experiment. Finally, we piloted the use of the low-cost device and the automatic mapping method to label training data for a Learning EMR (LEMR) which is a system that highlights the EMR elements a physician is predicted to use. PMID:28815151

Automatic speech recognition using a predictive echo state network classifier.

PubMed

Skowronski, Mark D; Harris, John G

2007-04-01

We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

The design and implementation of an automated system for logging clinical experiences using an anesthesia information management system.

PubMed

Simpao, Allan; Heitz, James W; McNulty, Stephen E; Chekemian, Beth; Brenn, B Randall; Epstein, Richard H

2011-02-01

Residents in anesthesia training programs throughout the world are required to document their clinical cases to help ensure that they receive adequate training. Current systems involve self-reporting, are subject to delayed updates and misreported data, and do not provide a practicable method of validation. Anesthesia information management systems (AIMS) are being used increasingly in training programs and are a logical source for verifiable documentation. We hypothesized that case logs generated automatically from an AIMS would be sufficiently accurate to replace the current manual process. We based our analysis on the data reporting requirements of the American College of Graduate Medical Education (ACGME). We conducted a systematic review of ACGME requirements and our AIMS record, and made modifications after identifying data element and attribution issues. We studied 2 methods (parsing of free text procedure descriptions and CPT4 procedure code mapping) to automatically determine ACGME case categories and generated AIMS-based case logs and compared these to assignments made by manual inspection of the anesthesia records. We also assessed under- and overreporting of cases entered manually by our residents into the ACGME website. The parsing and mapping methods assigned cases to a majority of the ACGME categories with accuracies of 95% and 97%, respectively, as compared with determinations made by 2 residents and 1 attending who manually reviewed all procedure descriptions. Comparison of AIMS-based case logs with reports from the ACGME Resident Case Log System website showed that >50% of residents either underreported or overreported their total case counts by at least 5%. The AIMS database is a source of contemporaneous documentation of resident experience that can be queried to generate valid, verifiable case logs. The extent of AIMS adoption by academic anesthesia departments should encourage accreditation organizations to support uploading of AIMS-based case log files to improve accuracy and to decrease the clerical burden on anesthesia residents.

Automatic speech recognition and training for severely dysarthric users of assistive technology: the STARDUST project.

PubMed

Parker, Mark; Cunningham, Stuart; Enderby, Pam; Hawley, Mark; Green, Phil

2006-01-01

The STARDUST project developed robust computer speech recognizers for use by eight people with severe dysarthria and concomitant physical disability to access assistive technologies. Independent computer speech recognizers trained with normal speech are of limited functional use by those with severe dysarthria due to limited and inconsistent proximity to "normal" articulatory patterns. Severe dysarthric output may also be characterized by a small mass of distinguishable phonetic tokens making the acoustic differentiation of target words difficult. Speaker dependent computer speech recognition using Hidden Markov Models was achieved by the identification of robust phonetic elements within the individual speaker output patterns. A new system of speech training using computer generated visual and auditory feedback reduced the inconsistent production of key phonetic tokens over time.

The European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool: A digital training and decision support system for optimized exercise prescription in cardiovascular disease. Concept, definitions and construction methodology.

PubMed

Hansen, Dominique; Dendale, Paul; Coninx, Karin; Vanhees, Luc; Piepoli, Massimo F; Niebauer, Josef; Cornelissen, Veronique; Pedretti, Roberto; Geurts, Eva; Ruiz, Gustavo R; Corrà, Ugo; Schmid, Jean-Paul; Greco, Eugenio; Davos, Constantinos H; Edelmann, Frank; Abreu, Ana; Rauch, Bernhard; Ambrosetti, Marco; Braga, Simona S; Barna, Olga; Beckers, Paul; Bussotti, Maurizio; Fagard, Robert; Faggiano, Pompilio; Garcia-Porrero, Esteban; Kouidi, Evangelia; Lamotte, Michel; Neunhäuserer, Daniel; Reibis, Rona; Spruit, Martijn A; Stettler, Christoph; Takken, Tim; Tonoli, Cajsa; Vigorito, Carlo; Völler, Heinz; Doherty, Patrick

2017-07-01

Background Exercise rehabilitation is highly recommended by current guidelines on prevention of cardiovascular disease, but its implementation is still poor. Many clinicians experience difficulties in prescribing exercise in the presence of different concomitant cardiovascular diseases and risk factors within the same patient. It was aimed to develop a digital training and decision support system for exercise prescription in cardiovascular disease patients in clinical practice: the European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool. Methods EXPERT working group members were requested to define (a) diagnostic criteria for specific cardiovascular diseases, cardiovascular disease risk factors, and other chronic non-cardiovascular conditions, (b) primary goals of exercise intervention, (c) disease-specific prescription of exercise training (intensity, frequency, volume, type, session and programme duration), and (d) exercise training safety advices. The impact of exercise tolerance, common cardiovascular medications and adverse events during exercise testing were further taken into account for optimized exercise prescription. Results Exercise training recommendations and safety advices were formulated for 10 cardiovascular diseases, five cardiovascular disease risk factors (type 1 and 2 diabetes, obesity, hypertension, hypercholesterolaemia), and three common chronic non-cardiovascular conditions (lung and renal failure and sarcopaenia), but also accounted for baseline exercise tolerance, common cardiovascular medications and occurrence of adverse events during exercise testing. An algorithm, supported by an interactive tool, was constructed based on these data. This training and decision support system automatically provides an exercise prescription according to the variables provided. Conclusion This digital training and decision support system may contribute in overcoming barriers in exercise implementation in common cardiovascular diseases.

Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

NASA Astrophysics Data System (ADS)

Althuwaynee, Omar F.; Pradhan, Biswajeet; Ahmad, Noordin

2014-06-01

This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies.

Automatic behavior sensing for a bomb-detecting dog

NASA Astrophysics Data System (ADS)

Nguyen, Hoa G.; Nans, Adam; Talke, Kurt; Candela, Paul; Everett, H. R.

2015-05-01

Bomb-detecting dogs are trained to detect explosives through their sense of smell and often perform a specific behavior to indicate a possible bomb detection. This behavior is noticed by the dog handler, who confirms the probable explosives, determines the location, and forwards the information to an explosive ordnance disposal (EOD) team. To improve the speed and accuracy of this process and better integrate it with the EOD team's robotic explosive disposal operation, SPAWAR Systems Center Pacific has designed and prototyped an electronic dog collar that automatically tracks the dog's location and attitude, detects the indicative behavior, and records the data. To account for the differences between dogs, a 5-minute training routine can be executed before the mission to establish initial values for the k-mean clustering algorithm that classifies a specific dog's behavior. The recorded data include GPS location of the suspected bomb, the path the dog took to approach this location, and a video clip covering the detection event. The dog handler reviews and confirms the data before it is packaged up and forwarded on to the EOD team. The EOD team uses the video clip to better identify the type of bomb and for awareness of the surrounding environment before they arrive at the scene. Before the robotic neutralization operation commences at the site, the location and path data (which are supplied in a format understandable by the next-generation EOD robots—the Advanced EOD Robotic System) can be loaded into the robotic controller to automatically guide the robot to the bomb site. This paper describes the project with emphasis on the dog-collar hardware, behavior-classification software, and feasibility testing.

A function approximation approach to anomaly detection in propulsion system test data

NASA Technical Reports Server (NTRS)

Whitehead, Bruce A.; Hoyt, W. A.

1993-01-01

Ground test data from propulsion systems such as the Space Shuttle Main Engine (SSME) can be automatically screened for anomalies by a neural network. The neural network screens data after being trained with nominal data only. Given the values of 14 measurements reflecting external influences on the SSME at a given time, the neural network predicts the expected nominal value of a desired engine parameter at that time. We compared the ability of three different function-approximation techniques to perform this nominal value prediction: a novel neural network architecture based on Gaussian bar basis functions, a conventional back propagation neural network, and linear regression. These three techniques were tested with real data from six SSME ground tests containing two anomalies. The basis function network trained more rapidly than back propagation. It yielded nominal predictions with, a tight enough confidence interval to distinguish anomalous deviations from the nominal fluctuations in an engine parameter. Since the function-approximation approach requires nominal training data only, it is capable of detecting unknown classes of anomalies for which training data is not available.

Fully automatic cervical vertebrae segmentation framework for X-ray images.

PubMed

Al Arif, S M Masudur Rahman; Knapp, Karen; Slabaugh, Greg

2018-04-01

The cervical spine is a highly flexible anatomy and therefore vulnerable to injuries. Unfortunately, a large number of injuries in lateral cervical X-ray images remain undiagnosed due to human errors. Computer-aided injury detection has the potential to reduce the risk of misdiagnosis. Towards building an automatic injury detection system, in this paper, we propose a deep learning-based fully automatic framework for segmentation of cervical vertebrae in X-ray images. The framework first localizes the spinal region in the image using a deep fully convolutional neural network. Then vertebra centers are localized using a novel deep probabilistic spatial regression network. Finally, a novel shape-aware deep segmentation network is used to segment the vertebrae in the image. The framework can take an X-ray image and produce a vertebrae segmentation result without any manual intervention. Each block of the fully automatic framework has been trained on a set of 124 X-ray images and tested on another 172 images, all collected from real-life hospital emergency rooms. A Dice similarity coefficient of 0.84 and a shape error of 1.69 mm have been achieved. Copyright © 2018 Elsevier B.V. All rights reserved.

Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning

NASA Astrophysics Data System (ADS)

Nguyen, Tan H.; Sridharan, Shamira; Macias, Virgilia; Kajdacsy-Balla, Andre; Melamed, Jonathan; Do, Minh N.; Popescu, Gabriel

2017-03-01

We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.

Automatic segmentation of the puborectalis muscle in 3D transperineal ultrasound.

PubMed

van den Noort, Frieda; Grob, Anique T M; Slump, Cornelis H; van der Vaart, Carl H; van Stralen, Marijn

2017-10-11

The introduction of 3D analysis of the puborectalis muscle, for diagnostic purposes, into daily practice is hindered by the need for appropriate training of the observers. Automatic 3D segmentation of the puborectalis muscle in 3D transperineal ultrasound may aid to its adaption in clinical practice. A manual 3D segmentation protocol was developed to segment the puborectalis muscle. The data of 20 women, in their first trimester of pregnancy, was used to validate the reproducibility of this protocol. For automatic segmentation, active appearance models of the puborectalis muscle were developed. Those models were trained using manual segmentation data of 50 women. The performance of both manual and automatic segmentation was analyzed by measuring the overlap and distance between the segmentations. Also, the interclass correlation coefficients and their 95% confidence intervals were determined for mean echogenicity and volume of the puborectalis muscle. The ICC values of mean echogenicity (0.968-0.991) and volume (0.626-0.910) are good to very good for both automatic and manual segmentation. The results of overlap and distance for manual segmentation are as expected, showing only few pixels (2-3) mismatch on average and a reasonable overlap. Based on overlap and distance 5 mismatches in automatic segmentation were detected, resulting in an automatic segmentation a success rate of 90%. In conclusion, this study presents a reliable manual and automatic 3D segmentation of the puborectalis muscle. This will facilitate future investigation of the puborectalis muscle. It also allows for reliable measurements of clinically potentially valuable parameters like mean echogenicity. This article is protected by copyright. All rights reserved.

Effect of consecutive driving on accident risk: a comparison between passenger and freight train driving.

PubMed

Chang, Hsin-Li; Ju, Lai-Shun

2008-11-01

This study combined driver-responsible accidents with on-board driving hours to examine the effect of consecutive driving on the accident risk of train operations. The data collected from the Taiwan Railway Administration for the period 1996-2006 was used to compute accident rates for varied accumulated driving hours for passenger and freight trains. The results showed that accident risk grew with increased consecutive driving hours for both passenger and freight trains, and doubled that of the first hour after four consecutive hours of driving. Additional accident risk was found for freight trains during the first hour due to required shunting in the marshalling yards where there are complex track layouts and semi-automatic traffic controls. Also, accident risk for train driving increased more quickly over consecutive driving hours than for automobile driving, and accumulated fatigue caused by high working pressure and monotony of the working environment are considered to be the part of the reason. To prevent human errors accidents, enhancing safety equipment, driver training programs, and establishing a sound auditing system are suggested and discussed.

Automated detection of heuristics and biases among pathologists in a computer-based system.

PubMed

Crowley, Rebecca S; Legowski, Elizabeth; Medvedeva, Olga; Reitmeyer, Kayse; Tseytlin, Eugene; Castine, Melissa; Jukic, Drazen; Mello-Thoms, Claudia

2013-08-01

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to diagnostic errors. The authors conducted the study using a computer-based system to view and diagnose virtual slide cases. The software recorded participant responses throughout the diagnostic process, and automatically classified participant actions based on definitions of eight common heuristics and/or biases. The authors measured frequency of heuristic use and bias across three levels of training. Biases studied were detected at varying frequencies, with availability and search satisficing observed most frequently. There were few significant differences by level of training. For representativeness and anchoring, the heuristic was used appropriately as often or more often than it was used in biased judgment. Approximately half of the diagnostic errors were associated with one or more biases. We conclude that heuristic use and biases were observed among physicians at all levels of training using the virtual slide system, although their frequencies varied. The system can be employed to detect heuristic use and to test methods for decreasing diagnostic errors resulting from cognitive biases.

Assessing the impact of graphical quality on automatic text recognition in digital maps

NASA Astrophysics Data System (ADS)

Chiang, Yao-Yi; Leyk, Stefan; Honarvar Nazari, Narges; Moghaddam, Sima; Tan, Tian Xiang

2016-08-01

Converting geographic features (e.g., place names) in map images into a vector format is the first step for incorporating cartographic information into a geographic information system (GIS). With the advancement in computational power and algorithm design, map processing systems have been considerably improved over the last decade. However, the fundamental map processing techniques such as color image segmentation, (map) layer separation, and object recognition are sensitive to minor variations in graphical properties of the input image (e.g., scanning resolution). As a result, most map processing results would not meet user expectations if the user does not "properly" scan the map of interest, pre-process the map image (e.g., using compression or not), and train the processing system, accordingly. These issues could slow down the further advancement of map processing techniques as such unsuccessful attempts create a discouraged user community, and less sophisticated tools would be perceived as more viable solutions. Thus, it is important to understand what kinds of maps are suitable for automatic map processing and what types of results and process-related errors can be expected. In this paper, we shed light on these questions by using a typical map processing task, text recognition, to discuss a number of map instances that vary in suitability for automatic processing. We also present an extensive experiment on a diverse set of scanned historical maps to provide measures of baseline performance of a standard text recognition tool under varying map conditions (graphical quality) and text representations (that can vary even within the same map sheet). Our experimental results help the user understand what to expect when a fully or semi-automatic map processing system is used to process a scanned map with certain (varying) graphical properties and complexities in map content.

Realistic radio communications in pilot simulator training

DOT National Transportation Integrated Search

2000-12-01

This report summarizes the first-year efforts of assessing the requirement and feasibility of simulating radio communication automatically. A review of the training and crew resource/task management literature showed both practical and theoretical su...

Gimli: open source and high-performance biomedical name recognition

PubMed Central

2013-01-01

Background Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. Results We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Conclusions Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli. PMID:23413997

PubMed Central

LINKE, R.; LEICHTLE, A.; SHEIKH, F.; SCHMIDT, C.; FRENZEL, H.; GRAEFE, H.; WOLLENBERG, B.; MEYER, J.E.

2013-01-01

SUMMARY Surgery on the temporal bone is technically challenging due to its complex anatomy. Precise anatomical dissection of the human temporal bone is essential and is fundamental for middle ear surgery. We assessed the possible application of a virtual reality temporal bone surgery simulator to the education of ear surgeons. Seventeen ENT physicians with different levels of surgical training and 20 medical students performed an antrotomy with a computer-based virtual temporal bone surgery simulator. The ease, accuracy and timing of the simulated temporal bone surgery were assessed using the automatic assessment software provided by the simulator device and additionally with a modified Final Product Analysis Scale. Trained ENT surgeons, physicians without temporal bone surgical training and medical students were all able to perform the antrotomy. However, the highly trained ENT surgeons were able to complete the surgery in approximately half the time, with better handling and accuracy as assessed by the significant reduction in injury to important middle ear structures. Trained ENT surgeons achieved significantly higher scores using both dissection analysis methods. Surprisingly, there were no significant differences in the results between medical students and physicians without experience in ear surgery. The virtual temporal bone training system can stratify users of known levels of experience. This system can be used not only to improve the surgical skills of trained ENT surgeons for more successful and injury-free surgeries, but also to train inexperienced physicians/medical students in developing their surgical skills for the ear. PMID:24043916

Blastocyst microinjection automation.

PubMed

Mattos, Leonardo S; Grant, Edward; Thresher, Randy; Kluckman, Kimberly

2009-09-01

Blastocyst microinjections are routinely involved in the process of creating genetically modified mice for biomedical research, but their efficiency is highly dependent on the skills of the operators. As a consequence, much time and resources are required for training microinjection personnel. This situation has been aggravated by the rapid growth of genetic research, which has increased the demand for mutant animals. Therefore, increased productivity and efficiency in this area are highly desired. Here, we pursue these goals through the automation of a previously developed teleoperated blastocyst microinjection system. This included the design of a new system setup to facilitate automation, the definition of rules for automatic microinjections, the implementation of video processing algorithms to extract feedback information from microscope images, and the creation of control algorithms for process automation. Experimentation conducted with this new system and operator assistance during the cells delivery phase demonstrated a 75% microinjection success rate. In addition, implantation of the successfully injected blastocysts resulted in a 53% birth rate and a 20% yield of chimeras. These results proved that the developed system was capable of automatic blastocyst penetration and retraction, demonstrating the success of major steps toward full process automation.

Automatic detection of pelvic lymph nodes using multiple MR sequences

NASA Astrophysics Data System (ADS)

Yan, Michelle; Lu, Yue; Lu, Renzhi; Requardt, Martin; Moeller, Thomas; Takahashi, Satoru; Barentsz, Jelle

2007-03-01

A system for automatic detection of pelvic lymph nodes is developed by incorporating complementary information extracted from multiple MR sequences. A single MR sequence lacks sufficient diagnostic information for lymph node localization and staging. Correct diagnosis often requires input from multiple complementary sequences which makes manual detection of lymph nodes very labor intensive. Small lymph nodes are often missed even by highly-trained radiologists. The proposed system is aimed at assisting radiologists in finding lymph nodes faster and more accurately. To the best of our knowledge, this is the first such system reported in the literature. A 3-dimensional (3D) MR angiography (MRA) image is employed for extracting blood vessels that serve as a guide in searching for pelvic lymph nodes. Segmentation, shape and location analysis of potential lymph nodes are then performed using a high resolution 3D T1-weighted VIBE (T1-vibe) MR sequence acquired by Siemens 3T scanner. An optional contrast-agent enhanced MR image, such as post ferumoxtran-10 T2*-weighted MEDIC sequence, can also be incorporated to further improve detection accuracy of malignant nodes. The system outputs a list of potential lymph node locations that are overlaid onto the corresponding MR sequences and presents them to users with associated confidence levels as well as their sizes and lengths in each axis. Preliminary studies demonstrates the feasibility of automatic lymph node detection and scenarios in which this system may be used to assist radiologists in diagnosis and reporting.

PPP effectiveness study. [automatic procedures recording and crew performance monitoring system

NASA Technical Reports Server (NTRS)

Arbet, J. D.; Benbow, R. L.

1976-01-01

This design note presents a study of the Procedures and Performance Program (PPP) effectiveness. The intent of the study is to determine manpower time savings and the improvements in job performance gained through PPP automated techniques. The discussion presents a synopsis of PPP capabilities and identifies potential users and associated applications, PPP effectiveness, and PPP applications to other simulation/training facilities. Appendix A provides a detailed description of each PPP capability.

A Scalable and Dynamic Testbed for Conducting Penetration-Test Training in a Laboratory Environment

DTIC Science & Technology

2015-03-01

entry point through which to execute a payload to accomplish a higher-level goal: executing arbitrary code, escalating privileges , pivoting...Mobile Ad Hoc Network Emulator (EMANE)26 can emulate the entire network stack (physical to application -layer protocols). 2. Methodology To build a...to host Windows, Linux, MacOS, Android , and other operating systems without much effort. 4 E. A simple and automatic “restore” function: Many

Technological Areas to Improve Soldier Decisiveness: Insights From the Soldier-System Design Perspective

DTIC Science & Technology

2012-03-01

learning state of the Soldier (e.g., frustrated, confused, engaged), to select the best learning strategies (e.g., feedback, reflection, hints), and...targeted to areas of weakness. This training can be enhanced by the use of “intelligent” agents to perceive learner attributes (e.g., competence...auditory scene would be made, and outlying objects and sounds, or missing activity, could be automatically identified and displayed aurally or visually

14 CFR 60.27 - Automatic loss of qualification and procedures for restoration of qualification.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 14 Aeronautics and Space 2 2014-01-01 2014-01-01 false Automatic loss of qualification and procedures for restoration of qualification. 60.27 Section 60.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) AIRMEN FLIGHT SIMULATION TRAINING DEVICE INITIAL AND...

14 CFR 60.27 - Automatic loss of qualification and procedures for restoration of qualification.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 14 Aeronautics and Space 2 2013-01-01 2013-01-01 false Automatic loss of qualification and procedures for restoration of qualification. 60.27 Section 60.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) AIRMEN FLIGHT SIMULATION TRAINING DEVICE INITIAL AND...

14 CFR 60.27 - Automatic loss of qualification and procedures for restoration of qualification.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 2 2011-01-01 2011-01-01 false Automatic loss of qualification and procedures for restoration of qualification. 60.27 Section 60.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) AIRMEN FLIGHT SIMULATION TRAINING DEVICE INITIAL AND...

14 CFR 60.27 - Automatic loss of qualification and procedures for restoration of qualification.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 2 2010-01-01 2010-01-01 false Automatic loss of qualification and procedures for restoration of qualification. 60.27 Section 60.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) AIRMEN FLIGHT SIMULATION TRAINING DEVICE INITIAL AND...

14 CFR 60.27 - Automatic loss of qualification and procedures for restoration of qualification.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 14 Aeronautics and Space 2 2012-01-01 2012-01-01 false Automatic loss of qualification and procedures for restoration of qualification. 60.27 Section 60.27 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) AIRMEN FLIGHT SIMULATION TRAINING DEVICE INITIAL AND...

Automatic methods of the processing of data from track detectors on the basis of the PAVICOM facility

NASA Astrophysics Data System (ADS)

Aleksandrov, A. B.; Goncharova, L. A.; Davydov, D. A.; Publichenko, P. A.; Roganova, T. M.; Polukhina, N. G.; Feinberg, E. L.

2007-02-01

New automatic methods essentially simplify and increase the rate of the processing of data from track detectors. This provides a possibility of processing large data arrays and considerably improves their statistical significance. This fact predetermines the development of new experiments which plan to use large-volume targets, large-area emulsion, and solid-state track detectors [1]. In this regard, the problem of training qualified physicists who are capable of operating modern automatic equipment is very important. Annually, about ten Moscow students master the new methods, working at the Lebedev Physical Institute at the PAVICOM facility [2 4]. Most students specializing in high-energy physics are only given an idea of archaic manual methods of the processing of data from track detectors. In 2005, on the basis of the PAVICOM facility and the physicstraining course of Moscow State University, a new training work was prepared. This work is devoted to the determination of the energy of neutrons passing through a nuclear emulsion. It provides the possibility of acquiring basic practical skills of the processing of data from track detectors using automatic equipment and can be included in the educational process of students of any physical faculty. Those who have mastered the methods of automatic data processing in a simple and pictorial example of track detectors will be able to apply their knowledge in various fields of science and technique. Formulation of training works for pregraduate and graduate students is a new additional aspect of application of the PAVICOM facility described earlier in [4].

Towards a mLearning training solution to the adoption of a CPOE system.

PubMed

Pakonstantinou, Despoina; Poulymenopoulou, Mikaela; Malamateniou, Flora; Vassilacopoulos, George

2012-01-01

Computerized Physician Order Entry (CPOE) has been introduced as a solution that can fundamentally change the way healthcare is provided, affecting all types of healthcare stakeholders and improving healthcare decisions, patient outcomes, patient safety and efficiency. However, a relatively small proportion of healthcare organizations have implemented CPOE systems, due to its technological complexity and to its low acceptance rate by healthcare professionals who largely disregard the value of CPOE in efficient healthcare delivery. An online training facility embedded within a CPOE service may increase the likelihood of its adoption by healthcare professionals as it offers them guidelines on how to perform each task of the CPOE service. In contrast to CPOE, on the other hand, handheld devices and other mobile technologies have showed an increased adoption rate. This paper considers a CPOE service that can be accessed by authorized healthcare professionals through their mobile devices anytime anywhere, and allows embedded training content, which has been developed through a learning management system (LMS) to be presented to the user automatically upon request.

Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning.

PubMed

DeChant, Chad; Wiesner-Hanks, Tyr; Chen, Siyuan; Stewart, Ethan L; Yosinski, Jason; Gore, Michael A; Nelson, Rebecca J; Lipson, Hod

2017-11-01

Northern leaf blight (NLB) can cause severe yield loss in maize; however, scouting large areas to accurately diagnose the disease is time consuming and difficult. We demonstrate a system capable of automatically identifying NLB lesions in field-acquired images of maize plants with high reliability. This approach uses a computational pipeline of convolutional neural networks (CNNs) that addresses the challenges of limited data and the myriad irregularities that appear in images of field-grown plants. Several CNNs were trained to classify small regions of images as containing NLB lesions or not; their predictions were combined into separate heat maps, then fed into a final CNN trained to classify the entire image as containing diseased plants or not. The system achieved 96.7% accuracy on test set images not used in training. We suggest that such systems mounted on aerial- or ground-based vehicles can help in automated high-throughput plant phenotyping, precision breeding for disease resistance, and reduced pesticide use through targeted application across a variety of plant and disease categories.

Monotone Increasing Binary Similarity and Its Application to Automatic Document-Acquisition of a Category

NASA Astrophysics Data System (ADS)

Suzuki, Izumi; Mikami, Yoshiki; Ohsato, Ario

A technique that acquires documents in the same category with a given short text is introduced. Regarding the given text as a training document, the system marks up the most similar document, or sufficiently similar documents, from among the document domain (or entire Web). The system then adds the marked documents to the training set to learn the set, and this process is repeated until no more documents are marked. Setting a monotone increasing property to the similarity as it learns enables the system to 1) detect the correct timing so that no more documents remain to be marked and to 2) decide the threshold value that the classifier uses. In addition, under the condition that the normalization process is limited to what term weights are divided by a p-norm of the weights, the linear classifier in which training documents are indexed in a binary manner is the only instance that satisfies the monotone increasing property. The feasibility of the proposed technique was confirmed through an examination of binary similarity and using English and German documents randomly selected from the Web.

The development of a novel cricket bowling system: recreating spin and swing bowling deliveries at the elite level

NASA Astrophysics Data System (ADS)

West, A. A.; Justham, L.

2008-03-01

During the game of cricket, bowlers create different deliveries by altering the manner in which they release the ball from their hand. The orientation of the seam, the speed at which the ball is released and the magnitude and direction of the spin combine to determine the motion of the ball through the air and its movement after impact with the wicket. These factors have to be considered if automatic training machines are to be capable of replicating elite bowling deliveries. The need for automotive systems for batting and fielding training at the elite level has arisen due to: (i) the capabilities of human bowlers are limited by the onset of fatigue and the risk of injury and (ii) a large number of accurate and repeatable deliveries to be ''programmable'' by coaches to ensure batsmen and fielders are tested to the limits of their abilities and a training benefit is achieved.

The Electronic Nose Training Automation Development

NASA Technical Reports Server (NTRS)

Schattke, Nathan

2002-01-01

The electronic nose is a method of using several sensors in conjunction to identify an unknown gas. Statistical analysis has shown that a large number of training exposures need to be performed in order to get a model that can be depended on. The number of training exposures needed is on the order of 1000. Data acquisition from the noses are generally automatic and built in. The gas generation equipment consists of a Miller-Nelson (MN) flow/temperature/humidity controller and a Kin-Tek (KT) trace gas generator. This equipment has been controlled in the past by an old data acquisition and control system. The new system will use new control boards and an easy graphical user interface. The programming for this is in the LabVIEW G programming language. A language easy for the user to make modifications to. This paper details some of the issues in selecting the components and programming the connections. It is not a primer on LabVIEW programming, a separate CD is being delivered with website files to teach that.

Applying deep neural networks to HEP job classification

NASA Astrophysics Data System (ADS)

Wang, L.; Shi, J.; Yan, X.

2015-12-01

The cluster of IHEP computing center is a middle-sized computing system which provides 10 thousands CPU cores, 5 PB disk storage, and 40 GB/s IO throughput. Its 1000+ users come from a variety of HEP experiments. In such a system, job classification is an indispensable task. Although experienced administrator can classify a HEP job by its IO pattern, it is unpractical to classify millions of jobs manually. We present how to solve this problem with deep neural networks in a supervised learning way. Firstly, we built a training data set of 320K samples by an IO pattern collection agent and a semi-automatic process of sample labelling. Then we implemented and trained DNNs models with Torch. During the process of model training, several meta-parameters was tuned with cross-validations. Test results show that a 5- hidden-layer DNNs model achieves 96% precision on the classification task. By comparison, it outperforms a linear model by 8% precision.

Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictionary Approach.

PubMed

Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

2018-06-25

The objective of the work described in this paper is development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach: A novel patient-specific seizure detection approach based on signal-derived Empirical Mode Decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results: The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability of the approach for seizure detection in long-term multi-channel EEG recordings is discussed. Significance: The proposed approach describes a computationally efficient method for automatic seizure detection in long-term multi-channel EEG recordings. The method does not rely on hand-engineered features, as are required in traditional approaches. Furthermore, the approach is suitable for scenarios where the dictionary once formed and trained can be used for automatic seizure detection of newly recorded data, making the approach suitable for long-term multi-channel EEG recordings. © 2018 IOP Publishing Ltd.

An Algorithm for Automatically Modifying Train Crew Schedule

NASA Astrophysics Data System (ADS)

Takahashi, Satoru; Kataoka, Kenji; Kojima, Teruhito; Asami, Masayuki

Once the break-down of the train schedule occurs, the crew schedule as well as the train schedule has to be modified as quickly as possible to restore them. In this paper, we propose an algorithm for automatically modifying a crew schedule that takes all constraints into consideration, presenting a model of the combined problem of crews and trains. The proposed algorithm builds an initial solution by relaxing some of the constraint conditions, and then uses a Taboo-search method to revise this solution in order to minimize the degree of constraint violation resulting from these relaxed conditions. Then we show not only that the algorithm can generate a constraint satisfaction solution, but also that the solution will satisfy the experts. That is, we show the proposed algorithm is capable of producing a usable solution in a short time by applying to actual cases of train-schedule break-down, and that the solution is at least as good as those produced manually, by comparing the both solutions with several point of view.

Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error.

PubMed

Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

2016-12-01

This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Automatic control of negative emotions: evidence that structured practice increases the efficiency of emotion regulation.

PubMed

Christou-Champi, Spyros; Farrow, Tom F D; Webb, Thomas L

2015-01-01

Emotion regulation (ER) is vital to everyday functioning. However, the effortful nature of many forms of ER may lead to regulation being inefficient and potentially ineffective. The present research examined whether structured practice could increase the efficiency of ER. During three training sessions, comprising a total of 150 training trials, participants were presented with negatively valenced images and asked either to "attend" (control condition) or "reappraise" (ER condition). A further group of participants did not participate in training but only completed follow-up measures. Practice increased the efficiency of ER as indexed by decreased time required to regulate emotions and increased heart rate variability (HRV). Furthermore, participants in the ER condition spontaneously regulated their negative emotions two weeks later and reported being more habitual in their use of ER. These findings indicate that structured practice can facilitate the automatic control of negative emotions and that these effects persist beyond training.

Automatic decoding of facial movements reveals deceptive pain expressions

PubMed Central

Bartlett, Marian Stewart; Littlewort, Gwen C.; Frank, Mark G.; Lee, Kang

2014-01-01

Summary In highly social species such as humans, faces have evolved to convey rich information for social interaction, including expressions of emotions and pain [1–3]. Two motor pathways control facial movement [4–7]. A subcortical extrapyramidal motor system drives spontaneous facial expressions of felt emotions. A cortical pyramidal motor system controls voluntary facial expressions. The pyramidal system enables humans to simulate facial expressions of emotions not actually experienced. Their simulation is so successful that they can deceive most observers [8–11]. Machine vision may, however, be able to distinguish deceptive from genuine facial signals by identifying the subtle differences between pyramidally and extrapyramidally driven movements. Here we show that human observers could not discriminate real from faked expressions of pain better than chance, and after training, improved accuracy to a modest 55%. However a computer vision system that automatically measures facial movements and performs pattern recognition on those movements attained 85% accuracy. The machine system’s superiority is attributable to its ability to differentiate the dynamics of genuine from faked expressions. Thus by revealing the dynamics of facial action through machine vision systems, our approach has the potential to elucidate behavioral fingerprints of neural control systems involved in emotional signaling. PMID:24656830

Artificial intelligence in sports on the example of weight training.

PubMed

Novatchkov, Hristo; Baca, Arnold

2013-01-01

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key pointsArtificial intelligence is a promising field for sport-related analysis.Implementations integrating pattern recognition techniques enable the automatic evaluation of data measurements.Artificial neural networks applied for the analysis of weight training data show good performance and high classification rates.

Artificial Intelligence in Sports on the Example of Weight Training

PubMed Central

Novatchkov, Hristo; Baca, Arnold

2013-01-01

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key points Artificial intelligence is a promising field for sport-related analysis. Implementations integrating pattern recognition techniques enable the automatic evaluation of data measurements. Artificial neural networks applied for the analysis of weight training data show good performance and high classification rates. PMID:24149722

Automatic polyp detection in colonoscopy videos

NASA Astrophysics Data System (ADS)

Yuan, Zijie; IzadyYazdanabadi, Mohammadhassan; Mokkapati, Divya; Panvalkar, Rujuta; Shin, Jae Y.; Tajbakhsh, Nima; Gurudu, Suryakanth; Liang, Jianming

2017-02-01

Colon cancer is the second cancer killer in the US [1]. Colonoscopy is the primary method for screening and prevention of colon cancer, but during colonoscopy, a significant number (25% [2]) of polyps (precancerous abnormal growths inside of the colon) are missed; therefore, the goal of our research is to reduce the polyp miss-rate of colonoscopy. This paper presents a method to detect polyp automatically in a colonoscopy video. Our system has two stages: Candidate generation and candidate classification. In candidate generation (stage 1), we chose 3,463 frames (including 1,718 with-polyp frames) from real-time colonoscopy video database. We first applied processing procedures, namely intensity adjustment, edge detection and morphology operations, as pre-preparation. We extracted each connected component (edge contour) as one candidate patch from the pre-processed image. With the help of ground truth (GT) images, 2 constraints were implemented on each candidate patch, dividing and saving them into polyp group and non-polyp group. In candidate classification (stage 2), we trained and tested convolutional neural networks (CNNs) with AlexNet architecture [3] to classify each candidate into with-polyp or non-polyp class. Each with-polyp patch was processed by rotation, translation and scaling for invariant to get a much robust CNNs system. We applied leave-2-patients-out cross-validation on this model (4 of 6 cases were chosen as training set and the rest 2 were as testing set). The system accuracy and sensitivity are 91.47% and 91.76%, respectively.

A speech-controlled environmental control system for people with severe dysarthria.

PubMed

Hawley, Mark S; Enderby, Pam; Green, Phil; Cunningham, Stuart; Brownsell, Simon; Carmichael, James; Parker, Mark; Hatzis, Athanassios; O'Neill, Peter; Palmer, Rebecca

2007-06-01

Automatic speech recognition (ASR) can provide a rapid means of controlling electronic assistive technology. Off-the-shelf ASR systems function poorly for users with severe dysarthria because of the increased variability of their articulations. We have developed a limited vocabulary speaker dependent speech recognition application which has greater tolerance to variability of speech, coupled with a computerised training package which assists dysarthric speakers to improve the consistency of their vocalisations and provides more data for recogniser training. These applications, and their implementation as the interface for a speech-controlled environmental control system (ECS), are described. The results of field trials to evaluate the training program and the speech-controlled ECS are presented. The user-training phase increased the recognition rate from 88.5% to 95.4% (p<0.001). Recognition rates were good for people with even the most severe dysarthria in everyday usage in the home (mean word recognition rate 86.9%). Speech-controlled ECS were less accurate (mean task completion accuracy 78.6% versus 94.8%) but were faster to use than switch-scanning systems, even taking into account the need to repeat unsuccessful operations (mean task completion time 7.7s versus 16.9s, p<0.001). It is concluded that a speech-controlled ECS is a viable alternative to switch-scanning systems for some people with severe dysarthria and would lead, in many cases, to more efficient control of the home.

"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing.

PubMed

Xiao, Bo; Imel, Zac E; Georgiou, Panayiotis G; Atkins, David C; Narayanan, Shrikanth S

2015-01-01

The technology for evaluating patient-provider interactions in psychotherapy-observational coding-has not changed in 70 years. It is labor-intensive, error prone, and expensive, limiting its use in evaluating psychotherapy in the real world. Engineering solutions from speech and language processing provide new methods for the automatic evaluation of provider ratings from session recordings. The primary data are 200 Motivational Interviewing (MI) sessions from a study on MI training methods with observer ratings of counselor empathy. Automatic Speech Recognition (ASR) was used to transcribe sessions, and the resulting words were used in a text-based predictive model of empathy. Two supporting datasets trained the speech processing tasks including ASR (1200 transcripts from heterogeneous psychotherapy sessions and 153 transcripts and session recordings from 5 MI clinical trials). The accuracy of computationally-derived empathy ratings were evaluated against human ratings for each provider. Computationally-derived empathy scores and classifications (high vs. low) were highly accurate against human-based codes and classifications, with a correlation of 0.65 and F-score (a weighted average of sensitivity and specificity) of 0.86, respectively. Empathy prediction using human transcription as input (as opposed to ASR) resulted in a slight increase in prediction accuracies, suggesting that the fully automatic system with ASR is relatively robust. Using speech and language processing methods, it is possible to generate accurate predictions of provider performance in psychotherapy from audio recordings alone. This technology can support large-scale evaluation of psychotherapy for dissemination and process studies.

Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

PubMed Central

Ribeiro, Sidarta; Pereira, Danillo R.; Papa, João P.; de Albuquerque, Victor Hugo C.

2016-01-01

Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available. PMID:27654941

Creating a medical English-Swedish dictionary using interactive word alignment.

PubMed

Nyström, Mikael; Merkel, Magnus; Ahrenberg, Lars; Zweigenbaum, Pierre; Petersson, Håkan; Ahlfeldt, Hans

2006-10-12

This paper reports on a parallel collection of rubrics from the medical terminology systems ICD-10, ICF, MeSH, NCSP and KSH97-P and its use for semi-automatic creation of an English-Swedish dictionary of medical terminology. The methods presented are relevant for many other West European language pairs than English-Swedish. The medical terminology systems were collected in electronic format in both English and Swedish and the rubrics were extracted in parallel language pairs. Initially, interactive word alignment was used to create training data from a sample. Then the training data were utilised in automatic word alignment in order to generate candidate term pairs. The last step was manual verification of the term pair candidates. A dictionary of 31,000 verified entries has been created in less than three man weeks, thus with considerably less time and effort needed compared to a manual approach, and without compromising quality. As a side effect of our work we found 40 different translation problems in the terminology systems and these results indicate the power of the method for finding inconsistencies in terminology translations. We also report on some factors that may contribute to making the process of dictionary creation with similar tools even more expedient. Finally, the contribution is discussed in relation to other ongoing efforts in constructing medical lexicons for non-English languages. In three man weeks we were able to produce a medical English-Swedish dictionary consisting of 31,000 entries and also found hidden translation errors in the utilized medical terminology systems.

Creating a medical English-Swedish dictionary using interactive word alignment

PubMed Central

Nyström, Mikael; Merkel, Magnus; Ahrenberg, Lars; Zweigenbaum, Pierre; Petersson, Håkan; Åhlfeldt, Hans

2006-01-01

Background This paper reports on a parallel collection of rubrics from the medical terminology systems ICD-10, ICF, MeSH, NCSP and KSH97-P and its use for semi-automatic creation of an English-Swedish dictionary of medical terminology. The methods presented are relevant for many other West European language pairs than English-Swedish. Methods The medical terminology systems were collected in electronic format in both English and Swedish and the rubrics were extracted in parallel language pairs. Initially, interactive word alignment was used to create training data from a sample. Then the training data were utilised in automatic word alignment in order to generate candidate term pairs. The last step was manual verification of the term pair candidates. Results A dictionary of 31,000 verified entries has been created in less than three man weeks, thus with considerably less time and effort needed compared to a manual approach, and without compromising quality. As a side effect of our work we found 40 different translation problems in the terminology systems and these results indicate the power of the method for finding inconsistencies in terminology translations. We also report on some factors that may contribute to making the process of dictionary creation with similar tools even more expedient. Finally, the contribution is discussed in relation to other ongoing efforts in constructing medical lexicons for non-English languages. Conclusion In three man weeks we were able to produce a medical English-Swedish dictionary consisting of 31,000 entries and also found hidden translation errors in the utilized medical terminology systems. PMID:17034649

Developing and Evaluating an Oral Skills Training Website Supported by Automatic Speech Recognition Technology

ERIC Educational Resources Information Center

Chen, Howard Hao-Jan

2011-01-01

Oral communication ability has become increasingly important to many EFL students. Several commercial software programs based on automatic speech recognition (ASR) technologies are available but their prices are not affordable for many students. This paper will demonstrate how the Microsoft Speech Application Software Development Kit (SASDK), a…

Model-Based Reasoning in Humans Becomes Automatic with Training.

PubMed

Economides, Marcos; Kurth-Nelson, Zeb; Lübbert, Annika; Guitart-Masip, Marc; Dolan, Raymond J

2015-09-01

Model-based and model-free reinforcement learning (RL) have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load--a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders.

Lead Exposure in Military Outdoor Firing Ranges.

PubMed

Greenberg, Nili; Frimer, Ron; Meyer, Robert; Derazne, Estella; Chodick, Gabrial

2016-09-01

Several studies have reported significant airborne lead exposures during training at indoor firing ranges. Scarce attention has been given to airborne lead exposures in outdoor shooting ranges with automatic weapons. To assess the prevalence and magnitude of airborne and blood lead levels (BLL) among firing instructors and shooters in military outdoor ranges. Exposure assessment, for both trainees and instructors, included airborne and BLL during basic and advanced training at outdoor firing ranges. Personal airborne samples were collected in both day and night shooting during both training periods. During basic training, there is 95% likelihood that up to 25% of instructors and 99% likelihood that up to 5% of trainees might be exposed above the action level (AL) (25 μg/m(3)). During advanced training, there is 90% likelihood that 10% of instructors and 99% likelihood that up to 10% of trainees might be exposed above the AL. Military personnel participating in automatic weapon marksmanship training can be exposed to considerable levels of airborne lead during outdoor firing range training. As a result, the Israel Defense Force Medical Corp has classified firing range instructors as workers that require periodic medical examinations. Reprint & Copyright © 2016 Association of Military Surgeons of the U.S.

Design description of the Tangaye Village photovoltaic power system

NASA Astrophysics Data System (ADS)

Martz, J. E.; Ratajczak, A. F.

1982-06-01

The engineering design of a stand alone photovoltaic (PV) powered grain mill and water pump for the village of Tangaye, Upper Volta is described. The socioeconomic effects of reducing the time required by women in rural areas for drawing water and grinding grain were studied. The suitability of photovoltaic technology for use in rural areas by people of limited technical training was demonstrated. The PV system consists of a 1.8-kW (peak) solar cell array, 540 ampere hours of battery storage, instrumentation, automatic controls, and a data collection and storage system. The PV system is situated near an improved village well and supplies d.c. power to a grain mill and a water pump. The array is located in a fenced area and the mill, battery, instruments, controls, and data system are in a mill building. A water storage tank is located near the well. The system employs automatic controls which provide battery charge regulation and system over and under voltage protection. This report includes descriptions of the engineering design of the system and of the load that it serves; a discussion of PV array and battery sizing methodology; descriptions of the mechanical and electrical designs including the array, battery, controls, and instrumentation; and a discussion of the safety features. The system became operational on March 1, 1979.

Design description of the Tangaye Village photovoltaic power system

NASA Technical Reports Server (NTRS)

Martz, J. E.; Ratajczak, A. F.

1982-01-01

The engineering design of a stand alone photovoltaic (PV) powered grain mill and water pump for the village of Tangaye, Upper Volta is described. The socioeconomic effects of reducing the time required by women in rural areas for drawing water and grinding grain were studied. The suitability of photovoltaic technology for use in rural areas by people of limited technical training was demonstrated. The PV system consists of a 1.8-kW (peak) solar cell array, 540 ampere hours of battery storage, instrumentation, automatic controls, and a data collection and storage system. The PV system is situated near an improved village well and supplies d.c. power to a grain mill and a water pump. The array is located in a fenced area and the mill, battery, instruments, controls, and data system are in a mill building. A water storage tank is located near the well. The system employs automatic controls which provide battery charge regulation and system over and under voltage protection. This report includes descriptions of the engineering design of the system and of the load that it serves; a discussion of PV array and battery sizing methodology; descriptions of the mechanical and electrical designs including the array, battery, controls, and instrumentation; and a discussion of the safety features. The system became operational on March 1, 1979.

Automatic interpretation of ERTS data for forest management

NASA Technical Reports Server (NTRS)

Kirvida, L.; Johnson, G. R.

1973-01-01

Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wild life management, forest inventory and forest condition monitoring. Automatic procedures based on both multi-spectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74% was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 99% was obtained.

Temporal Cyber Attack Detection.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ingram, Joey Burton; Draelos, Timothy J.; Galiardi, Meghan

Rigorous characterization of the performance and generalization ability of cyber defense systems is extremely difficult, making it hard to gauge uncertainty, and thus, confidence. This difficulty largely stems from a lack of labeled attack data that fully explores the potential adversarial space. Currently, performance of cyber defense systems is typically evaluated in a qualitative manner by manually inspecting the results of the system on live data and adjusting as needed. Additionally, machine learning has shown promise in deriving models that automatically learn indicators of compromise that are more robust than analyst-derived detectors. However, to generate these models, most algorithms requiremore » large amounts of labeled data (i.e., examples of attacks). Algorithms that do not require annotated data to derive models are similarly at a disadvantage, because labeled data is still necessary when evaluating performance. In this work, we explore the use of temporal generative models to learn cyber attack graph representations and automatically generate data for experimentation and evaluation. Training and evaluating cyber systems and machine learning models requires significant, annotated data, which is typically collected and labeled by hand for one-off experiments. Automatically generating such data helps derive/evaluate detection models and ensures reproducibility of results. Experimentally, we demonstrate the efficacy of generative sequence analysis techniques on learning the structure of attack graphs, based on a realistic example. These derived models can then be used to generate more data. Additionally, we provide a roadmap for future research efforts in this area.« less

A technology prototype system for rating therapist empathy from audio recordings in addiction counseling.

PubMed

Xiao, Bo; Huang, Chewei; Imel, Zac E; Atkins, David C; Georgiou, Panayiotis; Narayanan, Shrikanth S

2016-04-01

Scaling up psychotherapy services such as for addiction counseling is a critical societal need. One challenge is ensuring quality of therapy, due to the heavy cost of manual observational assessment. This work proposes a speech technology-based system to automate the assessment of therapist empathy-a key therapy quality index-from audio recordings of the psychotherapy interactions. We designed a speech processing system that includes voice activity detection and diarization modules, and an automatic speech recognizer plus a speaker role matching module to extract the therapist's language cues. We employed Maximum Entropy models, Maximum Likelihood language models, and a Lattice Rescoring method to characterize high vs. low empathic language. We estimated therapy-session level empathy codes using utterance level evidence obtained from these models. Our experiments showed that the fully automated system achieved a correlation of 0.643 between expert annotated empathy codes and machine-derived estimations, and an accuracy of 81% in classifying high vs. low empathy, in comparison to a 0.721 correlation and 86% accuracy in the oracle setting using manual transcripts. The results show that the system provides useful information that can contribute to automatic quality insurance and therapist training.

A technology prototype system for rating therapist empathy from audio recordings in addiction counseling

PubMed Central

Xiao, Bo; Huang, Chewei; Imel, Zac E.; Atkins, David C.; Georgiou, Panayiotis; Narayanan, Shrikanth S.

2016-01-01

Scaling up psychotherapy services such as for addiction counseling is a critical societal need. One challenge is ensuring quality of therapy, due to the heavy cost of manual observational assessment. This work proposes a speech technology-based system to automate the assessment of therapist empathy—a key therapy quality index—from audio recordings of the psychotherapy interactions. We designed a speech processing system that includes voice activity detection and diarization modules, and an automatic speech recognizer plus a speaker role matching module to extract the therapist's language cues. We employed Maximum Entropy models, Maximum Likelihood language models, and a Lattice Rescoring method to characterize high vs. low empathic language. We estimated therapy-session level empathy codes using utterance level evidence obtained from these models. Our experiments showed that the fully automated system achieved a correlation of 0.643 between expert annotated empathy codes and machine-derived estimations, and an accuracy of 81% in classifying high vs. low empathy, in comparison to a 0.721 correlation and 86% accuracy in the oracle setting using manual transcripts. The results show that the system provides useful information that can contribute to automatic quality insurance and therapist training. PMID:28286867

Collaborative filtering for brain-computer interaction using transfer learning and active class selection.

PubMed

Wu, Dongrui; Lance, Brent J; Parsons, Thomas D

2013-01-01

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.

Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection

PubMed Central

Wu, Dongrui; Lance, Brent J.; Parsons, Thomas D.

2013-01-01

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing. PMID:23437188

Automatic Earthquake Detection by Active Learning

NASA Astrophysics Data System (ADS)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

PubMed

Wang, Jun Yi; Ngo, Michael M; Hessl, David; Hagerman, Randi J; Rivera, Susan M

2016-01-01

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well.

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem

PubMed Central

Wang, Jun Yi; Ngo, Michael M.; Hessl, David; Hagerman, Randi J.; Rivera, Susan M.

2016-01-01

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer’s segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well. PMID:27213683

49 CFR 236.744 - Element, roadway.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 4 2013-10-01 2013-10-01 false Element, roadway. 236.744 Section 236.744 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Element, roadway. That portion of the roadway apparatus of automatic train stop, train control, or cab...

49 CFR 236.744 - Element, roadway.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Element, roadway. 236.744 Section 236.744 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Element, roadway. That portion of the roadway apparatus of automatic train stop, train control, or cab...

49 CFR 236.744 - Element, roadway.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false Element, roadway. 236.744 Section 236.744 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Element, roadway. That portion of the roadway apparatus of automatic train stop, train control, or cab...

49 CFR 236.744 - Element, roadway.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 4 2012-10-01 2012-10-01 false Element, roadway. 236.744 Section 236.744 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Element, roadway. That portion of the roadway apparatus of automatic train stop, train control, or cab...

Practical vision based degraded text recognition system

NASA Astrophysics Data System (ADS)

Mohammad, Khader; Agaian, Sos; Saleh, Hani

2011-02-01

Rapid growth and progress in the medical, industrial, security and technology fields means more and more consideration for the use of camera based optical character recognition (OCR) Applying OCR to scanned documents is quite mature, and there are many commercial and research products available on this topic. These products achieve acceptable recognition accuracy and reasonable processing times especially with trained software, and constrained text characteristics. Even though the application space for OCR is huge, it is quite challenging to design a single system that is capable of performing automatic OCR for text embedded in an image irrespective of the application. Challenges for OCR systems include; images are taken under natural real world conditions, Surface curvature, text orientation, font, size, lighting conditions, and noise. These and many other conditions make it extremely difficult to achieve reasonable character recognition. Performance for conventional OCR systems drops dramatically as the degradation level of the text image quality increases. In this paper, a new recognition method is proposed to recognize solid or dotted line degraded characters. The degraded text string is localized and segmented using a new algorithm. The new method was implemented and tested using a development framework system that is capable of performing OCR on camera captured images. The framework allows parameter tuning of the image-processing algorithm based on a training set of camera-captured text images. Novel methods were used for enhancement, text localization and the segmentation algorithm which enables building a custom system that is capable of performing automatic OCR which can be used for different applications. The developed framework system includes: new image enhancement, filtering, and segmentation techniques which enabled higher recognition accuracies, faster processing time, and lower energy consumption, compared with the best state of the art published techniques. The system successfully produced impressive OCR accuracies (90% -to- 93%) using customized systems generated by our development framework in two industrial OCR applications: water bottle label text recognition and concrete slab plate text recognition. The system was also trained for the Arabic language alphabet, and demonstrated extremely high recognition accuracy (99%) for Arabic license name plate text recognition with processing times of 10 seconds. The accuracy and run times of the system were compared to conventional and many states of art methods, the proposed system shows excellent results.

Fidelity of Automatic Speech Processing for Adult and Child Talker Classifications.

PubMed

VanDam, Mark; Silbert, Noah H

2016-01-01

Automatic speech processing (ASP) has recently been applied to very large datasets of naturalistically collected, daylong recordings of child speech via an audio recorder worn by young children. The system developed by the LENA Research Foundation analyzes children's speech for research and clinical purposes, with special focus on of identifying and tagging family speech dynamics and the at-home acoustic environment from the auditory perspective of the child. A primary issue for researchers, clinicians, and families using the Language ENvironment Analysis (LENA) system is to what degree the segment labels are valid. This classification study evaluates the performance of the computer ASP output against 23 trained human judges who made about 53,000 judgements of classification of segments tagged by the LENA ASP. Results indicate performance consistent with modern ASP such as those using HMM methods, with acoustic characteristics of fundamental frequency and segment duration most important for both human and machine classifications. Results are likely to be important for interpreting and improving ASP output.

Fidelity of Automatic Speech Processing for Adult and Child Talker Classifications

PubMed Central

2016-01-01

Automatic speech processing (ASP) has recently been applied to very large datasets of naturalistically collected, daylong recordings of child speech via an audio recorder worn by young children. The system developed by the LENA Research Foundation analyzes children's speech for research and clinical purposes, with special focus on of identifying and tagging family speech dynamics and the at-home acoustic environment from the auditory perspective of the child. A primary issue for researchers, clinicians, and families using the Language ENvironment Analysis (LENA) system is to what degree the segment labels are valid. This classification study evaluates the performance of the computer ASP output against 23 trained human judges who made about 53,000 judgements of classification of segments tagged by the LENA ASP. Results indicate performance consistent with modern ASP such as those using HMM methods, with acoustic characteristics of fundamental frequency and segment duration most important for both human and machine classifications. Results are likely to be important for interpreting and improving ASP output. PMID:27529813

Semantic Segmentation and Unregistered Building Detection from Uav Images Using a Deconvolutional Network

NASA Astrophysics Data System (ADS)

Ham, S.; Oh, Y.; Choi, K.; Lee, I.

2018-05-01

Detecting unregistered buildings from aerial images is an important task for urban management such as inspection of illegal buildings in green belt or update of GIS database. Moreover, the data acquisition platform of photogrammetry is evolving from manned aircraft to UAVs (Unmanned Aerial Vehicles). However, it is very costly and time-consuming to detect unregistered buildings from UAV images since the interpretation of aerial images still relies on manual efforts. To overcome this problem, we propose a system which automatically detects unregistered buildings from UAV images based on deep learning methods. Specifically, we train a deconvolutional network with publicly opened geospatial data, semantically segment a given UAV image into a building probability map and compare the building map with existing GIS data. Through this procedure, we could detect unregistered buildings from UAV images automatically and efficiently. We expect that the proposed system can be applied for various urban management tasks such as monitoring illegal buildings or illegal land-use change.

Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

NASA Astrophysics Data System (ADS)

Heracleous, Panikos; Kaino, Tomomi; Saruwatari, Hiroshi; Shikano, Kiyohiro

2006-12-01

We present the use of stethoscope and silicon NAM (nonaudible murmur) microphones in automatic speech recognition. NAM microphones are special acoustic sensors, which are attached behind the talker's ear and can capture not only normal (audible) speech, but also very quietly uttered speech (nonaudible murmur). As a result, NAM microphones can be applied in automatic speech recognition systems when privacy is desired in human-machine communication. Moreover, NAM microphones show robustness against noise and they might be used in special systems (speech recognition, speech transform, etc.) for sound-impaired people. Using adaptation techniques and a small amount of training data, we achieved for a 20 k dictation task a[InlineEquation not available: see fulltext.] word accuracy for nonaudible murmur recognition in a clean environment. In this paper, we also investigate nonaudible murmur recognition in noisy environments and the effect of the Lombard reflex on nonaudible murmur recognition. We also propose three methods to integrate audible speech and nonaudible murmur recognition using a stethoscope NAM microphone with very promising results.

Supervision strategies for improved reliability of bus routes. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1991-09-01

The synthesis will be of interest to transit agency managers and supervisors, as well as to operating and planning personnel who are concerned with the reliability and scheduling of buses. Information is provided on service monitoring, service supervision and control, and supervision strategies. Reliability of transit service is critical to bus transit ridership. The extent of service supervision has an important bearing on reliability. The report describes the various procedures that are used by transit agencies to monitor and maintain bus service reliability. Most transit systems conduct checks of the number of riders at maximum load points and monitor schedulemore » adherence at these locations. Other supervisory actions include service restoration techniques, and strategies such as schedule control, headway control, load control, extraboard management, and personnel selection and training. More sophisticated technologies, such as automatic passenger counting (APC) systems and automatic vehicle location and control (AVLC), have been employed by some transit agencies and are described in the synthesis.« less

Quality of Life Effects of Automatic External Defibrillators in the Home: Results from the Home Automatic External Defibrillator Trial (HAT)

PubMed Central

Mark, Daniel B.; Anstrom, Kevin J.; McNulty, Steven E.; Flaker, Greg C.; Tonkin, Andrew M.; Smith, Warren M.; Toff, William D.; Dorian, Paul; Clapp-Channing, Nancy E.; Anderson, Jill; Johnson, George; Schron, Eleanor B.; Poole, Jeanne E.; Lee, Kerry L.; Bardy, Gust H.

2010-01-01

Background Public access automatic external defibrillators (AEDs) can save lives, but most deaths from out-of-hospital sudden cardiac arrest occur at home. The Home Automatic External Defibrillator Trial (HAT) found no survival advantage for adding a home AED to cardiopulmonary resuscitation (CPR) training for 7001 patients with a prior anterior wall myocardial infarction. Quality of life (QOL) outcomes for both the patient and spouse/companion were secondary endpoints. Methods A subset of 1007 study patients and their spouse/companions was randomly selected for ascertainment of QOL by structured interview at baseline and 12 and 24 months following enrollment. The primary QOL measures were the Medical Outcomes Study 36-Item Short-Form (SF-36) psychological well-being (reflecting anxiety and depression) and vitality (reflecting energy and fatigue) subscales. Results For patients and spouse/companions, the psychological well-being and vitality scales did not differ significantly between those randomly assigned an AED plus CPR training and controls who received CPR training only. None of the other QOL measures collected showed a clinically and statistically significant difference between treatment groups. Patients in the AED group were more likely to report being extremely or quite a bit reassured by their treatment assignment. Spouse/companions in the AED group reported being less often nervous about the possibility of using AED/CPR treatment than those in the CPR group. Conclusions Adding access to a home AED to CPR training did not affect quality of life either for patients with a prior anterior myocardial infarction or their spouse/companion but did provide more reassurance to the patients without increasing anxiety for spouse/companions. PMID:20362722

Development of A Two-Stage Procedure for the Automatic Recognition of Dysfluencies in the Speech of Children Who Stutter: I. Psychometric Procedures Appropriate for Selection of Training Material for Lexical Dysfluency Classifiers

PubMed Central

Howell, Peter; Sackin, Stevie; Glenn, Kazan

2007-01-01

This program of work is intended to develop automatic recognition procedures to locate and assess stuttered dysfluencies. This and the following article together, develop and test recognizers for repetitions and prolongations. The automatic recognizers classify the speech in two stages: In the first, the speech is segmented and in the second the segments are categorized. The units that are segmented are words. Here assessments by human judges on the speech of 12 children who stutter are described using a corresponding procedure. The accuracy of word boundary placement across judges, categorization of the words as fluent, repetition or prolongation, and duration of the different fluency categories are reported. These measures allow reliable instances of repetitions and prolongations to be selected for training and assessing the recognizers in the subsequent paper. PMID:9328878

Postural perturbations: new insights for treatment of balance disorders

NASA Technical Reports Server (NTRS)

Horak, F. B.; Henry, S. M.; Shumway-Cook, A.; Peterson, B. W. (Principal Investigator)

1997-01-01

This article reviews the neural control of posture as understood through studies of automatic responses to mechanical perturbations. Recent studies of responses to postural perturbations have provided a new view of how postural stability is controlled, and this view has profound implications for physical therapy practice. We discuss the implications for rehabilitation of balance disorders and demonstrate how an understanding of the specific systems underlying postural control can help to focus and enrich our therapeutic approaches. By understanding the basic systems underlying control of balance, such as strategy selection, rapid latencies, coordinated temporal spatial patterns, force control, and context-specific adaptations, therapists can focus their treatment on each patient's specific impairments. Research on postural responses to surface translations has shown that balance is not based on a fixed set of equilibrium reflexes but on a flexible, functional motor skill that can adapt with training and experience. More research is needed to determine the extent to which quantification of automatic postural responses has practical implications for predicting falls in patients with constraints in their postural control system.

Lung nodule detection from CT scans using 3D convolutional neural networks without candidate selection

NASA Astrophysics Data System (ADS)

Jenuwine, Natalia M.; Mahesh, Sunny N.; Furst, Jacob D.; Raicu, Daniela S.

2018-02-01

Early detection of lung nodules from CT scans is key to improving lung cancer treatment, but poses a significant challenge for radiologists due to the high throughput required of them. Computer-Aided Detection (CADe) systems aim to automatically detect these nodules with computer algorithms, thus improving diagnosis. These systems typically use a candidate selection step, which identifies all objects that resemble nodules, followed by a machine learning classifier which separates true nodules from false positives. We create a CADe system that uses a 3D convolutional neural network (CNN) to detect nodules in CT scans without a candidate selection step. Using data from the LIDC database, we train a 3D CNN to analyze subvolumes from anywhere within a CT scan and output the probability that each subvolume contains a nodule. Once trained, we apply our CNN to detect nodules from entire scans, by systematically dividing the scan into overlapping subvolumes which we input into the CNN to obtain the corresponding probabilities. By enabling our network to process an entire scan, we expect to streamline the detection process while maintaining its effectiveness. Our results imply that with continued training using an iterative training scheme, the one-step approach has the potential to be highly effective.

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

PubMed

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-07

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography

NASA Astrophysics Data System (ADS)

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-01

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

Automated 3D Phenotype Analysis Using Data Mining

PubMed Central

Plyusnin, Ilya; Evans, Alistair R.; Karme, Aleksis; Gionis, Aristides; Jernvall, Jukka

2008-01-01

The ability to analyze and classify three-dimensional (3D) biological morphology has lagged behind the analysis of other biological data types such as gene sequences. Here, we introduce the techniques of data mining to the study of 3D biological shapes to bring the analyses of phenomes closer to the efficiency of studying genomes. We compiled five training sets of highly variable morphologies of mammalian teeth from the MorphoBrowser database. Samples were labeled either by dietary class or by conventional dental types (e.g. carnassial, selenodont). We automatically extracted a multitude of topological attributes using Geographic Information Systems (GIS)-like procedures that were then used in several combinations of feature selection schemes and probabilistic classification models to build and optimize classifiers for predicting the labels of the training sets. In terms of classification accuracy, computational time and size of the feature sets used, non-repeated best-first search combined with 1-nearest neighbor classifier was the best approach. However, several other classification models combined with the same searching scheme proved practical. The current study represents a first step in the automatic analysis of 3D phenotypes, which will be increasingly valuable with the future increase in 3D morphology and phenomics databases. PMID:18320060

Text-image alignment for historical handwritten documents

NASA Astrophysics Data System (ADS)

Zinger, S.; Nerbonne, J.; Schomaker, L.

2009-01-01

We describe our work on text-image alignment in context of building a historical document retrieval system. We aim at aligning images of words in handwritten lines with their text transcriptions. The images of handwritten lines are automatically segmented from the scanned pages of historical documents and then manually transcribed. To train automatic routines to detect words in an image of handwritten text, we need a training set - images of words with their transcriptions. We present our results on aligning words from the images of handwritten lines and their corresponding text transcriptions. Alignment based on the longest spaces between portions of handwriting is a baseline. We then show that relative lengths, i.e. proportions of words in their lines, can be used to improve the alignment results considerably. To take into account the relative word length, we define the expressions for the cost function that has to be minimized for aligning text words with their images. We apply right to left alignment as well as alignment based on exhaustive search. The quality assessment of these alignments shows correct results for 69% of words from 100 lines, or 90% of partially correct and correct alignments combined.

Assessment of skills using a virtual reality temporal bone surgery simulator.

PubMed

Linke, R; Leichtle, A; Sheikh, F; Schmidt, C; Frenzel, H; Graefe, H; Wollenberg, B; Meyer, J E

2013-08-01

Surgery on the temporal bone is technically challenging due to its complex anatomy. Precise anatomical dissection of the human temporal bone is essential and is fundamental for middle ear surgery. We assessed the possible application of a virtual reality temporal bone surgery simulator to the education of ear surgeons. Seventeen ENT physicians with different levels of surgical training and 20 medical students performed an antrotomy with a computer-based virtual temporal bone surgery simulator. The ease, accuracy and timing of the simulated temporal bone surgery were assessed using the automatic assessment software provided by the simulator device and additionally with a modified Final Product Analysis Scale. Trained ENT surgeons, physicians without temporal bone surgical training and medical students were all able to perform the antrotomy. However, the highly trained ENT surgeons were able to complete the surgery in approximately half the time, with better handling and accuracy as assessed by the significant reduction in injury to important middle ear structures. Trained ENT surgeons achieved significantly higher scores using both dissection analysis methods. Surprisingly, there were no significant differences in the results between medical students and physicians without experience in ear surgery. The virtual temporal bone training system can stratify users of known levels of experience. This system can be used not only to improve the surgical skills of trained ENT surgeons for more successful and injury-free surgeries, but also to train inexperienced physicians/medical students in developing their surgical skills for the ear.

Slip control for LIM propelled transit vehicles

NASA Astrophysics Data System (ADS)

Wallace, A. K.; Parker, J. H.; Dawson, G. E.

1980-09-01

Short stator linear induction motors, with an iron-backed aluminum sheet reaction rail and powered by a controlled inverter, have been selected as the propulsion system for transit vehicles in an intermediate capacity system (12-20,000 pphpd). The linear induction motor is capable of adhesion independent braking and acceleration levels which permit safe, close headways. In addition, simple control is possible allowing moving block automatic train control. This paper presents a slip frequency control scheme for the LIM. Experimental results for motoring and braking obtained from a test vehicle are also presented. These values are compared with theoretical predictions.

Macintosh/LabVIEW based control and data acquisition system for a single photon counting fluorometer

NASA Astrophysics Data System (ADS)

Stryjewski, Wieslaw J.

1991-08-01

A flexible software system has been developed for controlling fluorescence decay measurements using the virtual instrument approach offered by LabVIEW. The time-correlated single photon counting instrument operates under computer control in both manual and automatic mode. Implementation time was short and the equipment is now easier to use, reducing the training time required for new investigators. It is not difficult to customize the front panel or adapt the program to a different instrument. We found LabVIEW much more convenient to use for this application than traditional, textual computer languages.

Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

PubMed Central

Peng, Zhenyun; Zhang, Yaohui

2014-01-01

Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying. PMID:24592182

Detecting tympanostomy tubes from otoscopic images via offline and online training.

PubMed

Wang, Xin; Valdez, Tulio A; Bi, Jinbo

2015-06-01

Tympanostomy tube placement has been commonly used nowadays as a surgical treatment for otitis media. Following the placement, regular scheduled follow-ups for checking the status of the tympanostomy tubes are important during the treatment. The complexity of performing the follow up care mainly lies on identifying the presence and patency of the tympanostomy tube. An automated tube detection program will largely reduce the care costs and enhance the clinical efficiency of the ear nose and throat specialists and general practitioners. In this paper, we develop a computer vision system that is able to automatically detect a tympanostomy tube in an otoscopic image of the ear drum. The system comprises an offline classifier training process followed by a real-time refinement stage performed at the point of care. The offline training process constructs a three-layer cascaded classifier with each layer reflecting specific characteristics of the tube. The real-time refinement process enables the end users to interact and adjust the system over time based on their otoscopic images and patient care. The support vector machine (SVM) algorithm has been applied to train all of the classifiers. Empirical evaluation of the proposed system on both high quality hospital images and low quality internet images demonstrates the effectiveness of the system. The offline classifier trained using 215 images could achieve a 90% accuracy in terms of classifying otoscopic images with and without a tympanostomy tube, and then the real-time refinement process could improve the classification accuracy by 3-5% based on additional 20 images. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effectiveness of automated external defibrillators in high schools in greater Boston.

PubMed

England, Hannah; Hoffman, Caitlin; Hodgman, Thomas; Singh, Sushil; Homoud, Munther; Weinstock, Jonathan; Link, Mark; Estes, N A Mark

2005-06-15

A program using a strategy of donating a single automatic external defibrillator to 35 schools in the Boston area resulted in compliance with American Heart Association guidelines on automatic external defibrillator placement and training and 2 successful resuscitations from sudden cardiac arrest. Participating schools indicated a high degree of satisfaction with the program.

Mindfulness-Based Parent Training: Strategies to Lessen the Grip of Automaticity in Families with Disruptive Children

ERIC Educational Resources Information Center

Dumas, Jean E.

2005-01-01

Disagreements and conflicts in families with disruptive children often reflect rigid patterns of behavior that have become overlearned and automatized with repeated practice. These patterns are mindless: They are performed with little or no awareness and are highly resistant to change. This article introduces a new, mindfulness-based model of…

Automatic/Control Processing Concepts and Their Implications for the Training of Skills.

DTIC Science & Technology

1982-04-01

driving a car are examples of automatic processes. Controll p s is comparatively slow, serial, limited by short-term memory, and requires subject effort...development has convinced us that moivation a oftn more Jmportn nti mAn =other iJli velLJoa jjthpgy gI. njj Lautomatic U_2,LLjjk. Motivation Is much more

Automatic detection of apical roots in oral radiographs

NASA Astrophysics Data System (ADS)

Wu, Yi; Xie, Fangfang; Yang, Jie; Cheng, Erkang; Megalooikonomou, Vasileios; Ling, Haibin

2012-03-01

The apical root regions play an important role in analysis and diagnosis of many oral diseases. Automatic detection of such regions is consequently the first step toward computer-aided diagnosis of these diseases. In this paper we propose an automatic method for periapical root region detection by using the state-of-theart machine learning approaches. Specifically, we have adapted the AdaBoost classifier for apical root detection. One challenge in the task is the lack of training cases especially for diseased ones. To handle this problem, we boost the training set by including more root regions that are close to the annotated ones and decompose the original images to randomly generate negative samples. Based on these training samples, the Adaboost algorithm in combination with Haar wavelets is utilized in this task to train an apical root detector. The learned detector usually generates a large amount of true and false positives. In order to reduce the number of false positives, a confidence score for each candidate detection result is calculated for further purification. We first merge the detected regions by combining tightly overlapped detected candidate regions and then we use the confidence scores from the Adaboost detector to eliminate the false positives. The proposed method is evaluated on a dataset containing 39 annotated digitized oral X-Ray images from 21 patients. The experimental results show that our approach can achieve promising detection accuracy.

Possible End to an Endless Quest? Cognitive Bias Modification for Excessive Multiplayer Online Gamers.

PubMed

Rabinovitz, Sharon; Nagar, Maayan

2015-10-01

Cognitive biases have previously been recognized as key mechanisms that contribute to the development, maintenance, and relapse of addictive behaviors. The same mechanisms have been recently found in problematic computer gaming. The present study aims to investigate whether excessive massively multiplayer online role-playing gamers (EG) demonstrate an approach bias toward game-related cues compared to neutral stimuli; to test whether these automatic action tendencies can be implicitly modified in a single session training; and to test whether this training affects game urges and game-seeking behavior. EG (n=38) were randomly assigned to a condition in which they were implicitly trained to avoid or to approach gaming cues by pushing or pulling a joystick, using a computerized intervention (cognitive bias modification via the Approach Avoidance Task). EG demonstrated an approach bias for gaming cues compared with neutral, movie cues. Single session training significantly decreased automatic action tendencies to approach gaming cues. These effects occurred outside subjective awareness. Furthermore, approach bias retraining reduced subjective urges and intentions to play, as well as decreased game-seeking behavior. Retraining automatic processes may be beneficial in changing addictive impulses in EG. Yet, large-scale trials and long-term follow-up are warranted. The results extend the application of cognitive bias modification from substance use disorders to behavioral addictions, and specifically to Internet gaming disorder. Theoretical implications are discussed.

Relearning of Writing Skills in Parkinson's Disease After Intensive Amplitude Training.

PubMed

Nackaerts, Evelien; Heremans, Elke; Vervoort, Griet; Smits-Engelsman, Bouwien C M; Swinnen, Stephan P; Vandenberghe, Wim; Bergmans, Bruno; Nieuwboer, Alice

2016-08-01

Micrographia occurs in approximately 60% of people with Parkinson's disease (PD). Although handwriting is an important task in daily life, it is not clear whether relearning and consolidation (ie the solid storage in motor memory) of this skill is possible in PD. The objective was to conduct for the first time a controlled study into the effects of intensive motor learning to improve micrographia in PD. In this placebo-controlled study, 38 right-handed people with PD were randomized into 2 groups, receiving 1 of 2 equally time-intensive training programs (30 min/day, 5 days/week for 6 weeks). The experimental group (n = 18) performed amplitude training focused at improving writing size. The placebo group (n = 20) received stretch and relaxation exercises. Participants' writing skills were assessed using a touch-sensitive writing tablet and a pen-and-paper test, pre- and posttraining, and after a 6-week retention period. The primary outcome was change in amplitude during several tests of consolidation: (1) transfer, using trained and untrained sequences performed with and without target zones; and (2) automatization, using single- and dual-task sequences. The group receiving amplitude training significantly improved in amplitude and variability of amplitude on the transfer and automatization task. Effect sizes varied between 7% and 17%, and these benefits were maintained after the 6-week retention period. Moreover, there was transfer to daily life writing. These results show automatization, transfer, and retention of increased writing size (diminished micrographia) after intensive amplitude training, indicating that consolidation of motor learning is possible in PD. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG.

PubMed

Supratak, Akara; Dong, Hao; Wu, Chao; Guo, Yike

2017-11-01

This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG (left), Fpz-Cz, and Pz-Oz) from two public sleep data sets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared with the state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both data sets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different data sets without utilizing any hand-engineered features.

Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction.

PubMed

Kamphuis, C; Mollenhorst, H; Heesterbeek, J A P; Hogeveen, H

2010-08-01

The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM, at a very high specificity (Sp). In addition, an alert for CM should be generated preferably at the quarter milking (QM) at which the CM infection is visible for the first time. Data were collected from 9 Dutch dairy herds milking automatically during a 2.5-yr period. Data included sensor data (electrical conductivity, color, and yield) at the QM level and visual observations of quarters with CM recorded by the farmers. Visual observations of quarters with CM were combined with sensor data of the most recent automatic milking recorded for that same quarter, within a 24-h time window before the visual assessment time. Sensor data of 3.5 million QM were collected, of which 348 QM were combined with a CM observation. Data were divided into a training set, including two-thirds of all data, and a test set. Cows in the training set were not included in the test set and vice versa. A decision-tree model was trained using only clear examples of healthy (n=24,717) or diseased (n=243) QM. The model was tested on 105 QM with CM and a random sample of 50,000 QM without CM. While keeping the Se at a level comparable to that of models currently used by AMS, the decision-tree model was able to decrease the number of false-positive alerts by more than 50%. At an Sp of 99%, 40% of the CM cases were detected. Sixty-four percent of the severe CM cases were detected and only 12.5% of the CM that were scored as watery milk. The Se increased considerably from 40% to 66.7% when the time window increased from less than 24h before the CM observation, to a time window from 24h before to 24h after the CM observation. Even at very wide time windows, however, it was impossible to reach an Se of 100%. This indicates the inability to detect all CM cases based on sensor data alone. Sensitivity levels varied largely when the decision tree was validated per herd. This trend was confirmed when decision trees were trained using data from 8 herds and tested on data from the ninth herd. This indicates that when using the decision tree as a generic CM detection model in practice, some herds will continue having difficulties in detecting CM using mastitis alert lists, whereas others will perform well. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Effects of Word Recognition Training in a Picture-Word Interference Task: Automaticity vs. Speed.

ERIC Educational Resources Information Center

Ehri, Linnea C.

First and second graders were taught to recognize a set of written words either more accurately or more rapidly. Both before and after word training, they named pictures printed with and without these words as distractors. Of interest was whether training would enhance or diminish the interference created by these words in the picture naming task.…

49 CFR 236.567 - Restrictions imposed when device fails and/or is cut out en route.

Code of Federal Regulations, 2010 CFR

2010-10-01

... cut out en route. 236.567 Section 236.567 Transportation Other Regulations Relating to Transportation...; Locomotives § 236.567 Restrictions imposed when device fails and/or is cut out en route. Where an automatic train stop, train control, or cab signal device fails and/or is cut out enroute, train may proceed at...

Defense Management Education and Training Catalog.

ERIC Educational Resources Information Center

Office of the Assistant Secretary of Defense for Manpower and Reserve Affairs (DOD), Washington, DC.

This catalog provides information on a wide variety of courses, programs, and school made available by Department of Defense organizations. The program consists of eighteen primarily service-operated schools offering joint training in management covering a wide variety of subjects including automatic data processing, production management,…

49 CFR 236.722 - Circuit, cut-in.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Circuit, cut-in. 236.722 Section 236.722 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Circuit, cut-in. A roadway circuit at the entrance to automatic train stop, train control or cab signal...

Techniques and Training.

ERIC Educational Resources Information Center

Davies, Maire Messenger

1992-01-01

Provides an overview of articles included in this issue that address automatic evaluation of public service announcements about AIDS that are aimed at high-risk, low-literacy individuals; how children construe television programs; the concept of quality in broadcasting; and the use of video for inservice teacher training. (LRW)

49 CFR 236.722 - Circuit, cut-in.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Circuit, cut-in. 236.722 Section 236.722 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION... Circuit, cut-in. A roadway circuit at the entrance to automatic train stop, train control or cab signal...

Automatic measurement of voice onset time using discriminative structured prediction.

PubMed

Sonderegger, Morgan; Keshet, Joseph

2012-12-01

A discriminative large-margin algorithm for automatic measurement of voice onset time (VOT) is described, considered as a case of predicting structured output from speech. Manually labeled data are used to train a function that takes as input a speech segment of an arbitrary length containing a voiceless stop, and outputs its VOT. The function is explicitly trained to minimize the difference between predicted and manually measured VOT; it operates on a set of acoustic feature functions designed based on spectral and temporal cues used by human VOT annotators. The algorithm is applied to initial voiceless stops from four corpora, representing different types of speech. Using several evaluation methods, the algorithm's performance is near human intertranscriber reliability, and compares favorably with previous work. Furthermore, the algorithm's performance is minimally affected by training and testing on different corpora, and remains essentially constant as the amount of training data is reduced to 50-250 manually labeled examples, demonstrating the method's practical applicability to new datasets.

Automatic categorization of anatomical landmark-local appearances based on diffeomorphic demons and spectral clustering for constructing detector ensembles.

PubMed

Hanaoka, Shouhei; Masutani, Yoshitaka; Nemoto, Mitsutaka; Nomura, Yukihiro; Yoshikawa, Takeharu; Hayashi, Naoto; Ohtomo, Kuni

2012-01-01

A method for categorizing landmark-local appearances extracted from computed tomography (CT) datasets is presented. Anatomical landmarks in the human body inevitably have inter-individual variations that cause difficulty in automatic landmark detection processes. The goal of this study is to categorize subjects (i.e., training datasets) according to local shape variations of such a landmark so that each subgroup has less shape variation and thus the machine learning of each landmark detector is much easier. The similarity between each subject pair is measured based on the non-rigid registration result between them. These similarities are used by the spectral clustering process. After the clustering, all training datasets in each cluster, as well as synthesized intermediate images calculated from all subject-pairs in the cluster, are used to train the corresponding subgroup detector. All of these trained detectors compose a detector ensemble to detect the target landmark. Evaluation with clinical CT datasets showed great improvement in the detection performance.

Systems and technologies for objective evaluation of technical skills in laparoscopic surgery.

PubMed

Sánchez-Margallo, Juan A; Sánchez-Margallo, Francisco M; Oropesa, Ignacio; Gómez, Enrique J

2014-01-01

Minimally invasive surgery is a highly demanding surgical approach regarding technical requirements for the surgeon, who must be trained in order to perform a safe surgical intervention. Traditional surgical education in minimally invasive surgery is commonly based on subjective criteria to quantify and evaluate surgical abilities, which could be potentially unsafe for the patient. Authors, surgeons and associations are increasingly demanding the development of more objective assessment tools that can accredit surgeons as technically competent. This paper describes the state of the art in objective assessment methods of surgical skills. It gives an overview on assessment systems based on structured checklists and rating scales, surgical simulators, and instrument motion analysis. As a future work, an objective and automatic assessment method of surgical skills should be standardized as a means towards proficiency-based curricula for training in laparoscopic surgery and its certification.

Intelligent guidance and control for wind shear encounter

NASA Technical Reports Server (NTRS)

Stengel, Robert F.

1988-01-01

The principal objective is to develop methods for assessing the likelihood of wind shear encounter, for deciding what flight path to pursue, and for using the aircraft's full potential for combating wind shear. This study requires the definition of both deterministic and statistical techniques for fusing internal and external information, for making go/no-go decisions, and for generating commands to the aircraft's cockpit displays and autopilot for both manually controlled and automatic flight. The program has begun with the development of a real-time expert system for pilot aiding that is based on the results of the FAA Windshear Training Aids Program. A two-volume manual that presents an overview, pilot guide, training program, and substantiating data provides guidelines for this initial development. The Expert System to Avoid Wind Shear (ESAWS) currently contains over 140 rules and is coded in the LISP programming language for implementation on a Symbolics 3670 LISP machine.

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading.

PubMed

Deng, Yue; Bao, Feng; Kong, Youyong; Ren, Zhiquan; Dai, Qionghai

2017-03-01

Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automatically senses the dynamic market condition for informative feature learning. Then, the RL module interacts with deep representations and makes trading decisions to accumulate the ultimate rewards in an unknown environment. The learning system is implemented in a complex NN that exhibits both the deep and recurrent structures. Hence, we propose a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training. The robustness of the neural system is verified on both the stock and the commodity future markets under broad testing conditions.

Evaluating segmentation error without ground truth.

PubMed

Kohlberger, Timo; Singh, Vivek; Alvino, Chris; Bahlmann, Claus; Grady, Leo

2012-01-01

The automatic delineation of the boundaries of organs and other anatomical structures is a key component of many medical image processing systems. In this paper we present a generic learning approach based on a novel space of segmentation features, which can be trained to predict the overlap error and Dice coefficient of an arbitrary organ segmentation without knowing the ground truth delineation. We show the regressor to be much stronger a predictor of these error metrics than the responses of probabilistic boosting classifiers trained on the segmentation boundary. The presented approach not only allows us to build reliable confidence measures and fidelity checks, but also to rank several segmentation hypotheses against each other during online usage of the segmentation algorithm in clinical practice.

Automatic photointerpretation for plant species and stress identification (ERTS-A1)

NASA Technical Reports Server (NTRS)

Swanlund, G. D. (Principal Investigator); Kirvida, L.; Johnson, G. R.

1973-01-01

The author has identified the following significant results. Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wildlife management, forest inventory, and forest condition monitoring. Automatic procedures based on both multispectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74 percent was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 90 percent was obtained.

41 CFR 102-80.100 - What performance objective should an automatic sprinkler system be capable of meeting?

Code of Federal Regulations, 2010 CFR

2010-07-01

... objective should an automatic sprinkler system be capable of meeting? 102-80.100 Section 102-80.100 Public... Automatic Sprinkler Systems § 102-80.100 What performance objective should an automatic sprinkler system be capable of meeting? The performance objective of the automatic sprinkler system is that it must be capable...

41 CFR 102-80.100 - What performance objective should an automatic sprinkler system be capable of meeting?

Code of Federal Regulations, 2013 CFR

2013-07-01

... objective should an automatic sprinkler system be capable of meeting? 102-80.100 Section 102-80.100 Public... Automatic Sprinkler Systems § 102-80.100 What performance objective should an automatic sprinkler system be capable of meeting? The performance objective of the automatic sprinkler system is that it must be capable...

41 CFR 102-80.100 - What performance objective should an automatic sprinkler system be capable of meeting?

Code of Federal Regulations, 2014 CFR

2014-01-01

... objective should an automatic sprinkler system be capable of meeting? 102-80.100 Section 102-80.100 Public... Automatic Sprinkler Systems § 102-80.100 What performance objective should an automatic sprinkler system be capable of meeting? The performance objective of the automatic sprinkler system is that it must be capable...

41 CFR 102-80.100 - What performance objective should an automatic sprinkler system be capable of meeting?

Code of Federal Regulations, 2012 CFR

2012-01-01

... objective should an automatic sprinkler system be capable of meeting? 102-80.100 Section 102-80.100 Public... Automatic Sprinkler Systems § 102-80.100 What performance objective should an automatic sprinkler system be capable of meeting? The performance objective of the automatic sprinkler system is that it must be capable...

41 CFR 102-80.100 - What performance objective should an automatic sprinkler system be capable of meeting?

Code of Federal Regulations, 2011 CFR

2011-01-01

... objective should an automatic sprinkler system be capable of meeting? 102-80.100 Section 102-80.100 Public... Automatic Sprinkler Systems § 102-80.100 What performance objective should an automatic sprinkler system be capable of meeting? The performance objective of the automatic sprinkler system is that it must be capable...

Large-scale machine learning and evaluation platform for real-time traffic surveillance

NASA Astrophysics Data System (ADS)

Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

2016-09-01

In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing

PubMed Central

Xiao, Bo; Imel, Zac E.; Georgiou, Panayiotis G.; Atkins, David C.; Narayanan, Shrikanth S.

2015-01-01

The technology for evaluating patient-provider interactions in psychotherapy–observational coding–has not changed in 70 years. It is labor-intensive, error prone, and expensive, limiting its use in evaluating psychotherapy in the real world. Engineering solutions from speech and language processing provide new methods for the automatic evaluation of provider ratings from session recordings. The primary data are 200 Motivational Interviewing (MI) sessions from a study on MI training methods with observer ratings of counselor empathy. Automatic Speech Recognition (ASR) was used to transcribe sessions, and the resulting words were used in a text-based predictive model of empathy. Two supporting datasets trained the speech processing tasks including ASR (1200 transcripts from heterogeneous psychotherapy sessions and 153 transcripts and session recordings from 5 MI clinical trials). The accuracy of computationally-derived empathy ratings were evaluated against human ratings for each provider. Computationally-derived empathy scores and classifications (high vs. low) were highly accurate against human-based codes and classifications, with a correlation of 0.65 and F-score (a weighted average of sensitivity and specificity) of 0.86, respectively. Empathy prediction using human transcription as input (as opposed to ASR) resulted in a slight increase in prediction accuracies, suggesting that the fully automatic system with ASR is relatively robust. Using speech and language processing methods, it is possible to generate accurate predictions of provider performance in psychotherapy from audio recordings alone. This technology can support large-scale evaluation of psychotherapy for dissemination and process studies. PMID:26630392

[Sudden cardiac death out of the hospital and early defibrillation].

PubMed

Marín-Huerta, E; Peinado, R; Asso, A; Loma, A; Villacastín, J P; Muñiz, J; Brugada, J

2000-06-01

Since most sudden cardiac death victims show neither symptoms before the event nor other signs or risk factors that would have identified them as a high risk population before their cardiac arrest, emergency out-of-hospital medical services must be improved in order to obtain a higher survival in these patients. Early defibrillation is an essential part of the chain of survival that also includes the early identification of the victim, activation of the emergency medical system, immediate arrival of trained personnel who can perform basic cardiopulmonary resuscitation and early initiation of advanced cardiac life support that would raise the survival rate for sudden cardiac arrest victims. Many studies have demonstrated the enormous importance of early defibrillation in patients with a cardiac arrest due to ventricular fibrillation. The most important predictor of survival in these individuals is the time that elapses until electric defibrillation, the longer the time to defbrillation the lower the number of patients who are eventually discharged. Multiple studies have demonstrated that automatic external defibrillation will reduce the time elapsed to defibrillation and thus improve survival. For these reason, public access defibrillation to allow the use of automatic external defibrillators by minimally trained members of the lay public, has received increasing interest on the part of a groving number of companies, cities or countries. The automatic external defibrillaton, as performed by a lay person is being investigated. The liberalization of its application, if is demonstrated to be effective, will need to be accompanied by legal measures to endorse it and appropriate health education, probably during secondary education.

The expert surgical assistant. An intelligent virtual environment with multimodal input.

PubMed

Billinghurst, M; Savage, J; Oppenheimer, P; Edmond, C

1996-01-01

Virtual Reality has made computer interfaces more intuitive but not more intelligent. This paper shows how an expert system can be coupled with multimodal input in a virtual environment to provide an intelligent simulation tool or surgical assistant. This is accomplished in three steps. First, voice and gestural input is interpreted and represented in a common semantic form. Second, a rule-based expert system is used to infer context and user actions from this semantic representation. Finally, the inferred user actions are matched against steps in a surgical procedure to monitor the user's progress and provide automatic feedback. In addition, the system can respond immediately to multimodal commands for navigational assistance and/or identification of critical anatomical structures. To show how these methods are used we present a prototype sinus surgery interface. The approach described here may easily be extended to a wide variety of medical and non-medical training applications by making simple changes to the expert system database and virtual environment models. Successful implementation of an expert system in both simulated and real surgery has enormous potential for the surgeon both in training and clinical practice.

Experimental study of visual accommodation

NASA Technical Reports Server (NTRS)

Cornsweet, T. N.; Crane, H. D.

1972-01-01

A summary report of a research effort related to the human visual accommodation system is presented. A theoretical study of the accommodation system was made. Subsequent effort was aimed at the development of specialized instrumentation for experiments designed to lead to understanding the nature of the control system in human accommodation. The necessary instrumentation consisted primarily of: (1) an automatic optometer to measure the state of eye focus, (2) a focus stimulator device to control the apparent optical distance to any target, and (3) a two-dimensional eye tracker. The concepts and designs of the first two instruments have been published in the open literature, but this report contains the first detailed treatment of the Purkinje eye tracker developed under this program. The report also discusses an accommodation lag model to explain the ability of the eye to apparently know the polarity of focus error even though the blur on the retina is to a first-approximation an even function. The interaction of the accommodation and eye movement systems is also discussed, as is the ability to train the visual accommodation system to a surprisingly responsive condition in only a few hours of training.

Neural Coding for Effective Rehabilitation

PubMed Central

2014-01-01

Successful neurological rehabilitation depends on accurate diagnosis, effective treatment, and quantitative evaluation. Neural coding, a technology for interpretation of functional and structural information of the nervous system, has contributed to the advancements in neuroimaging, brain-machine interface (BMI), and design of training devices for rehabilitation purposes. In this review, we summarized the latest breakthroughs in neuroimaging from microscale to macroscale levels with potential diagnostic applications for rehabilitation. We also reviewed the achievements in electrocorticography (ECoG) coding with both animal models and human beings for BMI design, electromyography (EMG) interpretation for interaction with external robotic systems, and robot-assisted quantitative evaluation on the progress of rehabilitation programs. Future rehabilitation would be more home-based, automatic, and self-served by patients. Further investigations and breakthroughs are mainly needed in aspects of improving the computational efficiency in neuroimaging and multichannel ECoG by selection of localized neuroinformatics, validation of the effectiveness in BMI guided rehabilitation programs, and simplification of the system operation in training devices. PMID:25258708

An expert system for wind shear avoidance

NASA Technical Reports Server (NTRS)

Stengel, Robert F.; Stratton, D. Alexander

1990-01-01

The principal objectives are to develop methods for assessing the likelihood of wind shear encounter (based on real-time information in the cockpit), for deciding what flight path to pursue (e.g., takeoff abort, landing go-around, or normal climbout or glide slope), and for using the aircraft's full potential for combating wind shear. This study requires the definition of both deterministic and statistical techniques for fusing internal and external information, for making go/no-go decisions, and for generating commands to the aircraft's autopilot and flight directors for both automatic and manually controlled flight. The expert system for pilot aiding is based on the results of the FAA Windshear Training Aids Program, a two-volume manual that presents an overview, pilot guide, training program, and substantiating data that provides guidelines for this initial development. The Windshear Safety Advisor expert system currently contains over 140 rules and is coded in the LISP programming language for implementation on a Symbolics 3670 LISP Machine.

SYRIAC: The systematic review information automated collection system a data warehouse for facilitating automated biomedical text classification.

PubMed

Yang, Jianji J; Cohen, Aaron M; Cohen, Aaron; McDonagh, Marian S

2008-11-06

Automatic document classification can be valuable in increasing the efficiency in updating systematic reviews (SR). In order for the machine learning process to work well, it is critical to create and maintain high-quality training datasets consisting of expert SR inclusion/exclusion decisions. This task can be laborious, especially when the number of topics is large and source data format is inconsistent.To approach this problem, we build an automated system to streamline the required steps, from initial notification of update in source annotation files to loading the data warehouse, along with a web interface to monitor the status of each topic. In our current collection of 26 SR topics, we were able to standardize almost all of the relevance judgments and recovered PMIDs for over 80% of all articles. Of those PMIDs, over 99% were correct in a manual random sample study. Our system performs an essential function in creating training and evaluation data sets for SR text mining research.

SYRIAC: The SYstematic Review Information Automated Collection System A Data Warehouse for Facilitating Automated Biomedical Text Classification

PubMed Central

Yang, Jianji J.; Cohen, Aaron M.; McDonagh, Marian S.

2008-01-01

Automatic document classification can be valuable in increasing the efficiency in updating systematic reviews (SR). In order for the machine learning process to work well, it is critical to create and maintain high-quality training datasets consisting of expert SR inclusion/exclusion decisions. This task can be laborious, especially when the number of topics is large and source data format is inconsistent. To approach this problem, we build an automated system to streamline the required steps, from initial notification of update in source annotation files to loading the data warehouse, along with a web interface to monitor the status of each topic. In our current collection of 26 SR topics, we were able to standardize almost all of the relevance judgments and recovered PMIDs for over 80% of all articles. Of those PMIDs, over 99% were correct in a manual random sample study. Our system performs an essential function in creating training and evaluation datasets for SR text mining research. PMID:18999194

Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

NASA Astrophysics Data System (ADS)

Assaleh, Khaled; Al-Rousan, M.

2005-12-01

Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.

AutoNR: an automated system that measures ECAP thresholds with the Nucleus Freedom cochlear implant via machine intelligence.

PubMed

Botros, Andrew; van Dijk, Bas; Killian, Matthijs

2007-05-01

AutoNRT is an automated system that measures electrically evoked compound action potential (ECAP) thresholds from the auditory nerve with the Nucleus Freedom cochlear implant. ECAP thresholds along the electrode array are useful in objectively fitting cochlear implant systems for individual use. This paper provides the first detailed description of the AutoNRT algorithm and its expert systems, and reports the clinical success of AutoNRT to date. AutoNRT determines thresholds by visual detection, using two decision tree expert systems that automatically recognise ECAPs. The expert systems are guided by a dataset of 5393 neural response measurements. The algorithm approaches threshold from lower stimulus levels, ensuring recipient safety during postoperative measurements. Intraoperative measurements use the same algorithm but proceed faster by beginning at stimulus levels much closer to threshold. When searching for ECAPs, AutoNRT uses a highly specific expert system (specificity of 99% during training, 96% during testing; sensitivity of 91% during training, 89% during testing). Once ECAPs are established, AutoNRT uses an unbiased expert system to determine an accurate threshold. Throughout the execution of the algorithm, recording parameters (such as implant amplifier gain) are automatically optimised when needed. In a study that included 29 intraoperative and 29 postoperative subjects (a total of 418 electrodes), AutoNRT determined a threshold in 93% of cases where a human expert also determined a threshold. When compared to the median threshold of multiple human observers on 77 randomly selected electrodes, AutoNRT performed as accurately as the 'average' clinician. AutoNRT has demonstrated a high success rate and a level of performance that is comparable with human experts. It has been used in many clinics worldwide throughout the clinical trial and commercial launch of Nucleus Custom Sound Suite, significantly streamlining the clinical procedures associated with cochlear implant use.

Novel Symbol Learning-Induced Stroop Effect: Evidence for a Strategy-Based, Utility Learning Model

ERIC Educational Resources Information Center

Wang, Jin; Tang, Huijun; Deng, Yuan

2016-01-01

The automaticity level and attention priority/strategy are two major theories that have attempted to explain the mechanism underlying the Stroop effect. Training is an effective way to manipulate the experience with the two dimensions (ink color and color word) in the Stroop task. In order to distinguish the above two factors (the automaticity or…

Microwave Radiometers for Fire Detection in Trains: Theory and Feasibility Study.

PubMed

Alimenti, Federico; Roselli, Luca; Bonafoni, Stefania

2016-06-17

This paper introduces the theory of fire detection in moving vehicles by microwave radiometers. The system analysis is discussed and a feasibility study is illustrated on the basis of two implementation hypotheses. The basic idea is to have a fixed radiometer and to look inside the glass windows of the wagon when it passes in front of the instrument antenna. The proposed sensor uses a three-pixel multi-beam configuration that allows an image to be formed by the movement of the train itself. Each pixel is constituted by a direct amplification microwave receiver operating at 31.4 GHz. At this frequency, the antenna can be a 34 cm offset parabolic dish, whereas a 1 K brightness temperature resolution is achievable with an overall system noise figure of 6 dB, an observation bandwidth of 2 GHz and an integration time of 1 ms. The effect of the detector noise is also investigated and several implementation hypotheses are discussed. The presented study is important since it could be applied to the automatic fire alarm in trains and moving vehicles with dielectric wall/windows.

Microwave Radiometers for Fire Detection in Trains: Theory and Feasibility Study †

PubMed Central

Alimenti, Federico; Roselli, Luca; Bonafoni, Stefania

2016-01-01

This paper introduces the theory of fire detection in moving vehicles by microwave radiometers. The system analysis is discussed and a feasibility study is illustrated on the basis of two implementation hypotheses. The basic idea is to have a fixed radiometer and to look inside the glass windows of the wagon when it passes in front of the instrument antenna. The proposed sensor uses a three-pixel multi-beam configuration that allows an image to be formed by the movement of the train itself. Each pixel is constituted by a direct amplification microwave receiver operating at 31.4 GHz. At this frequency, the antenna can be a 34 cm offset parabolic dish, whereas a 1 K brightness temperature resolution is achievable with an overall system noise figure of 6 dB, an observation bandwidth of 2 GHz and an integration time of 1 ms. The effect of the detector noise is also investigated and several implementation hypotheses are discussed. The presented study is important since it could be applied to the automatic fire alarm in trains and moving vehicles with dielectric wall/windows. PMID:27322280

Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

NASA Technical Reports Server (NTRS)

Huang, Yiteng; Chen, Jingdong; Chen, Shaoyan

2010-01-01

A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.

PubMed

Kong, Xiangyi; Gong, Shun; Su, Lijuan; Howard, Newton; Kong, Yanguo

2018-01-01

Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

A machine learning approach for classification of anatomical coverage in CT

NASA Astrophysics Data System (ADS)

Wang, Xiaoyong; Lo, Pechin; Ramakrishna, Bharath; Goldin, Johnathan; Brown, Matthew

2016-03-01

Automatic classification of anatomical coverage of medical images is critical for big data mining and as a pre-processing step to automatically trigger specific computer aided diagnosis systems. The traditional way to identify scans through DICOM headers has various limitations due to manual entry of series descriptions and non-standardized naming conventions. In this study, we present a machine learning approach where multiple binary classifiers were used to classify different anatomical coverages of CT scans. A one-vs-rest strategy was applied. For a given training set, a template scan was selected from the positive samples and all other scans were registered to it. Each registered scan was then evenly split into k × k × k non-overlapping blocks and for each block the mean intensity was computed. This resulted in a 1 × k3 feature vector for each scan. The feature vectors were then used to train a SVM based classifier. In this feasibility study, four classifiers were built to identify anatomic coverages of brain, chest, abdomen-pelvis, and chest-abdomen-pelvis CT scans. Each classifier was trained and tested using a set of 300 scans from different subjects, composed of 150 positive samples and 150 negative samples. Area under the ROC curve (AUC) of the testing set was measured to evaluate the performance in a two-fold cross validation setting. Our results showed good classification performance with an average AUC of 0.96.

High Reliability and the Evaluation of ATC System Configuration by Communizing Resources

NASA Astrophysics Data System (ADS)

Yamamoto, Masanori

Automatic Train Control (ATC) in the railway signalling system is required high safety, high availability, reduction of unit, energy saving and cost reduction. This paper described the resources communization redundancy of the ATC system that shared the redundant units in preparation for common use units in order to accommodate with this issue by keeping safety and availability in the same level of conventional ATC. It was evaluated on N+2 redundant system which established 2 spares for the common use system N piece in transmission division. It was done the safety evaluation of the N+2 redundant system by way of hazard analysis of FTA method and safety issue was confirmed by FMEA. The new redundant system concludes that 19% of downsizing and 36% of the energy saving are surely possible.

Automatic detection and decoding of honey bee waggle dances.

PubMed

Wario, Fernando; Wild, Benjamin; Rojas, Raúl; Landgraf, Tim

2017-01-01

The waggle dance is one of the most popular examples of animal communication. Forager bees direct their nestmates to profitable resources via a complex motor display. Essentially, the dance encodes the polar coordinates to the resource in the field. Unemployed foragers follow the dancer's movements and then search for the advertised spots in the field. Throughout the last decades, biologists have employed different techniques to measure key characteristics of the waggle dance and decode the information it conveys. Early techniques involved the use of protractors and stopwatches to measure the dance orientation and duration directly from the observation hive. Recent approaches employ digital video recordings and manual measurements on screen. However, manual approaches are very time-consuming. Most studies, therefore, regard only small numbers of animals in short periods of time. We have developed a system capable of automatically detecting, decoding and mapping communication dances in real-time. In this paper, we describe our recording setup, the image processing steps performed for dance detection and decoding and an algorithm to map dances to the field. The proposed system performs with a detection accuracy of 90.07%. The decoded waggle orientation has an average error of -2.92° (± 7.37°), well within the range of human error. To evaluate and exemplify the system's performance, a group of bees was trained to an artificial feeder, and all dances in the colony were automatically detected, decoded and mapped. The system presented here is the first of this kind made publicly available, including source code and hardware specifications. We hope this will foster quantitative analyses of the honey bee waggle dance.

49 CFR 236.567 - Restrictions imposed when device fails and/or is cut out en route.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 4 2014-10-01 2014-10-01 false Restrictions imposed when device fails and/or is...; Locomotives § 236.567 Restrictions imposed when device fails and/or is cut out en route. Where an automatic train stop, train control, or cab signal device fails and/or is cut out enroute, train may proceed at...

Utility of Automatic Lighting Design in 3-D Virtual Training Environment

DTIC Science & Technology

2004-01-01

Many training environments have emphasized realism . Although realism may be important for many training applications, it is not essential for...achieving presence, attention, and emotional engagement (Zimmons, 2004). Also, realism is not always in conflict with providing atmosphere or mood, as...applied to the scene to heighten the audience’s emotional experience, while maintaining the perceived realism of the environment portrayed (Block, 2001

Machine Tool Technology. Automatic Screw Machine Troubleshooting & Set-Up Training Outlines [and] Basic Operator's Skills Set List.

ERIC Educational Resources Information Center

Anoka-Hennepin Technical Coll., Minneapolis, MN.

This set of two training outlines and one basic skills set list are designed for a machine tool technology program developed during a project to retrain defense industry workers at risk of job loss or dislocation because of conversion of the defense industry. The first troubleshooting training outline lists the categories of problems that develop…