Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity.

PubMed

Kesting, Arne; Treiber, Martin; Helbing, Dirk

2010-10-13

With an increasing number of vehicles equipped with adaptive cruise control (ACC), the impact of such vehicles on the collective dynamics of traffic flow becomes relevant. By means of simulation, we investigate the influence of variable percentages of ACC vehicles on traffic flow characteristics. For simulating the ACC vehicles, we propose a new car-following model that also serves as the basis of an ACC implementation in real cars. The model is based on the intelligent driver model (IDM) and inherits its intuitive behavioural parameters: desired velocity, acceleration, comfortable deceleration and desired minimum time headway. It eliminates, however, the sometimes unrealistic behaviour of the IDM in cut-in situations with ensuing small gaps that regularly are caused by lane changes of other vehicles in dense or congested traffic. We simulate the influence of different ACC strategies on the maximum capacity before breakdown and the (dynamic) bottleneck capacity after breakdown. With a suitable strategy, we find sensitivities of the order of 0.3, i.e. 1 per cent more ACC vehicles will lead to an increase in the capacities by about 0.3 per cent. This sensitivity multiplies when considering travel times at actual breakdowns.

A learning-based autonomous driver: emulate human driver's intelligence in low-speed car following

NASA Astrophysics Data System (ADS)

Wei, Junqing; Dolan, John M.; Litkouhi, Bakhtiar

2010-04-01

In this paper, an offline learning mechanism based on the genetic algorithm is proposed for autonomous vehicles to emulate human driver behaviors. The autonomous driving ability is implemented based on a Prediction- and Cost function-Based algorithm (PCB). PCB is designed to emulate a human driver's decision process, which is modeled as traffic scenario prediction and evaluation. This paper focuses on using a learning algorithm to optimize PCB with very limited training data, so that PCB can have the ability to predict and evaluate traffic scenarios similarly to human drivers. 80 seconds of human driving data was collected in low-speed (< 30miles/h) car-following scenarios. In the low-speed car-following tests, PCB was able to perform more human-like carfollowing after learning. A more general 120 kilometer-long simulation showed that PCB performs robustly even in scenarios that are not part of the training set.

Aggregate driver model to enable predictable behaviour

NASA Astrophysics Data System (ADS)

Chowdhury, A.; Chakravarty, T.; Banerjee, T.; Balamuralidhar, P.

2015-09-01

The categorization of driving styles, particularly in terms of aggressiveness and skill is an emerging area of interest under the broader theme of intelligent transportation. There are two possible discriminatory techniques that can be applied for such categorization; a microscale (event based) model and a macro-scale (aggregate) model. It is believed that an aggregate model will reveal many interesting aspects of human-machine interaction; for example, we may be able to understand the propensities of individuals to carry out a given task over longer periods of time. A useful driver model may include the adaptive capability of the human driver, aggregated as the individual propensity to control speed/acceleration. Towards that objective, we carried out experiments by deploying smartphone based application to be used for data collection by a group of drivers. Data is primarily being collected from GPS measurements including position & speed on a second-by-second basis, for a number of trips over a two months period. Analysing the data set, aggregate models for individual drivers were created and their natural aggressiveness were deduced. In this paper, we present the initial results for 12 drivers. It is shown that the higher order moments of the acceleration profile is an important parameter and identifier of journey quality. It is also observed that the Kurtosis of the acceleration profiles stores major information about the driving styles. Such an observation leads to two different ranking systems based on acceleration data. Such driving behaviour models can be integrated with vehicle and road model and used to generate behavioural model for real traffic scenario.

Perturbation and Stability Analysis of the Multi-Anticipative Intelligent Driver Model

NASA Astrophysics Data System (ADS)

Chen, Xi-Qun; Xie, Wei-Jun; Shi, Jing; Shi, Qi-Xin

This paper discusses three kinds of IDM car-following models that consider both the multi-anticipative behaviors and the reaction delays of drivers. Here, the multi-anticipation comes from two ways: (1) the driver is capable of evaluating the dynamics of several preceding vehicles, and (2) the autonomous vehicles can obtain the velocity and distance information of several preceding vehicles via inter-vehicle communications. In this paper, we study the stability of homogeneous traffic flow. The linear stability analysis indicates that the stable region will generally be enlarged by the multi-anticipative behaviors and reduced by the reaction delays. The temporal amplification and the spatial divergence of velocities for local perturbation are also studied, where the results further prove this conclusion. Simulation results also show that the multi-anticipative behaviors near the bottleneck will lead to a quicker backwards propagation of oscillations.

On an efficient and effective intelligent transportation system (ITS) safety and traffic efficiency application with corresponding driver behavior

NASA Astrophysics Data System (ADS)

Ekedebe, Nnanna; Yu, Wei; Lu, Chao

2015-06-01

Driver distraction could result in safety compromises attributable to distractions from in-vehicle equipment usage [1]. The effective design of driver-vehicle interfaces (DVIs) and other human-machine interfaces (HMIs) together with their usability, and accessibility while driving become important [2]. Driving distractions can be classified as: visual distractions (any activity that takes your eyes away from the road), cognitive distraction (any activity that takes your mind away from the course of driving), and manual distractions (any activity that takes your hands away from the steering wheel [2]). Besides, multitasking during driving is a distractive activity that can increase the risks of vehicular accidents. To study the driver's behaviors on the safety of transportation system, using an in-vehicle driver notification application, we examined the effects of increasing driver distraction levels on the evaluation metrics of traffic efficiency and safety by using two types of driver models: young drivers (ages 16-25 years) and middle-age drivers (ages 30-45 years). Our evaluation data demonstrates that as a drivers distraction level is increased, less heed is given to change route directives from the in-vehicle on-board unit (OBU) using textual, visual, audio, and haptic notifications. Interestingly, middle-age drivers proved more effective/resilient in mitigating the negative effects of driver distraction over young drivers [2].

Driver Distraction with Wireless Telecommunications and Route Guidance Systems

DOT National Transportation Integrated Search

2000-07-01

Concerns have been raised in recent years about the distraction potential of Intelligent Transportation Systems (ITS) technologies including driver : information systems such as route navigation systems. The research described in this report had the ...

Driving safely into the future with applied technology

DOT National Transportation Integrated Search

1999-10-01

Driver error remains the leading cause of highway crashes. Through the Intelligent Vehicle Initiative (IVI), the Department of Transportation hopes to reduce crashes by helping drivers avoid hazardous mistakes. IVI aims to accelerate the development ...

Deal or no deal: can incentives encourage widespread adoption of intelligent speed adaptation devices?

PubMed

Chorlton, Kathryn; Hess, Stephane; Jamson, Samantha; Wardman, Mark

2012-09-01

Given the burden of injury, economic, environmental and social consequences associated with speeding, reducing road traffic speed remains a major priority. Intelligent speed adaptation (ISA) is a promising but controversial new in-vehicle system that provides drivers with support on the speed-control task. In order to model potential system uptake, this paper explores drivers' preferences for two different types of ISA given a number of alternative fiscal incentives and non-fiscal measures, using a stated preference approach. As would be expected with such a contentious issue, the analysis revealed the presence of significant variations in sensitivities and preferences in the sample. While a non-negligible part of the sample population has such strong opposition to ISA that no reasonable discounts or incentives would lead to them buying or accepting such a system, there is also a large part of the population that, if given the right incentives, would be willing or even keen to equip their vehicle with an ISA device. Copyright © 2011. Published by Elsevier Ltd.

Backup Warning Signals: Driver Perception and Response

DOT National Transportation Integrated Search

1996-08-01

This report describes the findings of three experiments that concern driver reaction to acoustic signals that might be used for backup warning devices. Intelligent warning devices are under development that will use vehicle-based sensors to warn back...

Basic Collision Warning and Driver Information Systems: Human Factors Research Needs

DOT National Transportation Integrated Search

1998-11-01

As part of the U.S. Department of Transportation's Intelligent Vehicle Initiative (IVI) program, the Federal Highway Administration (FHWA) investigated the human factors research needs for integrating in-vehicle safety and driver information technolo...

Air quality impacts of intercity freight. Volume 1 : guidebook

DOT National Transportation Integrated Search

2000-01-01

Driver error remains the leading cause of highway crashes. Through the Intelligent Vehicle Initiative (IVI), the Department of Transportation hopes to reduce crashes by helping drivers avoid hazardous mistakes. IVI aims to accelerate the development ...

White paper : Mn/DOT driver acceptance : IVI FOT evaluation report

DOT National Transportation Integrated Search

2003-06-30

This white paper provides findings from surveys and interviews for the evaluation of driver acceptance as a component of Battelles independent evaluation of the Mn/DOT Intelligent Vehicle Initiative (IVI) Field Operational Test (FOT), sponsored by th...

Investigation Of Alternative Displays For Side Collision Avoidance Systems, Final Report

DOT National Transportation Integrated Search

1996-12-01

DRIVER-VEHICLE INTERFACE OR DVI, HUMAN FACTORS, DRIVER PREFERENCES, INTELLIGENT VEHICLE INITIATIVE OR IVI : SIDE COLLISION AVOIDANCE SYSTEMS (SCAS) ARE DESIGNED TO WARN OF IMPENDING COLLISIONS AND CAN DETECT NOT ONLY ADJACENT VEHICLES BUT VEHICLES...

2003 status report on transit intelligent vehicle initiative studies

DOT National Transportation Integrated Search

2003-06-01

This 2003 Status Report provides an overview and updates on studies in the transit Intelligent Vehicle Initiative (IVI) area. IVI emphasizes the significant and continuing role of drivers in roadway safety. IVI is aimed at accelerating the developmen...

Can enforced behaviour change attitudes: exploring the influence of Intelligent Speed Adaptation.

PubMed

Chorlton, Kathryn; Conner, Mark

2012-09-01

The Theory of Planned Behaviour model (Ajzen, 1985) was used to determine whether long-term experience with Intelligent Speed Adaption (ISA) prompts a change in speed related cognitions. The study examines data collected as part of a project examining driver behaviour with an intervening but overridable ISA system. Data was collected in four six-month field trials. The trials followed an A-B-A design (28 days driving with no ISA, 112 days driving with ISA, 28 days driving without ISA) to monitor changes in speeding behaviour as a result of the ISA system and any carry-over effect of the system. Findings suggested that following experience with the system, drivers' intention to speed significantly weakened, beyond the removal of ISA support. Drivers were also less likely to believe that exceeding the speed would 'get them to their destination more quickly' and less likely to believe that 'being in a hurry' would facilitate speeding. However, the positive change in intentions and beliefs failed to translate into behaviour. Experience with the ISA system significantly reduced the percentage of distance travelled whilst exceeding the speed limit but this effect was not evident when the ISA support was removed. Copyright © 2010 Elsevier Ltd. All rights reserved.

Perspectives on driver preferences for dynamic route guidance systems

DOT National Transportation Integrated Search

1997-01-01

Insights about the design of route guidance systems based on the needs and desires of drivers who are familiar with the travel network are provided. Results from the ADVANCE Intelligent Transportation System operational test, in which more than 100 d...

Driver decision making in response to alternate routes.

DOT National Transportation Integrated Search

2011-04-11

The issue of driver route choice has been studied fairly extensively as part of the body of research on intelligent : transportation systems. A few studies cited in this literature search focus directly on Route Choice Analysis, while : many other st...

What benefit does Intelligent Speed Adaptation deliver: a close examination of its effect on vehicle speeds.

PubMed

Lai, Frank; Carsten, Oliver

2012-09-01

Intelligent Speed Adaptation (ISA) is a driver support system which brings the speed limit information into the vehicle. This paper describes the UK ISA field trials taken place between 2004 and 2006 and presents evidence on how drivers' choice of speed is altered. The ISA system was observed to have a distinctive effect in transforming the speed distribution from a conventional bell shape to an asymmetric distribution biased towards the high speed end. ISA not only diminished excessive speeding, but also led to a reduction in speed variation, prompting a positive implication to accident reduction. The use of an overridable ISA system also provided an opportunity to investigate where drivers would choose to have ISA based on observed behaviour instead of opinion. Evidence shows that ISA tends to be overridden on roads where it was perhaps needed most. Behavioural difference among driver groups also suggests that ISA tends to be overridden by those drivers who in safety terms stand to benefit most from using it, as with other safety systems. Copyright © 2010 Elsevier Ltd. All rights reserved.

The Role Of Driver Inattention In Crashes; New Statistics From The 1995 Crashworthiness Data System

DOT National Transportation Integrated Search

1996-08-08

INTELLIGENT VEHICLE INITIATIVE OR IVI : IN 1995, NHTSA BEGAN EMPLOYING THE CRASHWORTHINESS DATA SYSTEM (CDS) TO OBTAIN MORE IN-DEPTH INFORMATION ON DRIVER INATTENTION-RELATED CRASH CAUSES, INCLUDING DROWSINESS AND MANY FORMS OF DISTRACTION. CDS IS PO...

Automated feedback to foster safe driving in young drivers : Phase 2.

DOT National Transportation Integrated Search

2015-12-01

Intelligent Speed Adaptation (ISA) represents a promising approach to reduce speeding. A core principle for ISA systems is that they provide real-time feedback to drivers, prompting them to reduce speed when some threshold at or above the limit is re...

Primal Leadership: The Hidden Driver of Great Performance.

ERIC Educational Resources Information Center

Goleman, Daniel; Boyatzis, Richard; McKee, Annie

2001-01-01

An extension of emotional intelligence research demonstrated that leaders' moods play a key role in organizational climate and effectiveness. A process for developing emotionally intelligent behaviors emerged: developing self-awareness, collecting 360 feedback, action planning, learning new habits, and cultivating a community of supporters. (SK)

Argonne simulation framework for intelligent transportation systems

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

Ewing, T.; Doss, E.; Hanebutte, U.

1996-04-01

A simulation framework has been developed which defines a high-level architecture for a large-scale, comprehensive, scalable simulation of an Intelligent Transportation System (ITS). The simulator is designed to run on parallel computers and distributed (networked) computer systems; however, a version for a stand alone workstation is also available. The ITS simulator includes an Expert Driver Model (EDM) of instrumented ``smart`` vehicles with in-vehicle navigation units. The EDM is capable of performing optimal route planning and communicating with Traffic Management Centers (TMC). A dynamic road map data base is sued for optimum route planning, where the data is updated periodically tomore » reflect any changes in road or weather conditions. The TMC has probe vehicle tracking capabilities (display position and attributes of instrumented vehicles), and can provide 2-way interaction with traffic to provide advisories and link times. Both the in-vehicle navigation module and the TMC feature detailed graphical user interfaces that includes human-factors studies to support safety and operational research. Realistic modeling of variations of the posted driving speed are based on human factor studies that take into consideration weather, road conditions, driver`s personality and behavior and vehicle type. The simulator has been developed on a distributed system of networked UNIX computers, but is designed to run on ANL`s IBM SP-X parallel computer system for large scale problems. A novel feature of the developed simulator is that vehicles will be represented by autonomous computer processes, each with a behavior model which performs independent route selection and reacts to external traffic events much like real vehicles. Vehicle processes interact with each other and with ITS components by exchanging messages. With this approach, one will be able to take advantage of emerging massively parallel processor (MPP) systems.« less

Automated feedback to foster safe driving in young drivers: phase 2 : traffic tech.

DOT National Transportation Integrated Search

2015-12-01

Intelligent Speed Adaptation (ISA) provides a promising approach to reduce speeding. A core principle of ISA is real-time feedback that lets drivers know when they are driving over the speed limit. The overall goal of the study was to provide insight...

Could Intelligent Speed Adaptation make overtaking unsafe?

PubMed

Jamson, Samantha; Chorlton, Kathryn; Carsten, Oliver

2012-09-01

This driving simulator study investigated how mandatory and voluntary ISA might affect a driver's overtaking decisions on rural roads, by presenting drivers with a variety of overtaking scenarios designed to evaluate both the frequency and safety of the manoeuvres. In half the overtaking scenarios, ISA was active and in the remainder ISA was switched off. A rural road was modelled with a number of 2+1 road sections, thus allowing drivers a protected overtaking opportunity. The results indicate that drivers became less inclined to initiate an overtaking manoeuvre when the mandatory ISA was active and this was particularly so when the overtaking opportunity was short. In addition to this, when ISA was activated drivers were more likely to have to abandon an overtaking, presumably due to running out of road. They also spent more time in the critical hatched area-a potentially unsafe behaviour. The quality of the overtaking manoeuvre was also affected when mandatory ISA was active, with drivers pulling out and cutting back in more sharply. In contrast, when driving with a voluntary ISA, overtaking behaviour remained mostly unchanged: drivers disengaged the function in approximately 70% of overtaking scenarios. The results of this study suggest that mandatory ISA could affect the safety of overtaking manoeuvres unless coupled with an adaptation period or other driver support functions that support safe overtaking. Copyright © 2010 Elsevier Ltd. All rights reserved.

Acquisition of business intelligence from human experience in route planning

NASA Astrophysics Data System (ADS)

Bello Orgaz, Gema; Barrero, David F.; R-Moreno, María D.; Camacho, David

2015-04-01

The logistic sector raises a number of highly challenging problems. Probably one of the most important ones is the shipping planning, i.e. plan the routes that the shippers have to follow to deliver the goods. In this article, we present an artificial intelligence-based solution that has been designed to help a logistic company to improve its routes planning process. In order to achieve this goal, the solution uses the knowledge acquired by the company drivers to propose optimised routes. Hence, the proposed solution gathers the experience of the drivers, processes it and optimises the delivery process. The solution uses data mining to extract knowledge from the company information systems and prepares it for analysis with a case-based reasoning (CBR) algorithm. The CBR obtains critical business intelligence knowledge from the drivers experience that is needed by the planner. The design of the routes is done by a genetic algorithm that, given the processed information, optimises the routes following several objectives, such as minimise the distance or time. Experimentation shows that the proposed approach is able to find routes that improve, on average, the routes made by the human experts.

Lane change warning threshold based on driver perception characteristics.

PubMed

Wang, Chang; Sun, Qinyu; Fu, Rui; Li, Zhen; Zhang, Qiong

2018-08-01

Lane Change Warning system (LCW) is exploited to alleviate driver workload and improve the safety performance of lane changes. Depending on the secure threshold, the lane change warning system could transmit caution to drivers. Although the system possesses substantial benefits, it may perturb the conventional operating of the driver and affect driver judgment if the warning threshold does not conform to the driver perception of safety. Therefore, it is essential to establish an appropriate warning threshold to enhance the accuracy rate and acceptability of the lane change warning system. This research aims to identify the threshold that conforms to the driver perception of the ability to safely change lanes with a rear vehicle fast approaching. We propose a theoretical warning model of lane change based on a safe minimum distance and deceleration of the rear vehicle. For the purpose of acquiring the different safety levels of lane changes, 30 licensed drivers are recruited and we obtain the extreme moments represented by driver perception characteristics from a Front Extremity Test and a Rear Extremity Test implemented on the freeway. The required deceleration of the rear vehicle corresponding to the extreme time is calculated according to the proposed model. In light of discrepancies in the deceleration in these extremity experiments, we determine two levels of a hierarchical warning system. The purpose of the primary warning is to remind drivers of the existence of potentially dangerous vehicles and the second warning is used to warn the driver to stop changing lanes immediately. We use the signal detection theory to analyze the data. Ultimately, we confirm that the first deceleration threshold is 1.5 m/s 2 and the second deceleration threshold is 2.7 m/s 2 . The findings provide the basis for the algorithm design of LCW and enhance the acceptability of the intelligent system. Copyright © 2018 Elsevier Ltd. All rights reserved.

LAVIA--an evaluation of the potential safety benefits of the French intelligent speed adaptation project.

PubMed

Driscoll, R; Page, Y; Lassarre, S; Ehrlich, J

2007-01-01

This paper presents the potential safety benefits of the experimental French LAVIA Intelligent Speed Adaptation system, according to road network and system mode, based on observed driving speeds, distributions of crash severity and crash injury risk. Results are given for car frontal and side impacts that together, represent 80% of all serious and fatal injuries in France. Of the three system modes tested (advisory, driver select, mandatory), our results suggest that driver select would most significantly reduce serious injuries and death. We estimate this 100% utilization of cars equipped with this type of speed adaptation system would decrease injury rates by 6% to 16% over existing conditions depending on the type of crash (frontal or side) and road environment considered. Some limitations associated with the analysis are also identified. LAVIA is the acronym for Limiteur s'Adaptant à la VItesse Autorisée, a French Intelligent Speed Adaptation (ISA) project that was set up towards the end of 1999. At the time, 1998 French national road safety statistics recorded 8437 road related deaths, a figure which had shown virtually no positive evolution since 1994. Detailed analysis of the contributory factors involved in fatal road crashes highlighted the time-honoured crash and injury causation mechanisms - alcohol, speed and seatbelts. Of the three, excessive speed (over and above the posted speed limit) was a contributory factor in half of all fatal crashes Inappropriate behaviour such as excessive speeding can be dealt with either by legislative or driver-incentive programmes. The first of these two solutions involves the introduction of new legislation and/or the enforcement of existing laws. This is the domain of Public Authorities and will not be discussed in detail here. Alternatively, incentive schemes can involve the implementation of speed related driver assistance systems, categorised according to their voluntary or mandatory character and the degree of autonomy proposed to or imposed on the driver. The LAVIA project set out to address several possible combinations of these two factors. The generic term Intelligent Speed Adaptation (ISA) encompasses a wide range of different technologies aimed at improving road safety by reducing traffic speed and homogenising traffic flow, within the limit of posted speed limits. "Fixed speed limit" systems inform the vehicle of the posted speed limit whereas "variable speed limit" systems take into account certain locations on the road network where a speed below the posted limit is desirable, such as sharp curves, pedestrian crossings or crash black spots. Taken one step further, speed limit systems may also take into account weather and traffic flow conditions. These systems are known as "dynamic speed limit" systems and benefit from real time updates for a specific location. The different ISA systems are generally characterised by the degree of freedom of choice given to the driver in moderating his or her speed. Speed limit technologies may be advisory (informing drivers of the current speed limit and speed limit changes), voluntary (allowing the driver to decide whether or not to implement speed limitation) or mandatory (imposing the current speed limit). The information supplied may be provided by way of the road infrastructure (and associated equipment), may be acquired autonomously by the vehicle or may be based on an interaction between the infrastructure and the vehicle. Even the most basic of these systems should be considered as a very useful driver aid, helping the driver to stay within the posted speed limit, avoiding "unnecessary" speeding fines through inattention, modelling driver behaviour through the long term reduction of speeds and reducing driver workload by limiting visual speedometer controls. Vehicle-based ISA systems should not be confused with internal systems. These latter systems rely upon the driver entering the desired travel speed, which is then maintained by cruise control or set as a maximum value by automatic speed regulators. Although these systems will not be discussed in detail here, it should be noted that the engine management technologies that they employ are a vital component of ISA systems.

A Vision-Based Driver Nighttime Assistance and Surveillance System Based on Intelligent Image Sensing Techniques and a Heterogamous Dual-Core Embedded System Architecture

PubMed Central

Chen, Yen-Lin; Chiang, Hsin-Han; Chiang, Chuan-Yen; Liu, Chuan-Ming; Yuan, Shyan-Ming; Wang, Jenq-Haur

2012-01-01

This study proposes a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system) implemented by a set of embedded software components and modules, and integrates these modules to accomplish a component-based system framework on an embedded heterogamous dual-core platform. Therefore, this study develops and implements computer vision and sensing techniques of nighttime vehicle detection, collision warning determination, and traffic event recording. The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle. These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform. Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system. PMID:22736956

A vision-based driver nighttime assistance and surveillance system based on intelligent image sensing techniques and a heterogamous dual-core embedded system architecture.

PubMed

Chen, Yen-Lin; Chiang, Hsin-Han; Chiang, Chuan-Yen; Liu, Chuan-Ming; Yuan, Shyan-Ming; Wang, Jenq-Haur

2012-01-01

This study proposes a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system) implemented by a set of embedded software components and modules, and integrates these modules to accomplish a component-based system framework on an embedded heterogamous dual-core platform. Therefore, this study develops and implements computer vision and sensing techniques of nighttime vehicle detection, collision warning determination, and traffic event recording. The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle. These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform. Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system.

Does assisted driving behavior lead to safety-critical encounters with unequipped vehicles' drivers?

PubMed

Preuk, Katharina; Stemmler, Eric; Schießl, Caroline; Jipp, Meike

2016-10-01

With Intelligent Transport Systems (e.g., traffic light assistance systems) assisted drivers are able to show driving behavior in anticipation of upcoming traffic situations. In the years to come, the penetration rate of such systems will be low. Therefore, the majority of vehicles will not be equipped with these systems. Unequipped vehicles' drivers may not expect the driving behavior of assisted drivers. However, drivers' predictions and expectations can play a significant role in their reaction times. Thus, safety issues could arise when unequipped vehicles' drivers encounter driving behavior of assisted drivers. This is why we tested how unequipped vehicles' drivers (N=60) interpreted and reacted to the driving behavior of an assisted driver. We used a multi-driver simulator with three drivers. The three drivers were driving in a line. The lead driver in the line was a confederate who was followed by two unequipped vehicles' drivers. We varied the equipment of the confederate with an Intelligent Transport System: The confederate was equipped either with or without a traffic light assistance system. The traffic light assistance system provided a start-up maneuver before a light turned green. Therefore, the assisted confederate seemed to show unusual deceleration behavior by coming to a halt at an unusual distance from the stop line at the red traffic light. The unusual distance was varied as we tested a moderate (4m distance from the stop line) and an extreme (10m distance from the stop line) parameterization of the system. Our results showed that the extreme parametrization resulted in shorter minimal time-to-collision of the unequipped vehicles' drivers. One rear-end crash was observed. These results provided initial evidence that safety issues can arise when unequipped vehicles' drivers encounter assisted driving behavior. We recommend that future research identifies counteractions to prevent these safety issues. Moreover, we recommend that system developers discuss the best parameterizations of their systems to ensure benefits but also the safety in encounters with unequipped vehicles' drivers. Copyright © 2016 Elsevier Ltd. All rights reserved.

DEPSCOR: Research on ARL’s Intelligent Control Architecture: Hierarchical Hybrid-Model Based Design, Verification, Simulation, and Synthesis of Mission Control for Autonomous Underwater Vehicles

DTIC Science & Technology

2007-02-01

shown in Figure 13 and the abstracted commanded environment is shown in Figure 14. Abort? Start Intl End itmi! Aborti Figure 13: Driver for loiter module...module in UPPAAL Aborti ? start Idle *- SteerToPoirt lot er<=2 Stee Doý2 I Abort? 65 66 Figure 14: Stub for loiter module module in UPPAAL Queries

Instruct: An Example of the Role of Artificial Intelligence in Voice-Based Training Systems.

DTIC Science & Technology

1983-01-01

Lockhart , R.S. Levels of processing : A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 1972, LT, pp. 6𔄁T-684. Ericsson...of covert student processing and knowledge levels from overt student behavior; and a curriculum driver that could use the student model to determine...concentration), then the task is said to be resource-limited. Whenever the performance level remains invariant to increased allocations of processing

Intelligent Middle-Ware Architecture for Mobile Networks

NASA Astrophysics Data System (ADS)

Rayana, Rayene Ben; Bonnin, Jean-Marie

Recent advances in electronic and automotive industries as well as in wireless telecommunication technologies have drawn a new picture where each vehicle became “fully networked”. Multiple stake-holders (network operators, drivers, car manufacturers, service providers, etc.) will participate in this emerging market, which could grow following various models. To free the market from technical constraints, it is important to return to the basics of the Internet, i.e., providing embarked devices with a fully operational Internet connectivity (IPv6).

Intelligent Sensors: Strategies for an Integrated Systems Approach

NASA Technical Reports Server (NTRS)

Chitikeshi, Sanjeevi; Mahajan, Ajay; Bandhil, Pavan; Utterbach, Lucas; Figueroa, Fernando

2005-01-01

This paper proposes the development of intelligent sensors as an integrated systems approach, i.e. one treats the sensors as a complete system with its own sensing hardware (the traditional sensor), A/D converters, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow them to get better with time. Under a project being undertaken at the Stennis Space Center, an integrated framework is being developed for the intelligent monitoring of smart elements. These smart elements can be sensors, actuators or other devices. The immediate application is the monitoring of the rocket test stands, but the technology should be generally applicable to the Intelligent Systems Health Monitoring (ISHM) vision. This paper outlines progress made in the development of intelligent sensors by describing the work done till date on Physical Intelligent Sensors (PIS) and Virtual Intelligent Sensors (VIS).

Memory effects in microscopic traffic models and wide scattering in flow-density data

NASA Astrophysics Data System (ADS)

Treiber, Martin; Helbing, Dirk

2003-10-01

By means of microscopic simulations we show that noninstantaneous adaptation of the driving behavior to the traffic situation together with the conventional method to measure flow-density data provides a possible explanation for the observed inverse-λ shape and the wide scattering of flow-density data in “synchronized” congested traffic. We model a memory effect in the response of drivers to the traffic situation for a wide class of car-following models by introducing an additional dynamical variable (the “subjective level of service”) describing the adaptation of drivers to the surrounding traffic situation during the past few minutes and couple this internal state to parameters of the underlying model that are related to the driving style. For illustration, we use the intelligent-driver model (IDM) as the underlying model, characterize the level of service solely by the velocity, and couple the internal variable to the IDM parameter “time gap” to model an increase of the time gap in congested traffic (“frustration effect”), which is supported by single-vehicle data. We simulate open systems with a bottleneck and obtain flow-density data by implementing “virtual detectors.” The shape, relative size, and apparent “stochasticity” of the region of the scattered data points agree nearly quantitatively with empirical data. Wide scattering is even observed for identical vehicles, although the proposed model is a time-continuous, deterministic, single-lane car-following model with a unique fundamental diagram.

Sensemaking: A Structure for an Intelligence Revolution

DTIC Science & Technology

2011-03-01

level of distraction among drivers who are using cell phones reveal an associated , diminished driver capacity.32 Non...relationships among sparse and ambiguous data .”87 Th is book accepts that perspective and develops the psychological, behavioral , and social levels of...correlations revealed . Benjamin Kleinmuntz obtained a similar result using the “Twenty Questions” game and a set of test cases to add structure

DriveID: safety innovation through individuation.

PubMed

Sawyer, Ben; Teo, Grace; Mouloua, Mustapha

2012-01-01

The driving task is highly complex and places considerable perceptual, physical and cognitive demands on the driver. As driving is fundamentally an information processing activity, distracted or impaired drivers have diminished safety margins compared with non- distracted drivers (Hancock and Parasuraman, 1992; TRB 1998 a & b). This competition for sensory and decision making capacities can lead to failures that cost lives. Some groups, teens and elderly drivers for example, have patterns of systematically poor perceptual, physical and cognitive performance while driving. Although there are technologies developed to aid these different drivers, these systems are often misused and underutilized. The DriveID project aims to design and develop a passive, automated face identification system capable of robustly identifying the driver of the vehicle, retrieve a stored profile, and intelligently prescribing specific accident prevention systems and driving environment customizations.

ITS Technologies in Military Wheeled Tactical Vehicles: Status Quo and the Future

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

Knee, H.E.

2001-07-02

The U.S. Army operates and maintains the largest trucking fleet in the United States. Its fleet consists of over 246,000 trucks, and it is responsible for buying and developing trucks for all branches of the armed forces. The Army's tactical wheeled vehicle fleet is the logistical backbone of the Army, and annually, the fleet logs about 823 million miles. The fleet consists of a number of types of vehicles. They include eight different families of trucks from the High Mobility Multi-Purpose Wheeled Vehicles to M900 series line haul tractors and special bodies. The average age of all the trucks withinmore » the Army fleet is 15 years, and very few have more than traditional driving instrumentation on-board. Over the past decade, the Department of Transportation's (DOT's) Intelligent Transportation Systems (ITS) Program has conducted research and deployment activities in a number of areas including in-vehicle systems, communication and telematics technologies. Many current model passenger vehicles have demonstrated the assimilation of these technologies to enhance safety and trip quality. Commercial vehicles are also demonstrating many new electronic devices that are assisting in making them safer and more efficient. Moreover, a plethora of new technologies are about to be introduced to drivers that promise greater safety, enhanced efficiency, congestion avoidance, fuel usage reduction, and enhanced trip quality. The U.S. Army has special needs with regard to fleet management, logistics, sustainability, reliability, survivability, and fuel consumption that goes beyond similar requirements within the private industry. In order to effectively apply emerging ITS technologies to the special needs of the U.S. Army, planning for the conduct of the Army's Vehicle Intelligence Program (AVIP) has now commenced. The AVIP will be focused on the conduct of research that: (1) will apply ITS technologies to the special needs of the Army, and (2) will conduct research for special needs wi th regard to vehicle control, driver assistance, integration of vehicle intelligence and robotic technologies, managing effectively the information flow to drivers, enhanced logistics capabilities and sustainability of the Army's fleet during battlefield conditions. This paper will highlight the special needs of the Army, briefly describe two programs, which are embracing ITS technologies to a limited extent, will outline the AVIP, and will provide some insight into future Army vehicle intelligence efforts.« less

Intelligent Automatic Right-Left Sign Lamp Based on Brain Signal Recognition System

NASA Astrophysics Data System (ADS)

Winda, A.; Sofyan; Sthevany; Vincent, R. S.

2017-12-01

Comfort as a part of the human factor, plays important roles in nowadays advanced automotive technology. Many of the current technologies go in the direction of automotive driver assistance features. However, many of the driver assistance features still require physical movement by human to enable the features. In this work, the proposed method is used in order to make certain feature to be functioning without any physical movement, instead human just need to think about it in their mind. In this work, brain signal is recorded and processed in order to be used as input to the recognition system. Right-Left sign lamp based on the brain signal recognition system can potentially replace the button or switch of the specific device in order to make the lamp work. The system then will decide whether the signal is ‘Right’ or ‘Left’. The decision of the Right-Left side of brain signal recognition will be sent to a processing board in order to activate the automotive relay, which will be used to activate the sign lamp. Furthermore, the intelligent system approach is used to develop authorized model based on the brain signal. Particularly Support Vector Machines (SVMs)-based classification system is used in the proposed system to recognize the Left-Right of the brain signal. Experimental results confirm the effectiveness of the proposed intelligent Automatic brain signal-based Right-Left sign lamp access control system. The signal is processed by Linear Prediction Coefficient (LPC) and Support Vector Machines (SVMs), and the resulting experiment shows the training and testing accuracy of 100% and 80%, respectively.

Intelligent vehicle safety control strategy in various driving situations

NASA Astrophysics Data System (ADS)

Moon, Seungwuk; Cho, Wanki; Yi, Kyongsu

2010-12-01

This paper describes a safety control strategy for intelligent vehicles with the objective of optimally coordinating the throttle, brake, and active front steering actuator inputs to obtain both lateral stability and longitudinal safety. The control system consists of a supervisor, control algorithms, and a coordinator. From the measurement and estimation signals, the supervisor determines the active control modes among normal driving, longitudinal safety, lateral stability, and integrated safety control mode. The control algorithms consist of longitudinal and lateral stability controllers. The longitudinal controller is designed to improve the driver's comfort during normal, safe-driving situations, and to avoid rear-end collision in vehicle-following situations. The lateral stability controller is designed to obtain the required manoeuvrability and to limit the vehicle body's side-slip angle. To obtain both longitudinal safety and lateral stability control in various driving situations, the coordinator optimally determines the throttle, brake, and active front steering inputs based on the current status of the subject vehicle. Closed-loop simulations with the driver-vehicle-controller system are conducted to investigate the performance of the proposed control strategy. From these simulation results, it is shown that the proposed control algorithm assists the driver in combined severe braking/large steering manoeuvring so that the driver can maintain good manoeuvrability and prevent the vehicle from crashing in vehicle-following situations.

A model for the role of emotional intelligence in patient safety

PubMed Central

Codier, Estelle; Codier, David

2015-01-01

Medical errors are the third leading cause of death in the USA, resulting in over 440,000 deaths/year. Although over a decade has passed since the first Institute of Medicine study that documented such horrific statistics and despite significant safety improvement efforts, serious progress has yet to be achieved. It is estimated that 80% of medical errors result from miscommunication among health care providers and between providers and patients. There is preliminary research evidence that communication skills programs can improve safety outcomes, but a systematic theoretical framework for such programs has not been identified. Because of the connection between emotional intelligence (EI) ability and communication effectiveness, EI has been called by some “one of the largest drivers of patient safety.” Little literature has explored this relationship. The purpose of this article was to present a theoretical model for the relationship between EI, communication and patient safety, with conceptual and clinical illustrations used to describe such a relationship. PMID:27981102

Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions

PubMed Central

Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Mª; de la Escalera, Arturo

2010-01-01

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle. PMID:22163639

Identifying and tracking pedestrians based on sensor fusion and motion stability predictions.

PubMed

Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Maria; de la Escalera, Arturo

2010-01-01

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.

Resource Aware Intelligent Network Services (RAINS) Final Technical Report

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

Lehman, Tom; Yang, Xi

The Resource Aware Intelligent Network Services (RAINS) project conducted research and developed technologies in the area of cyber infrastructure resource modeling and computation. The goal of this work was to provide a foundation to enable intelligent, software defined services which spanned the network AND the resources which connect to the network. A Multi-Resource Service Plane (MRSP) was defined, which allows resource owners/managers to locate and place themselves from a topology and service availability perspective within the dynamic networked cyberinfrastructure ecosystem. The MRSP enables the presentation of integrated topology views and computation results which can include resources across the spectrum ofmore » compute, storage, and networks. The RAINS project developed MSRP includes the following key components: i) Multi-Resource Service (MRS) Ontology/Multi-Resource Markup Language (MRML), ii) Resource Computation Engine (RCE), iii) Modular Driver Framework (to allow integration of a variety of external resources). The MRS/MRML is a general and extensible modeling framework that allows for resource owners to model, or describe, a wide variety of resource types. All resources are described using three categories of elements: Resources, Services, and Relationships between the elements. This modeling framework defines a common method for the transformation of cyber infrastructure resources into data in the form of MRML models. In order to realize this infrastructure datification, the RAINS project developed a model based computation system, i.e. “RAINS Computation Engine (RCE)”. The RCE has the ability to ingest, process, integrate, and compute based on automatically generated MRML models. The RCE interacts with the resources thru system drivers which are specific to the type of external network or resource controller. The RAINS project developed a modular and pluggable driver system which facilities a variety of resource controllers to automatically generate, maintain, and distribute MRML based resource descriptions. Once all of the resource topologies are absorbed by the RCE, a connected graph of the full distributed system topology is constructed, which forms the basis for computation and workflow processing. The RCE includes a Modular Computation Element (MCE) framework which allows for tailoring of the computation process to the specific set of resources under control, and the services desired. The input and output of an MCE are both model data based on MRS/MRML ontology and schema. Some of the RAINS project accomplishments include: Development of general and extensible multi-resource modeling framework; Design of a Resource Computation Engine (RCE) system which includes the following key capabilities; Absorb a variety of multi-resource model types and build integrated models; Novel architecture which uses model based communications across the full stack for all Flexible provision of abstract or intent based user facing interfaces; Workflow processing based on model descriptions; Release of the RCE as an open source software; Deployment of RCE in the University of Maryland/Mid-Atlantic Crossroad ScienceDMZ in prototype mode with a plan under way to transition to production; Deployment at the Argonne National Laboratory DTN Facility in prototype mode; Selection of RCE by the DOE SENSE (SDN for End-to-end Networked Science at the Exascale) project as the basis for their orchestration service.« less

Drivers of Emerging Infectious Disease Events as a Framework for Digital Detection.

PubMed

Olson, Sarah H; Benedum, Corey M; Mekaru, Sumiko R; Preston, Nicholas D; Mazet, Jonna A K; Joly, Damien O; Brownstein, John S

2015-08-01

The growing field of digital disease detection, or epidemic intelligence, attempts to improve timely detection and awareness of infectious disease (ID) events. Early detection remains an important priority; thus, the next frontier for ID surveillance is to improve the recognition and monitoring of drivers (antecedent conditions) of ID emergence for signals that precede disease events. These data could help alert public health officials to indicators of elevated ID risk, thereby triggering targeted active surveillance and interventions. We believe that ID emergence risks can be anticipated through surveillance of their drivers, just as successful warning systems of climate-based, meteorologically sensitive diseases are supported by improved temperature and precipitation data. We present approaches to driver surveillance, gaps in the current literature, and a scientific framework for the creation of a digital warning system. Fulfilling the promise of driver surveillance will require concerted action to expand the collection of appropriate digital driver data.

Collision Avoidance, Driver Support and Safety Intervention Systems

NASA Astrophysics Data System (ADS)

Gilling, Simon P.

Autonomous Intelligent Cruise Control (AICC) will be marketed by a number of vehicle manufacturers before the end of the decade. This paper will describe AICC and the next generation systems currently being developed and validated within the EC Fourth Framework project, Anti-Collision Autonomous Support and Safety Intervention SysTem (AC ASSIST).The currently available cruise control systems which maintain a fixed speed are a well-known form of longitudinal driver support. The fixed speed cruise control becomes less useful with increased traffic volumes, as the driver must disable the system when a slower preceding vehicle is encountered.

The use of technology to address patterns of risk among teenage drivers.

PubMed

Brovold, Shawn; Ward, Nic; Donath, Max; Simon, Stephen; Shankwitz, Craig; Creaser, Janet

2007-01-01

The crash risk of teens is high, with fatal crash rates of teen drivers higher than any other age group. New approaches to reduce teen traffic fatalities are clearly needed. A possible approach to reduce the incidence of teen driver crashes and fatalities is through the use of vehicle-based intelligent driver support systems. To be most effective, the system should address the behaviors associated with an overwhelming number of teen fatal crashes: speed, low seatbelt use, and alcohol impairment. In-vehicle technology also offers an opportunity to address the issue of inexperience through enforcement of certain Graduated Driver's License provisions. To fully understand the capability of such technologies, there should be a concerted effort to further their development, and human factors testing should take place to understand their effects on the driver. If successfully implemented, a Teen Driver Support System (TDSS), such as the one described here, could significantly decrease the number of teens killed in traffic crashes.

Qualifications of drivers - vision and diabetes

DOT National Transportation Integrated Search

2011-01-01

San Francisco UPA projects focus on reducing traffic congestion related to parking in downtown San Francisco. Intelligent transportation systems (ITS) technologies underlie many of the San Francisco UPA projects, including parking and roadway sensors...

Humans, Intelligent Technology, and Their Interface: A Study of Brown’s Point

DTIC Science & Technology

2017-12-01

known about the role of drivers. When combining humans and intelligent technology (machines), such as self-driving vehicles, how people think about...disrupt the entire transportation industry and potentially change how society moves people and goods. The findings of the investigation are likely...The power of suggestion is very important to understand and consider when framing and bringing meaning to new technology, which points to looking at

Lavia – an Evaluation of the Potential Safety Benefits of the French Intelligent Speed Adaptation Project

PubMed Central

Driscoll, R.; Page, Y.; Lassarre, S.; Ehrlich, J.

2007-01-01

This paper presents the potential safety benefits of the experimental French LAVIA Intelligent Speed Adaptation system, according to road network and system mode, based on observed driving speeds, distributions of crash severity and crash injury risk. Results are given for car frontal and side impacts that together, represent 80% of all serious and fatal injuries in France. Of the three system modes tested (advisory, driver select, mandatory), our results suggest that driver select would most significantly reduce serious injuries and death. We estimate this 100% utilization of cars equipped with this type of speed adaptation system would decrease injury rates by 6% to 16% over existing conditions depending on the type of crash (frontal or side) and road environment considered. Some limitations associated with the analysis are also identified. PMID:18184509

Application of intelligent sensors in the integrated systems health monitoring of a rocket test stand

NASA Astrophysics Data System (ADS)

Mahajan, Ajay; Chitikeshi, Sanjeevi; Utterbach, Lucas; Bandhil, Pavan; Figueroa, Fernando

2006-05-01

This paper describes the application of intelligent sensors in the Integrated Systems Health Monitoring (ISHM) as applied to a rocket test stand. The development of intelligent sensors is attempted as an integrated system approach, i.e. one treats the sensors as a complete system with its own physical transducer, A/D converters, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow them to get better with time. Under a project being undertaken at the NASA Stennis Space Center, an integrated framework is being developed for the intelligent monitoring of smart elements associated with the rocket tests stands. These smart elements can be sensors, actuators or other devices. Though the immediate application is the monitoring of the rocket test stands, the technology should be generally applicable to the ISHM vision. This paper outlines progress made in the development of intelligent sensors by describing the work done till date on Physical Intelligent sensors (PIS) and Virtual Intelligent Sensors (VIS).

Contributing Factors to Driver's Over-trust in a Driving Support System for Workload Reduction

NASA Astrophysics Data System (ADS)

Itoh, Makoto

Avoiding over-trust in machines is a vital issue in order to establish intelligent driver support systems. It is necessary to distinguish systems for workload reduction from systems for accident prevention/mitigation. This study focuses on over-trust in an Adaptive Cruise Control (ACC) system as a typical driving support system for workload reduction. By conducting an experiment, we obtained a case in which a driver trusted the ACC system too much. Concretely speaking, the driver just watched the ACC system crashing into a stopped car even though the ACC system was designed to ignore such stopped cars. This paper investigates possible contributing factors to the driver' s over-trust in the ACC system. The results suggest that emerging trust in the dimension of performance may cause over-trust in the dimension of method or purpose.

Self-Learning Intelligent Agents for Dynamic Traffic Routing on Transportation Networks

NASA Astrophysics Data System (ADS)

Sadek, Add; Basha, Nagi

Intelligent Transportation Systems (ITS) are designed to take advantage of recent advances in communications, electronics, and Information Technology in improving the efficiency and safety of transportation systems. Among the several ITS applications is the notion of Dynamic Traffic Routing (DTR), which involves generating "optimal" routing recommendations to drivers with the aim of maximizing network utilizing. In this paper, we demonstrate the feasibility of using a self-learning intelligent agent to solve the DTR problem to achieve traffic user equilibrium in a transportation network. The core idea is to deploy an agent to a simulation model of a highway. The agent then learns by itself by interacting with the simulation model. Once the agent reaches a satisfactory level of performance, it can then be deployed to the real-world, where it would continue to learn how to refine its control policies over time. To test this concept in this paper, the Cell Transmission Model (CTM) developed by Carlos Daganzo of the University of California at Berkeley is used to simulate a simple highway with two main alternative routes. With the model developed, a Reinforcement Learning Agent (RLA) is developed to learn how to best dynamically route traffic, so as to maximize the utilization of existing capacity. Preliminary results obtained from our experiments are promising. RL, being an adaptive online learning technique, appears to have a great potential for controlling a stochastic dynamic systems such as a transportation system. Furthermore, the approach is highly scalable and applicable to a variety of networks and roadways.

SAE J2735 standard : applying the systems engineering process.

DOT National Transportation Integrated Search

1998-11-01

As part of the U.S. Department of Transportations Intelligent Vehicle Initiative (IVI) program, the Federal Highway Administration investigated the human factors research needs for integrating in-vehicle safety and driver information technologies ...

Integrated Capabilities in Heavy Vehicles: Human Factors Research Needs

DOT National Transportation Integrated Search

1998-11-01

As part of the U.S. Department of Transportation's Intelligent Vehicle Initiative (IVI) program, the Federal Highway Administration (FHWA) investigated the human factors research needs for integrating in-vehicle safety and driver information technolo...

Integrated ITS Capabilities In Transit Vehicles: Human Factors Research Needs

DOT National Transportation Integrated Search

1998-11-01

As part of the U.S. Department of Transportation's Intelligent Vehicle Initiative (IVI) program, the Federal Highway Administration (FHWA) investigated human factors research needs for integrating in-vehicle safety and driver information technologies...

Integrated ITS capabilities in transit vehicles : human factors research needs

DOT National Transportation Integrated Search

1998-11-01

As part of the U.S. Department of Transportation's Intelligent Vehicle Initiative (IVI) program, the Federal Highway Administration (FHWA) investigated human factors research needs for integrating in-vehicle safety and driver information technologies...

Advanced Traveler Information System Capabilities : Human Factors Research Needs

DOT National Transportation Integrated Search

1998-11-01

As part of the U.S. Department of Transportation's Intelligent Vehicle Initiative (IVI) program, the Federal Highway Administration investigated the human factors research needs for integrating in-vehicle safety and driver information technologies in...

Travtek Evaluation Yoked Driver Study

DOT National Transportation Integrated Search

1998-11-01

The purpose of this paper is to present estimates of potential safety benefits resulting from full implementation of Intelligent Transportation Systems (ITS) in the United States. These estimates were derived by integrating results from a number of d...

When intelligence loses its impact: neural efficiency during reasoning in a familiar area.

PubMed

Grabner, Roland H; Stern, Elsbeth; Neubauer, Aljoscha C

2003-08-01

Several studies have revealed that persons with a lower IQ show more cortical activity when solving intelligence-related tasks than more intelligent persons do. Such results are interpreted in terms of neural efficiency: the more intelligent a person is, the fewer mental resources have to be activated. In an experiment with 31 experienced taxi drivers of varying IQs (measured by Raven's advanced progressive matrices test), we investigated cortical activation by measuring the amount of event-related desynchronization in the electroencephalogram during a familiar task (thinking about routes to take in their city) and a novel task (memorizing routes of an artificial map). A comparison of participants with lower and higher IQs (median split) revealed higher cortical activation in the less intelligent group for the novel task, but not for the familiar task. These results suggest that long-term experience can compensate for lower intellectual ability, even at the level of cortical activation.

Review of and preliminary guidelines for integrating transit into transportation management centers

DOT National Transportation Integrated Search

1994-07-01

The advent of intelligent vehicle-highway system (IVHS) : technologies has fostered the development and implementation of : automated systems that control traffic and provide traffic : information to drivers. However, one very important element of : ...

Analysis of older driver safety interventions : a human factors taxonomic approach

DOT National Transportation Integrated Search

1999-03-01

The careful application of human factors design principles and guidelines is integral to : the development of safe, efficient and usable Intelligent Transportation Systems (ITS). One : segment of the driving population that may significantly benefit ...

Examples of variable speed limit applications : speed management workshop

DOT National Transportation Integrated Search

2000-01-09

VSL systems are a type of Intelligent Transportation System (ITS) that utilizes traffic : speed and volume detection, weather information, and road surface condition technology to determine appropriate speeds at which drivers should be traveling, giv...

An RFID-based intelligent vehicle speed controller using active traffic signals.

PubMed

Pérez, Joshué; Seco, Fernando; Milanés, Vicente; Jiménez, Antonio; Díaz, Julio C; de Pedro, Teresa

2010-01-01

These days, mass-produced vehicles benefit from research on Intelligent Transportation System (ITS). One prime example of ITS is vehicle Cruise Control (CC), which allows it to maintain a pre-defined reference speed, to economize on fuel or energy consumption, to avoid speeding fines, or to focus all of the driver's attention on the steering of the vehicle. However, achieving efficient Cruise Control is not easy in roads or urban streets where sudden changes of the speed limit can happen, due to the presence of unexpected obstacles or maintenance work, causing, in inattentive drivers, traffic accidents. In this communication we present a new Infrastructure to Vehicles (I2V) communication and control system for intelligent speed control, which is based upon Radio Frequency Identification (RFID) technology for identification of traffic signals on the road, and high accuracy vehicle speed measurement with a Hall effect-based sensor. A fuzzy logic controller, based on sensor fusion of the information provided by the I2V infrastructure, allows the efficient adaptation of the speed of the vehicle to the circumstances of the road. The performance of the system is checked empirically, with promising results.

Information Mining Technologies to Enable Discovery of Actionable Intelligence to Facilitate Maritime Situational Awareness: I-MINE

DTIC Science & Technology

2013-01-01

website). Data mining tools are in-house code developed in Python, C++ and Java . • NGA The National Geospatial-Intelligence Agency (NGA) performs data...as PostgreSQL (with PostGIS), MySQL , Microsoft SQL Server, SQLite, etc. using the appropriate JDBC driver. 14 The documentation and ease to learn are...written in Java that is able to perform various types of regressions, classi- fications, and other data mining tasks. There is also a commercial version

Intelligent transportation systems for planned special events : a cross-cutting study

DOT National Transportation Integrated Search

2008-11-01

This cross-cutting study examines how six agencies in five states used and continue to use ITS to reduce congestion generated by planned special events, thereby reducing crashes, increasing travel time reliability, and reducing driver frustration.

Preliminary Human Factors Design Guidelines For Driver Information Systems

DOT National Transportation Integrated Search

2001-01-01

During the summer and fall of 2000, a group of high level public safety and transportation officials was brought together by the US Department of Transportations (USDOT) Intelligent Transportation Systems (ITS) Program to consider the interaction bet...

Technology in rural transportation. Simple solution #7, lane drop driver awareness

DOT National Transportation Integrated Search

1997-01-01

This application was identified as a promising rural Intelligent Transportation Systems (ITS) solution under a project sponsored by the Federal Highway Administration (FHWA) and the ENTERPRISE program. This summary describes the solution as well as o...

Intelligent transportation systems field operational test cross-cutting study : advanced traveler information systems

DOT National Transportation Integrated Search

1998-09-01

Approximately 2 million roadside inspections of commercial motor vehicles (CMVs) are conducted annually, primarily through the joint Federal and State Motor Carrier Safety Assistance Program (MCSAP). Vehicles and drivers with serious safety problems ...

Safe distance car-following model including backward-looking and its stability analysis

NASA Astrophysics Data System (ADS)

Yang, Da; Jin, Peter Jing; Pu, Yun; Ran, Bin

2013-03-01

The focus of this paper is the car-following behavior including backward-looking, simply called the bi-directional looking car-following behavior. This study is motivated by the potential changes of the physical properties of traffic flow caused by the fast developing intelligent transportation system (ITS), especially the new connected vehicle technology. Existing studies on this topic focused on general motors (GM) models and optimal velocity (OV) models. The safe distance car-following model, Gipps' model, which is more widely used in practice have not drawn too much attention in the bi-directional looking context. This paper explores the property of the bi-directional looking extension of Gipps' safe distance model. The stability condition of the proposed model is derived using the linear stability theory and is verified using numerical simulations. The impacts of the driver and vehicle characteristics appeared in the proposed model on the traffic flow stability are also investigated. It is found that taking into account the backward-looking effect in car-following has three types of effect on traffic flow: stabilizing, destabilizing and producing non-physical phenomenon. This conclusion is more sophisticated than the study results based on the OV bi-directional looking car-following models. Moreover, the drivers who have the smaller reaction time or the larger additional delay and think the other vehicles have larger maximum decelerations can stabilize traffic flow.

In-vehicle communication systems: the safety aspect

PubMed Central

Pauzie, A

2002-01-01

Communication and information technology are developing very rapidly at present. At the same time, the number of older drivers is increasing. When designing systems for elderly drivers, it has been shown that: (1) simplifying a task reduces performance differences between old and young; and (2) the optimization of onboard systems (better legibility and intelligibility of the information, simplified dialogue) in relation to the abilities of elderly drivers benefits the rest of the user population. Elderly people do not automatically reject new information and assistance technologies especially when the systems are user friendly. However, the ergonomics of these new technologies must be studied, with particular attention to the specific needs of the elderly, in order not to marginalize them PMID:12460953

Emotional intelligence--essential for trauma nursing.

PubMed

Holbery, Natalie

2015-01-01

Patients and their relatives are increasingly considered partners in health and social care decision-making. Numerous political drivers in the UK reflect a commitment to this partnership and to improving the experience of patients and relatives in emergency care environments. As a Lecturer/Practitioner in Emergency Care I recently experienced the London Trauma System as a relative. My dual perspective, as nurse and relative, allowed me to identify a gap in the quality of care akin to emotional intelligence. This paper aims to raise awareness of emotional intelligence (EI), highlight its importance in trauma care and contribute to the development of this concept in trauma nursing and education across the globe. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automatic vehicle identification technology applications to toll collection services

DOT National Transportation Integrated Search

1997-01-01

Intelligent transportation systems technologies are being developed and applied through transportation systems in the United States. An example of this type of innovation can be seen on toll roads where a driver is required to deposit a toll in order...

The smartphone and the driver's cognitive workload: A comparison of Apple, Google, and Microsoft's intelligent personal assistants.

PubMed

Strayer, David L; Cooper, Joel M; Turrill, Jonna; Coleman, James R; Hopman, Rachel J

2017-06-01

The goal of this research was to examine the impact of voice-based interactions using 3 different intelligent personal assistants (Apple's Siri , Google's Google Now for Android phones, and Microsoft's Cortana ) on the cognitive workload of the driver. In 2 experiments using an instrumented vehicle on suburban roadways, we measured the cognitive workload of drivers when they used the voice-based features of each smartphone to place a call, select music, or send text messages. Cognitive workload was derived from primary task performance through video analysis, secondary-task performance using the Detection Response Task (DRT), and subjective mental workload. We found that workload was significantly higher than that measured in the single-task drive. There were also systematic differences between the smartphones: The Google system placed lower cognitive demands on the driver than the Apple and Microsoft systems, which did not differ. Video analysis revealed that the difference in mental workload between the smartphones was associated with the number of system errors, the time to complete an action, and the complexity and intuitiveness of the devices. Finally, surprisingly high levels of cognitive workload were observed when drivers were interacting with the devices: "on-task" workload measures did not systematically differ from that associated with a mentally demanding Operation Span (OSPAN) task. The analysis also found residual costs associated using each of the smartphones that took a significant time to dissipate. The data suggest that caution is warranted in the use of smartphone voice-based technology in the vehicle because of the high levels of cognitive workload associated with these interactions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Identifying Key Features, Cutting Edge Cloud Resources, and Artificial Intelligence Tools to Achieve User-Friendly Water Science in the Cloud

NASA Astrophysics Data System (ADS)

Pierce, S. A.

2017-12-01

Decision making for groundwater systems is becoming increasingly important, as shifting water demands increasingly impact aquifers. As buffer systems, aquifers provide room for resilient responses and augment the actual timeframe for hydrological response. Yet the pace impacts, climate shifts, and degradation of water resources is accelerating. To meet these new drivers, groundwater science is transitioning toward the emerging field of Integrated Water Resources Management, or IWRM. IWRM incorporates a broad array of dimensions, methods, and tools to address problems that tend to be complex. Computational tools and accessible cyberinfrastructure (CI) are needed to cross the chasm between science and society. Fortunately cloud computing environments, such as the new Jetstream system, are evolving rapidly. While still targeting scientific user groups systems such as, Jetstream, offer configurable cyberinfrastructure to enable interactive computing and data analysis resources on demand. The web-based interfaces allow researchers to rapidly customize virtual machines, modify computing architecture and increase the usability and access for broader audiences to advanced compute environments. The result enables dexterous configurations and opening up opportunities for IWRM modelers to expand the reach of analyses, number of case studies, and quality of engagement with stakeholders and decision makers. The acute need to identify improved IWRM solutions paired with advanced computational resources refocuses the attention of IWRM researchers on applications, workflows, and intelligent systems that are capable of accelerating progress. IWRM must address key drivers of community concern, implement transdisciplinary methodologies, adapt and apply decision support tools in order to effectively support decisions about groundwater resource management. This presentation will provide an overview of advanced computing services in the cloud using integrated groundwater management case studies to highlight how Cloud CI streamlines the process for setting up an interactive decision support system. Moreover, advances in artificial intelligence offer new techniques for old problems from integrating data to adaptive sensing or from interactive dashboards to optimizing multi-attribute problems. The combination of scientific expertise, flexible cloud computing solutions, and intelligent systems opens new research horizons.

Observed and projected drivers of emerging infectious diseases in Europe.

PubMed

Semenza, Jan C; Rocklöv, Joacim; Penttinen, Pasi; Lindgren, Elisabet

2016-10-01

Emerging infectious diseases are of international concern because of the potential for, and impact of, pandemics; however, they are difficult to predict. To identify the drivers of disease emergence, we analyzed infectious disease threat events (IDTEs) detected through epidemic intelligence collected at the European Centre for Disease Prevention and Control (ECDC) between 2008 and 2013, and compared the observed results with a 2008 ECDC foresight study of projected drivers of future IDTEs in Europe. Among 10 categories of IDTEs, foodborne and waterborne IDTEs were the most common, vaccine-preventable IDTEs caused the highest number of cases, and airborne IDTEs caused the most deaths. Observed drivers for each IDTE were sorted into three main groups: globalization and environmental drivers contributed to 61% of all IDTEs, public health system drivers contributed to 21%, and social and demographic drivers to 18%. A multiple logistic regression analysis showed that four of the top five drivers for observed IDTEs were in the globalization and environment group. In the observational study, the globalization and environment group was related to all IDTE categories, but only to five of eight categories in the foresight study. Directly targeting these drivers with public health interventions may diminish the chances of IDTE occurrence from the outset. © 2016 New York Academy of Sciences.

Driver's behavioural changes with new intelligent transport system interventions at railway level crossings--A driving simulator study.

PubMed

Larue, Grégoire S; Kim, Inhi; Rakotonirainy, Andry; Haworth, Narelle L; Ferreira, Luis

2015-08-01

Improving safety at railway level crossings is an important issue for the Australian transport system. Governments, the rail industry and road organisations have tried a variety of countermeasures for many years to improve railway level crossing safety. New types of intelligent transport system (ITS) interventions are now emerging due to the availability and the affordability of technology. These interventions target both actively and passively protected railway level crossings and attempt to address drivers' errors at railway crossings, which are mainly a failure to detect the crossing or the train and misjudgement of the train approach speed and distance. This study aims to assess the effectiveness of three emerging ITS that the rail industry considers implementing in Australia: a visual in-vehicle ITS, an audio in-vehicle ITS, as well as an on-road flashing beacons intervention. The evaluation was conducted on an advanced driving simulator with 20 participants per trialled technology, each participant driving once without any technology and once with one of the ITS interventions. Every participant drove through a range of active and passive crossings with and without trains approaching. Their speed approach of the crossing, head movements and stopping compliance were measured. Results showed that driver behaviour was changed with the three ITS interventions at passive crossings, while limited effects were found at active crossings, even with reduced visibility. The on-road intervention trialled was unsuccessful in improving driver behaviour; the audio and visual ITS improved driver behaviour when a train was approaching. A trend toward worsening driver behaviour with the visual ITS was observed when no trains were approaching. This trend was not observed for the audio ITS intervention, which appears to be the ITS intervention with the highest potential for improving safety at passive crossings. Copyright © 2015 Elsevier Ltd. All rights reserved.

Intelligent dilemma zone protection system at high-speed intersections : final report.

DOT National Transportation Integrated Search

2017-07-01

Drivers actions in an intersections dilemma zone the area where the decision to stop at a yellow light or continue through it is not clear-cut can lead to side-angle and rear-end crashes. In Maryland, researchers developed an intelligen...

Driver response to the TetraStar Navigation Assistance System by age and sex

DOT National Transportation Integrated Search

1997-07-01

This study is part of the evaluation of the FAST-TRAC operational test of an Intelligent Transportation System (ITS) in Michigan and is concerned with user perceptions and behaviors with Advanced Traveler Information Systems (ATIS). The use and perce...

Intelligent dilemma zone protection system at high-speed intersections : research summary.

DOT National Transportation Integrated Search

2017-07-01

Drivers actions in an intersections dilemma zone the area where the decision to stop at a yellow light or continue through it is not clear-cut can lead to side-angle and rear-end crashes. In Maryland, researchers developed an intelligen...

Pay as You Speed, ISA with incentives for not speeding: a case of test driver recruitment.

PubMed

Lahrmann, Harry; Agerholm, Niels; Tradisauskas, Nerius; Næss, Teresa; Juhl, Jens; Harms, Lisbeth

2012-09-01

The Intelligent Speed Adaptation (ISA) project we describe in this article is based on Pay as You Drive principles. These principles assume that the ISA equipment informs a driver of the speed limit, warns the driver when speeding and calculates penalty points. Each penalty point entails the reduction of a 30% discount on the driver's car insurance premium, which therefore produced the name, Pay as You Speed. The ISA equipment consists of a GPS-based On Board Unit with a mobile phone connection to a web server. The project was planned for a three-year test period with 300 young car drivers, but it never succeeded in recruiting that number of drivers. After several design changes, the project eventually went forward with 153 test drivers of all ages. This number represents approximately one thousandth of all car owners in the proving ground of North Jutland in Denmark. Furthermore the project was terminated before its scheduled closing date. This article describes the project with an emphasis on recruitment efforts and the project's progress. We include a discussion of possible explanations for the failure to recruit volunteers for the project and reflect upon the general barriers to using ISA with ordinary drivers. Copyright © 2011 Elsevier Ltd. All rights reserved.

Intelligent Transportation Systems, The National Architecture, A Framework For Integrated Transportation Into The 21St Century

DOT National Transportation Integrated Search

1985-08-01

The primary objective of this study was to determine if freeway lighting can be reduced or eliminated during nighttime periods when traffic volume is much lower than design capacity without causing significant reductions in the ability of drivers to ...

Intelligent transportation systems field operational test cross-cutting study : emissions management using ITS technology

DOT National Transportation Integrated Search

1999-09-01

The Federal Highway Administration (FHWA) began the Federal Waiver Study Program in 1992. The Vision Waiver Program began in July 1992, when 2,686 drivers were accepted into the program. The Diabetes Waiver Program began in mid-1993 when 139 waivers ...

Technology and teen drivers.

PubMed

Lee, John D

2007-01-01

The rapid evolution of computing, communication, and sensor technology is likely to affect young drivers more than others. The distraction potential of infotainment technology stresses the same vulnerabilities that already lead young drivers to crash more frequently than other drivers. Cell phones, text messaging, MP3 players, and other nomadic devices all present a threat because young drivers may lack the spare attentional capacity for vehicle control and the ability to anticipate and manage hazards. Moreover, young drivers are likely to be the first and most aggressive users of new technology. Fortunately, emerging technology can also support safe driving. Electronic stability control, collision avoidance systems, intelligent speed adaptation, and vehicle tracking systems can all help mitigate the threats to young drivers. However, technology alone is unlikely to make young drivers safer. One promising approach to tailoring technology to teen drivers is to extend proven methods for enhancing young driver safety. The success of graduated drivers license programs (GDL) and the impressive safety benefit of supervised driving suggest ways of tailoring technology to the needs of young drivers. To anticipate the effects of technology on teen driving it may be useful to draw an analogy between the effects of passengers and the effects of technology. Technology can act as a teen passenger and undermine safety or it can act as an adult passenger and enhance safety. Rapidly developing technology may have particularly large effects on teen drivers. To maximize the positive effects and minimize the negative effects will require a broad range of industries to work together. Ideally, vehicle manufacturers would work with infotainment providers, insurance companies, and policy makers to craft new technologies so that they accommodate the needs of young drivers. Without such collaboration young drivers will face even greater challenges to their safety as new technologies emerge.

Neurocognitive Correlates of Young Drivers' Performance in a Driving Simulator.

PubMed

Guinosso, Stephanie A; Johnson, Sara B; Schultheis, Maria T; Graefe, Anna C; Bishai, David M

2016-04-01

Differences in neurocognitive functioning may contribute to driving performance among young drivers. However, few studies have examined this relation. This pilot study investigated whether common neurocognitive measures were associated with driving performance among young drivers in a driving simulator. Young drivers (19.8 years (standard deviation [SD] = 1.9; N = 74)) participated in a battery of neurocognitive assessments measuring general intellectual capacity (Full-Scale Intelligence Quotient, FSIQ) and executive functioning, including the Stroop Color-Word Test (cognitive inhibition), Wisconsin Card Sort Test-64 (cognitive flexibility), and Attention Network Task (alerting, orienting, and executive attention). Participants then drove in a simulated vehicle under two conditions-a baseline and driving challenge. During the driving challenge, participants completed a verbal working memory task to increase demand on executive attention. Multiple regression models were used to evaluate the relations between the neurocognitive measures and driving performance under the two conditions. FSIQ, cognitive inhibition, and alerting were associated with better driving performance at baseline. FSIQ and cognitive inhibition were also associated with better driving performance during the verbal challenge. Measures of cognitive flexibility, orienting, and conflict executive control were not associated with driving performance under either condition. FSIQ and, to some extent, measures of executive function are associated with driving performance in a driving simulator. Further research is needed to determine if executive function is associated with more advanced driving performance under conditions that demand greater cognitive load. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Advanced Traveler Information Systems and Commercial Vehicle Operations Components of the Intelligent Transportation Systems: On-road Evaluation of ATIS Messages

DOT National Transportation Integrated Search

1998-03-01

This report describes the results of an on-road study that examined how Advanced Traveler Information Systems (ATIS) information influences driver behavior. The objective of the study was to develop ATIS design guidelines, primarily for In-Vehicle Si...

A high accuracy vehicle positioning system implemented in a lane assistance system when GPS Is unavailable.

DOT National Transportation Integrated Search

2011-07-01

The use of lane assistance systems can reduce the stress levels experienced by drivers and allow for better lane : keeping in narrow, bus-dedicated lanes. In 2008, the Intelligent Vehicles (IV) Lab at the University of Minnesota : has developed such ...

What have we learned about intelligent transportation systems? Chapter 5, What have we learned about advanced public transportation systems?

DOT National Transportation Integrated Search

1995-12-01

THE PURPOSE OF THE STUDY REPORTED HERE WAS TO EXAMINE WHETHER AGE AND SPATIAL ABILITY ARE FACTORS THAT INFLUENCE A DRIVER'S ABILITY TO NAVIGATE AND TO USE NAVIGATIONAL DISPLAYS. THESE FACTORS WERE EXAMINED BECAUSE PREVIOUS RESEARCH SUGGESTS THAT SPAT...

Analyzing Vehicle Fuel Saving Opportunities through Intelligent Driver Feedback

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

Gonder, J.; Earleywine, M.; Sparks, W.

2012-06-01

Driving style changes, e.g., improving driver efficiency and motivating driver behavior changes, could deliver significant petroleum savings. This project examines eliminating stop-and-go driving and unnecessary idling, and also adjusting acceleration rates and cruising speeds to ideal levels to quantify fuel savings. Such extreme adjustments can result in dramatic fuel savings of over 30%, but would in reality only be achievable through automated control of vehicles and traffic flow. In real-world driving, efficient driving behaviors could reduce fuel use by 20% on aggressively driven cycles and by 5-10% on more moderately driven trips. A literature survey was conducted of driver behaviormore » influences, and pertinent factors from on-road experiments with different driving styles were observed. This effort highlighted important driver influences such as surrounding vehicle behavior, anxiety over trying to get somewhere quickly, and the power/torque available from the vehicle. Existing feedback approaches often deliver efficiency information and instruction. Three recommendations for maximizing fuel savings from potential drive cycle improvement are: (1) leveraging applications with enhanced incentives, (2) using an approach that is easy and widely deployable to motivate drivers, and (3) utilizing connected vehicle and automation technologies to achieve large and widespread efficiency improvements.« less

The simulation of emergent dispatch of cars for intelligent driving autos

NASA Astrophysics Data System (ADS)

Zheng, Ziao

2018-03-01

It is widely acknowledged that it is important for the development of intelligent cars to be widely accepted by the majority of car users. While most of the intelligent cars have the system of monitoring itself whether it is on the good situation to drive, it is also clear that studies should be performed on the way of cars for the emergent rescue of the intelligent vehicles. In this study, writer focus mainly on how to derive a separate system for the car caring teams to arrive as soon as they get the signal sent out by the intelligent driving autos. This simulation measure the time for the rescuing team to arrive, the cost it spent on arriving on the site of car problem happens, also how long the queue is when the rescuing auto is waiting to cross a road. This can be definitely in great use when there are a team of intelligent cars with one car immediately having problems causing it's not moving and can be helpful in other situations. Through this way, the interconnection of cars can be a safety net for the drivers encountering difficulties in any time.

The comparing analysis of simulation of emergent dispatch of cars for intelligent driving autos in crossroads

NASA Astrophysics Data System (ADS)

Zheng, Ziao

2018-03-01

It is widely acknowledged that it is important for the development of intelligent cars to be widely accepted by the majority of car users. While most of the intelligent cars have the system of monitoring itself whether it is on the good situation to drive, it is also clear that studies should be performed on the way of cars for the emergent rescue of the intelligent vehicles. In this study, writer focus mainly on how to derive a separate system for the car caring teams to arrive as soon as they get the signal sent out by the intelligent driving autos. This simulation measure the time for the rescuing team to arrive, the cost it spent on arriving on the site of car problem happens, also how long the queue is when the rescuing auto is waiting to cross a road. This can be definitely in great use when there are a team of intelligent cars with one car immediately having problems causing its not moving and can be helpful in other situations. Through this way, the interconnection of cars can be a safety net for the drivers encountering difficulties in any time.

Driver fatigue alarm based on eye detection and gaze estimation

NASA Astrophysics Data System (ADS)

Sun, Xinghua; Xu, Lu; Yang, Jingyu

2007-11-01

The driver assistant system has attracted much attention as an essential component of intelligent transportation systems. One task of driver assistant system is to prevent the drivers from fatigue. For the fatigue detection it is natural that the information about eyes should be utilized. The driver fatigue can be divided into two types, one is the sleep with eyes close and another is the sleep with eyes open. Considering that the fatigue detection is related with the prior knowledge and probabilistic statistics, the dynamic Bayesian network is used as the analysis tool to perform the reasoning of fatigue. Two kinds of experiments are performed to verify the system effectiveness, one is based on the video got from the laboratory and another is based on the video got from the real driving situation. Ten persons participate in the test and the experimental result is that, in the laboratory all the fatigue events can be detected, and in the practical vehicle the detection ratio is about 85%. Experiments show that in most of situations the proposed system works and the corresponding performance is satisfying.

An Identification of Operating and Support Cost Drivers for Command, Control, Communications, and Intelligence Systems.

DTIC Science & Technology

1985-09-01

8217 - -51 0 0~ ) 4 -~~~~~~~~~ *-1)4 q.4444 I) ~ 44200 . ):~F40 c . U 40 𔃾)C40󈧄 440O ..- .. : 0’ 44).𔃿 S4~44)4044).0~4’) 804) Appendix ±3: Tnesis

Advanced Traveler Information Systems and Commercial Vehicle Operations Components of the Intelligent Transportation Systems: Head-up Displays and Driver Attention for Navigation Information

DOT National Transportation Integrated Search

1998-03-01

Since the initial development of prototype automotive head-up displays (HUDs), there has been a concern that the presence of the HUD image may interfere with the driving task and negatively impact driving performance. The overall goal of this experim...

A Qualitative Synthesis of the Flynn Effect

ERIC Educational Resources Information Center

Ceci, Stephen J.; Williams, Wendy M.

2016-01-01

Clark et al. focus on the likely drivers of the Flynn effect (sociocultural, educational, technological), and imply that it is not a single causal agent driving the upward climb in IQ scores but perhaps multiple causes with different onsets. Given, the authors' conception of intelligence in terms of underlying attentional and cognitive resources…

Increasing Intelligence in Inter-Vehicle Communications to Reduce Traffic Congestions: Experiments in Urban and Highway Environments.

PubMed

Meneguette, Rodolfo I; Filho, Geraldo P R; Guidoni, Daniel L; Pessin, Gustavo; Villas, Leandro A; Ueyama, Jó

2016-01-01

Intelligent Transportation Systems (ITS) rely on Inter-Vehicle Communication (IVC) to streamline the operation of vehicles by managing vehicle traffic, assisting drivers with safety and sharing information, as well as providing appropriate services for passengers. Traffic congestion is an urban mobility problem, which causes stress to drivers and economic losses. In this context, this work proposes a solution for the detection, dissemination and control of congested roads based on inter-vehicle communication, called INCIDEnT. The main goal of the proposed solution is to reduce the average trip time, CO emissions and fuel consumption by allowing motorists to avoid congested roads. The simulation results show that our proposed solution leads to short delays and a low overhead. Moreover, it is efficient with regard to the coverage of the event and the distance to which the information can be propagated. The findings of the investigation show that the proposed solution leads to (i) high hit rate in the classification of the level of congestion, (ii) a reduction in average trip time, (iii) a reduction in fuel consumption, and (iv) reduced CO emissions.

Increasing Intelligence in Inter-Vehicle Communications to Reduce Traffic Congestions: Experiments in Urban and Highway Environments

PubMed Central

Filho, Geraldo P. R.; Guidoni, Daniel L.; Pessin, Gustavo; Villas, Leandro A.; Ueyama, Jó

2016-01-01

Intelligent Transportation Systems (ITS) rely on Inter-Vehicle Communication (IVC) to streamline the operation of vehicles by managing vehicle traffic, assisting drivers with safety and sharing information, as well as providing appropriate services for passengers. Traffic congestion is an urban mobility problem, which causes stress to drivers and economic losses. In this context, this work proposes a solution for the detection, dissemination and control of congested roads based on inter-vehicle communication, called INCIDEnT. The main goal of the proposed solution is to reduce the average trip time, CO emissions and fuel consumption by allowing motorists to avoid congested roads. The simulation results show that our proposed solution leads to short delays and a low overhead. Moreover, it is efficient with regard to the coverage of the event and the distance to which the information can be propagated. The findings of the investigation show that the proposed solution leads to (i) high hit rate in the classification of the level of congestion, (ii) a reduction in average trip time, (iii) a reduction in fuel consumption, and (iv) reduced CO emissions PMID:27526048

Extended time-to-collision measures for road traffic safety assessment.

PubMed

Minderhoud, M M; Bovy, P H

2001-01-01

This article describes two new safety indicators based on the time-to-collision notion suitable for comparative road traffic safety analyses. Such safety indicators can be applied in the comparison of a do-nothing case with an adapted situation, e.g. the introduction of intelligent driver support systems. In contrast to the classical time-to-collision value, measured at a cross section, the improved safety indicators use vehicle trajectories collected over a specific time horizon for a certain roadway segment to calculate the overall safety indicator value. Vehicle-specific indicator values as well as safety-critical probabilities can easily be determined from the developed safety measures. Application of the derived safety indicators is demonstrated for the assessment of the potential safety impacts of driver support systems from which it appears that some Autonomous Intelligent Cruise Control (AICC) designs are more safety-critical than the reference case without these systems. It is suggested that the indicator threshold value to be applied in the safety assessment has to be adapted when advanced AICC-systems with safe characteristics are introduced.

Boredom begets creativity: A solution to the exploitation-exploration trade-off in predictive coding.

PubMed

Gomez-Ramirez, Jaime; Costa, Tommaso

2017-12-01

Here we investigate whether systems that minimize prediction error e.g. predictive coding, can also show creativity, or on the contrary, prediction error minimization unqualifies for the design of systems that respond in creative ways to non-recurrent problems. We argue that there is a key ingredient that has been overlooked by researchers that needs to be incorporated to understand intelligent behavior in biological and technical systems. This ingredient is boredom. We propose a mathematical model based on the Black-Scholes-Merton equation which provides mechanistic insights into the interplay between boredom and prediction pleasure as the key drivers of behavior. Copyright © 2017 Elsevier B.V. All rights reserved.

Intelligent Sensors: An Integrated Systems Approach

NASA Technical Reports Server (NTRS)

Mahajan, Ajay; Chitikeshi, Sanjeevi; Bandhil, Pavan; Utterbach, Lucas; Figueroa, Fernando

2005-01-01

The need for intelligent sensors as a critical component for Integrated System Health Management (ISHM) is fairly well recognized by now. Even the definition of what constitutes an intelligent sensor (or smart sensor) is well documented and stems from an intuitive desire to get the best quality measurement data that forms the basis of any complex health monitoring and/or management system. If the sensors, i.e. the elements closest to the measurand, are unreliable then the whole system works with a tremendous handicap. Hence, there has always been a desire to distribute intelligence down to the sensor level, and give it the ability to assess its own health thereby improving the confidence in the quality of the data at all times. This paper proposes the development of intelligent sensors as an integrated systems approach, i.e. one treats the sensors as a complete system with its own sensing hardware (the traditional sensor), A/D converters, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow them to get better with time. Under a project being undertaken at the NASA Stennis Space Center, an integrated framework is being developed for the intelligent monitoring of smart elements. These smart elements can be sensors, actuators or other devices. The immediate application is the monitoring of the rocket test stands, but the technology should be generally applicable to the Intelligent Systems Health Monitoring (ISHM) vision. This paper outlines some fundamental issues in the development of intelligent sensors under the following two categories: Physical Intelligent Sensors (PIS) and Virtual Intelligent Sensors (VIS).

A Strain-Based Method to Estimate Slip Angle and Tire Working Conditions for Intelligent Tires Using Fuzzy Logic.

PubMed

Garcia-Pozuelo, Daniel; Yunta, Jorge; Olatunbosun, Oluremi; Yang, Xiaoguang; Diaz, Vicente

2017-04-16

Tires equipped with sensors, the so-called "intelligent tires", can provide vital information for control systems, drivers and external users. In this research, tire dynamic strain characteristics in cornering conditions are collected and analysed in relation to the variation of tire working conditions, such as inflation pressure, rolling speed, vertical load and slip angle. An experimental tire strain-based prototype and an indoor tire test rig are used to demonstrate the suitability of strain sensors to establish relations between strain data and lateral force. The results of experiments show that strain values drop sharply when lateral force is decreasing, which can be used to predict tire slip conditions. As a first approach to estimate some tire working conditions, such as the slip angle and vertical load, a fuzzy logic method has been developed. The simulation and test results confirm the feasibility of strain sensors and the proposed computational model to solve the non-linearity characteristics of the tires' parameters and turn tires into a source of useful information.

A Strain-Based Method to Estimate Slip Angle and Tire Working Conditions for Intelligent Tires Using Fuzzy Logic

PubMed Central

Garcia-Pozuelo, Daniel; Yunta, Jorge; Olatunbosun, Oluremi; Yang, Xiaoguang; Diaz, Vicente

2017-01-01

Tires equipped with sensors, the so-called “intelligent tires”, can provide vital information for control systems, drivers and external users. In this research, tire dynamic strain characteristics in cornering conditions are collected and analysed in relation to the variation of tire working conditions, such as inflation pressure, rolling speed, vertical load and slip angle. An experimental tire strain-based prototype and an indoor tire test rig are used to demonstrate the suitability of strain sensors to establish relations between strain data and lateral force. The results of experiments show that strain values drop sharply when lateral force is decreasing, which can be used to predict tire slip conditions. As a first approach to estimate some tire working conditions, such as the slip angle and vertical load, a fuzzy logic method has been developed. The simulation and test results confirm the feasibility of strain sensors and the proposed computational model to solve the non-linearity characteristics of the tires’ parameters and turn tires into a source of useful information. PMID:28420156

A quantum leap into the IED age

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

Patterson, R.C.

1996-11-01

The integration of pattern recognition, artificial intelligence and advanced communication technologies in utility substation IED`s (Intelligent Electronic Devices) has opened the door to practical and cost effective automation of power distribution systems. A major driver for the application of these new technologies has been the research directed toward the detection of high-impedance faults. The commercial products which embody these complex detection functions have already expanded to include most of the protection, control, and monitoring required at a utility substation. These new Super-IED`s enable major utility initiatives, such as power quality management, improved public safety, operation and maintenance productivity, and powermore » system automation.« less

Evaluating impacts of different longitudinal driver assistance systems on reducing multi-vehicle rear-end crashes during small-scale inclement weather.

PubMed

Li, Ye; Xing, Lu; Wang, Wei; Wang, Hao; Dong, Changyin; Liu, Shanwen

2017-10-01

Multi-vehicle rear-end (MVRE) crashes during small-scale inclement (SSI) weather cause high fatality rates on freeways, which cannot be solved by traditional speed limit strategies. This study aimed to reduce MVRE crash risks during SSI weather using different longitudinal driver assistance systems (LDAS). The impact factors on MVRE crashes during SSI weather were firstly analyzed. Then, four LDAS, including Forward collision warning (FCW), Autonomous emergency braking (AEB), Adaptive cruise control (ACC) and Cooperative ACC (CACC), were modeled based on a unified platform, the Intelligent Driver Model (IDM). Simulation experiments were designed and a large number of simulations were then conducted to evaluate safety effects of different LDAS. Results indicate that the FCW and ACC system have poor performance on reducing MVRE crashes during SSI weather. The slight improvement of sight distance of FCW and the limitation of perception-reaction time of ACC lead the failure of avoiding MVRE crashes in most scenarios. The AEB system has the better effect due to automatic perception and reaction, as well as performing the full brake when encountering SSI weather. The CACC system has the best performance because wireless communication provides a larger sight distance and a shorter time delay at the sub-second level. Sensitivity analyses also indicated that the larger number of vehicles and speed changes after encountering SSI weather have negative impacts on safety performances. Results of this study provide useful information for accident prevention during SSI weather. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adapting ISA system warnings to enhance user acceptance.

PubMed

Jiménez, Felipe; Liang, Yingzhen; Aparicio, Francisco

2012-09-01

Inappropriate speed is a major cause of traffic accidents. Different measures have been considered to control traffic speed, and intelligent speed adaptation (ISA) systems are one of the alternatives. These systems know the speed limits and try to improve compliance with them. This paper deals with an informative ISA system that provides the driver with an advance warning before reaching a road section with singular characteristics that require a lower safe speed than the current speed. In spite of the extensive tests performed using ISA systems, few works show how warnings can be adapted to the driver. This paper describes a method to adapt warning parameters (safe speed on curves, zone of influence of a singular stretch, deceleration process and reaction time) to normal driving behavior. The method is based on a set of tests with and without the ISA system. This adjustment, as well as the analysis of driver acceptance before and after the adaptation and changes in driver behavior (changes in speed and path) resulting from the tested ISA regarding a driver's normal driving style, is shown in this paper. The main conclusion is that acceptance by drivers increased significantly after redefining the warning parameters, but the effect of speed homogenization was not reduced. Copyright © 2010 Elsevier Ltd. All rights reserved.

Simulation framework for intelligent transportation systems

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

Ewing, T.; Doss, E.; Hanebutte, U.

1996-10-01

A simulation framework has been developed for a large-scale, comprehensive, scaleable simulation of an Intelligent Transportation System (ITS). The simulator is designed for running on parallel computers and distributed (networked) computer systems, but can run on standalone workstations for smaller simulations. The simulator currently models instrumented smart vehicles with in-vehicle navigation units capable of optimal route planning and Traffic Management Centers (TMC). The TMC has probe vehicle tracking capabilities (display position and attributes of instrumented vehicles), and can provide two-way interaction with traffic to provide advisories and link times. Both the in-vehicle navigation module and the TMC feature detailed graphicalmore » user interfaces to support human-factors studies. Realistic modeling of variations of the posted driving speed are based on human factors studies that take into consideration weather, road conditions, driver personality and behavior, and vehicle type. The prototype has been developed on a distributed system of networked UNIX computers but is designed to run on parallel computers, such as ANL`s IBM SP-2, for large-scale problems. A novel feature of the approach is that vehicles are represented by autonomous computer processes which exchange messages with other processes. The vehicles have a behavior model which governs route selection and driving behavior, and can react to external traffic events much like real vehicles. With this approach, the simulation is scaleable to take advantage of emerging massively parallel processor (MPP) systems.« less

Effectiveness and acceptance of the intelligent speeding prediction system (ISPS).

PubMed

Zhao, Guozhen; Wu, Changxu

2013-03-01

The intelligent speeding prediction system (ISPS) is an in-vehicle speed assistance system developed to provide quantitative predictions of speeding. Although the ISPS's prediction of speeding has been validated, whether the ISPS can regulate a driver's speed behavior or whether a driver accepts the ISPS needs further investigation. Additionally, compared to the existing intelligent speed adaptation (ISA) system, whether the ISPS performs better in terms of reducing excessive speeds and improving driving safety needs more direct evidence. An experiment was conducted to assess and compare the effectiveness and acceptance of the ISPS and the ISA. We conducted a driving simulator study with 40 participants. System type served as a between-subjects variable with four levels: no speed assistance system, pre-warning system developed based on the ISPS, post-warning system ISA, and combined pre-warning and ISA system. Speeding criterion served as a within-subjects variable with two levels: lower (posted speed limit plus 1 mph) and higher (posted speed limit plus 5 mph) speed threshold. Several aspects of the participants' driving speed, speeding measures, lead vehicle response, and subjective measures were collected. Both pre-warning and combined systems led to greater minimum time-to-collision. The combined system resulted in slower driving speed, fewer speeding exceedances, shorter speeding duration, and smaller speeding magnitude. The results indicate that both pre-warning and combined systems have the potential to improve driving safety and performance. Copyright © 2012 Elsevier Ltd. All rights reserved.

Semantically-enabled sensor plug & play for the sensor web.

PubMed

Bröring, Arne; Maúe, Patrick; Janowicz, Krzysztof; Nüst, Daniel; Malewski, Christian

2011-01-01

Environmental sensors have continuously improved by becoming smaller, cheaper, and more intelligent over the past years. As consequence of these technological advancements, sensors are increasingly deployed to monitor our environment. The large variety of available sensor types with often incompatible protocols complicates the integration of sensors into observing systems. The standardized Web service interfaces and data encodings defined within OGC's Sensor Web Enablement (SWE) framework make sensors available over the Web and hide the heterogeneous sensor protocols from applications. So far, the SWE framework does not describe how to integrate sensors on-the-fly with minimal human intervention. The driver software which enables access to sensors has to be implemented and the measured sensor data has to be manually mapped to the SWE models. In this article we introduce a Sensor Plug & Play infrastructure for the Sensor Web by combining (1) semantic matchmaking functionality, (2) a publish/subscribe mechanism underlying the SensorWeb, as well as (3) a model for the declarative description of sensor interfaces which serves as a generic driver mechanism. We implement and evaluate our approach by applying it to an oil spill scenario. The matchmaking is realized using existing ontologies and reasoning engines and provides a strong case for the semantic integration capabilities provided by Semantic Web research.

Semantically-Enabled Sensor Plug & Play for the Sensor Web

PubMed Central

Bröring, Arne; Maúe, Patrick; Janowicz, Krzysztof; Nüst, Daniel; Malewski, Christian

2011-01-01

Environmental sensors have continuously improved by becoming smaller, cheaper, and more intelligent over the past years. As consequence of these technological advancements, sensors are increasingly deployed to monitor our environment. The large variety of available sensor types with often incompatible protocols complicates the integration of sensors into observing systems. The standardized Web service interfaces and data encodings defined within OGC’s Sensor Web Enablement (SWE) framework make sensors available over the Web and hide the heterogeneous sensor protocols from applications. So far, the SWE framework does not describe how to integrate sensors on-the-fly with minimal human intervention. The driver software which enables access to sensors has to be implemented and the measured sensor data has to be manually mapped to the SWE models. In this article we introduce a Sensor Plug & Play infrastructure for the Sensor Web by combining (1) semantic matchmaking functionality, (2) a publish/subscribe mechanism underlying the SensorWeb, as well as (3) a model for the declarative description of sensor interfaces which serves as a generic driver mechanism. We implement and evaluate our approach by applying it to an oil spill scenario. The matchmaking is realized using existing ontologies and reasoning engines and provides a strong case for the semantic integration capabilities provided by Semantic Web research. PMID:22164033

Sensor Systems for Vehicle Environment Perception in a Highway Intelligent Space System

PubMed Central

Tang, Xiaofeng; Gao, Feng; Xu, Guoyan; Ding, Nenggen; Cai, Yao; Ma, Mingming; Liu, Jianxing

2014-01-01

A Highway Intelligent Space System (HISS) is proposed to study vehicle environment perception in this paper. The nature of HISS is that a space sensors system using laser, ultrasonic or radar sensors are installed in a highway environment and communication technology is used to realize the information exchange between the HISS server and vehicles, which provides vehicles with the surrounding road information. Considering the high-speed feature of vehicles on highways, when vehicles will be passing a road ahead that is prone to accidents, the vehicle driving state should be predicted to ensure drivers have road environment perception information in advance, thereby ensuring vehicle driving safety and stability. In order to verify the accuracy and feasibility of the HISS, a traditional vehicle-mounted sensor system for environment perception is used to obtain the relative driving state. Furthermore, an inter-vehicle dynamics model is built and model predictive control approach is used to predict the driving state in the following period. Finally, the simulation results shows that using the HISS for environment perception can arrive at the same results detected by a traditional vehicle-mounted sensors system. Meanwhile, we can further draw the conclusion that using HISS to realize vehicle environment perception can ensure system stability, thereby demonstrating the method's feasibility. PMID:24834907

Soft optics in intelligent optical networks

NASA Astrophysics Data System (ADS)

Shue, Chikong; Cao, Yang

2001-10-01

In addition to the recent advances in Hard-optics that pushes the optical transmission speed, distance, wave density and optical switching capacity, Soft-optics provides the necessary intelligence and control software that reduces operational costs, increase efficiency, and enhances revenue generating services by automating optimal optical circuit placement and restoration, and enabling value-added new services like Optical VPN. This paper describes the advances in 1) Overall Hard-optics and Soft-optics 2) Layered hierarchy of Soft-optics 3) Component of Soft-optics, including hard-optics drivers, Management Soft-optics, Routing Soft-optics and System Soft-optics 4) Key component of Routing and System Soft-optics, namely optical routing and signaling (including UNI/NNI and GMPLS signaling). In summary, the soft-optics on a new generation of OXC's enables Intelligent Optical Networks to provide just-in-time service delivery and fast restoration, and real-time capacity management that eliminates stranded bandwidth. It reduces operational costs and provides new revenue opportunities.

Warfighter decision making performance analysis as an investment priority driver

NASA Astrophysics Data System (ADS)

Thornley, David J.; Dean, David F.; Kirk, James C.

2010-04-01

Estimating the relative value of alternative tactics, techniques and procedures (TTP) and information systems requires measures of the costs and benefits of each, and methods for combining and comparing those measures. The NATO Code of Best Practice for Command and Control Assessment explains that decision making quality would ideally be best assessed on outcomes. Lessons learned in practice can be assessed statistically to support this, but experimentation with alternate measures in live conflict is undesirable. To this end, the development of practical experimentation to parameterize effective constructive simulation and analytic modelling for system utility prediction is desirable. The Land Battlespace Systems Department of Dstl has modeled human development of situational awareness to support constructive simulation by empirically discovering how evidence is weighed according to circumstance, personality, training and briefing. The human decision maker (DM) provides the backbone of the information processing activity associated with military engagements because of inherent uncertainty associated with combat operations. To develop methods for representing the process in order to assess equipment and non-technological interventions such as training and TTPs we are developing componentized or modularized timed analytic stochastic model components and instruments as part of a framework to support quantitative assessment of intelligence production and consumption methods in a human decision maker-centric mission space. In this paper, we formulate an abstraction of the human intelligence fusion process from the Defence Science and Technology Laboratory's (Dstl's) INCIDER model to include in our framework, and synthesize relevant cost and benefit characteristics.

How much benefit does Intelligent Speed Adaptation deliver: an analysis of its potential contribution to safety and environment.

PubMed

Lai, Frank; Carsten, Oliver; Tate, Fergus

2012-09-01

The UK Intelligent Speed Adaptation (ISA) project produced a rich database with high-resolution data on driver behaviour covering a comprehensive range of road environment. The field trials provided vital information on driver behaviour in the presence of ISA. The purpose of this paper is to exploit the information gathered in the field trials to predict the impacts of various forms of ISA and to assess whether ISA is viable in terms of benefit-to-cost ratio. ISA is predicted to save up to 33% of accidents on urban roads, and to reduce CO(2) emissions by up to 5.8% on 70 mph roads. In order to investigate the long-term impacts of ISA, two hypothetical deployment scenarios were envisaged covering a 60-year appraisal period. The results indicate that ISA could deliver a very healthy benefit-to-cost ratio, ranging from 3.4 to 7.4, depending on the deployment scenarios. Under both deployment scenarios, ISA has recovered its implementation costs in less than 15 years. It can be concluded that implementation of ISA is clearly justified from a social cost and benefit perspective. Of the two deployment scenarios, the Market Driven one is substantially outperformed by the Authority Driven one. The benefits of ISA on fuel saving and emission reduction are real but not substantial, in comparison with the benefits on accident reduction; up to 98% of benefits are attributable to accident savings. Indeed, ISA is predicted to lead to a savings of 30% in fatal crashes and 25% in serious crashes over the 60-year period modelled. Copyright © 2011 Elsevier Ltd. All rights reserved.

Brain limbic system-based intelligent controller application to lane change manoeuvre

NASA Astrophysics Data System (ADS)

Kim, Changwon; Langari, Reza

2011-12-01

This paper presents the application of a novel neuromorphic control strategy for lane change manoeuvres in the highway environment. The lateral dynamics of a vehicle with and without wind disturbance are derived and utilised to implement a control strategy based on the brain limbic system. To show the robustness of the proposed controller, several disturbance conditions including wind, uncertainty in the cornering stiffness, and changes in the vehicle mass are investigated. To demonstrate the performance of the suggested strategy, simulation results of the proposed method are compared with the human driver model-based control scheme, which has been discussed in the literature. The simulation results demonstrate the superiority of the proposed controller in energy efficiency, driving comfort, and robustness.

Compact, Intelligent, Digitally Controlled IGBT Gate Drivers for a PEBB-Based ILC Marx Modulator

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

Nguyen, M.N.; Burkhart, C.; Olsen, J.J.

2010-06-07

SLAC National Accelerator Laboratory has built and is currently operating a first generation prototype Marx klystron modulator to meet ILC specifications. Under development is a second generation prototype, aimed at improving overall performance, serviceability, and manufacturability as compared to its predecessor. It is designed around 32 cells, each operating at 3.75 kV and correcting for its own capacitor droop. Due to the uniqueness of this application, high voltage gate drivers needed to be developed for the main 6.5 kV and droop correction 1.7 kV IGBTs. The gate driver provides vital functions such as protection of the IGBT from over-voltage andmore » over-current, detection of gate-emitter open and short circuit conditions, and monitoring of IGBT degradation (based on collector-emitter saturation voltage). Gate drive control, diagnostic processing capabilities, and communication are digitally implemented using an FPGA. This paper details the design of the gate driver circuitry, component selection, and construction layout. In addition, experimental results are included to illustrate the effectiveness of the protection circuit.« less

The Relationship between Student-Centred Lectures, Emotional Intelligence, and Study Teams: A Social Telemetry Study with Mobile Telephony

ERIC Educational Resources Information Center

Senior, Carl; Howard, Christopher; Reddy, Peter; Clark, Robin; Lim, Ming

2012-01-01

A student-centred approach to teaching has been conceptualized as a key driver in higher education to facilitate understanding of concepts and improve attainment. The occurrence of student study team behaviours is diagnostic of this approach to teaching. However, the extent to which team behaviours are performed outside the parameters of formal…

Distribution of dilemma zone after intelligent transportation system established

NASA Astrophysics Data System (ADS)

Deng, Yuanchang; Yang, Huiqin; Wu, Linying

2017-03-01

Dilemma zone refers to an area where vehicles can neither clear the intersection during the yellow interval nor stop safely before the stop line. The purpose of this paper is to analyzing the distribution of two types of dilemma zone after intelligent transportation system (ITS) established at Outer Ring Roads signalized intersections in Guangzhou Higher Education Mega Center. To collect field data a drone aircraft was used. When calculating the type II dilemma zone's distribution, we considered the information of drivers' aggressiveness, which was classified by driving speed and type I dilemma zone as well. We also compared the two types dilemma zone's distribution before and after ITS established and analyzed the changes, which was brought by ITS.

Intelligent advisory speed limit dedication in highway using VANET.

PubMed

Jalooli, Ali; Shaghaghi, Erfan; Jabbarpour, Mohammad Reza; Noor, Rafidah Md; Yeo, Hwasoo; Jung, Jason J

2014-01-01

Variable speed limits (VSLs) as a mean for enhancing road traffic safety are studied for decades to modify the speed limit based on the prevailing road circumstances. In this study the pros and cons of VSL systems and their effects on traffic controlling efficiency are summarized. Despite the potential effectiveness of utilizing VSLs, we have witnessed that the effectiveness of this system is impacted by factors such as VSL control strategy used and the level of driver compliance. Hence, the proposed approach called Intelligent Advisory Speed Limit Dedication (IASLD) as the novel VSL control strategy which considers the driver compliance aims to improve the traffic flow and occupancy of vehicles in addition to amelioration of vehicle's travel times. The IASLD provides the advisory speed limit for each vehicle exclusively based on the vehicle's characteristics including the vehicle type, size, and safety capabilities as well as traffic and weather conditions. The proposed approach takes advantage of vehicular ad hoc network (VANET) to accelerate its performance, in the way that simulation results demonstrate the reduction of incident detection time up to 31.2% in comparison with traditional VSL strategy. The simulation results similarly indicate the improvement of traffic flow efficiency, occupancy, and travel time in different conditions.

Time series modeling in traffic safety research.

PubMed

Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue

2018-08-01

The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.

A measurement model of multiple intelligence profiles of management graduates

NASA Astrophysics Data System (ADS)

Krishnan, Heamalatha; Awang, Siti Rahmah

2017-05-01

In this study, developing a fit measurement model and identifying the best fitting items to represent Howard Gardner's nine intelligences namely, musical intelligence, bodily-kinaesthetic intelligence, mathematical/logical intelligence, visual/spatial intelligence, verbal/linguistic intelligence, interpersonal intelligence, intrapersonal intelligence, naturalist intelligence and spiritual intelligence are the main interest in order to enhance the opportunities of the management graduates for employability. In order to develop a fit measurement model, Structural Equation Modeling (SEM) was applied. A psychometric test which is the Ability Test in Employment (ATIEm) was used as the instrument to measure the existence of nine types of intelligence of 137 University Teknikal Malaysia Melaka (UTeM) management graduates for job placement purposes. The initial measurement model contains nine unobserved variables and each unobserved variable is measured by ten observed variables. Finally, the modified measurement model deemed to improve the Normed chi-square (NC) = 1.331; Incremental Fit Index (IFI) = 0.940 and Root Mean Square of Approximation (RMSEA) = 0.049 was developed. The findings showed that the UTeM management graduates possessed all nine intelligences either high or low. Musical intelligence, mathematical/logical intelligence, naturalist intelligence and spiritual intelligence contributed highest loadings on certain items. However, most of the intelligences such as bodily kinaesthetic intelligence, visual/spatial intelligence, verbal/linguistic intelligence interpersonal intelligence and intrapersonal intelligence possessed by UTeM management graduates are just at the borderline.

Effect of audio in-vehicle red light-running warning message on driving behavior based on a driving simulator experiment.

PubMed

Yan, Xuedong; Liu, Yang; Xu, Yongcun

2015-01-01

Drivers' incorrect decisions of crossing signalized intersections at the onset of the yellow change may lead to red light running (RLR), and RLR crashes result in substantial numbers of severe injuries and property damage. In recent years, some Intelligent Transport System (ITS) concepts have focused on reducing RLR by alerting drivers that they are about to violate the signal. The objective of this study is to conduct an experimental investigation on the effectiveness of the red light violation warning system using a voice message. In this study, the prototype concept of the RLR audio warning system was modeled and tested in a high-fidelity driving simulator. According to the concept, when a vehicle is approaching an intersection at the onset of yellow and the time to the intersection is longer than the yellow interval, the in-vehicle warning system can activate the following audio message "The red light is impending. Please decelerate!" The intent of the warning design is to encourage drivers who cannot clear an intersection during the yellow change interval to stop at the intersection. The experimental results showed that the warning message could decrease red light running violations by 84.3 percent. Based on the logistic regression analyses, drivers without a warning were about 86 times more likely to make go decisions at the onset of yellow and about 15 times more likely to run red lights than those with a warning. Additionally, it was found that the audio warning message could significantly reduce RLR severity because the RLR drivers' red-entry times without a warning were longer than those with a warning. This driving simulator study showed a promising effect of the audio in-vehicle warning message on reducing RLR violations and crashes. It is worthwhile to further develop the proposed technology in field applications.

Real-world effects of using a phone while driving on lateral and longitudinal control of vehicles.

PubMed

Dozza, Marco; Flannagan, Carol A C; Sayer, James R

2015-12-01

Technologies able to augment human communication, such as smartphones, are increasingly present during all daily activities. Their use while driving, in particular, is of great potential concern, because of the high risk that distraction poses during this activity. Current countermeasures to distraction from phone use are considerably different across countries and not always widely accepted/adopted by the drivers. This study utilized naturalistic driving data collected from 108 drivers in the Integrated Vehicle-Based Safety Systems (IVBSS) program in 2009 and 2010 to assess the extent to which using a phone changes lateral or longitudinal control of a vehicle. The IVBSS study included drivers from three age groups: 20–30 (younger), 40–50 (middle-aged), and 60–70 (older). Results from this study show that younger drivers are more likely to use a phone while driving than older and middle-aged drivers. Furthermore, younger drivers exhibited smaller safety margins while using a phone. Nevertheless, younger drivers did not experience more severe lateral/longitudinal threats than older and middle-aged drivers, probably because of faster reaction times. While manipulating the phone (i.e., dialing, texting), drivers exhibited larger lateral safety margins and experienced less severe lateral threats than while conversing on the phone. Finally, longitudinal threats were more critical soon after phone interaction, suggesting that drivers terminate phone interactions when driving becomes more demanding. These findings suggest that drivers are aware of the potential negative effect of phone use on their safety. This awareness guides their decision to engage/disengage in phone use and to increase safety margins (self-regulation). This compensatory behavior may be a natural countermeasure to distraction that is hard to measure in controlled studies. Practical Applications: Intelligent systems able to amplify this natural compensatory behavior may become a widely accepted/adopted countermeasure to the potential distraction from phone operation while driving.

An integrated telemedicine platform for the assessment of affective physiological states

PubMed Central

Katsis, Christos D; Ganiatsas, George; Fotiadis, Dimitrios I

2006-01-01

AUBADE is an integrated platform built for the affective assessment of individuals. The system performs evaluation of the emotional state by classifying vectors of features extracted from: facial Electromyogram, Respiration, Electrodermal Activity and Electrocardiogram. The AUBADE system consists of: (a) a multisensorial wearable, (b) a data acquisition and wireless communication module, (c) a feature extraction module, (d) a 3D facial animation module which is used for the projection of the obtained data through a generic 3D face model; whereas the end-user will be able to view the facial expression of the subject in real time, (e) an intelligent emotion recognition module, and (f) the AUBADE databases where the acquired signals along with the subject's animation videos are saved. The system is designed to be applied to human subjects operating under extreme stress conditions, in particular car racing drivers, and also to patients suffering from neurological and psychological disorders. AUBADE's classification accuracy into five predefined emotional classes (high stress, low stress, disappointment, euphoria and neutral face) is 86.0%. The pilot system applications and components are being tested and evaluated on Maserati's car. racing drivers. PMID:16879757

Design and development of an IoT-based web application for an intelligent remote SCADA system

NASA Astrophysics Data System (ADS)

Kao, Kuang-Chi; Chieng, Wei-Hua; Jeng, Shyr-Long

2018-03-01

This paper presents a design of an intelligent remote electrical power supervisory control and data acquisition (SCADA) system based on the Internet of Things (IoT), with Internet Information Services (IIS) for setting up web servers, an ASP.NET model-view- controller (MVC) for establishing a remote electrical power monitoring and control system by using responsive web design (RWD), and a Microsoft SQL Server as the database. With the web browser connected to the Internet, the sensing data is sent to the client by using the TCP/IP protocol, which supports mobile devices with different screen sizes. The users can provide instructions immediately without being present to check the conditions, which considerably reduces labor and time costs. The developed system incorporates a remote measuring function by using a wireless sensor network and utilizes a visual interface to make the human-machine interface (HMI) more instinctive. Moreover, it contains an analog input/output and a basic digital input/output that can be applied to a motor driver and an inverter for integration with a remote SCADA system based on IoT, and thus achieve efficient power management.

Combat Service Support Enabler Functional Assessment (CEFA). Volume 2: Individual (65) Mini-CEFA Assessments

DTIC Science & Technology

1997-12-01

SLOT) 356 57. Sensor Artificial Intelligence Communications Interactive Maintenance System (SACIMS) 361 58. Soldier’s Portable On- System Repair...increased mobility ; NBC overpressure, environmental control; and situational awareness.] 19. Related changes in CSS effectiveness. Increase The AMEV...to paragraph 17 above.] 19. Related changes in CSS effectiveness. Increase. Driver Minder alerts the maintainer about equipment and operator faults

Driver Vision Based Perception-Response Time Prediction and Assistance Model on Mountain Highway Curve.

PubMed

Li, Yi; Chen, Yuren

2016-12-30

To make driving assistance system more humanized, this study focused on the prediction and assistance of drivers' perception-response time on mountain highway curves. Field tests were conducted to collect real-time driving data and driver vision information. A driver-vision lane model quantified curve elements in drivers' vision. A multinomial log-linear model was established to predict perception-response time with traffic/road environment information, driver-vision lane model, and mechanical status (last second). A corresponding assistance model showed a positive impact on drivers' perception-response times on mountain highway curves. Model results revealed that the driver-vision lane model and visual elements did have important influence on drivers' perception-response time. Compared with roadside passive road safety infrastructure, proper visual geometry design, timely visual guidance, and visual information integrality of a curve are significant factors for drivers' perception-response time.

Introduction and Highlights of the Workshop

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.; Venneri, Samuel L.

1997-01-01

Four generations of CAD/CAM systems can be identified, corresponding to changes in both modeling functionality and software architecture. The systems evolved from 2D and wireframes to solid modeling, to parametric/variational modelers to the current simulation-embedded systems. Recent developments have enabled design engineers to perform many of the complex analysis tasks, typically performed by analysis experts. Some of the characteristics of the current and emerging CAD/CAM/CAE systems are described in subsequent presentations. The focus of the workshop is on the potential of CAD/CAM/CAE systems for use in simulating the entire mission and life-cycle of future aerospace systems, and the needed development to realize this potential. First, the major features of the emerging computing, communication and networking environment are outlined; second, the characteristics and design drivers of future aerospace systems are identified; third, the concept of intelligent synthesis environment being planned by NASA, the UVA ACT Center and JPL is presented; and fourth, the objectives and format of the workshop are outlined.

Evolution and intelligent design in drug development.

PubMed

Agafonov, Roman V; Wilson, Christopher; Kern, Dorothee

2015-01-01

Sophisticated protein kinase networks, empowering complexity in higher organisms, are also drivers of devastating diseases such as cancer. Accordingly, these enzymes have become major drug targets of the twenty-first century. However, the holy grail of designing specific kinase inhibitors aimed at specific cancers has not been found. Can new approaches in cancer drug design help win the battle with this multi-faced and quickly evolving enemy? In this perspective we discuss new strategies and ideas that were born out of a recent breakthrough in understanding the molecular basis underlying the clinical success of the cancer drug Gleevec. An "old" method, stopped-flow kinetics, combined with old enzymes, the ancestors dating back up to about billion years, provides an unexpected outlook for future intelligent design of drugs.

Intelligent Advisory Speed Limit Dedication in Highway Using VANET

PubMed Central

Md Noor, Rafidah; Yeo, Hwasoo; Jung, Jason J.

2014-01-01

Variable speed limits (VSLs) as a mean for enhancing road traffic safety are studied for decades to modify the speed limit based on the prevailing road circumstances. In this study the pros and cons of VSL systems and their effects on traffic controlling efficiency are summarized. Despite the potential effectiveness of utilizing VSLs, we have witnessed that the effectiveness of this system is impacted by factors such as VSL control strategy used and the level of driver compliance. Hence, the proposed approach called Intelligent Advisory Speed Limit Dedication (IASLD) as the novel VSL control strategy which considers the driver compliance aims to improve the traffic flow and occupancy of vehicles in addition to amelioration of vehicle's travel times. The IASLD provides the advisory speed limit for each vehicle exclusively based on the vehicle's characteristics including the vehicle type, size, and safety capabilities as well as traffic and weather conditions. The proposed approach takes advantage of vehicular ad hoc network (VANET) to accelerate its performance, in the way that simulation results demonstrate the reduction of incident detection time up to 31.2% in comparison with traditional VSL strategy. The simulation results similarly indicate the improvement of traffic flow efficiency, occupancy, and travel time in different conditions. PMID:24999493

An IEEE 1451.1 Architecture for ISHM Applications

NASA Technical Reports Server (NTRS)

Morris, Jon A.; Turowski, Mark; Schmalzel, John L.; Figueroa, Jorge F.

2007-01-01

The IEEE 1451.1 Standard for a Smart Transducer Interface defines a common network information model for connecting and managing smart elements in control and data acquisition networks using network-capable application processors (NCAPs). The Standard is a network-neutral design model that is easily ported across operating systems and physical networks for implementing complex acquisition and control applications by simply plugging in the appropriate network level drivers. To simplify configuration and tracking of transducer and actuator details, the family of 1451 standards defines a Transducer Electronic Data Sheet (TEDS) that is associated with each physical element. The TEDS contains all of the pertinent information about the physical operations of a transducer (such as operating regions, calibration tables, and manufacturer information), which the NCAP uses to configure the system to support a specific transducer. The Integrated Systems Health Management (ISHM) group at NASA's John C. Stennis Space Center (SSC) has been developing an ISHM architecture that utilizes IEEE 1451.1 as the primary configuration and data acquisition mechanism for managing and collecting information from a network of distributed intelligent sensing elements. This work has involved collaboration with other NASA centers, universities and aerospace industries to develop IEEE 1451.1 compliant sensors and interfaces tailored to support health assessment of complex systems. This paper and presentation describe the development and implementation of an interface for the configuration, management and communication of data, information and knowledge generated by a distributed system of IEEE 1451.1 intelligent elements monitoring a rocket engine test system. In this context, an intelligent element is defined as one incorporating support for the IEEE 1451.x standards and additional ISHM functions. Our implementation supports real-time collection of both measurement data (raw ADC counts and converted engineering units) and health statistics produced by each intelligent element. The handling of configuration, calibration and health information is automated by using the TEDS in combination with other electronic data sheets extensions to convey health parameters. By integrating the IEEE 1451.1 Standard for a Smart Transducer Interface with ISHM technologies, each element within a complex system becomes a highly flexible computation engine capable of self-validation and performing other measures of the quality of information it is producing.

COMPARING THE IMPAIRMENT PROFILES OF OLDER DRIVERS AND NON-DRIVERS: TOWARD THE DEVELOPMENT OF A FITNESS-TO-DRIVE MODEL

PubMed Central

Antin, Jonathan F.; Stanley, Laura M.; Guo, Feng

2011-01-01

The purpose of this research effort was to compare older driver and non-driver functional impairment profiles across some 60 assessment metrics in an initial effort to contribute to the development of fitness-to-drive assessment models. Of the metrics evaluated, 21 showed statistically significant differences, almost all favoring the drivers. Also, it was shown that a logistic regression model comprised of five of the assessment scores could completely and accurately separate the two groups. The results of this study imply that older drivers are far less functionally impaired than non-drivers of similar ages, and that a parsimonious model can accurately assign individuals to either group. With such models, any driver classified or diagnosed as a non-driver would be a strong candidate for further investigation and intervention. PMID:22058607

Congested traffic states in empirical observations and microscopic simulations

NASA Astrophysics Data System (ADS)

Treiber, Martin; Hennecke, Ansgar; Helbing, Dirk

2000-08-01

We present data from several German freeways showing different kinds of congested traffic forming near road inhomogeneities, specifically lane closings, intersections, or uphill gradients. The states are localized or extended, homogeneous or oscillating. Combined states are observed as well, like the coexistence of moving localized clusters and clusters pinned at road inhomogeneities, or regions of oscillating congested traffic upstream of nearly homogeneous congested traffic. The experimental findings are consistent with a recently proposed theoretical phase diagram for traffic near on-ramps [D. Helbing, A. Hennecke, and M. Treiber, Phys. Rev. Lett. 82, 4360 (1999)]. We simulate these situations with a continuous microscopic single-lane model, the ``intelligent driver model,'' using empirical boundary conditions. All observations, including the coexistence of states, are qualitatively reproduced by describing inhomogeneities with local variations of one model parameter. We show that the results of the microscopic model can be understood by formulating the theoretical phase diagram for bottlenecks in a more general way. In particular, a local drop of the road capacity induced by parameter variations has essentially the same effect as an on-ramp.

Tank-automotive robotics

NASA Astrophysics Data System (ADS)

Lane, Gerald R.

1999-07-01

To provide an overview of Tank-Automotive Robotics. The briefing will contain program overviews & inter-relationships and technology challenges of TARDEC managed unmanned and robotic ground vehicle programs. Specific emphasis will focus on technology developments/approaches to achieve semi- autonomous operation and inherent chassis mobility features. Programs to be discussed include: DemoIII Experimental Unmanned Vehicle (XUV), Tactical Mobile Robotics (TMR), Intelligent Mobility, Commanders Driver Testbed, Collision Avoidance, International Ground Robotics Competition (ICGRC). Specifically, the paper will discuss unique exterior/outdoor challenges facing the IGRC competing teams and the synergy created between the IGRC and ongoing DoD semi-autonomous Unmanned Ground Vehicle and DoT Intelligent Transportation System programs. Sensor and chassis approaches to meet the IGRC challenges and obstacles will be shown and discussed. Shortfalls in performance to meet the IGRC challenges will be identified.

Modeling intelligent adversaries for terrorism risk assessment: some necessary conditions for adversary models.

PubMed

Guikema, Seth

2012-07-01

Intelligent adversary modeling has become increasingly important for risk analysis, and a number of different approaches have been proposed for incorporating intelligent adversaries in risk analysis models. However, these approaches are based on a range of often-implicit assumptions about the desirable properties of intelligent adversary models. This "Perspective" paper aims to further risk analysis for situations involving intelligent adversaries by fostering a discussion of the desirable properties for these models. A set of four basic necessary conditions for intelligent adversary models is proposed and discussed. These are: (1) behavioral accuracy to the degree possible, (2) computational tractability to support decision making, (3) explicit consideration of uncertainty, and (4) ability to gain confidence in the model. It is hoped that these suggested necessary conditions foster discussion about the goals and assumptions underlying intelligent adversary modeling in risk analysis. © 2011 Society for Risk Analysis.

Physical Intelligent Sensors

NASA Technical Reports Server (NTRS)

Bandhil, Pavan; Chitikeshi, Sanjeevi; Mahajan, Ajay; Figueroa, Fernando

2005-01-01

This paper proposes the development of intelligent sensors as part of an integrated systems approach, i.e. one treats the sensors as a complete system with its own sensing hardware (the traditional sensor), A/D converters, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow them to get better with time. Under a project being undertaken at the NASA s Stennis Space Center, an integrated framework is being developed for the intelligent monitoring of smart elements. These smart elements can be sensors, actuators or other devices. The immediate application is the monitoring of the rocket test stands, but the technology should be generally applicable to the Integrated Systems Health Monitoring (ISHM) vision. This paper outlines progress made in the development of intelligent sensors by describing the work done till date on Physical Intelligent Sensors (PIS). The PIS discussed here consists of a thermocouple used to read temperature in an analog form which is then converted into digital values. A microprocessor collects the sensor readings and runs numerous embedded event detection routines on the collected data and if any event is detected, it is reported, stored and sent to a remote system through an Ethernet connection. Hence the output of the PIS is data coupled with confidence factor in the reliability of the data which leads to information on the health of the sensor at all times. All protocols are consistent with IEEE 1451.X standards. This work lays the foundation for the next generation of smart devices that have embedded intelligence for distributed decision making capabilities.

Potential of Cognitive Computing and Cognitive Systems

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.

2015-01-01

Cognitive computing and cognitive technologies are game changers for future engineering systems, as well as for engineering practice and training. They are major drivers for knowledge automation work, and the creation of cognitive products with higher levels of intelligence than current smart products. This paper gives a brief review of cognitive computing and some of the cognitive engineering systems activities. The potential of cognitive technologies is outlined, along with a brief description of future cognitive environments, incorporating cognitive assistants - specialized proactive intelligent software agents designed to follow and interact with humans and other cognitive assistants across the environments. The cognitive assistants engage, individually or collectively, with humans through a combination of adaptive multimodal interfaces, and advanced visualization and navigation techniques. The realization of future cognitive environments requires the development of a cognitive innovation ecosystem for the engineering workforce. The continuously expanding major components of the ecosystem include integrated knowledge discovery and exploitation facilities (incorporating predictive and prescriptive big data analytics); novel cognitive modeling and visual simulation facilities; cognitive multimodal interfaces; and cognitive mobile and wearable devices. The ecosystem will provide timely, engaging, personalized / collaborative, learning and effective decision making. It will stimulate creativity and innovation, and prepare the participants to work in future cognitive enterprises and develop new cognitive products of increasing complexity. http://www.aee.odu.edu/cognitivecomp

The experiment of cooperative learning model type team assisted individualization (TAI) on three-dimensional space subject viewed from spatial intelligence

NASA Astrophysics Data System (ADS)

Manapa, I. Y. H.; Budiyono; Subanti, S.

2018-03-01

The aim of this research is to determine the effect of TAI or direct learning (DL) on student’s mathematics achievement viewed from spatial intelligence. This research was quasi experiment. The population was 10th grade senior high school students in Alor Regency on academic year of 2015/2016 chosen by stratified cluster random sampling. The data were collected through achievement and spatial intelligence test. The data were analyzed by two ways, ANOVA with unequal cell and scheffe test. This research showed that student’s mathematics achievement used in TAI had better results than DL models one. In spatial intelligence category, student’s mathematics achievement with high spatial intelligence has better result than the other spatial intelligence category and students with high spatial intelligence have better results than those with middle spatial intelligence category. At TAI, student’s mathematics achievement with high spatial intelligence has better result than those with the other spatial intelligence category and students with middle spatial intelligence have better results than students with low spatial intelligence. In DL model, student’s mathematics achievement with high and middle spatial intelligence has better result than those with low spatial intelligence, but students with high spatial intelligence and middle spatial intelligence have no significant difference. In each category of spatial intelligence and learning model, mathematics achievement has no significant difference.

Intelligence: Real or artificial?

PubMed Central

Schlinger, Henry D.

1992-01-01

Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally referred to behavior-environment relations and not to inferred internal structures and processes. It is concluded that if workers in artificial intelligence are to succeed in their general goal, then they must design machines that are adaptive, that is, that can learn. Thus, artificial intelligence researchers must discard their essentialist model of natural intelligence and adopt a selectionist model instead. Such a strategic change should lead them to the science of behavior analysis. PMID:22477051

Modeling of Driver Steering Operations in Lateral Wind Disturbances toward Driver Assistance System

NASA Astrophysics Data System (ADS)

Kurata, Yoshinori; Wada, Takahiro; Kamiji, Norimasa; Doi, Shun'ichi

Disturbances decrease vehicle stability and increase driver's mental and physical workload. Especially unexpected disturbances such as lateral winds have severe effect on vehicle stability and driver's workload. This study aims at building a driver model of steering operations in lateral wind toward developing effective driver assistance system. First, the relationship between the driver's lateral motion and its reactive quick steering behavior is investigated using driving simulator with lateral 1dof motion. In the experiments, four different wind patterns are displayed by the simulator. As the results, strong correlation was found between the driver's head lateral jerk by the lateral disturbance and the angular acceleration of the steering wheel. Then, we build a mathematical model of driver's steering model from lateral disturbance input to steering torque of the reactive quick feed-forward steering based on the experimental results. Finally, validity of the proposed model is shown by comparing the steering torque of experimental results and that of simulation results.

Neuropsychological assessment of driving ability and self-evaluation: a comparison between driving offenders and a control group.

PubMed

Zingg, Christina; Puelschen, Dietrich; Soyka, Michael

2009-12-01

The relationship between performance in neuropsychological tests and actual driving performance is unclear and results of studies on this topic differ. This makes it difficult to use neuropsychological tests to assess driving ability. The ability to compensate cognitive deficits plays a crucial role in this context. We compared neuropsychological test results and self-evaluation ratings between three groups: driving offenders with a psychiatric diagnosis relevant for driving ability (mainly alcohol dependence), driving offenders without such a diagnosis and a control group of non-offending drivers. Subjects were divided into two age categories (19-39 and 40-66 years). It was assumed that drivers with a psychiatric diagnosis relevant for driving ability and younger driving offenders without a psychiatric diagnosis would be less able to adequately assess their own capabilities than the control group. The driving offenders with a psychiatric diagnosis showed poorer concentration, reactivity, cognitive flexibility and problem solving, and tended to overassess their abilities in intelligence and attentional functions, compared to the other two groups. Conversely, younger drivers rather underassessed their performance.

Experimentation of cooperative learning model Numbered Heads Together (NHT) type by concept maps and Teams Games Tournament (TGT) by concept maps in terms of students logical mathematics intellegences

NASA Astrophysics Data System (ADS)

Irawan, Adi; Mardiyana; Retno Sari Saputro, Dewi

2017-06-01

This research is aimed to find out the effect of learning model towards learning achievement in terms of students’ logical mathematics intelligences. The learning models that were compared were NHT by Concept Maps, TGT by Concept Maps, and Direct Learning model. This research was pseudo experimental by factorial design 3×3. The population of this research was all of the students of class XI Natural Sciences of Senior High School in all regency of Karanganyar in academic year 2016/2017. The conclusions of this research were: 1) the students’ achievements with NHT learning model by Concept Maps were better than students’ achievements with TGT model by Concept Maps and Direct Learning model. The students’ achievements with TGT model by Concept Maps were better than the students’ achievements with Direct Learning model. 2) The students’ achievements that exposed high logical mathematics intelligences were better than students’ medium and low logical mathematics intelligences. The students’ achievements that exposed medium logical mathematics intelligences were better than the students’ low logical mathematics intelligences. 3) Each of student logical mathematics intelligences with NHT learning model by Concept Maps has better achievement than students with TGT learning model by Concept Maps, students with NHT learning model by Concept Maps have better achievement than students with the direct learning model, and the students with TGT by Concept Maps learning model have better achievement than students with Direct Learning model. 4) Each of learning model, students who have logical mathematics intelligences have better achievement then students who have medium logical mathematics intelligences, and students who have medium logical mathematics intelligences have better achievement than students who have low logical mathematics intelligences.

A driver-adaptive stability control strategy for sport utility vehicles

NASA Astrophysics Data System (ADS)

Zhu, Shenjin; He, Yuping

2017-08-01

Conventional vehicle stability control (VSC) systems are designed for average drivers. For a driver with a good driving skill, the VSC systems may be redundant; for a driver with a poor driving skill, the VSC intervention may be inadequate. To increase safety of sport utility vehicles (SUVs), this paper proposes a novel driver-adaptive VSC (DAVSC) strategy based on scaling the target yaw rate commanded by the driver. The DAVSC system is adaptive to drivers' driving skills. More control effort would be exerted for drivers with poor driving skills, and vice versa. A sliding mode control (SMC)-based differential braking (DB) controller is designed using a three degrees of freedom (DOF) yaw-plane model. An eight DOF nonlinear yaw-roll model is used to simulate the SUV dynamics. Two driver models, namely longitudinal and lateral, are used to 'drive' the virtual SUV. By integrating the virtual SUV, the DB controller, and the driver models, the performance of the DAVSC system is investigated. The simulations demonstrate the effectiveness of the DAVSC strategy.

Sharing control with haptics: seamless driver support from manual to automatic control.

PubMed

Mulder, Mark; Abbink, David A; Boer, Erwin R

2012-10-01

Haptic shared control was investigated as a human-machine interface that can intuitively share control between drivers and an automatic controller for curve negotiation. As long as automation systems are not fully reliable, a role remains for the driver to be vigilant to the system and the environment to catch any automation errors. The conventional binary switches between supervisory and manual control has many known issues, and haptic shared control is a promising alternative. A total of 42 respondents of varying age and driving experience participated in a driving experiment in a fixed-base simulator, in which curve negotiation behavior during shared control was compared to during manual control, as well as to three haptic tunings of an automatic controller without driver intervention. Under the experimental conditions studied, the main beneficial effect of haptic shared control compared to manual control was that less control activity (16% in steering wheel reversal rate, 15% in standard deviation of steering wheel angle) was needed for realizing an improved safety performance (e.g., 11% in peak lateral error). Full automation removed the need for any human control activity and improved safety performance (e.g., 35% in peak lateral error) but put the human in a supervisory position. Haptic shared control kept the driver in the loop, with enhanced performance at reduced control activity, mitigating the known issues that plague full automation. Haptic support for vehicular control ultimately seeks to intuitively combine human intelligence and creativity with the benefits of automation systems.

User modeling for distributed virtual environment intelligent agents

NASA Astrophysics Data System (ADS)

Banks, Sheila B.; Stytz, Martin R.

1999-07-01

This paper emphasizes the requirement for user modeling by presenting the necessary information to motivate the need for and use of user modeling for intelligent agent development. The paper will present information on our current intelligent agent development program, the Symbiotic Information Reasoning and Decision Support (SIRDS) project. We then discuss the areas of intelligent agents and user modeling, which form the foundation of the SIRDS project. Included in the discussion of user modeling are its major components, which are cognitive modeling and behavioral modeling. We next motivate the need for and user of a methodology to develop user models to encompass work within cognitive task analysis. We close the paper by drawing conclusions from our current intelligent agent research project and discuss avenues of future research in the utilization of user modeling for the development of intelligent agents for virtual environments.

Modeling of driver's collision avoidance maneuver based on controller switching model.

PubMed

Kim, Jong-Hae; Hayakawa, Soichiro; Suzuki, Tatsuya; Hayashi, Koji; Okuma, Shigeru; Tsuchida, Nuio; Shimizu, Masayuki; Kido, Shigeyuki

2005-12-01

This paper presents a modeling strategy of human driving behavior based on the controller switching model focusing on the driver's collision avoidance maneuver. The driving data are collected by using the three-dimensional (3-D) driving simulator based on the CAVE Automatic Virtual Environment (CAVE), which provides stereoscopic immersive virtual environment. In our modeling, the control scenario of the human driver, that is, the mapping from the driver's sensory information to the operation of the driver such as acceleration, braking, and steering, is expressed by Piecewise Polynomial (PWP) model. Since the PWP model includes both continuous behaviors given by polynomials and discrete logical conditions, it can be regarded as a class of Hybrid Dynamical System (HDS). The identification problem for the PWP model is formulated as the Mixed Integer Linear Programming (MILP) by transforming the switching conditions into binary variables. From the obtained results, it is found that the driver appropriately switches the "control law" according to the sensory information. In addition, the driving characteristics of the beginner driver and the expert driver are compared and discussed. These results enable us to capture not only the physical meaning of the driving skill but the decision-making aspect (switching conditions) in the driver's collision avoidance maneuver as well.

Driver's mental workload prediction model based on physiological indices.

PubMed

Yan, Shengyuan; Tran, Cong Chi; Wei, Yingying; Habiyaremye, Jean Luc

2017-09-15

Developing an early warning model to predict the driver's mental workload (MWL) is critical and helpful, especially for new or less experienced drivers. The present study aims to investigate the correlation between new drivers' MWL and their work performance, regarding the number of errors. Additionally, the group method of data handling is used to establish the driver's MWL predictive model based on subjective rating (NASA task load index [NASA-TLX]) and six physiological indices. The results indicate that the NASA-TLX and the number of errors are positively correlated, and the predictive model shows the validity of the proposed model with an R 2 value of 0.745. The proposed model is expected to provide a reference value for the new drivers of their MWL by providing the physiological indices, and the driving lesson plans can be proposed to sustain an appropriate MWL as well as improve the driver's work performance.

Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: methodology and preliminary report.

PubMed

Fu, Jicheng; Jones, Maria; Jan, Yih-Kuen

2014-01-01

Wheelchair tilt and recline functions are two of the most desirable features for relieving seating pressure to decrease the risk of pressure ulcers. The effective guidance on wheelchair tilt and recline usage is therefore critical to pressure ulcer prevention. The aim of this study was to demonstrate the feasibility of using machine learning techniques to construct an intelligent model to provide personalized guidance to individuals with spinal cord injury (SCI). The motivation stems from the clinical evidence that the requirements of individuals vary greatly and that no universal guidance on tilt and recline usage could possibly satisfy all individuals with SCI. We explored all aspects involved in constructing the intelligent model and proposed approaches tailored to suit the characteristics of this preliminary study, such as the way of modeling research participants, using machine learning techniques to construct the intelligent model, and evaluating the performance of the intelligent model. We further improved the intelligent model's prediction accuracy by developing a two-phase feature selection algorithm to identify important attributes. Experimental results demonstrated that our approaches held the promise: they could effectively construct the intelligent model, evaluate its performance, and refine the participant model so that the intelligent model's prediction accuracy was significantly improved.

Traffic Games: Modeling Freeway Traffic with Game Theory.

PubMed

Cortés-Berrueco, Luis E; Gershenson, Carlos; Stephens, Christopher R

2016-01-01

We apply game theory to a vehicular traffic model to study the effect of driver strategies on traffic flow. The resulting model inherits the realistic dynamics achieved by a two-lane traffic model and aims to incorporate phenomena caused by driver-driver interactions. To achieve this goal, a game-theoretic description of driver interaction was developed. This game-theoretic formalization allows one to model different lane-changing behaviors and to keep track of mobility performance. We simulate the evolution of cooperation, traffic flow, and mobility performance for different modeled behaviors. The analysis of these results indicates a mobility optimization process achieved by drivers' interactions.

Social dilemma structure hidden behind traffic flow with route selection

NASA Astrophysics Data System (ADS)

Tanimoto, Jun; Nakamura, Kousuke

2016-10-01

Several traffic flows contain social dilemma structures. Herein, we explored a route-selection problem using a cellular automaton simulation dovetailed with evolutionary game theory. In our model, two classes of driver-agents coexist: D agents (defective strategy), which refer to traffic information for route selection to move fast, and C agents (cooperative strategy), which are insensitive to information and less inclined to move fast. Although no evidence suggests that the social dilemma structure in low density causes vehicles to move freely and that in high density causes traffic jams, we found a structure that corresponds to an n-person (multiplayer) Chicken (n-Chicken) game if the provided traffic information is inappropriate. If appropriate traffic information is given to the agents, the n-Chicken game can be solved. The information delivered to vehicles is crucial for easing the social dilemma due to urban traffic congestion when developing technologies to support the intelligent transportation system (ITS).

Traffic Sign Detection Based on Biologically Visual Mechanism

NASA Astrophysics Data System (ADS)

Hu, X.; Zhu, X.; Li, D.

2012-07-01

TSR (Traffic sign recognition) is an important problem in ITS (intelligent traffic system), which is being paid more and more attention for realizing drivers assisting system and unmanned vehicle etc. TSR consists of two steps: detection and recognition, and this paper describe a new traffic sign detection method. The design principle of the traffic sign is comply with the visual attention mechanism of human, so we propose a method using visual attention mechanism to detect traffic sign ,which is reasonable. In our method, the whole scene will firstly be analyzed by visual attention model to acquire the area where traffic signs might be placed. And then, these candidate areas will be analyzed according to the shape characteristics of the traffic sign to detect traffic signs. In traffic sign detection experiments, the result shows the proposed method is effectively and robust than other existing saliency detection method.

Model driver screening and evaluation program. Volume 2, Maryland pilot older driver study

DOT National Transportation Integrated Search

2003-05-01

This research project studied the feasibility as well as the scientific validity and utility of performing functional capacity screening with older drivers. A Model Program was described encompassing procedures to detect functionally impaired drivers...

Intelligent systems of the vehicles’ suspension

NASA Astrophysics Data System (ADS)

Yurlin, D.

2018-02-01

The article is devoted to the current condition of car’s active suspension system. It presents the tendencies in development of the active systems of suspension system, adjustable elements incorporated in them and the companies succeeded in designing such systems. It also mirrors the problem of impact of active systems on car’s safety and their importance for the driver. Advantages and disadvantages of the most common types of active elements are being described, analyzed and compared. The author concludes about the perspectives of these systems’ development.

Vision-based algorithms for near-host object detection and multilane sensing

NASA Astrophysics Data System (ADS)

Kenue, Surender K.

1995-01-01

Vision-based sensing can be used for lane sensing, adaptive cruise control, collision warning, and driver performance monitoring functions of intelligent vehicles. Current computer vision algorithms are not robust for handling multiple vehicles in highway scenarios. Several new algorithms are proposed for multi-lane sensing, near-host object detection, vehicle cut-in situations, and specifying regions of interest for object tracking. These algorithms were tested successfully on more than 6000 images taken from real-highway scenes under different daytime lighting conditions.

iDriving (Intelligent Driving)

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

Malikopoulos, Andreas

2012-09-17

iDriving identifies the driving style factors that have a major impact on fuel economy. An optimization framework is used with the aim of optimizing a driving style with respect to these driving factors. A set of polynomial metamodels is constructed to reflect the responses produced in fuel economy by changing the driving factors. The optimization framework is used to develop a real-time feedback system, including visual instructions, to enable drivers to alter their driving styles in responses to actual driving conditions to improve fuel efficiency.

Modeling driver behavior in a cognitive architecture.

PubMed

Salvucci, Dario D

2006-01-01

This paper explores the development of a rigorous computational model of driver behavior in a cognitive architecture--a computational framework with underlying psychological theories that incorporate basic properties and limitations of the human system. Computational modeling has emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior. An integrated driver model developed in the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture is described that focuses on the component processes of control, monitoring, and decision making in a multilane highway environment. This model accounts for the steering profiles, lateral position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing. The model demonstrates how cognitive architectures facilitate understanding of driver behavior in the context of general human abilities and constraints and how the driving domain benefits cognitive architectures by pushing model development toward more complex, realistic tasks. The model can also serve as a core computational engine for practical applications that predict and recognize driver behavior and distraction.

Exploratory multinomial logit model-based driver injury severity analyses for teenage and adult drivers in intersection-related crashes.

PubMed

Wu, Qiong; Zhang, Guohui; Ci, Yusheng; Wu, Lina; Tarefder, Rafiqul A; Alcántara, Adélamar Dely

2016-05-18

Teenage drivers are more likely to be involved in severely incapacitating and fatal crashes compared to adult drivers. Moreover, because two thirds of urban vehicle miles traveled are on signal-controlled roadways, significant research efforts are needed to investigate intersection-related teenage driver injury severities and their contributing factors in terms of driver behavior, vehicle-infrastructure interactions, environmental characteristics, roadway geometric features, and traffic compositions. Therefore, this study aims to explore the characteristic differences between teenage and adult drivers in intersection-related crashes, identify the significant contributing attributes, and analyze their impacts on driver injury severities. Using crash data collected in New Mexico from 2010 to 2011, 2 multinomial logit regression models were developed to analyze injury severities for teenage and adult drivers, respectively. Elasticity analyses and transferability tests were conducted to better understand the quantitative impacts of these factors and the teenage driver injury severity model's generality. The results showed that although many of the same contributing factors were found to be significant in the both teenage and adult driver models, certain different attributes must be distinguished to specifically develop effective safety solutions for the 2 driver groups. The research findings are helpful to better understand teenage crash uniqueness and develop cost-effective solutions to reduce intersection-related teenage injury severities and facilitate driver injury mitigation research.

Cognitive Modeling of Social Behaviors

NASA Technical Reports Server (NTRS)

Clancey, William J.; Sierhuis, Maarten; Damer. Bruce; Brodsky, Boris

2004-01-01

The driving theme of cognitive modeling for many decades has been that knowledge affects how and which goals are accomplished by an intelligent being (Newell 1991). But when one examines groups of people living and working together, one is forced to recognize that whose knowledge is called into play, at a particular time and location, directly affects what the group accomplishes. Indeed, constraints on participation, including roles, procedures, and norms, affect whether an individual is able to act at all (Lave & Wenger 1991; Jordan 1992; Scribner & Sachs 1991). To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual &nd as ways of carrying out activities (Clancey 1997a, 2002b). This requires for the psychologist a shift from only modeling goals and tasks - why people do what they do - to modeling behavioral patterns-what people do-as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). This analysis is particular inspired by activity theory (Leont ev 1979). While acknowledging that knowledge (relating goals and operations) is fundamental for intelligent behavior, activity theory claims that a broader driver is the person s motives and conceptualization of activities. Such understanding of human interaction is normative (i.e., viewed with respect to social standards), affecting how knowledge is called into play and applied in practice. Put another way, how problems are discovered and framed, what methods are chosen, and indeed who even cares or has the authority to act, are all constrained by norms, which are conceived and enacted by individuals.

Student Modeling in an Intelligent Tutoring System

DTIC Science & Technology

1996-12-17

Multi-Agent Architecture." Advances in Artificial Intelligence : Proceedings of the 12 th Brazilian Symposium on Aritificial Intelligence , edited by...STUDENT MODELING IN AN INTELLIGENT TUTORING SYSTEM THESIS Jeremy E. Thompson Captain, USAF AFIT/GCS/ENG/96D-27 DIMTVMON* fCKAJWINT A Appr"v*d t=i...Air Force Base, Ohio AFIT/GCS/ENG/96D-27 STUDENT MODELING IN AN INTELLIGENT TUTORING SYSTEM THESIS Jeremy E. Thompson Captain, USAF AFIT/GCS/ENG/96D

Lane-changing model with dynamic consideration of driver's propensity

NASA Astrophysics Data System (ADS)

Wang, Xiaoyuan; Wang, Jianqiang; Zhang, Jinglei; Ban, Xuegang Jeff

2015-07-01

Lane-changing is the driver's selection result of the satisfaction degree in different lane driving conditions. There are many different factors influencing lane-changing behavior, such as diversity, randomicity and difficulty of measurement. So it is hard to accurately reflect the uncertainty of drivers' lane-changing behavior. As a result, the research of lane-changing models is behind that of car-following models. Driver's propensity is her/his emotion state or the corresponding preference of a decision or action toward the real objective traffic situations under the influence of various dynamic factors. It represents the psychological characteristics of the driver in the process of vehicle operation and movement. It is an important factor to influence lane-changing. In this paper, dynamic recognition of driver's propensity is considered during simulation based on its time-varying discipline and the analysis of the driver's psycho-physic characteristics. The Analytic Hierarchy Process (AHP) method is used to quantify the hierarchy of driver's dynamic lane-changing decision-making process, especially the influence of the propensity. The model is validated using real data. Test results show that the developed lane-changing model with the dynamic consideration of a driver's time-varying propensity and the AHP method are feasible and with improved accuracy.

On the efficiency of driver state monitoring systems

NASA Astrophysics Data System (ADS)

Dementienko, V. V.; Dorokhov, V. B.; Gerus, S. V.; Markov, A. G.; Shakhnarovich, V. M.

2007-06-01

Statistical data on road traffic and the results of laboratory studies are used to construct a mathematical model of a driver-driver state monitor-automobile-traffic system. In terms of the model, the probability of an accident resulting from the drowsy state of the driver is determined both in the absence and presence of a monitor. The model takes into account the efficiency and safety level provided by different monitoring systems, as well as psychological factors associated with the excessive reliance of drivers upon monitoring.

Examining driver injury severity outcomes in rural non-interstate roadway crashes using a hierarchical ordered logit model.

PubMed

Chen, Cong; Zhang, Guohui; Huang, Helai; Wang, Jiangfeng; Tarefder, Rafiqul A

2016-11-01

Rural non-interstate crashes induce a significant amount of severe injuries and fatalities. Examination of such injury patterns and the associated contributing factors is of practical importance. Taking into account the ordinal nature of injury severity levels and the hierarchical feature of crash data, this study employs a hierarchical ordered logit model to examine the significant factors in predicting driver injury severities in rural non-interstate crashes based on two-year New Mexico crash records. Bayesian inference is utilized in model estimation procedure and 95% Bayesian Credible Interval (BCI) is applied to testing variable significance. An ordinary ordered logit model omitting the between-crash variance effect is evaluated as well for model performance comparison. Results indicate that the model employed in this study outperforms ordinary ordered logit model in model fit and parameter estimation. Variables regarding crash features, environment conditions, and driver and vehicle characteristics are found to have significant influence on the predictions of driver injury severities in rural non-interstate crashes. Factors such as road segments far from intersection, wet road surface condition, collision with animals, heavy vehicle drivers, male drivers and driver seatbelt used tend to induce less severe driver injury outcomes than the factors such as multiple-vehicle crashes, severe vehicle damage in a crash, motorcyclists, females, senior drivers, driver with alcohol or drug impairment, and other major collision types. Research limitations regarding crash data and model assumptions are also discussed. Overall, this research provides reasonable results and insight in developing effective road safety measures for crash injury severity reduction and prevention. Copyright © 2016 Elsevier Ltd. All rights reserved.

Organisational Intelligence

ERIC Educational Resources Information Center

Yolles, Maurice

2005-01-01

Purpose: Seeks to explore the notion of organisational intelligence as a simple extension of the notion of the idea of collective intelligence. Design/methodology/approach: Discusses organisational intelligence using previous research, which includes the Purpose, Properties and Practice model of Dealtry, and the Viable Systems model. Findings: The…

Lane Detection on the iPhone

NASA Astrophysics Data System (ADS)

Ren, Feixiang; Huang, Jinsheng; Terauchi, Mutsuhiro; Jiang, Ruyi; Klette, Reinhard

A robust and efficient lane detection system is an essential component of Lane Departure Warning Systems, which are commonly used in many vision-based Driver Assistance Systems (DAS) in intelligent transportation. Various computation platforms have been proposed in the past few years for the implementation of driver assistance systems (e.g., PC, laptop, integrated chips, PlayStation, and so on). In this paper, we propose a new platform for the implementation of lane detection, which is based on a mobile phone (the iPhone). Due to physical limitations of the iPhone w.r.t. memory and computing power, a simple and efficient lane detection algorithm using a Hough transform is developed and implemented on the iPhone, as existing algorithms developed based on the PC platform are not suitable for mobile phone devices (currently). Experiments of the lane detection algorithm are made both on PC and on iPhone.

Intelligent imaging systems for automotive applications

NASA Astrophysics Data System (ADS)

Thompson, Chris; Huang, Yingping; Fu, Shan

2004-03-01

In common with many other application areas, visual signals are becoming an increasingly important information source for many automotive applications. For several years CCD cameras have been used as research tools for a range of automotive applications. Infrared cameras, RADAR and LIDAR are other types of imaging sensors that have also been widely investigated for use in cars. This paper will describe work in this field performed in C2VIP over the last decade - starting with Night Vision Systems and looking at various other Advanced Driver Assistance Systems. Emerging from this experience, we make the following observations which are crucial for "intelligent" imaging systems: 1. Careful arrangement of sensor array. 2. Dynamic-Self-Calibration. 3. Networking and processing. 4. Fusion with other imaging sensors, both at the image level and the feature level, provides much more flexibility and reliability in complex situations. We will discuss how these problems can be addressed and what are the outstanding issues.

Road Nail: Experimental Solar Powered Intelligent Road Marking System

NASA Astrophysics Data System (ADS)

Samardžija, Dragan; Teslić, Nikola; Todorović, Branislav M.; Kovač, Erne; Isailović, Đorđe; Miladinović, Bojan

2012-03-01

Driving in low visibility conditions (night time, fog or heavy precipitation) is particularly challenging task with an increased probability of traffic accidents and possible injuries. Road Nail is a solar powered intelligent road marking system of wirelessly networked signaling devices that improve driver safety in low visibility conditions along hazardous roadways. Nails or signaling devices are autonomous nodes with capability to accumulate energy, exchange wireless messages, detect approaching vehicles and emit signalization light. We have built an experimental test-bed that consists of 20 nodes and a cellular gateway. Implementation details of the above system, including extensive measurements and performance evaluations in realistic field deployments are presented. A novel distributed network topology discovery scheme is proposed which integrates both sensor and wireless communication aspects, where nodes act autonomously. Finally, integration of the Road Nail system with the cellular network and the Internet is described.

Steering disturbance rejection using a physics-based neuromusculoskeletal driver model

NASA Astrophysics Data System (ADS)

Mehrabi, Naser; Sharif Razavian, Reza; McPhee, John

2015-10-01

The aim of this work is to develop a comprehensive yet practical driver model to be used in studying driver-vehicle interactions. Drivers interact with their vehicle and the road through the steering wheel. This interaction forms a closed-loop coupled human-machine system, which influences the driver's steering feel and control performance. A hierarchical approach is proposed here to capture the complexity of the driver's neuromuscular dynamics and the central nervous system in the coordination of the driver's upper extremity activities, especially in the presence of external disturbance. The proposed motor control framework has three layers: the first (or the path planning) plans a desired vehicle trajectory and the required steering angles to perform the desired trajectory; the second (or the musculoskeletal controller) actuates the musculoskeletal arm to rotate the steering wheel accordingly; and the final layer ensures the precision control and disturbance rejection of the motor control units. The physics-based driver model presented here can also provide insights into vehicle control in relaxed and tensed driving conditions, which are simulated by adjusting the driver model parameters such as cognition delay and muscle co-contraction dynamics.

An Analysis of Student Model Portability

ERIC Educational Resources Information Center

Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict

2016-01-01

Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…

Biosignal Analysis to Assess Mental Stress in Automatic Driving of Trucks: Palmar Perspiration and Masseter Electromyography

PubMed Central

Zheng, Rencheng; Yamabe, Shigeyuki; Nakano, Kimihiko; Suda, Yoshihiro

2015-01-01

Nowadays insight into human-machine interaction is a critical topic with the large-scale development of intelligent vehicles. Biosignal analysis can provide a deeper understanding of driver behaviors that may indicate rationally practical use of the automatic technology. Therefore, this study concentrates on biosignal analysis to quantitatively evaluate mental stress of drivers during automatic driving of trucks, with vehicles set at a closed gap distance apart to reduce air resistance to save energy consumption. By application of two wearable sensor systems, a continuous measurement was realized for palmar perspiration and masseter electromyography, and a biosignal processing method was proposed to assess mental stress levels. In a driving simulator experiment, ten participants completed automatic driving with 4, 8, and 12 m gap distances from the preceding vehicle, and manual driving with about 25 m gap distance as a reference. It was found that mental stress significantly increased when the gap distances decreased, and an abrupt increase in mental stress of drivers was also observed accompanying a sudden change of the gap distance during automatic driving, which corresponded to significantly higher ride discomfort according to subjective reports. PMID:25738768

Development of an Intelligent Digital Watershed to understand water-human interaction for a sustainable Agroeconomy in Midwest USA

NASA Astrophysics Data System (ADS)

Mishra, S. K.; Rapolu, U.; Ding, D.; Muste, M.; Bennett, D.; Schnoor, J. L.

2011-12-01

Human activity is intricately linked to the quality and quantity of water resources. Although many studies have examined water-human interaction, the complexity of such coupled systems is not well understood largely because of gaps in our knowledge of water-cycle processes which are heavily influenced by socio-economic drivers. Considerable research has been performed to develop an understanding of the impact of local land use decisions on field and catchment processes at an annual basis. Still less is known about the impact of economic and environmental outcomes on decision-making processes at the local and national level. Traditional geographic information management systems lack the ability to support the modeling and analysis of complex spatial processes. New frameworks are needed to track, query, and analyze the massive amounts of data generated by ensembles of simulations produced by multiple models that couple socioeconomic and natural system processes. On this context, we propose to develop an Intelligent Digital Watershed (IDW) which fuses emerging concepts of Digital Watershed (DW). DW is a comprehensive characterization of the eco hydrologic systems based on the best available digital data generated by measurements and simulations models. Prototype IDW in the form of a cyber infrastructure based engineered system will facilitate novel insights into human/environment interactions through multi-disciplinary research focused on watershed-related processes at multiple spatio-temporal scales. In ongoing effort, the prototype IDW is applied to Clear Creek watershed, an agricultural dominating catchment in Iowa, to understand water-human processes relevant to management decisions by farmers regarding agro ecosystems. This paper would also lay out the database design that stores metadata about simulation scenarios, scenario inputs and outputs, and connections among these elements- essentially the database. The paper describes the cyber infrastructure and workflows developed for connecting the IDW modeling tools: ABM, Data-Driven Modeling, and SWAT.

The psychology of intelligence analysis: drivers of prediction accuracy in world politics.

PubMed

Mellers, Barbara; Stone, Eric; Atanasov, Pavel; Rohrbaugh, Nick; Metz, S Emlen; Ungar, Lyle; Bishop, Michael M; Horowitz, Michael; Merkle, Ed; Tetlock, Philip

2015-03-01

This article extends psychological methods and concepts into a domain that is as profoundly consequential as it is poorly understood: intelligence analysis. We report findings from a geopolitical forecasting tournament that assessed the accuracy of more than 150,000 forecasts of 743 participants on 199 events occurring over 2 years. Participants were above average in intelligence and political knowledge relative to the general population. Individual differences in performance emerged, and forecasting skills were surprisingly consistent over time. Key predictors were (a) dispositional variables of cognitive ability, political knowledge, and open-mindedness; (b) situational variables of training in probabilistic reasoning and participation in collaborative teams that shared information and discussed rationales (Mellers, Ungar, et al., 2014); and (c) behavioral variables of deliberation time and frequency of belief updating. We developed a profile of the best forecasters; they were better at inductive reasoning, pattern detection, cognitive flexibility, and open-mindedness. They had greater understanding of geopolitics, training in probabilistic reasoning, and opportunities to succeed in cognitively enriched team environments. Last but not least, they viewed forecasting as a skill that required deliberate practice, sustained effort, and constant monitoring of current affairs. PsycINFO Database Record (c) 2015 APA, all rights reserved.

An RFID-Based Intelligent Vehicle Speed Controller Using Active Traffic Signals

PubMed Central

Pérez, Joshué; Seco, Fernando; Milanés, Vicente; Jiménez, Antonio; Díaz, Julio C.; de Pedro, Teresa

2010-01-01

These days, mass-produced vehicles benefit from research on Intelligent Transportation System (ITS). One prime example of ITS is vehicle Cruise Control (CC), which allows it to maintain a pre-defined reference speed, to economize on fuel or energy consumption, to avoid speeding fines, or to focus all of the driver’s attention on the steering of the vehicle. However, achieving efficient Cruise Control is not easy in roads or urban streets where sudden changes of the speed limit can happen, due to the presence of unexpected obstacles or maintenance work, causing, in inattentive drivers, traffic accidents. In this communication we present a new Infrastructure to Vehicles (I2V) communication and control system for intelligent speed control, which is based upon Radio Frequency Identification (RFID) technology for identification of traffic signals on the road, and high accuracy vehicle speed measurement with a Hall effect-based sensor. A fuzzy logic controller, based on sensor fusion of the information provided by the I2V infrastructure, allows the efficient adaptation of the speed of the vehicle to the circumstances of the road. The performance of the system is checked empirically, with promising results. PMID:22219692

Addressing Diverse Learner Preferences and Intelligences with Emerging Technologies: Matching Models to Online Opportunities

ERIC Educational Resources Information Center

Zhang, Ke; Bonk, Curtis J.

2008-01-01

This paper critically reviews various learning preferences and human intelligence theories and models with a particular focus on the implications for online learning. It highlights a few key models, Gardner's multiple intelligences, Fleming and Mills' VARK model, Honey and Mumford's Learning Styles, and Kolb's Experiential Learning Model, and…

Transforming Systems Engineering through Model-Centric Engineering

DTIC Science & Technology

2018-02-28

intelligence (e.g., Artificial Intelligence , etc.), because they provide a means for representing knowledge. We see these capabilities coming to use in both...level, including:  Performance is measured by degree of success of a mission  Artificial Intelligence (AI) is applied to counterparties so that they...Modeling, Artificial Intelligence , Simulation and Modeling, 1989. [140] SAE ARP4761. Guidelines and Methods for Conducting the Safety Assessment Process

Modelling the influence of sensory dynamics on linear and nonlinear driver steering control

NASA Astrophysics Data System (ADS)

Nash, C. J.; Cole, D. J.

2018-05-01

A recent review of the literature has indicated that sensory dynamics play an important role in the driver-vehicle steering task, motivating the design of a new driver model incorporating human sensory systems. This paper presents a full derivation of the linear driver model developed in previous work, and extends the model to control a vehicle with nonlinear tyres. Various nonlinear controllers and state estimators are compared with different approximations of the true system dynamics. The model simulation time is found to increase significantly with the complexity of the controller and state estimator. In general the more complex controllers perform best, although with certain vehicle and tyre models linearised controllers perform as well as a full nonlinear optimisation. Various extended Kalman filters give similar results, although the driver's sensory dynamics reduce control performance compared with full state feedback. The new model could be used to design vehicle systems which interact more naturally and safely with a human driver.

Model driver screening and evaluation program. Volume 1, Project summary and model program recommendations

DOT National Transportation Integrated Search

2003-05-01

This research project studied the feasibility as well as the scientific validity and utility of performing functional capacity screening with older drivers. A Model Program was described encompassing procedures to detect functionally impaired drivers...

The codification of spiritual intelligence measurement model in librarianship and medical information science students of medical universities in Iran

PubMed Central

Zarrinabadi, Zarrin; Isfandyari-Moghaddam, Alireza; Erfani, Nasrolah; Tahour Soltani, Mohsen Ahmadi

2018-01-01

INTRODUCTION: According to the research mission of the librarianship and information sciences field, it is necessary to have the ability to communicate constructively between the user of the information and information in these students, and it appears more important in medical librarianship and information sciences because of the need for quick access to information for clinicians. Considering the role of spiritual intelligence in capability to establish effective and balanced communication makes it important to study this variable in librarianship and information students. One of the main factors that can affect the results of any research is conceptual model of measure variables. Accordingly, the purpose of this study was codification of spiritual intelligence measurement model. METHODS: This correlational study was conducted through structural equation model, and 270 students were opted from library and medical information students of nationwide medical universities by simple random sampling and responded to the King spiritual intelligence questionnaire (2008). Initially, based on the data, the model parameters were estimated using maximum likelihood method; then, spiritual intelligence measurement model was tested by fit indices. Data analysis was performed by Smart-Partial Least Squares software. RESULTS: Preliminary results showed that due to the positive indicators of predictive association and t-test results for spiritual intelligence parameters, the King measurement model has the acceptable fit and internal correlation of the questionnaire items was significant. Composite reliability and Cronbach's alpha of parameters indicated high reliability of spiritual intelligence model. CONCLUSIONS: The spiritual intelligence measurement model was evaluated, and results showed that the model has a good fit, so it is recommended that domestic researchers use this questionnaire to assess spiritual intelligence. PMID:29922688

The codification of spiritual intelligence measurement model in librarianship and medical information science students of medical universities in Iran.

PubMed

Zarrinabadi, Zarrin; Isfandyari-Moghaddam, Alireza; Erfani, Nasrolah; Tahour Soltani, Mohsen Ahmadi

2018-01-01

According to the research mission of the librarianship and information sciences field, it is necessary to have the ability to communicate constructively between the user of the information and information in these students, and it appears more important in medical librarianship and information sciences because of the need for quick access to information for clinicians. Considering the role of spiritual intelligence in capability to establish effective and balanced communication makes it important to study this variable in librarianship and information students. One of the main factors that can affect the results of any research is conceptual model of measure variables. Accordingly, the purpose of this study was codification of spiritual intelligence measurement model. This correlational study was conducted through structural equation model, and 270 students were opted from library and medical information students of nationwide medical universities by simple random sampling and responded to the King spiritual intelligence questionnaire (2008). Initially, based on the data, the model parameters were estimated using maximum likelihood method; then, spiritual intelligence measurement model was tested by fit indices. Data analysis was performed by Smart-Partial Least Squares software. Preliminary results showed that due to the positive indicators of predictive association and t -test results for spiritual intelligence parameters, the King measurement model has the acceptable fit and internal correlation of the questionnaire items was significant. Composite reliability and Cronbach's alpha of parameters indicated high reliability of spiritual intelligence model. The spiritual intelligence measurement model was evaluated, and results showed that the model has a good fit, so it is recommended that domestic researchers use this questionnaire to assess spiritual intelligence.

Adult correlates of early behavioral maladjustment: a study of injured drivers.

PubMed

Ryb, Gabriel; Dischinger, Patricia; Smith, Gordon; Soderstrom, Carl

2008-10-01

To establish whether a history of school suspension (HSS) predicts adult driver behavior. 323 injured drivers were interviewed as part of a study of psychoactive substance use disorders (PSUD) and injury. Drivers with a HSS were compared to those without HSS in relation to demographics, SES, PSUD, risky behaviors, trauma history and driving history using student's t test and chi-square. Multiple logistic regression models were constructed to adjust for demographics, SES and PSUD. HSS drivers represented 31% of the population and were younger, more likely to be male and had higher rates of alcohol and drug dependence than drivers without HSS. Educational achievement was worse for drivers with HSS. Drivers with HSS were more likely to have a history of prior vehicular trauma and assault. Seat-belt non-use, drinking and driving, riding with drunk driver, binge drinking, driving fast for the thrill, license suspension and drinking and driving convictions were more common among drivers with HSS. In multiple logistic regression models adjusting for demographics and SES, HSS revealed higher odds ratios for the same outcomes. After adding PSUD to the models HSS remained significant only for seat belt non use, binge drinking and previous assault history. HSS is associated with risky behaviors, repeated vehicular injury, and poor driver history. The association with driver history, however, disappears when PSUD are included in the models. The association of HSS (a marker of early behavioral maladjustment) with behavioral risks suggests that undiagnosed psychopathology may be linked to injury recidivism.

Revisiting measurement invariance in intelligence testing in aging research: Evidence for almost complete metric invariance across age groups.

PubMed

Sprague, Briana N; Hyun, Jinshil; Molenaar, Peter C M

2017-01-01

Invariance of intelligence across age is often assumed but infrequently explicitly tested. Horn and McArdle (1992) tested measurement invariance of intelligence, providing adequate model fit but might not consider all relevant aspects such as sub-test differences. The goal of the current paper is to explore age-related invariance of the WAIS-R using an alternative model that allows direct tests of age on WAIS-R subtests. Cross-sectional data on 940 participants aged 16-75 from the WAIS-R normative values were used. Subtests examined were information, comprehension, similarities, vocabulary, picture completion, block design, picture arrangement, and object assembly. The two intelligence factors considered were fluid and crystallized intelligence. Self-reported ages were divided into young (16-22, n = 300), adult (29-39, n = 275), middle (40-60, n = 205), and older (61-75, n = 160) adult groups. Results suggested partial metric invariance holds. Although most of the subtests reflected fluid and crystalized intelligence similarly across different ages, invariance did not hold for block design on fluid intelligence and picture arrangement on crystallized intelligence for older adults. Additionally, there was evidence of a correlated residual between information and vocabulary for the young adults only. This partial metric invariance model yielded acceptable model fit compared to previously-proposed invariance models of Horn and McArdle (1992). Almost complete metric invariance holds for a two-factor model of intelligence. Most of the subtests were invariant across age groups, suggesting little evidence for age-related bias in the WAIS-R. However, we did find unique relationships between two subtests and intelligence. Future studies should examine age-related differences in subtests when testing measurement invariance in intelligence.

Impacts of the driver's bounded rationality on the traffic running cost under the car-following model

NASA Astrophysics Data System (ADS)

Tang, Tie-Qiao; Luo, Xiao-Feng; Liu, Kai

2016-09-01

The driver's bounded rationality has significant influences on the micro driving behavior and researchers proposed some traffic flow models with the driver's bounded rationality. However, little effort has been made to explore the effects of the driver's bounded rationality on the trip cost. In this paper, we use our recently proposed car-following model to study the effects of the driver's bounded rationality on his running cost and the system's total cost under three traffic running costs. The numerical results show that considering the driver's bounded rationality will enhance his each running cost and the system's total cost under the three traffic running costs.

Emotional intelligence is a second-stratum factor of intelligence: evidence from hierarchical and bifactor models.

PubMed

MacCann, Carolyn; Joseph, Dana L; Newman, Daniel A; Roberts, Richard D

2014-04-01

This article examines the status of emotional intelligence (EI) within the structure of human cognitive abilities. To evaluate whether EI is a 2nd-stratum factor of intelligence, data were fit to a series of structural models involving 3 indicators each for fluid intelligence, crystallized intelligence, quantitative reasoning, visual processing, and broad retrieval ability, as well as 2 indicators each for emotion perception, emotion understanding, and emotion management. Unidimensional, multidimensional, hierarchical, and bifactor solutions were estimated in a sample of 688 college and community college students. Results suggest adequate fit for 2 models: (a) an oblique 8-factor model (with 5 traditional cognitive ability factors and 3 EI factors) and (b) a hierarchical solution (with cognitive g at the highest level and EI representing a 2nd-stratum factor that loads onto g at λ = .80). The acceptable relative fit of the hierarchical model confirms the notion that EI is a group factor of cognitive ability, marking the expression of intelligence in the emotion domain. The discussion proposes a possible expansion of Cattell-Horn-Carroll theory to include EI as a 2nd-stratum factor of similar standing to factors such as fluid intelligence and visual processing.

Analyzing the severity of accidents on the German Autobahn.

PubMed

Manner, Hans; Wünsch-Ziegler, Laura

2013-08-01

We study the severity of accidents on the German Autobahn in the state of North Rhine-Westphalia using data for the years 2009 until 2011. We use a multinomial logit model to identify statistically relevant factors explaining the severity of the most severe injury, which is classified into the four classes fatal, severe injury, light injury and property damage. Furthermore, to account for unobserved heterogeneity we use a random parameter model. We study the effect of a number of factors including traffic information, road conditions, type of accidents, speed limits, presence of intelligent traffic control systems, age and gender of the driver and location of the accident. Our findings are in line with studies in different settings and indicate that accidents during daylight and at interchanges or construction sites are less severe in general. Accidents caused by the collision with roadside objects, involving pedestrians and motorcycles, or caused by bad sight conditions tend to be more severe. We discuss the measures of the 2011 German traffic safety programm in the light of our results. Copyright © 2013 Elsevier Ltd. All rights reserved.

The implementation of multiple intelligences based teaching model to improve mathematical problem solving ability for student of junior high school

NASA Astrophysics Data System (ADS)

Fasni, Nurli; Fatimah, Siti; Yulanda, Syerli

2017-05-01

This research aims to achieve some purposes such as: to know whether mathematical problem solving ability of students who have learned mathematics using Multiple Intelligences based teaching model is higher than the student who have learned mathematics using cooperative learning; to know the improvement of the mathematical problem solving ability of the student who have learned mathematics using Multiple Intelligences based teaching model., to know the improvement of the mathematical problem solving ability of the student who have learned mathematics using cooperative learning; to know the attitude of the students to Multiple Intelligences based teaching model. The method employed here is quasi-experiment which is controlled by pre-test and post-test. The population of this research is all of VII grade in SMP Negeri 14 Bandung even-term 2013/2014, later on two classes of it were taken for the samples of this research. A class was taught using Multiple Intelligences based teaching model and the other one was taught using cooperative learning. The data of this research were gotten from the test in mathematical problem solving, scale questionnaire of the student attitudes, and observation. The results show the mathematical problem solving of the students who have learned mathematics using Multiple Intelligences based teaching model learning is higher than the student who have learned mathematics using cooperative learning, the mathematical problem solving ability of the student who have learned mathematics using cooperative learning and Multiple Intelligences based teaching model are in intermediate level, and the students showed the positive attitude in learning mathematics using Multiple Intelligences based teaching model. As for the recommendation for next author, Multiple Intelligences based teaching model can be tested on other subject and other ability.

Assessing Intelligence in Children and Youth Living in the Netherlands

ERIC Educational Resources Information Center

Hurks, Petra P. M.; Bakker, Helen

2016-01-01

In this article, we briefly describe the history of intelligence test use with children and youth in the Netherlands, explain which models of intelligence guide decisions about test use, and detail how intelligence tests are currently being used in Dutch school settings. Empirically supported and theoretical models studying the structure of human…

Multiple logistic regression model of signalling practices of drivers on urban highways

NASA Astrophysics Data System (ADS)

Puan, Othman Che; Ibrahim, Muttaka Na'iya; Zakaria, Rozana

2015-05-01

Giving signal is a way of informing other road users, especially to the conflicting drivers, the intention of a driver to change his/her movement course. Other users are exposed to hazard situation and risks of accident if the driver who changes his/her course failed to give signal as required. This paper describes the application of logistic regression model for the analysis of driver's signalling practices on multilane highways based on possible factors affecting driver's decision such as driver's gender, vehicle's type, vehicle's speed and traffic flow intensity. Data pertaining to the analysis of such factors were collected manually. More than 2000 drivers who have performed a lane changing manoeuvre while driving on two sections of multilane highways were observed. Finding from the study shows that relatively a large proportion of drivers failed to give any signals when changing lane. The result of the analysis indicates that although the proportion of the drivers who failed to provide signal prior to lane changing manoeuvre is high, the degree of compliances of the female drivers is better than the male drivers. A binary logistic model was developed to represent the probability of a driver to provide signal indication prior to lane changing manoeuvre. The model indicates that driver's gender, type of vehicle's driven, speed of vehicle and traffic volume influence the driver's decision to provide a signal indication prior to a lane changing manoeuvre on a multilane urban highway. In terms of types of vehicles driven, about 97% of motorcyclists failed to comply with the signal indication requirement. The proportion of non-compliance drivers under stable traffic flow conditions is much higher than when the flow is relatively heavy. This is consistent with the data which indicates a high degree of non-compliances when the average speed of the traffic stream is relatively high.

Drinking driver and traffic safety project. Volume 2, Probabilities for drinking drivers

DOT National Transportation Integrated Search

1973-10-01

This is the second volume of a final report of a four-year study of drinking drivers. It includes a brief description of a prediction model developed from over 4000 cases, including drinking drivers, recidivist drinking drivers and drivers license ap...

MESA: An Interactive Modeling and Simulation Environment for Intelligent Systems Automation

NASA Technical Reports Server (NTRS)

Charest, Leonard

1994-01-01

This report describes MESA, a software environment for creating applications that automate NASA mission opterations. MESA enables intelligent automation by utilizing model-based reasoning techniques developed in the field of Artificial Intelligence. Model-based reasoning techniques are realized in Mesa through native support of causal modeling and discrete event simulation.

Mobile phone use during driving: Effects on speed and effectiveness of driver compensatory behaviour.

PubMed

Choudhary, Pushpa; Velaga, Nagendra R

2017-09-01

This study analysed and modelled the effects of conversation and texting (each with two difficulty levels) on driving performance of Indian drivers in terms of their mean speed and accident avoiding abilities; and further explored the relationship between speed reduction strategy of the drivers and their corresponding accident frequency. 100 drivers of three different age groups (young, mid-age and old-age) participated in the simulator study. Two sudden events of Indian context: unexpected crossing of pedestrians and joining of parked vehicles from road side, were simulated for estimating the accident probabilities. Generalized linear mixed models approach was used for developing linear regression models for mean speed and binary logistic regression models for accident probability. The results of the models showed that the drivers significantly compensated the increased workload by reducing their mean speed by 2.62m/s and 5.29m/s in the presence of conversation and texting tasks respectively. The logistic models for accident probabilities showed that the accident probabilities increased by 3 and 4 times respectively when the drivers were conversing or texting on a phone during driving. Further, the relationship between the speed reduction patterns and their corresponding accident frequencies showed that all the drivers compensated differently; but, among all the drivers, only few drivers, who compensated by reducing the speed by 30% or more, were able to fully offset the increased accident risk associated with the phone use. Copyright © 2017 Elsevier Ltd. All rights reserved.

An investigation on fatality of drivers in vehicle-fixed object accidents on expressways in China: Using multinomial logistic regression model.

PubMed

Peng, Yong; Peng, Shuangling; Wang, Xinghua; Tan, Shiyang

2018-06-01

This study aims to identify the effects of characteristics of vehicle, roadway, driver, and environment on fatality of drivers in vehicle-fixed object accidents on expressways in Changsha-Zhuzhou-Xiangtan district of Hunan province in China by developing multinomial logistic regression models. For this purpose, 121 vehicle-fixed object accidents from 2011-2017 are included in the modeling process. First, descriptive statistical analysis is made to understand the main characteristics of the vehicle-fixed object crashes. Then, 19 explanatory variables are selected, and correlation analysis of each two variables is conducted to choose the variables to be concluded. Finally, five multinomial logistic regression models including different independent variables are compared, and the model with best fitting and prediction capability is chosen as the final model. The results showed that the turning direction in avoiding fixed objects raised the possibility that drivers would die. About 64% of drivers died in the accident were found being ejected out of the car, of which 50% did not use a seatbelt before the fatal accidents. Drivers are likely to die when they encounter bad weather on the expressway. Drivers with less than 10 years of driving experience are more likely to die in these accidents. Fatigue or distracted driving is also a significant factor in fatality of drivers. Findings from this research provide an insight into reducing fatality of drivers in vehicle-fixed object accidents.

Spatially-explicit modeling of multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States.

PubMed

Ajaz Ahmed, Mukhtar Ahmed; Abd-Elrahman, Amr; Escobedo, Francisco J; Cropper, Wendell P; Martin, Timothy A; Timilsina, Nilesh

2017-09-01

Understanding ecosystem processes and the influence of regional scale drivers can provide useful information for managing forest ecosystems. Examining more local scale drivers of forest biomass and water yield can also provide insights for identifying and better understanding the effects of climate change and management on forests. We used diverse multi-scale datasets, functional models and Geographically Weighted Regression (GWR) to model ecosystem processes at the watershed scale and to interpret the influence of ecological drivers across the Southeastern United States (SE US). Aboveground forest biomass (AGB) was determined from available geospatial datasets and water yield was estimated using the Water Supply and Stress Index (WaSSI) model at the watershed level. Our geostatistical model examined the spatial variation in these relationships between ecosystem processes, climate, biophysical, and forest management variables at the watershed level across the SE US. Ecological and management drivers at the watershed level were analyzed locally to identify whether drivers contribute positively or negatively to aboveground forest biomass and water yield ecosystem processes and thus identifying potential synergies and tradeoffs across the SE US region. Although AGB and water yield drivers varied geographically across the study area, they were generally significantly influenced by climate (rainfall and temperature), land-cover factor1 (Water and barren), land-cover factor2 (wetland and forest), organic matter content high, rock depth, available water content, stand age, elevation, and LAI drivers. These drivers were positively or negatively associated with biomass or water yield which significantly contributes to ecosystem interactions or tradeoff/synergies. Our study introduced a spatially-explicit modelling framework to analyze the effect of ecosystem drivers on forest ecosystem structure, function and provision of services. This integrated model approach facilitates multi-scale analyses of drivers and interactions at the local to regional scale. Copyright © 2017 Elsevier Ltd. All rights reserved.

Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems.

PubMed

Rahman, Md Mahmudur; Lesch, Mary F; Horrey, William J; Strawderman, Lesley

2017-11-01

Advanced Driver Assistance Systems (ADAS) are intended to enhance driver performance and improve transportation safety. The potential benefits of these technologies, such as reduction in number of crashes, enhancing driver comfort or convenience, decreasing environmental impact, etc., have been acknowledged by transportation safety researchers and federal transportation agencies. Although these systems afford safety advantages, they may also challenge the traditional role of drivers in operating vehicles. Driver acceptance, therefore, is essential for the implementation of these systems into the transportation system. Recognizing the need for research into the factors affecting driver acceptance, this study assessed the utility of the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Use of Technology (UTAUT) for modelling driver acceptance in terms of Behavioral Intention to use an ADAS. Each of these models propose a set of factors that influence acceptance of a technology. Data collection was done using two approaches: a driving simulator approach and an online survey approach. In both approaches, participants interacted with either a fatigue monitoring system or an adaptive cruise control system combined with a lane-keeping system. Based on their experience, participants responded to several survey questions to indicate their attitude toward using the ADAS and their perception of its usefulness, usability, etc. A sample of 430 surveys were collected for this study. Results found that all the models (TAM, TPB, and UTAUT) can explain driver acceptance with their proposed sets of factors, each explaining 71% or more of the variability in Behavioral Intention. Among the models, TAM was found to perform the best in modelling driver acceptance followed by TPB. The findings of this study confirm that these models can be applied to ADAS technologies and that they provide a basis for understanding driver acceptance. Copyright © 2017 Elsevier Ltd. All rights reserved.

The mediating effect of emotional intelligence between emotional labour, job stress, burnout and nurses' turnover intention.

PubMed

Hong, Eunyoung; Lee, Young Sook

2016-12-01

This study was designed to construct and test the structural equation modelling on nurses' turnover intention including emotional labour, job stress, emotional intelligence and burnout in order to identify the mediating effect of emotional intelligence between those variables. Emotional labour, job stress and burnout increase turnover intention of nurses. However, emotional intelligence is negatively correlated with emotional labour and reduces job stress, burnout and turnover intention. Structural equation modelling was used to analyse the goodness of fit of the hypothetical model of nurses' turnover intention. Research data were collected via questionnaires from 4 to 22 August 2014 and analysed using SPSS version 18.0 and AMOS version 20.0. The model fit indices for the hypothetical model were suitable for recommended. Emotional intelligence has decreasing effect on turnover intention through burnout, although its direct effect on turnover intention is not significant. Emotional intelligence has mediation effect between emotional labour and burnout. This study's results suggest that increasing emotional intelligence might critically decrease nurses' turnover intention by reducing the effect of emotional labour on burnout. © 2016 John Wiley & Sons Australia, Ltd.

Modelling intelligent behavior

NASA Technical Reports Server (NTRS)

Green, H. S.; Triffet, T.

1993-01-01

An introductory discussion of the related concepts of intelligence and consciousness suggests criteria to be met in the modeling of intelligence and the development of intelligent materials. Methods for the modeling of actual structure and activity of the animal cortex have been found, based on present knowledge of the ionic and cellular constitution of the nervous system. These have led to the development of a realistic neural network model, which has been used to study the formation of memory and the process of learning. An account is given of experiments with simple materials which exhibit almost all properties of biological synapses and suggest the possibility of a new type of computer architecture to implement an advanced type of artificial intelligence.

Computational intelligence for the Balanced Scorecard: studying performance trends of hemodialysis clinics.

PubMed

Cattinelli, Isabella; Bolzoni, Elena; Chermisi, Milena; Bellocchio, Francesco; Barbieri, Carlo; Mari, Flavio; Amato, Claudia; Menzer, Marcus; Stopper, Andrea; Gatti, Emanuele

2013-07-01

The Balanced Scorecard (BSC) is a general, widely employed instrument for enterprise performance monitoring based on the periodic assessment of strategic Key Performance Indicators that are scored against preset targets. The BSC is currently employed as an effective management support tool within Fresenius Medical Care (FME) and is routinely analyzed via standard statistical methods. More recently, the application of computational intelligence techniques (namely, self-organizing maps) to BSC data has been proposed as a way to enhance the quantity and quality of information that can be extracted from it. In this work, additional methods are presented to analyze the evolution of clinic performance over time. Performance evolution is studied at the single-clinic level by computing two complementary indexes that measure the proportion of time spent within performance clusters and improving/worsening trends. Self-organizing maps are used in conjunction with these indexes to identify the specific drivers of the observed performance. The performance evolution for groups of clinics is modeled under a probabilistic framework by resorting to Markov chain properties. These allow a study of the probability of transitioning between performance clusters as time progresses for the identification of the performance level that is expected to become dominant over time. We show the potential of the proposed methods through illustrative results derived from the analysis of BSC data of 109 FME clinics in three countries. We were able to identify the performance drivers for specific groups of clinics and to distinguish between countries whose performances are likely to improve from those where a decline in performance might be expected. According to the stationary distribution of the Markov chain, the expected trend is best in Turkey (where the highest performance cluster has the highest probability, P=0.46), followed by Portugal (where the second best performance cluster dominates, with P=0.50), and finally Italy (where the second best performance cluster has P=0.34). These results highlight the ability of the proposed methods to extract insights about performance trends that cannot be easily extrapolated using standard analyses and that are valuable in directing management strategies within a continuous quality improvement policy. Copyright © 2013 Elsevier B.V. All rights reserved.

Isolation contactor state control system

DOEpatents

Bissontz, Jay E.

2017-05-16

A controller area network (CAN) installed on a hybrid electric vehicle provides one node with control of high voltage power distribution system isolation contactors and the capacity to energize a secondary electro-mechanical relay device. The output of the secondary relay provides a redundant and persistent backup signal to the output of the node. The secondary relay is relatively immune to CAN message traffic interruptions and, as a result, the high voltage isolation contactor(s) are less likely to transition open in the event that the intelligent output driver should fail.

Driver performance modelling and its practical application to railway safety.

PubMed

Hamilton, W Ian; Clarke, Theresa

2005-11-01

This paper reports on the development and main features of a model of driver information processing. The work was conducted on behalf of Network Rail to meet a requirement to understand and manage the driver's interaction with the infrastructure through lineside reminder appliances. The model utilises cognitive theory and modelling techniques to describe driver performance in relation to infrastructure features and operational conditions. The model is capable of predicting the performance time, workload and error consequences of different operational conditions. The utility of the model is demonstrated through reports of its application to the following studies: Research on the effect of line speed on driver interaction with signals and signs. Calculation of minimum reading times for signals. Development of a human factors signals passed at danger (SPAD) hazard checklist, and a method to resolve conflicts between signal sighting solutions. Research on the demands imposed on drivers by European train control system (ETCS) driving in a UK context. The paper also reports on a validation of the model's utility as a tool for assessing cab and infrastructure drivability.

Effect of driver's age and side of impact on crash severity along urban freeways: a mixed logit approach.

PubMed

Haleem, Kirolos; Gan, Albert

2013-09-01

This study identifies geometric, traffic, environmental, vehicle-related, and driver-related predictors of crash injury severity on urban freeways. The study takes advantage of the mixed logit model's ability to account for unobserved effects that are difficult to quantify and may affect the model estimation, such as the driver's reaction at the time of crash. Crashes of 5 years occurring on 89 urban freeway segments throughout the state of Florida in the United States were used. Examples of severity predictors explored include traffic volume, distance of the crash to the nearest ramp, and detailed driver's age, vehicle types, and sides of impact. To show how the parameter estimates could vary, a binary logit model was compared with the mixed logit model. It was found that the at-fault driver's age, traffic volume, distance of the crash to the nearest ramp, vehicle type, side of impact, and percentage of trucks significantly influence severity on urban freeways. Additionally, young at-fault drivers were associated with a significant severity risk increase relative to other age groups. It was also observed that some variables in the binary logit model yielded illogic estimates due to ignoring the random variation of the estimation. Since the at-fault driver's age and side of impact were significant random parameters in the mixed logit model, an in-depth investigation was performed. It was noticed that back, left, and right impacts had the highest risk among middle-aged drivers, followed by young drivers, very young drivers, and finally, old and very old drivers. To reduce side impacts due to lane changing, two primary strategies can be recommended. The first strategy is to conduct campaigns to convey the hazardous effect of changing lanes at higher speeds. The second is to devise in-vehicle side crash avoidance systems to alert drivers of a potential crash risk. The study provided a promising approach to screening the predictors before fitting the mixed logit model using the random forest technique. Furthermore, potential countermeasures were proposed to reduce the severity of impacts. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

Towards a social psychology-based microscopic model of driver behavior and decision-making : modifying Lewin's field theory

DOT National Transportation Integrated Search

2014-01-01

Central to effective roadway design is the ability to understand how drivers behave as they traverse a segment of : roadway. While simple and complex microscopic models have been used over the years to analyse driver behaviour, : most models: 1.) inc...

Traffic Games: Modeling Freeway Traffic with Game Theory

PubMed Central

Cortés-Berrueco, Luis E.; Gershenson, Carlos; Stephens, Christopher R.

2016-01-01

We apply game theory to a vehicular traffic model to study the effect of driver strategies on traffic flow. The resulting model inherits the realistic dynamics achieved by a two-lane traffic model and aims to incorporate phenomena caused by driver-driver interactions. To achieve this goal, a game-theoretic description of driver interaction was developed. This game-theoretic formalization allows one to model different lane-changing behaviors and to keep track of mobility performance. We simulate the evolution of cooperation, traffic flow, and mobility performance for different modeled behaviors. The analysis of these results indicates a mobility optimization process achieved by drivers’ interactions. PMID:27855176

Contributory fault and level of personal injury to drivers involved in head-on collisions: Application of copula-based bivariate ordinal models.

PubMed

Wali, Behram; Khattak, Asad J; Xu, Jingjing

2018-01-01

The main objective of this study is to simultaneously investigate the degree of injury severity sustained by drivers involved in head-on collisions with respect to fault status designation. This is complicated to answer due to many issues, one of which is the potential presence of correlation between injury outcomes of drivers involved in the same head-on collision. To address this concern, we present seemingly unrelated bivariate ordered response models by analyzing the joint injury severity probability distribution of at-fault and not-at-fault drivers. Moreover, the assumption of bivariate normality of residuals and the linear form of stochastic dependence implied by such models may be unduly restrictive. To test this, Archimedean copula structures and normal mixture marginals are integrated into the joint estimation framework, which can characterize complex forms of stochastic dependencies and non-normality in residual terms. The models are estimated using 2013 Virginia police reported two-vehicle head-on collision data, where exactly one driver is at-fault. The results suggest that both at-fault and not-at-fault drivers sustained serious/fatal injuries in 8% of crashes, whereas, in 4% of the cases, the not-at-fault driver sustained a serious/fatal injury with no injury to the at-fault driver at all. Furthermore, if the at-fault driver is fatigued, apparently asleep, or has been drinking the not-at-fault driver is more likely to sustain a severe/fatal injury, controlling for other factors and potential correlations between the injury outcomes. While not-at-fault vehicle speed affects injury severity of at-fault driver, the effect is smaller than the effect of at-fault vehicle speed on at-fault injury outcome. Contrarily, and importantly, the effect of at-fault vehicle speed on injury severity of not-at-fault driver is almost equal to the effect of not-at-fault vehicle speed on injury outcome of not-at-fault driver. Compared to traditional ordered probability models, the study provides evidence that copula based bivariate models can provide more reliable estimates and richer insights. Practical implications of the results are discussed. Published by Elsevier Ltd.

Emotional intelligence and affective events in nurse education: A narrative review.

PubMed

Lewis, Gillian M; Neville, Christine; Ashkanasy, Neal M

2017-06-01

To investigate the current state of knowledge about emotional intelligence and affective events that arise during nursing students' clinical placement experiences. Narrative literature review. CINAHL, MEDLINE, PsycINFO, Scopus, Web of Science, ERIC and APAIS-Health databases published in English between 1990 and 2016. Data extraction from and constant comparative analysis of ten (10) research articles. We found four main themes: (1) emotional intelligence buffers stress; (2) emotional intelligence reduces anxiety associated with end of life care; (3) emotional intelligence promotes effective communication; and (4) emotional intelligence improves nursing performance. The articles we analysed adopted a variety of emotional intelligence models. Using the Ashkanasy and Daus "three-stream" taxonomy (Stream 1: ability models; 2: self-report; 3: mixed models), we found that Stream 2 self-report measures were the most popular followed by Stream 3 mixed model measures. None of the studies we surveyed used the Stream 1 approach. Findings nonetheless indicated that emotional intelligence was important in maintaining physical and psychological well-being. We concluded that developing emotional intelligence should be a useful adjunct to improve academic and clinical performance and to reduce the risk of emotional distress during clinical placement experiences. We call for more consistency in the use of emotional intelligence tests as a means to create an empirical evidence base in the field of nurse education. Copyright © 2017 Elsevier Ltd. All rights reserved.

A watershed model of individual differences in fluid intelligence.

PubMed

Kievit, Rogier A; Davis, Simon W; Griffiths, John; Correia, Marta M; Cam-Can; Henson, Richard N

2016-10-01

Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Drivers' communicative interactions: on-road observations and modelling for integration in future automation systems.

PubMed

Portouli, Evangelia; Nathanael, Dimitris; Marmaras, Nicolas

2014-01-01

Social interactions with other road users are an essential component of the driving activity and may prove critical in view of future automation systems; still up to now they have received only limited attention in the scientific literature. In this paper, it is argued that drivers base their anticipations about the traffic scene to a large extent on observations of social behaviour of other 'animate human-vehicles'. It is further argued that in cases of uncertainty, drivers seek to establish a mutual situational awareness through deliberate communicative interactions. A linguistic model is proposed for modelling these communicative interactions. Empirical evidence from on-road observations and analysis of concurrent running commentary by 25 experienced drivers support the proposed model. It is suggested that the integration of a social interactions layer based on illocutionary acts in future driving support and automation systems will improve their performance towards matching human driver's expectations. Practitioner Summary: Interactions between drivers on the road may play a significant role in traffic coordination. On-road observations and running commentaries are presented as empirical evidence to support a model of such interactions; incorporation of drivers' interactions in future driving support and automation systems may improve their performance towards matching driver's expectations.

SBCDDB: Sleeping Beauty Cancer Driver Database for gene discovery in mouse models of human cancers

PubMed Central

Mann, Michael B

2018-01-01

Abstract Large-scale oncogenomic studies have identified few frequently mutated cancer drivers and hundreds of infrequently mutated drivers. Defining the biological context for rare driving events is fundamentally important to increasing our understanding of the druggable pathways in cancer. Sleeping Beauty (SB) insertional mutagenesis is a powerful gene discovery tool used to model human cancers in mice. Our lab and others have published a number of studies that identify cancer drivers from these models using various statistical and computational approaches. Here, we have integrated SB data from primary tumor models into an analysis and reporting framework, the Sleeping Beauty Cancer Driver DataBase (SBCDDB, http://sbcddb.moffitt.org), which identifies drivers in individual tumors or tumor populations. Unique to this effort, the SBCDDB utilizes a single, scalable, statistical analysis method that enables data to be grouped by different biological properties. This allows for SB drivers to be evaluated (and re-evaluated) under different contexts. The SBCDDB provides visual representations highlighting the spatial attributes of transposon mutagenesis and couples this functionality with analysis of gene sets, enabling users to interrogate relationships between drivers. The SBCDDB is a powerful resource for comparative oncogenomic analyses with human cancer genomics datasets for driver prioritization. PMID:29059366

The experimentation of LC7E learning model on the linear program material in terms of interpersonal intelligence on Wonogiri vocational school students

NASA Astrophysics Data System (ADS)

Antinah; Kusmayadi, T. A.; Husodo, B.

2018-05-01

This study aims to determine the effect of learning model on student achievement in terms of interpersonal intelligence. The compared learning models are LC7E and Direct learning model. This type of research is a quasi-experimental with 2x3 factorial design. The population in this study is a Grade XI student of Wonogiri Vocational Schools. The sample selection had done by stratified cluster random sampling. Data collection technique used questionnaires, documentation and tests. The data analysis technique used two different unequal cell variance analysis which previously conducted prerequisite analysis for balance test, normality test and homogeneity test. he conclusions of this research are: 1) student learning achievement of mathematics given by LC7E learning model is better when compared with direct learning; 2) Mathematics learning achievement of students who have a high level of interpersonal intelligence is better than students with interpersonal intelligence in medium and low level. Students' mathematics learning achievement with interpersonal level of intelligence is better than those with low interpersonal intelligence on linear programming; 3) LC7E learning model resulted better on mathematics learning achievement compared with direct learning model for each category of students’ interpersonal intelligence level on linear program material.

The experimentation of LC7E learning model on the linear program material in terms of interpersonal intelligence on Wonogiri Vocational School students

NASA Astrophysics Data System (ADS)

Antinah; Kusmayadi, T. A.; Husodo, B.

2018-03-01

This study aimed to determine the effect of learning model on student achievement in terms of interpersonal intelligence. The compared learning models are LC7E and Direct learning model. This type of research is a quasi-experimental with 2x3 factorial design. The population in this study is a Grade XI student of Wonogiri Vocational Schools. The sample selection had done by stratified cluster random sampling. Data collection technique used questionnaires, documentation and tests. The data analysis technique used two different unequal cell variance analysis which previously conducted prerequisite analysis for balance test, normality test and homogeneity test. he conclusions of this research are: 1) student learning achievement of mathematics given by LC7E learning model is better when compared with direct learning; 2) Mathematics learning achievement of students who have a high level of interpersonal intelligence is better than students with interpersonal intelligence in medium and low level. Students’ mathematics learning achievement with interpersonal level of intelligence is better than those with low interpersonal intelligence on linear programming; 3) LC7E learning model resulted better on mathematics learning achievement compared with direct learning model for each category of students’ interpersonal intelligence level on linear program material.

Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation.

PubMed

Chen, Cong; Zhang, Guohui; Liu, Xiaoyue Cathy; Ci, Yusheng; Huang, Helai; Ma, Jianming; Chen, Yanyan; Guan, Hongzhi

2016-12-01

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles.

PubMed

Pasquier, M; Quek, C; Toh, M

2001-10-01

This paper presents part of our research work concerned with the realisation of an Intelligent Vehicle and the technologies required for its routing, navigation, and control. An automated driver prototype has been developed using a self-organising fuzzy rule-based system (POPFNN-CRI(S)) to model and subsequently emulate human driving expertise. The ability of fuzzy logic to represent vague information using linguistic variables makes it a powerful tool to develop rule-based control systems when an exact working model is not available, as is the case of any vehicle-driving task. Designing a fuzzy system, however, is a complex endeavour, due to the need to define the variables and their associated fuzzy sets, and determine a suitable rule base. Many efforts have thus been devoted to automating this process, yielding the development of learning and optimisation techniques. One of them is the family of POP-FNNs, or Pseudo-Outer Product Fuzzy Neural Networks (TVR, AARS(S), AARS(NS), CRI, Yager). These generic self-organising neural networks developed at the Intelligent Systems Laboratory (ISL/NTU) are based on formal fuzzy mathematical theory and are able to objectively extract a fuzzy rule base from training data. In this application, a driving simulator has been developed, that integrates a detailed model of the car dynamics, complete with engine characteristics and environmental parameters, and an OpenGL-based 3D-simulation interface coupled with driving wheel and accelerator/ brake pedals. The simulator has been used on various road scenarios to record from a human pilot driving data consisting of steering and speed control actions associated to road features. Specifically, the POPFNN-CRI(S) system is used to cluster the data and extract a fuzzy rule base modelling the human driving behaviour. Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.

U.S. intelligence system: model for corporate chiefs?

PubMed

Gilad, B

1991-01-01

A fully dedicated intelligence support function for senior management is no longer a luxury but a necessity. Companies can enhance their intelligence capabilities by using the government model as a rough blueprint to structure such a program.

Model architecture of intelligent data mining oriented urban transportation information

NASA Astrophysics Data System (ADS)

Yang, Bogang; Tao, Yingchun; Sui, Jianbo; Zhang, Feizhou

2007-06-01

Aiming at solving practical problems in urban traffic, the paper presents model architecture of intelligent data mining from hierarchical view. With artificial intelligent technologies used in the framework, the intelligent data mining technology improves, which is more suitable for the change of real-time road condition. It also provides efficient technology support for the urban transport information distribution, transmission and display.

Measuring the Performance and Intelligence of Systems: Proceedings of the 2002 PerMIS Workshop

NASA Technical Reports Server (NTRS)

Messina, E. R.; Meystel, A. M.

2002-01-01

Contents include the following: Performance Metrics; Performance of Multiple Agents; Performance of Mobility Systems; Performance of Planning Systems; General Discussion Panel 1; Uncertainty of Representation I; Performance of Robots in Hazardous Domains; Modeling Intelligence; Modeling of Mind; Measuring Intelligence; Grouping: A Core Procedure of Intelligence; Uncertainty in Representation II; Towards Universal Planning/Control Systems.

Comparing Models of Intelligence in Project TALENT: The VPR Model Fits Better than the CHC and Extended Gf-Gc Models

ERIC Educational Resources Information Center

Major, Jason T.; Johnson, Wendy; Deary, Ian J.

2012-01-01

Three prominent theories of intelligence, the Cattell-Horn-Carroll (CHC), extended fluid-crystallized (Gf-Gc) and verbal-perceptual-image rotation (VPR) theories, provide differing descriptions of the structure of intelligence (McGrew, 2009; Horn & Blankson, 2005; Johnson & Bouchard, 2005b). To compare these theories, models representing them were…

Survival pattern of first accident among commercial drivers in the Greater Accra Region of Ghana.

PubMed

Nanga, Salifu; Odai, Nii Afotey; Lotsi, Anani

2017-06-01

In this study, the average accident risk of commercial drivers in the Greater Accra region of Ghana and its associated risks were examined based on a survey data collected using paper-based questionnaires from 204 commercial drivers from the Greater Accra Region of Ghana. The Cox Proportional Hazards Model was used for multivariate analysis while the Kaplan-Meier (KM) Model was used to study the survival patterns of the commercial drivers. The study revealed that the median survival time for an accident to happen is 2.50 years. Good roads provided a better chance of survival than bad roads and experienced drivers have a better chance of survival than the inexperienced drivers. Age of driver, alcohol usage of driver, marital status, condition of road and duration of driver's license were found to be related to the risk of accident. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modeling Driver Behavior near Intersections in Hidden Markov Model

PubMed Central

Li, Juan; He, Qinglian; Zhou, Hang; Guan, Yunlin; Dai, Wei

2016-01-01

Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in the dilemma zone, a Hidden Markov Model (HMM) is utilized in this study. With the discrete data processing, the observed dynamic data of vehicles are used for the inference of the Hidden Markov Model. The Baum-Welch (B-W) estimation algorithm is applied to calculate the vehicle state transition probability matrix and the observation probability matrix. When combined with the Forward algorithm, the most likely state of the driver can be obtained. Thus the model can be used to measure the stability and risk of driver behavior. It is found that drivers’ behaviors in the dilemma zone are of lower stability and higher risk compared with those in other regions around intersections. In addition to the B-W estimation algorithm, the Viterbi Algorithm is utilized to predict the potential dangers of vehicles. The results can be applied to driving assistance systems to warn drivers to avoid possible accidents. PMID:28009838

Predicting Speech Intelligibility Decline in Amyotrophic Lateral Sclerosis Based on the Deterioration of Individual Speech Subsystems

PubMed Central

Yunusova, Yana; Wang, Jun; Zinman, Lorne; Pattee, Gary L.; Berry, James D.; Perry, Bridget; Green, Jordan R.

2016-01-01

Purpose To determine the mechanisms of speech intelligibility impairment due to neurologic impairments, intelligibility decline was modeled as a function of co-occurring changes in the articulatory, resonatory, phonatory, and respiratory subsystems. Method Sixty-six individuals diagnosed with amyotrophic lateral sclerosis (ALS) were studied longitudinally. The disease-related changes in articulatory, resonatory, phonatory, and respiratory subsystems were quantified using multiple instrumental measures, which were subjected to a principal component analysis and mixed effects models to derive a set of speech subsystem predictors. A stepwise approach was used to select the best set of subsystem predictors to model the overall decline in intelligibility. Results Intelligibility was modeled as a function of five predictors that corresponded to velocities of lip and jaw movements (articulatory), number of syllable repetitions in the alternating motion rate task (articulatory), nasal airflow (resonatory), maximum fundamental frequency (phonatory), and speech pauses (respiratory). The model accounted for 95.6% of the variance in intelligibility, among which the articulatory predictors showed the most substantial independent contribution (57.7%). Conclusion Articulatory impairments characterized by reduced velocities of lip and jaw movements and resonatory impairments characterized by increased nasal airflow served as the subsystem predictors of the longitudinal decline of speech intelligibility in ALS. Declines in maximum performance tasks such as the alternating motion rate preceded declines in intelligibility, thus serving as early predictors of bulbar dysfunction. Following the rapid decline in speech intelligibility, a precipitous decline in maximum performance tasks subsequently occurred. PMID:27148967

Predicting Speech Intelligibility Decline in Amyotrophic Lateral Sclerosis Based on the Deterioration of Individual Speech Subsystems.

PubMed

Rong, Panying; Yunusova, Yana; Wang, Jun; Zinman, Lorne; Pattee, Gary L; Berry, James D; Perry, Bridget; Green, Jordan R

2016-01-01

To determine the mechanisms of speech intelligibility impairment due to neurologic impairments, intelligibility decline was modeled as a function of co-occurring changes in the articulatory, resonatory, phonatory, and respiratory subsystems. Sixty-six individuals diagnosed with amyotrophic lateral sclerosis (ALS) were studied longitudinally. The disease-related changes in articulatory, resonatory, phonatory, and respiratory subsystems were quantified using multiple instrumental measures, which were subjected to a principal component analysis and mixed effects models to derive a set of speech subsystem predictors. A stepwise approach was used to select the best set of subsystem predictors to model the overall decline in intelligibility. Intelligibility was modeled as a function of five predictors that corresponded to velocities of lip and jaw movements (articulatory), number of syllable repetitions in the alternating motion rate task (articulatory), nasal airflow (resonatory), maximum fundamental frequency (phonatory), and speech pauses (respiratory). The model accounted for 95.6% of the variance in intelligibility, among which the articulatory predictors showed the most substantial independent contribution (57.7%). Articulatory impairments characterized by reduced velocities of lip and jaw movements and resonatory impairments characterized by increased nasal airflow served as the subsystem predictors of the longitudinal decline of speech intelligibility in ALS. Declines in maximum performance tasks such as the alternating motion rate preceded declines in intelligibility, thus serving as early predictors of bulbar dysfunction. Following the rapid decline in speech intelligibility, a precipitous decline in maximum performance tasks subsequently occurred.

U.S. truck driver anthropometric study and multivariate anthropometric models for cab designs.

PubMed

Guan, Jinhua; Hsiao, Hongwei; Bradtmiller, Bruce; Kau, Tsui-Ying; Reed, Matthew R; Jahns, Steven K; Loczi, Josef; Hardee, H Lenora; Piamonte, Dominic Paul T

2012-10-01

This study presents data from a large-scale anthropometric study of U.S. truck drivers and the multivariate anthropometric models developed for the design of next-generation truck cabs. Up-to-date anthropometric information of the U.S. truck driver population is needed for the design of safe and ergonomically efficient truck cabs. We collected 35 anthropometric dimensions for 1,950 truck drivers (1,779 males and 171 females) across the continental United States using a sampling plan designed to capture the appropriate ethnic, gender, and age distributions of the truck driver population. Truck drivers are heavier than the U.S.general population, with a difference in mean body weight of 13.5 kg for males and 15.4 kg for females. They are also different in physique from the U.S. general population. In addition, the current truck drivers are heavier and different in physique compared to their counterparts of 25 to 30 years ago. The data obtained in this study provide more accurate anthropometric information for cab designs than do the current U.S. general population data or truck driver data collected 25 to 30 years ago. Multivariate anthropometric models, spanning 95% of the current truck driver population on the basis of a set of 12 anthropometric measurements, have been developed to facilitate future cab designs. The up-to-date truck driver anthropometric data and multivariate anthropometric models will benefit the design of future truck cabs which, in turn, will help promote the safety and health of the U.S. truck drivers.

Cooperative learning model with high order thinking skills questions: an understanding on geometry

NASA Astrophysics Data System (ADS)

Sari, P. P.; Budiyono; Slamet, I.

2018-05-01

Geometry, a branch of mathematics, has an important role in mathematics learning. This research aims to find out the effect of learning model, emotional intelligence, and the interaction between learning model and emotional intelligence toward students’ mathematics achievement. This research is quasi-experimental research with 2 × 3 factorial design. The sample in this research included 179 Senior High School students on 11th grade in Sukoharjo Regency, Central Java, Indonesia in academic year of 2016/2017. The sample was taken by using stratified cluster random sampling. The results showed that: the student are taught by Thinking Aloud Pairs Problem-Solving using HOTs questions provides better mathematics learning achievement than Make A Match using HOTs questions. High emotional intelligence students have better mathematics learning achievement than moderate and low emotional intelligence students, and moderate emotional intelligence students have better mathematics learning achievement than low emotional intelligence students. There is an interaction between learning model and emotional intelligence, and these affect mathematics learning achievement. We conclude that appropriate learning model can support learning activities become more meaningful and facilitate students to understand material. For further research, we suggest to explore the contribution of other aspects in cooperative learning modification to mathematics achievement.

Longitudinal safety evaluation of electric vehicles with the partial wireless charging lane on freeways.

PubMed

Li, Ye; Wang, Wei; Xing, Lu; Fan, Qi; Wang, Hao

2018-02-01

As an environment friendly transportation mode, the electric vehicle (EV) has drawn an increasing amount of attention from governments, vehicle manufactories and researchers recently. One of the biggest issue impeding EV's popularization associates with the charging process. The wireless charging lane (WCL) has been proposed as a convenient charging facility for EVs. Due to the high costs, the application of WCL on the entire freeways is impractical in the near future, while the partial WCL (PWCL) may be a feasible solution. This study aims to evaluate longitudinal safety of EVs with PWCL on freeways based on simulations. The simulation experiments are firstly designed, including deployment of PWCL on freeways and distribution of state of charge (SOC) of EVs. Then, a vehicle behavior model for EVs is proposed based on the intelligent driver model (IDM). Two surrogate safety measures, derived from time-to-collision (TTC), are utilized as indicators for safety evaluations. Sensitivity analysis is also conducted for related factors. Results show that the distribution of EVs' SOC significantly affect longitudinal safety when the PWCL is utilized. The low SOC in traffic consisting of EVs has the negative effect on longitudinal safety. The randomness and incompliance of EV drivers worsens the safety performance. The sensitivity analysis indicates that the larger maximum deceleration rate results in the higher longitudinal crash risks of EVs, while the length of PWCL has no monotonous effect. Different TTC thresholds also show no impact on results. A case study shows the consistent results. Based on the findings, several suggestions are discussed for EVs' safety improvement. Results of this study provide useful information for freeway safety when EVs are applied in the future. Copyright © 2017 Elsevier Ltd. All rights reserved.

Discovering potential driver genes through an integrated model of somatic mutation profiles and gene functional information.

PubMed

Xi, Jianing; Wang, Minghui; Li, Ao

2017-09-26

The accumulating availability of next-generation sequencing data offers an opportunity to pinpoint driver genes that are causally implicated in oncogenesis through computational models. Despite previous efforts made regarding this challenging problem, there is still room for improvement in the driver gene identification accuracy. In this paper, we propose a novel integrated approach called IntDriver for prioritizing driver genes. Based on a matrix factorization framework, IntDriver can effectively incorporate functional information from both the interaction network and Gene Ontology similarity, and detect driver genes mutated in different sets of patients at the same time. When evaluated through known benchmarking driver genes, the top ranked genes of our result show highly significant enrichment for the known genes. Meanwhile, IntDriver also detects some known driver genes that are not found by the other competing approaches. When measured by precision, recall and F1 score, the performances of our approach are comparable or increased in comparison to the competing approaches.

Models of Learning in ICAI.

ERIC Educational Resources Information Center

Duchastel, P.; And Others

1989-01-01

Discusses intelligent computer assisted instruction (ICAI) and presents various models of learning which have been proposed. Topics discussed include artificial intelligence; intelligent tutorial systems; tutorial strategies; learner control; system design; learning theory; and knowledge representation of proper and improper (i.e., incorrect)…

Intelligence Fusion Modeling. A Proposed Approach.

DTIC Science & Technology

1983-09-16

based techniques developed by artificial intelligence researchers. This paper describes the application of these techniques in the modeling of an... intelligence requirements, although the methods presented are applicable . We treat PIR/IR as given. -7- -- -W V"W v* 1.- . :71.,v It k*~ ~-- Movement...items from the PIR/IR/HVT decomposition are received from the CMDS. Formatted tactical intelligence reports are received from sensors of like types

Generalizing on Multiple Grounds: Performance Learning in Model-Based Troubleshooting

DTIC Science & Technology

1989-02-01

Aritificial Intelligence , 24, 1984. [Ble88] Guy E. Blelloch. Scan Primitives and Parallel Vector Models. PhD thesis, Artificial Intelligence Laboratory...Diagnostic reasoning based on strcture and behavior. Aritificial Intelligence , 24, 1984. [dK86] J. de Kleer. An assumption-based truth maintenance system...diagnosis. Aritificial Intelligence , 24. �. )3 94 BIBLIOGRAPHY [Ham87] Kristian J. Hammond. Learning to anticipate and avoid planning prob- lems

An Integrated Modeling and Simulation Methodology for Intelligent Systems Design and Testing

DTIC Science & Technology

2002-08-01

simulation and actual execution. KEYWORDS: Model Continuity, Modeling, Simulation, Experimental Frame, Real Time Systems , Intelligent Systems...the methodology for a stand-alone real time system. Then it will scale up to distributed real time systems . For both systems, step-wise simulation...MODEL CONTINUITY Intelligent real time systems monitor, respond to, or control, an external environment. This environment is connected to the digital

A novel AIDS/HIV intelligent medical consulting system based on expert systems.

PubMed

Ebrahimi, Alireza Pour; Toloui Ashlaghi, Abbas; Mahdavy Rad, Maryam

2013-01-01

The purpose of this paper is to propose a novel intelligent model for AIDS/HIV data based on expert system and using it for developing an intelligent medical consulting system for AIDS/HIV. In this descriptive research, 752 frequently asked questions (FAQs) about AIDS/HIV are gathered from numerous websites about this disease. To perform the data mining and extracting the intelligent model, the 6 stages of Crisp method has been completed for FAQs. The 6 stages include: Business understanding, data understanding, data preparation, modelling, evaluation and deployment. C5.0 Tree classification algorithm is used for modelling. Also, rational unified process (RUP) is used to develop the web-based medical consulting software. Stages of RUP are as follows: Inception, elaboration, construction and transition. The intelligent developed model has been used in the infrastructure of the software and based on client's inquiry and keywords related FAQs are displayed to the client, according to the rank. FAQs' ranks are gradually determined considering clients reading it. Based on displayed FAQs, test and entertainment links are also displayed. The accuracy of the AIDS/HIV intelligent web-based medical consulting system is estimated to be 78.76%. AIDS/HIV medical consulting systems have been developed using intelligent infrastructure. Being equipped with an intelligent model, providing consulting services on systematic textual data and providing side services based on client's activities causes the implemented system to be unique. The research has been approved by Iranian Ministry of Health and Medical Education for being practical.

Gratitude mediates the effect of emotional intelligence on subjective well-being: A structural equation modeling analysis.

PubMed

Geng, Yuan

2016-11-01

This study investigated the relationship among emotional intelligence, gratitude, and subjective well-being in a sample of university students. A total of 365 undergraduates completed the emotional intelligence scale, the gratitude questionnaire, and the subjective well-being measures. The results of the structural equation model showed that emotional intelligence is positively associated with gratitude and subjective well-being, that gratitude is positively associated with subjective well-being, and that gratitude partially mediates the positive relationship between emotional intelligence and subjective well-being. Bootstrap test results also revealed that emotional intelligence has a significant indirect effect on subjective well-being through gratitude.

Contemporary cybernetics and its facets of cognitive informatics and computational intelligence.

PubMed

Wang, Yingxu; Kinsner, Witold; Zhang, Du

2009-08-01

This paper explores the architecture, theoretical foundations, and paradigms of contemporary cybernetics from perspectives of cognitive informatics (CI) and computational intelligence. The modern domain and the hierarchical behavioral model of cybernetics are elaborated at the imperative, autonomic, and cognitive layers. The CI facet of cybernetics is presented, which explains how the brain may be mimicked in cybernetics via CI and neural informatics. The computational intelligence facet is described with a generic intelligence model of cybernetics. The compatibility between natural and cybernetic intelligence is analyzed. A coherent framework of contemporary cybernetics is presented toward the development of transdisciplinary theories and applications in cybernetics, CI, and computational intelligence.

Forensic intelligence framework--Part I: Induction of a transversal model by comparing illicit drugs and false identity documents monitoring.

PubMed

Morelato, Marie; Baechler, Simon; Ribaux, Olivier; Beavis, Alison; Tahtouh, Mark; Kirkbride, Paul; Roux, Claude; Margot, Pierre

2014-03-01

Forensic intelligence is a distinct dimension of forensic science. Forensic intelligence processes have mostly been developed to address either a specific type of trace or a specific problem. Even though these empirical developments have led to successes, they are trace-specific in nature and contribute to the generation of silos which hamper the establishment of a more general and transversal model. Forensic intelligence has shown some important perspectives but more general developments are required to address persistent challenges. This will ensure the progress of the discipline as well as its widespread implementation in the future. This paper demonstrates that the description of forensic intelligence processes, their architectures, and the methods for building them can, at a certain level, be abstracted from the type of traces considered. A comparative analysis is made between two forensic intelligence approaches developed independently in Australia and in Europe regarding the monitoring of apparently very different kind of problems: illicit drugs and false identity documents. An inductive effort is pursued to identify similarities and to outline a general model. Besides breaking barriers between apparently separate fields of study in forensic science and intelligence, this transversal model would assist in defining forensic intelligence, its role and place in policing, and in identifying its contributions and limitations. The model will facilitate the paradigm shift from the current case-by-case reactive attitude towards a proactive approach by serving as a guideline for the use of forensic case data in an intelligence-led perspective. A follow-up article will specifically address issues related to comparison processes, decision points and organisational issues regarding forensic intelligence (part II). Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Design and Implementation of C-iLearning: A Cloud-Based Intelligent Learning System

ERIC Educational Resources Information Center

Xiao, Jun; Wang, Minjuan; Wang, Lamei; Zhu, Xiaoxiao

2013-01-01

The gradual development of intelligent learning (iLearning) systems has prompted the changes of teaching and learning. This paper presents the architecture of an intelligent learning (iLearning) system built upon the recursive iLearning model and the key technologies associated with this model. Based on this model and the technical structure of a…

An Australasian model license reassessment procedure for identifying potentially unsafe drivers.

PubMed

Fildes, Brian N; Charlton, Judith; Pronk, Nicola; Langford, Jim; Oxley, Jennie; Koppel, Sjaanie

2008-08-01

Most licensing jurisdictions in Australia currently employ age-based assessment programs as a means to manage older driver safety, yet available evidence suggests that these programs have no safety benefits. This paper describes a community referral-based model license re assessment procedure for identifying and assessing potentially unsafe drivers. While the model was primarily developed for assessing older driver fitness to drive, it could be applicable to other forms of driver impairment associated with increased crash risk. It includes a three-tier process of assessment, involving the use of validated and relevant assessment instruments. A case is argued that this process is a more systematic, transparent and effective process for managing older driver safety and thus more likely to be widely acceptable to the target community and licensing authorities than age-based practices.

Emotional intelligence and psychological health in a sample of Kuwaiti college students.

PubMed

Alkhadher, Othman

2007-06-01

This summary investigated correlations between emotional intelligence and psychological health amongst 191 Kuwaiti undergraduate students in psychology, 98 men and 93 women (M age=20.6 yr., SD=2.8). There were two measures of emotional intelligence, one based on the ability model, the Arabic Test for Emotional Intelligence, and the other on the mixed model, the Emotional Intelligence Questionnaire. Participants' psychological health was assessed using scales from the Personality Assessment Inventory. A weak relationship between the two types of emotional intelligence was found. A correlation for scores on the Emotional Intelligence Questionnaire with the Personality Assessment Inventory was found but not with those of the Arabic Test for Emotional Intelligence. Regression analysis indicated scores on Managing Emotions and Self-awareness accounted for most of the variance in the association with the Personality Assessment Inventory. Significant sex differences were found only on the Arabic Test for Emotional Intelligence; women scored higher than men. On Emotional Intelligence Questionnaire measures, men had significantly higher means on Managing Emotions and Self-motivation. However, no significant differences were found between the sexes on the Total Emotional Intelligence Questionnaire scores.

Dielectric elastomer memory

NASA Astrophysics Data System (ADS)

O'Brien, Benjamin M.; McKay, Thomas G.; Xie, Sheng Q.; Calius, Emilio P.; Anderson, Iain A.

2011-04-01

Life shows us that the distribution of intelligence throughout flexible muscular networks is a highly successful solution to a wide range of challenges, for example: human hearts, octopi, or even starfish. Recreating this success in engineered systems requires soft actuator technologies with embedded sensing and intelligence. Dielectric Elastomer Actuator(s) (DEA) are promising due to their large stresses and strains, as well as quiet flexible multimodal operation. Recently dielectric elastomer devices were presented with built in sensor, driver, and logic capability enabled by a new concept called the Dielectric Elastomer Switch(es) (DES). DES use electrode piezoresistivity to control the charge on DEA and enable the distribution of intelligence throughout a DEA device. In this paper we advance the capabilities of DES further to form volatile memory elements. A set reset flip-flop with inverted reset line was developed based on DES and DEA. With a 3200V supply the flip-flop behaved appropriately and demonstrated the creation of dielectric elastomer memory capable of changing state in response to 1 second long set and reset pulses. This memory opens up applications such as oscillator, de-bounce, timing, and sequential logic circuits; all of which could be distributed throughout biomimetic actuator arrays. Future work will include miniaturisation to improve response speed, implementation into more complex circuits, and investigation of longer lasting and more sensitive switching materials.

Modeling of biological intelligence for SCM system optimization.

PubMed

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Modeling of Biological Intelligence for SCM System Optimization

PubMed Central

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

Missing data reconstruction using Gaussian mixture models for fingerprint images

NASA Astrophysics Data System (ADS)

Agaian, Sos S.; Yeole, Rushikesh D.; Rao, Shishir P.; Mulawka, Marzena; Troy, Mike; Reinecke, Gary

2016-05-01

Publisher's Note: This paper, originally published on 25 May 2016, was replaced with a revised version on 16 June 2016. If you downloaded the original PDF, but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. One of the most important areas in biometrics is matching partial fingerprints in fingerprint databases. Recently, significant progress has been made in designing fingerprint identification systems for missing fingerprint information. However, a dependable reconstruction of fingerprint images still remains challenging due to the complexity and the ill-posed nature of the problem. In this article, both binary and gray-level images are reconstructed. This paper also presents a new similarity score to evaluate the performance of the reconstructed binary image. The offered fingerprint image identification system can be automated and extended to numerous other security applications such as postmortem fingerprints, forensic science, investigations, artificial intelligence, robotics, all-access control, and financial security, as well as for the verification of firearm purchasers, driver license applicants, etc.

Creating healthy work environments: a strategic perspective.

PubMed

Adamson, Bonnie J

2010-01-01

Although I find Graham Lowe and Ben Chan's logic model and work environment metrics thought provoking, a healthy work environment framework must be more comprehensive and consider the addition of recommended diagnostic tools, vehicles to deliver the necessary change and a sustainability strategy that allows for the tweaking and refinement of ideas. Basic structure is required to frame and initiate an effective process, while allowing creativity and enhancements to be made by organizations as they learn. I support the construction of a suggested Canadian health sector framework for measuring the health of an organization, but I feel that organizations need to have some freedom in that design and the ability to incorporate their own indicators within the established proven drivers. Reflecting on my organization's experience with large-scale transformation efforts, I find that emotional intelligence along with formal leadership development and front-line engagement in Lean process improvement activities are essential for creating healthy work environments that produce the balanced set of outcomes listed in my hospital's Balanced Scorecard.

Longitudinal Mediation of Processing Speed on Age-Related Change in Memory and Fluid Intelligence

PubMed Central

Robitaille, Annie; Piccinin, Andrea M.; Muniz, Graciela; Hoffman, Lesa; Johansson, Boo; Deeg, Dorly J.H.; Aartsen, Marja J.; Comijs, Hannie C.; Hofer, Scott M.

2014-01-01

Age-related decline in processing speed has long been considered a key driver of cognitive aging. While the majority of empirical evidence for the processing speed hypothesis has been obtained from analyses of between-person age differences, longitudinal studies provide a direct test of within-person change. Using recent developments in longitudinal mediation analysis, we examine the speed–mediation hypothesis at both the within- and between-person levels in two longitudinal studies, LASA and OCTO-Twin. We found significant within-person indirect effects of change in age, such that increasing age was related to lower speed which, in turn, relates to lower performance across repeated measures on other cognitive outcomes. Although between-person indirect effects were also significant in LASA, they were not in OCTO-Twin. These differing magnitudes of direct and indirect effects across levels demonstrate the importance of separating between- and within-person effects in evaluating theoretical models of age-related change. PMID:23957224

Fuel Economy Improvement Potential of a Heavy Duty Truck using V2x Communication

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

LaClair, Tim J; Verma, Rajeev; Norris, Sarah

2014-01-01

In this paper, we introduce an intelligent driver assistance system to reduce fuel consumption in heavy duty vehicles irrespective of the driving style of the driver. We specifically study the potential of V2I and V2V communications to reduce fuel consumption in heavy duty trucks. Most ITS communications today are oriented towards vehicle safety, with communications strategies and hardware that tend to focus on low latency. This has resulted in technologies emerging with a relatively limited range for the communications. For fuel economy, it is expected that most benefits will be derived with greater communications distances, at the scale of manymore » hundred meters or several kilometers, due to the large inertia of heavy duty vehicles. It may therefore be necessary to employ different communications strategies for ITS applications aimed at fuel economy and other environmental benefits than what is used for safety applications in order to achieve the greatest benefits.« less

Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network.

PubMed

Fernandez, Susel; Hadfi, Rafik; Ito, Takayuki; Marsa-Maestre, Ivan; Velasco, Juan R

2016-08-15

Intelligent transportation systems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element for the success of these systems is the exchange of information, not only between vehicles, but also among other components in the road infrastructure through different applications. One of the most important information sources in this kind of systems is sensors. Sensors can be within vehicles or as part of the infrastructure, such as bridges, roads or traffic signs. Sensors can provide information related to weather conditions and traffic situation, which is useful to improve the driving process. To facilitate the exchange of information between the different applications that use sensor data, a common framework of knowledge is needed to allow interoperability. In this paper an ontology-driven architecture to improve the driving environment through a traffic sensor network is proposed. The system performs different tasks automatically to increase driver safety and comfort using the information provided by the sensors.

Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network

PubMed Central

Fernandez, Susel; Hadfi, Rafik; Ito, Takayuki; Marsa-Maestre, Ivan; Velasco, Juan R.

2016-01-01

Intelligent transportation systems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element for the success of these systems is the exchange of information, not only between vehicles, but also among other components in the road infrastructure through different applications. One of the most important information sources in this kind of systems is sensors. Sensors can be within vehicles or as part of the infrastructure, such as bridges, roads or traffic signs. Sensors can provide information related to weather conditions and traffic situation, which is useful to improve the driving process. To facilitate the exchange of information between the different applications that use sensor data, a common framework of knowledge is needed to allow interoperability. In this paper an ontology-driven architecture to improve the driving environment through a traffic sensor network is proposed. The system performs different tasks automatically to increase driver safety and comfort using the information provided by the sensors. PMID:27537878

HAS INCREASED BODY WEIGHT MADE DRIVING SAFER?†

PubMed Central

DUNN, RICHARD A.; TEFFT, NATHAN W.

2014-01-01

We develop a model of alcohol consumption that incorporates the negative biological relationship between body mass and inebriation conditional on total alcohol consumption. Our model predicts that the elasticity of inebriation with respect to weight is equal to the own-price elasticity of alcohol, consistent with body mass increasing the effective price of inebriation. Given that alcohol is generally considered price inelastic, this result implies that as individuals gain weight, they consume more alcohol but become less inebriated. We test this prediction and find that driver blood alcohol content (BAC) is negatively associated with driver weight. In fatal accidents with driver BAC above 0.10, the driver was 7.8 percentage points less likely to be obese than drivers in fatal accidents that did not involve alcohol. This relationship is not explained by driver attributes (age and sex), driver behaviors (speed and seatbelt use), vehicle attributes (weight class, model year, and number of occupants), or accident context (county of accident, time of day, and day of week). PMID:24038409

Analysis of injury severity of drivers involved in single- and two-vehicle crashes on highways in Ontario.

PubMed

Lee, Chris; Li, Xuancheng

2014-10-01

This study analyzes driver's injury severity in single- and two-vehicle crashes and compares the effects of explanatory variables among various types of crashes. The study identified factors affecting injury severity and their effects on severity levels using 5-year crash records for provincial highways in Ontario, Canada. Considering heteroscedasticity in the effects of explanatory variables on injury severity, the heteroscedastic ordered logit (HOL) models were developed for single- and two-vehicle crashes separately. The results of the models show that there exists heteroscedasticity for young drivers (≤30), safety equipment and ejection in the single-vehicle crash model, and female drivers, safety equipment and head-on collision in the two-vehicle crash models. The results also show that young car drivers have opposite effects between single-car and car-car crashes, and sideswipe crashes have opposite effects between car-car and truck-truck crashes. The study demonstrates that separate HOL models for single-vehicle and different types of two-vehicle crashes can identify differential effects of factors on driver's injury severity. Copyright © 2014 Elsevier Ltd. All rights reserved.

Connectionist Models for Intelligent Computation

DTIC Science & Technology

1989-07-26

Intelligent Canputation 12. PERSONAL AUTHOR(S) H.H. Chen and Y.C. Lee 13a. o R,POT Cal 13b TIME lVD/rED 14 DATE OF REPORT (Year, Month, Day) JS PAGE...fied Project Title: Connectionist Models-for Intelligent Computation Contract/Grant No.: AFOSR-87-0388 Contract/Grant Period of Performance: Sept. 1...underlying principles, architectures and appilications of artificial neural networks for intelligent computations.o, Approach: -) We use both numerical

Actors: A Model of Concurrent Computation in Distributed Systems.

DTIC Science & Technology

1985-06-01

Artificial Intelligence Labora- tory of the Massachusetts Institute of Technology. Support for the labora- tory’s aritificial intelligence research is...RD-A157 917 ACTORS: A MODEL OF CONCURRENT COMPUTATION IN 1/3- DISTRIBUTED SY𔃿TEMS(U) MASSACHUSETTS INST OF TECH CRMBRIDGE ARTIFICIAL INTELLIGENCE ...Computation In Distributed Systems Gui A. Aghai MIT Artificial Intelligence Laboratory Thsdocument ha. been cipp-oved I= pblicrelease and sale; itsI

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs.

PubMed

Marshall, Thomas; Champagne-Langabeer, Tiffiany; Castelli, Darla; Hoelscher, Deanna

2017-12-01

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience. The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research. By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

A parametric duration model of the reaction times of drivers distracted by mobile phone conversations.

PubMed

Haque, Md Mazharul; Washington, Simon

2014-01-01

The use of mobile phones while driving is more prevalent among young drivers-a less experienced cohort with elevated crash risk. The objective of this study was to examine and better understand the reaction times of young drivers to a traffic event originating in their peripheral vision whilst engaged in a mobile phone conversation. The CARRS-Q advanced driving simulator was used to test a sample of young drivers on various simulated driving tasks, including an event that originated within the driver's peripheral vision, whereby a pedestrian enters a zebra crossing from a sidewalk. Thirty-two licensed drivers drove the simulator in three phone conditions: baseline (no phone conversation), hands-free and handheld. In addition to driving the simulator each participant completed questionnaires related to driver demographics, driving history, usage of mobile phones while driving, and general mobile phone usage history. The participants were 21-26 years old and split evenly by gender. Drivers' reaction times to a pedestrian in the zebra crossing were modelled using a parametric accelerated failure time (AFT) duration model with a Weibull distribution. Also tested where two different model specifications to account for the structured heterogeneity arising from the repeated measures experimental design. The Weibull AFT model with gamma heterogeneity was found to be the best fitting model and identified four significant variables influencing the reaction times, including phone condition, driver's age, license type (provisional license holder or not), and self-reported frequency of usage of handheld phones while driving. The reaction times of drivers were more than 40% longer in the distracted condition compared to baseline (not distracted). Moreover, the impairment of reaction times due to mobile phone conversations was almost double for provisional compared to open license holders. A reduction in the ability to detect traffic events in the periphery whilst distracted presents a significant and measurable safety concern that will undoubtedly persist unless mitigated. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Effects of Vehicle Redesign on the Risk of Driver Death.

PubMed

Farmer, Charles M; Lund, Adrian K

2015-01-01

This study updates a 2006 report that estimated the historical effects of vehicle design changes on driver fatality rates in the United States, separate from the effects of environmental and driver behavior changes during the same period. In addition to extending the period covered by 8 years, this study estimated the effect of design changes by model year and vehicle type. Driver death rates for consecutive model years of vehicle models without design changes were used to estimate the vehicle aging effect and the death rates that would have been expected if the entire fleet had remained unchanged from the 1985 calendar year. These calendar year estimates are taken to be the combined effect of road environment and motorist behavioral changes, with the difference between them and the actual calendar year driver fatality rates reflecting the effect of changes in vehicle design and distribution of vehicle types. The effects of vehicle design changes by model year were estimated for cars, SUVs, and pickups by computing driver death rates for model years 1984-2009 during each of their first 3 full calendar years of exposure and comparing with the expected rates if there had been no design changes. As reported in the 2006 study, had there been no changes in the vehicle fleet, driver death risk would have declined during calendar years 1985-1993 and then slowly increased from 1993 to 2004. The updated results indicate that the gradual increase would have continued through 2006, after which driver fatality rates again would have declined through 2012. Overall, it is estimated that there were 7,700 fewer driver deaths in 2012 than there would have been had vehicle designs not changed. Cars were the first vehicle type whose design safety generally exceeded that of the 1984 model year (starting in model year 1996), followed by SUVs (1998 models) and pickups (2002 models). By the 2009 model year, car driver fatality risk had declined 51% from its high in 1994, pickup driver fatality risk had declined 61% from its high in 1988, and SUV risk had declined 79% from its high in 1988. The risk of driver death in 2009 model passenger vehicles was 8% lower than that in 2008 models and about half that in 1984 models. Changes in vehicles, whether from government regulations and consumer testing that led to advanced safety designs or from other factors such as consumer demand for different sizes and types of vehicles, have been key contributors to the decline in U.S. motor vehicle occupant crash death rates since the mid-1990s. Since the early 1990s, environmental and behavioral risk factors have not shown similar improvement, until the recession of 2007, even though there are many empirically proven countermeasures that have been inadequately applied.

Wheat stress indicator model, Crop Condition Assessment Division (CCAD) data base interface driver, user's manual

NASA Technical Reports Server (NTRS)

Hansen, R. F. (Principal Investigator)

1981-01-01

The use of the wheat stress indicator model CCAD data base interface driver is described. The purpose of this system is to interface the wheat stress indicator model with the CCAD operational data base. The interface driver routine decides what meteorological stations should be processed and calls the proper subroutines to process the stations.

Fatigue and crashes: the case of freight transport in Colombia.

PubMed

Torregroza-Vargas, Nathaly M; Bocarejo, Juan Pablo; Ramos-Bonilla, Juan P

2014-11-01

Truck drivers have been involved in a significant number of road fatalities in Colombia. To identify variables that could be associated with crashes in which truck drivers are involved, a logistic regression model was constructed. The model had as the response variable a dichotomous variable that included the presence or absence of a crash during a specific trip. As independent variables the model included information regarding a driver's work shift, with variables that could be associated with driver's fatigue. The model also included potential confounders related with road conditions. With the model, it was possible to determine the odds ratio of a crash in relation to several variables, adjusting for confounding. To collect the information about the trips included in the model, a survey among truck drivers was conducted. The results suggest strong associations between crashes (i.e., some of them statistically significant) with the number of stops made during the trip, and the average time of each stop. Survey analysis allowed us to identify the practices that contribute to generating fatigue and unhealthy conditions on the road among professional drivers. A review of national regulations confirmed the lack of legislation on this topic. Copyright © 2014 Elsevier Ltd. All rights reserved.

A quantum mechanics-based approach to model incident-induced dynamic driver behavior

NASA Astrophysics Data System (ADS)

Sheu, Jiuh-Biing

2008-08-01

A better understanding of the psychological factors influencing drivers, and the resulting driving behavior responding to incident-induced lane traffic phenomena while passing by an incident site is vital to the improvement of road safety. This paper presents a microscopic driver behavior model to explain the dynamics of the instantaneous driver decision process under lane-blocking incidents on adjacent lanes. The proposed conceptual framework decomposes the corresponding driver decision process into three sequential phases: (1) initial stimulus, (2) glancing-around car-following, and (3) incident-induced driving behavior. The theorem of quantum mechanics in optical flows is applied in the first phase to explain the motion-related perceptual phenomena while vehicles approach the incident site in adjacent lanes, followed by the incorporation of the effect of quantum optical flows in modeling the induced glancing-around car-following behavior in the second phase. Then, an incident-induced driving behavior model is formulated to reproduce the dynamics of driver behavior conducted in the process of passing by an incident site in the adjacent lanes. Numerical results of model tests using video-based incident data indicate the validity of the proposed traffic behavior model in analyzing the incident-induced lane traffic phenomena. It is also expected that such a proposed quantum-mechanics based methodology can throw more light if applied to driver psychology and response in anomalous traffic environments in order to improve road safety.

A predictive control framework for torque-based steering assistance to improve safety in highway driving

NASA Astrophysics Data System (ADS)

Ercan, Ziya; Carvalho, Ashwin; Tseng, H. Eric; Gökaşan, Metin; Borrelli, Francesco

2018-05-01

Haptic shared control framework opens up new perspectives on the design and implementation of the driver steering assistance systems which provide torque feedback to the driver in order to improve safety. While designing such a system, it is important to account for the human-machine interactions since the driver feels the feedback torque through the hand wheel. The controller should consider the driver's impact on the steering dynamics to achieve a better performance in terms of driver's acceptance and comfort. In this paper we present a predictive control framework which uses a model of driver-in-the-loop steering dynamics to optimise the torque intervention with respect to the driver's neuromuscular response. We first validate the system in simulations to compare the performance of the controller in nominal and model mismatch cases. Then we implement the controller in a test vehicle and perform experiments with a human driver. The results show the effectiveness of the proposed system in avoiding hazardous situations under different driver behaviours.

Driver steering dynamics measured in car simulator under a range of visibility and roadmaking conditions

NASA Technical Reports Server (NTRS)

Allen, R. W.; Mcruer, D. T.

1977-01-01

A simulation experiment was conducted to determine the effect of reduced visibility on driver lateral (steering) control. The simulator included a real car cab and a single lane road image projected on a screen six feet in front of the driver. Simulated equations of motion controlled apparent car lane position in response to driver steering actions, wind gusts, and road curvature. Six drivers experienced a range of visibility conditions at various speeds with assorted roadmaking configurations (mark and gap lengths). Driver describing functions were measured and detailed parametric model fits were determined. A pursuit model employing a road curvature feedforward was very effective in explaining driver behavior in following randomly curving roads. Sampled-data concepts were also effective in explaining the combined effects of reduced visibility and intermittent road markings on the driver's dynamic time delay. The results indicate the relative importance of various perceptual variables as the visual input to the driver's steering control process is changed.

Comparison of learning models based on mathematics logical intelligence in affective domain

NASA Astrophysics Data System (ADS)

Widayanto, Arif; Pratiwi, Hasih; Mardiyana

2018-04-01

The purpose of this study was to examine the presence or absence of different effects of multiple treatments (used learning models and logical-mathematical intelligence) on the dependent variable (affective domain of mathematics). This research was quasi experimental using 3x3 of factorial design. The population of this research was VIII grade students of junior high school in Karanganyar under the academic year 2017/2018. Data collected in this research was analyzed by two ways analysis of variance with unequal cells using 5% of significance level. The result of the research were as follows: (1) Teaching and learning with model TS lead to better achievement in affective domain than QSH, teaching and learning with model QSH lead to better achievement in affective domain than using DI; (2) Students with high mathematics logical intelligence have better achievement in affective domain than students with low mathematics logical intelligence have; (3) In teaching and learning mathematics using learning model TS, students with moderate mathematics logical intelligence have better achievement in affective domain than using DI; and (4) In teaching and learning mathematics using learning model TS, students with low mathematics logical intelligence have better achievement in affective domain than using QSH and DI.

Infrastructure Upgrades to Support Model Longevity and New Applications: The Variable Infiltration Capacity Model Version 5.0 (VIC 5.0)

NASA Astrophysics Data System (ADS)

Nijssen, B.; Hamman, J.; Bohn, T. J.

2015-12-01

The Variable Infiltration Capacity (VIC) model is a macro-scale semi-distributed hydrologic model. VIC development began in the early 1990s and it has been used extensively, applied from basin to global scales. VIC has been applied in a many use cases, including the construction of hydrologic data sets, trend analysis, data evaluation and assimilation, forecasting, coupled climate modeling, and climate change impact analysis. Ongoing applications of the VIC model include the University of Washington's drought monitor and forecast systems, and NASA's land data assimilation systems. The development of VIC version 5.0 focused on reconfiguring the legacy VIC source code to support a wider range of modern modeling applications. The VIC source code has been moved to a public Github repository to encourage participation by the model development community-at-large. The reconfiguration has separated the physical core of the model from the driver, which is responsible for memory allocation, pre- and post-processing and I/O. VIC 5.0 includes four drivers that use the same physical model core: classic, image, CESM, and Python. The classic driver supports legacy VIC configurations and runs in the traditional time-before-space configuration. The image driver includes a space-before-time configuration, netCDF I/O, and uses MPI for parallel processing. This configuration facilitates the direct coupling of streamflow routing, reservoir, and irrigation processes within VIC. The image driver is the foundation of the CESM driver; which couples VIC to CESM's CPL7 and a prognostic atmosphere. Finally, we have added a Python driver that provides access to the functions and datatypes of VIC's physical core from a Python interface. This presentation demonstrates how reconfiguring legacy source code extends the life and applicability of a research model.

Intelligent speed adaptation as an assistive device for drivers with acquired brain injury: a single-case field experiment.

PubMed

Klarborg, Brith; Lahrmann, Harry; NielsAgerholm; Tradisauskas, Nerius; Harms, Lisbeth

2012-09-01

Intelligent speed adaptation (ISA) was tested as an assistive device for drivers with an acquired brain injury (ABI). The study was part of the "Pay as You Speed" project (PAYS) and used the same equipment and technology as the main study (Lahrmann et al., in press-a, in press-b). Two drivers with ABI were recruited as subjects and had ISA equipment installed in their private vehicle. Their speed was logged with ISA equipment for a total of 30 weeks of which 12 weeks were with an active ISA user interface (6 weeks=Baseline 1; 12 weeks=ISA period; 12 weeks=Baseline 2). The subjects participated in two semi-structured interviews concerning their strategies for driving with ABI and for driving with ISA. Furthermore, they gave consent to have data from their clinical journals and be a part of the study. The two subjects did not report any instances of being distracted or confused by ISA, and in general they described driving with ISA as relaxed. ISA reduced the percentage of the total distance that was driven with a speed above the speed limit (PDA), but the subjects relapsed to their previous PDA level in Baseline 2. This suggests that ISA is more suited as a permanent assistive device (i.e. cognitive prosthesis) than as a temporary training device. As ABI is associated with a multitude of cognitive deficits, we developed a conceptual framework, which focused on the cognitive parameters that have been shown to relate to speeding behaviour, namely "intention to speed" and "inattention to speeding". The subjects' combined status on the two independent parameters made up their "speeding profile". A comparison of the speeding profiles and the speed logs indicated that ISA in the present study was more efficient in reducing inattention to speeding than affecting intention to speed. This finding suggests that ISA might be more suited for some neuropsychological profiles than for others, and that customisation of ISA for different neuropsychological profiles may be required. However, further studies with more subjects are needed in order to be conclusive on these issues. Copyright © 2011 Elsevier Ltd. All rights reserved.

Comparing and validating models of driver steering behaviour in collision avoidance and vehicle stabilisation

NASA Astrophysics Data System (ADS)

Markkula, G.; Benderius, O.; Wahde, M.

2014-12-01

A number of driver models were fitted to a large data set of human truck driving, from a simulated near-crash, low-friction scenario, yielding two main insights: steering to avoid a collision was best described as an open-loop manoeuvre of predetermined duration, but with situation-adapted amplitude, and subsequent vehicle stabilisation could to a large extent be accounted for by a simple yaw rate nulling control law. These two phenomena, which could be hypothesised to generalise to passenger car driving, were found to determine the ability of four driver models adopted from the literature to fit the human data. Based on the obtained results, it is argued that the concept of internal vehicle models may be less valuable when modelling driver behaviour in non-routine situations such as near-crashes, where behaviour may be better described as direct responses to salient perceptual cues. Some methodological issues in comparing and validating driver models are also discussed.

Development of an errorable car-following driver model

NASA Astrophysics Data System (ADS)

Yang, H.-H.; Peng, H.

2010-06-01

An errorable car-following driver model is presented in this paper. An errorable driver model is one that emulates human driver's functions and can generate both nominal (error-free), as well as devious (with error) behaviours. This model was developed for evaluation and design of active safety systems. The car-following data used for developing and validating the model were obtained from a large-scale naturalistic driving database. The stochastic car-following behaviour was first analysed and modelled as a random process. Three error-inducing behaviours were then introduced. First, human perceptual limitation was studied and implemented. Distraction due to non-driving tasks was then identified based on the statistical analysis of the driving data. Finally, time delay of human drivers was estimated through a recursive least-square identification process. By including these three error-inducing behaviours, rear-end collisions with the lead vehicle could occur. The simulated crash rate was found to be similar but somewhat higher than that reported in traffic statistics.

An intelligent anti-jamming network system of data link

NASA Astrophysics Data System (ADS)

Fan, Xiangrui; Lin, Jingyong; Liu, Jiarun; Zhou, Chunmei

2017-10-01

Data link is the key information system for the cooperation of weapons, single physical layer anti-jamming technology has been unable to meet its requirements. High dynamic precision-guided weapon nodes like missiles, anti-jamming design of data link system need to have stronger pertinence and effectiveness: the best anti-jamming communication mode can be selected intelligently in combat environment, in real time, guarantee the continuity of communication. We discuss an anti-jamming intelligent networking technology of data link based on interference awareness, put forward a model of intelligent anti-jamming system, and introduces the cognitive node protocol stack model and intelligent anti-jamming method, in order to improve the data chain of intelligent anti-jamming ability.

Identification of mutated driver pathways in cancer using a multi-objective optimization model.

PubMed

Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

2016-05-01

New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. Copyright © 2016 Elsevier Ltd. All rights reserved.

A novel AIDS/HIV intelligent medical consulting system based on expert systems

PubMed Central

Ebrahimi, Alireza Pour; Toloui Ashlaghi, Abbas; Mahdavy Rad, Maryam

2013-01-01

Background: The purpose of this paper is to propose a novel intelligent model for AIDS/HIV data based on expert system and using it for developing an intelligent medical consulting system for AIDS/HIV. Materials and Methods: In this descriptive research, 752 frequently asked questions (FAQs) about AIDS/HIV are gathered from numerous websites about this disease. To perform the data mining and extracting the intelligent model, the 6 stages of Crisp method has been completed for FAQs. The 6 stages include: Business understanding, data understanding, data preparation, modelling, evaluation and deployment. C5.0 Tree classification algorithm is used for modelling. Also, rational unified process (RUP) is used to develop the web-based medical consulting software. Stages of RUP are as follows: Inception, elaboration, construction and transition. The intelligent developed model has been used in the infrastructure of the software and based on client's inquiry and keywords related FAQs are displayed to the client, according to the rank. FAQs’ ranks are gradually determined considering clients reading it. Based on displayed FAQs, test and entertainment links are also displayed. Result: The accuracy of the AIDS/HIV intelligent web-based medical consulting system is estimated to be 78.76%. Conclusion: AIDS/HIV medical consulting systems have been developed using intelligent infrastructure. Being equipped with an intelligent model, providing consulting services on systematic textual data and providing side services based on client's activities causes the implemented system to be unique. The research has been approved by Iranian Ministry of Health and Medical Education for being practical. PMID:24251290

The Safety Performance of Passenger Carrier Drivers

DOT National Transportation Integrated Search

2012-12-01

This paper examines the safety performance of passenger carrier drivers. Special emphasis is given to the motorcoach segment. A model that investigates the contribution of driver factors on the number of State-reportable crashes in which the driver w...

Discovery of cancer common and specific driver gene sets

PubMed Central

2017-01-01

Abstract Cancer is known as a disease mainly caused by gene alterations. Discovery of mutated driver pathways or gene sets is becoming an important step to understand molecular mechanisms of carcinogenesis. However, systematically investigating commonalities and specificities of driver gene sets among multiple cancer types is still a great challenge, but this investigation will undoubtedly benefit deciphering cancers and will be helpful for personalized therapy and precision medicine in cancer treatment. In this study, we propose two optimization models to de novo discover common driver gene sets among multiple cancer types (ComMDP) and specific driver gene sets of one certain or multiple cancer types to other cancers (SpeMDP), respectively. We first apply ComMDP and SpeMDP to simulated data to validate their efficiency. Then, we further apply these methods to 12 cancer types from The Cancer Genome Atlas (TCGA) and obtain several biologically meaningful driver pathways. As examples, we construct a common cancer pathway model for BRCA and OV, infer a complex driver pathway model for BRCA carcinogenesis based on common driver gene sets of BRCA with eight cancer types, and investigate specific driver pathways of the liquid cancer lymphoblastic acute myeloid leukemia (LAML) versus other solid cancer types. In these processes more candidate cancer genes are also found. PMID:28168295

m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics.

PubMed

Istepanian, Robert S H; Al-Anzi, Turki

2018-06-08

Mobile health (m-Health) has been repeatedly called the biggest technological breakthrough of our modern times. Similarly, the concept of big data in the context of healthcare is considered one of the transformative drivers for intelligent healthcare delivery systems. In recent years, big data has become increasingly synonymous with mobile health, however key challenges of 'Big Data and mobile health', remain largely untackled. This is becoming particularly important with the continued deluge of the structured and unstructured data sets generated on daily basis from the proliferation of mobile health applications within different healthcare systems and products globally. The aim of this paper is of twofold. First we present the relevant big data issues from the mobile health (m-Health) perspective. In particular we discuss these issues from the technological areas and building blocks (communications, sensors and computing) of mobile health and the newly defined (m-Health 2.0) concept. The second objective is to present the relevant rapprochement issues of big m-Health data analytics with m-Health. Further, we also present the current and future roles of machine and deep learning within the current smart phone centric m-health model. The critical balance between these two important areas will depend on how different stakeholder from patients, clinicians, healthcare providers, medical and m-health market businesses and regulators will perceive these developments. These new perspectives are essential for better understanding the fine balance between the new insights of how intelligent and connected the future mobile health systems will look like and the inherent risks and clinical complexities associated with the big data sets and analytical tools used in these systems. These topics will be subject for extensive work and investigations in the foreseeable future for the areas of data analytics, computational and artificial intelligence methods applied for mobile health. Copyright © 2018 Elsevier Inc. All rights reserved.

The effect of learning models and emotional intelligence toward students learning outcomes on reaction rate

NASA Astrophysics Data System (ADS)

Sutiani, Ani; Silitonga, Mei Y.

2017-08-01

This research focused on the effect of learning models and emotional intelligence in students' chemistry learning outcomes on reaction rate teaching topic. In order to achieve the objectives of the research, with 2x2 factorial research design was used. There were two factors tested, namely: the learning models (factor A), and emotional intelligence (factor B) factors. Then, two learning models were used; problem-based learning/PBL (A1), and project-based learning/PjBL (A2). While, the emotional intelligence was divided into higher and lower types. The number of population was six classes containing 243 grade X students of SMAN 10 Medan, Indonesia. There were 15 students of each class were chosen as the sample of the research by applying purposive sampling technique. The data were analyzed by applying two-ways analysis of variance (2X2) at the level of significant α = 0.05. Based on hypothesis testing, there was the interaction between learning models and emotional intelligence in students' chemistry learning outcomes. Then, the finding of the research showed that students' learning outcomes in reaction rate taught by using PBL with higher emotional intelligence is higher than those who were taught by using PjBL. There was no significant effect between students with lower emotional intelligence taught by using both PBL and PjBL in reaction rate topic. Based on the finding, the students with lower emotional intelligence were quite hard to get in touch with other students in group discussion.

Application of artificial intelligence to the management of urological cancer.

PubMed

Abbod, Maysam F; Catto, James W F; Linkens, Derek A; Hamdy, Freddie C

2007-10-01

Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management. A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer. The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems. Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

Bits or Shots in Combat? The Generalized Deitchman Model of Guerrilla Warfare

DTIC Science & Technology

2013-08-13

fire; absence of intelligence leads to unaimed fire, dependent on targets’ density. We propose a new Lanchester -type model that mixes aimed and unaimed...military hardware. The idea of modeling the trade-off between firepower and intelligence in a Lanchester setting was first suggested by Schreiber [4...of intelligence leads to unaimed fire, dependent on targets? density. We propose a new Lanchester -type model that mixes aimed and unaimed fire, the

Teaching the Teachers: Emotional Intelligence Training for Teachers

ERIC Educational Resources Information Center

Hen, Meirav; Sharabi-Nov, Adi

2014-01-01

A growing body of research in recent years has supported the value of emotional intelligence in both effective teaching and student achievement. This paper presents a pre-post, quasi-experimental design study conducted to evaluate the contributions of a 56-h "Emotional Intelligence" training model. The model has been developed and…

Uncertainty in predictions of forest carbon dynamics: separating driver error from model error.

PubMed

Spadavecchia, L; Williams, M; Law, B E

2011-07-01

We present an analysis of the relative magnitude and contribution of parameter and driver uncertainty to the confidence intervals on estimates of net carbon fluxes. Model parameters may be difficult or impractical to measure, while driver fields are rarely complete, with data gaps due to sensor failure and sparse observational networks. Parameters are generally derived through some optimization method, while driver fields may be interpolated from available data sources. For this study, we used data from a young ponderosa pine stand at Metolius, Central Oregon, and a simple daily model of coupled carbon and water fluxes (DALEC). An ensemble of acceptable parameterizations was generated using an ensemble Kalman filter and eddy covariance measurements of net C exchange. Geostatistical simulations generated an ensemble of meteorological driving variables for the site, consistent with the spatiotemporal autocorrelations inherent in the observational data from 13 local weather stations. Simulated meteorological data were propagated through the model to derive the uncertainty on the CO2 flux resultant from driver uncertainty typical of spatially extensive modeling studies. Furthermore, the model uncertainty was partitioned between temperature and precipitation. With at least one meteorological station within 25 km of the study site, driver uncertainty was relatively small ( 10% of the total net flux), while parameterization uncertainty was larger, 50% of the total net flux. The largest source of driver uncertainty was due to temperature (8% of the total flux). The combined effect of parameter and driver uncertainty was 57% of the total net flux. However, when the nearest meteorological station was > 100 km from the study site, uncertainty in net ecosystem exchange (NEE) predictions introduced by meteorological drivers increased by 88%. Precipitation estimates were a larger source of bias in NEE estimates than were temperature estimates, although the biases partly compensated for each other. The time scales on which precipitation errors occurred in the simulations were shorter than the temporal scales over which drought developed in the model, so drought events were reasonably simulated. The approach outlined here provides a means to assess the uncertainty and bias introduced by meteorological drivers in regional-scale ecological forecasting.

An empirical assessment of driver motivation and emotional states in perceived safety margins under varied driving conditions.

PubMed

Zhang, Yu; Kaber, David B

2013-01-01

Motivation models in driving behaviour postulate that driver motives and emotional states dictate risk tolerance under various traffic conditions. The present study used time and driver performance-based payment systems to manipulate motivation and risk-taking behaviour. Ten participants drove to a predefined location in a simulated driving environment. Traffic patterns (density and velocity) were manipulated to cause driver behaviour adjustments due to the need to conform with the social norms of the roadway. The driving environment complexity was investigated as a mediating factor in risk tolerance. Results revealed the performance-based payment system to closely relate to risk-taking behaviour as compared with the time-based payment system. Drivers conformed with social norms associated with specific traffic patterns. Higher roadway complexity led to a more conservative safety margins and speeds. This research contributes to the further development of motivational models of driver behaviour. This study provides empirical justification for two motivation factors in driver risk-taking decisions, including compliance with social norm and emotions triggered by incentives. Environment complexity was identified as a mediating factor in motivational behaviour model. This study also recommended safety margin measures sensitive to changes in driver risk tolerance.

Injury severity analysis of commercially-licensed drivers in single-vehicle crashes: Accounting for unobserved heterogeneity and age group differences.

PubMed

Osman, Mohamed; Mishra, Sabyasachee; Paleti, Rajesh

2018-05-18

This study analyzes the injury severity of commercially-licensed drivers involved in single-vehicle crashes. Considering the discrete ordinal nature of injury severity data, the ordered response modeling framework was adopted. The moderating effect of driver's age on all other factors was examined by segmenting the parameters by driver's age group. Additional effects of the different drivers' age groups are taken into consideration through interaction terms. Unobserved heterogeneity of the different covariates was investigated using the Mixed Generalized Ordered Response Probit (MGORP) model. The empirical analysis was conducted using four years of the Highway Safety Information System (HSIS) data that included 6247 commercially-licensed drivers involved in single-vehicle crashes in the state of Minnesota. The MGORP model elasticity effects indicate that key factors that increase the likelihood of severe crashes for commercially-licensed drivers across all age groups include: lack of seatbelt usage, collision with a fixed object, speeding, vehicle age of 11 years or more, wind, night time, weekday, and female drivers. Also, the effects of several covariates were found to vary across different age groups. Copyright © 2018 Elsevier Ltd. All rights reserved.

The effects of drug and alcohol consumption on driver injury severities in single-vehicle crashes.

PubMed

Behnood, Ali; Mannering, Fred L

2017-07-04

It is well known that alcohol and drugs influence driving behavior by affecting the central nervous system, awareness, vision, and perception/reaction times, but the resulting effect on driver injuries in car crashes is not fully understood. The purpose of this study was to identify factors affecting the injury severities of unimpaired, alcohol-impaired, and drug-impaired drivers. The current article applies a random parameters logit model to study the differences in injury severities among unimpaired, alcohol-impaired, and drug-impaired drivers. Using data from single-vehicle crashes in Cook County, Illinois, over a 9-year period from January 1, 2004, to December 31, 2012, separate models for unimpaired, alcohol-impaired, and drug-impaired drivers were estimated. A wide range of variables potentially affecting driver injury severity was considered, including roadway and environmental conditions, driver attributes, time and location of the crash, and crash-specific factors. The estimation results show significant differences in the determinants of driver injury severities across groups of unimpaired, alcohol-impaired, and drug-impaired drivers. The findings also show that unimpaired drivers are understandably more responsive to variations in lighting, adverse weather, and road conditions, but these drivers also tend to have much more heterogeneity in their behavioral responses to these conditions, relative to impaired drivers. In addition, age and gender were found to be important determinants of injury severity, but the effects varied significantly across all drivers, particularly among alcohol-impaired drivers. The model estimation results show that statistically significant differences exist in driver injury severities among the unimpaired, alcohol-impaired, and drug-impaired driver groups considered. Specifically, we find that unimpaired drivers tend to have more heterogeneity in their injury outcomes in the presence potentially adverse weather and road surface conditions. This makes sense because one would expect unimpaired drivers to apply their full knowledge/judgment range to deal with these conditions, and the variability of this range across the driver population (with different driving experiences, etc.) should be great. In contrast, we find, for the most part, that alcohol-impaired and drug-impaired drivers have far less heterogeneity in the factors that affect injury severity, suggesting an equalizing effect resulting from the decision-impairing substance.

Use, perceptions, and benefits of automotive technologies among aging drivers.

PubMed

Eby, David W; Molnar, Lisa J; Zhang, Liang; St Louis, Renée M; Zanier, Nicole; Kostyniuk, Lidia P; Stanciu, Sergiu

2016-12-01

Advanced in-vehicle technologies have been proposed as a potential way to keep older adults driving for as long as they can safely do so, by taking into account the common declines in functional abilities experienced by older adults. The purpose of this report was to synthesize the knowledge about older drivers and advanced in-vehicle technologies, focusing on three areas: use (how older drivers use these technologies), perception (what they think about the technologies), and outcomes (the safety and/or comfort benefits of the technologies). Twelve technologies were selected for review and grouped into three categories: crash avoidance systems (lane departure warning, curve speed warning, forward collision warning, blind spot warning, parking assistance); in-vehicle information systems (navigation assistance, intelligent speed adaptation); and other systems (adaptive cruise control, automatic crash notification, night vision enhancement, adaptive headlight, voice activated control). A comprehensive and systematic search was conducted for each technology to collect related publications. 271 articles were included into the final review. Research findings for each of the 12 technologies are synthesized in relation to how older adults use and think about the technologies as well as potential benefits. These results are presented separately for each technology. Can advanced in-vehicle technologies help extend the period over which an older adult can drive safely? This report answers this question with an optimistic "yes." Some of the technologies reviewed in this report have been shown to help older drivers avoid crashes, improve the ease and comfort of driving, and travel to places and at times that they might normally avoid.

ECO-DRIVING MODELING ENVIRONMENT

DOT National Transportation Integrated Search

2015-11-01

This research project aims to examine the eco-driving modeling capabilities of different traffic modeling tools available and to develop a driver-simulator-based eco-driving modeling tool to evaluate driver behavior and to reliably estimate or measur...

Improving the All-Hazards Homeland Security Enterprise Through the Use of an Emergency Management Intelligence Model

DTIC Science & Technology

2013-09-01

Office of the Inspector General OSINT Open Source Intelligence PPD Presidential Policy Directive SIGINT Signals Intelligence SLFC State/Local Fusion...Geospatial Intelligence (GEOINT) from Geographic Information Systems (GIS), and Open Source Intelligence ( OSINT ) from Social Media. GIS is widely...and monitor make it a feasible tool to capitalize on for OSINT . A formalized EM intelligence process would help expedite the processing of such

Intelligent Integrated System Health Management

NASA Technical Reports Server (NTRS)

Figueroa, Fernando

2012-01-01

Intelligent Integrated System Health Management (ISHM) is the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system (Management: storage, distribution, sharing, maintenance, processing, reasoning, and presentation). Presentation discusses: (1) ISHM Capability Development. (1a) ISHM Knowledge Model. (1b) Standards for ISHM Implementation. (1c) ISHM Domain Models (ISHM-DM's). (1d) Intelligent Sensors and Components. (2) ISHM in Systems Design, Engineering, and Integration. (3) Intelligent Control for ISHM-Enabled Systems

Knowledge Based Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle.

DTIC Science & Technology

1988-04-13

Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle Mark S. Fox, Nizwer Husain, Malcolm...McRoberts and Y.V.Reddy CMU-RI-TR-88-5 Intelligent Systems Laboratory The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania D T T 13...years of research in the application of Artificial Intelligence to Simulation. Our focus has been in two areas: the use of Al knowledge representation

ISG hybrid powertrain: a rule-based driver model incorporating look-ahead information

NASA Astrophysics Data System (ADS)

Shen, Shuiwen; Zhang, Junzhi; Chen, Xiaojiang; Zhong, Qing-Chang; Thornton, Roger

2010-03-01

According to European regulations, if the amount of regenerative braking is determined by the travel of the brake pedal, more stringent standards must be applied, otherwise it may adversely affect the existing vehicle safety system. The use of engine or vehicle speed to derive regenerative braking is one way to avoid strict design standards, but this introduces discontinuity in powertrain torque when the driver releases the acceleration pedal or applies the brake pedal. This is shown to cause oscillations in the pedal input and powertrain torque when a conventional driver model is adopted. Look-ahead information, together with other predicted vehicle states, are adopted to control the vehicle speed, in particular, during deceleration, and to improve the driver model so that oscillations can be avoided. The improved driver model makes analysis and validation of the control strategy for an integrated starter generator (ISG) hybrid powertrain possible.

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.

PubMed

Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

2017-05-30

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

PubMed Central

Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

2017-01-01

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control. PMID:28556817

A Generalized Quantum-Inspired Decision Making Model for Intelligent Agent

PubMed Central

Loo, Chu Kiong

2014-01-01

A novel decision making for intelligent agent using quantum-inspired approach is proposed. A formal, generalized solution to the problem is given. Mathematically, the proposed model is capable of modeling higher dimensional decision problems than previous researches. Four experiments are conducted, and both empirical experiments results and proposed model's experiment results are given for each experiment. Experiments showed that the results of proposed model agree with empirical results perfectly. The proposed model provides a new direction for researcher to resolve cognitive basis in designing intelligent agent. PMID:24778580

The Cylindrical Structure of the Wechsler Intelligence Scale for Children--IV: A Retest of the Guttman Model of Intelligence

ERIC Educational Resources Information Center

Cohen, Arie; Fiorello, Catherine A.; Farley, Frank H.

2006-01-01

A previous study on the underlying structure of the Wechsler intelligence test (WISC-R; [Wechsler, D. (1974). Manual WISC-R: Wechsler intelligence scale for children-Revised. New York: Psychological Corporation]), using smallest space analysis (SSA) [Guttman, L., and Levy, S. (1991). Two structural laws for intelligence tests.…

Analyses of rear-end crashes based on classification tree models.

PubMed

Yan, Xuedong; Radwan, Essam

2006-09-01

Signalized intersections are accident-prone areas especially for rear-end crashes due to the fact that the diversity of the braking behaviors of drivers increases during the signal change. The objective of this article is to improve knowledge of the relationship between rear-end crashes occurring at signalized intersections and a series of potential traffic risk factors classified by driver characteristics, environments, and vehicle types. Based on the 2001 Florida crash database, the classification tree method and Quasi-induced exposure concept were used to perform the statistical analysis. Two binary classification tree models were developed in this study. One was used for the crash comparison between rear-end and non-rear-end to identify those specific trends of the rear-end crashes. The other was constructed for the comparison between striking vehicles/drivers (at-fault) and struck vehicles/drivers (not-at-fault) to find more complex crash pattern associated with the traffic attributes of driver, vehicle, and environment. The modeling results showed that the rear-end crashes are over-presented in the higher speed limits (45-55 mph); the rear-end crash propensity for daytime is apparently larger than nighttime; and the reduction of braking capacity due to wet and slippery road surface conditions would definitely contribute to rear-end crashes, especially at intersections with higher speed limits. The tree model segmented drivers into four homogeneous age groups: < 21 years, 21-31 years, 32-75 years, and > 75 years. The youngest driver group shows the largest crash propensity; in the 21-31 age group, the male drivers are over-involved in rear-end crashes under adverse weather conditions and the 32-75 years drivers driving large size vehicles have a larger crash propensity compared to those driving passenger vehicles. Combined with the quasi-induced exposure concept, the classification tree method is a proper statistical tool for traffic-safety analysis to investigate crash propensity. Compared to the logistic regression models, tree models have advantages for handling continuous independent variables and easily explaining the complex interaction effect with more than two independent variables. This research recommended that at signalized intersections with higher speed limits, reducing the speed limit to 40 mph efficiently contribute to a lower accident rate. Drivers involved in alcohol use may increase not only rear-end crash risk but also the driver injury severity. Education and enforcement countermeasures should focus on the driver group younger than 21 years. Further studies are suggested to compare crash risk distributions of the driver age for other main crash types to seek corresponding traffic countermeasures.

A spring-mass-damper system dynamics-based driver-vehicle integrated model for representing heterogeneous traffic

NASA Astrophysics Data System (ADS)

Munigety, Caleb Ronald

2018-04-01

The traditional traffic microscopic simulation models consider driver and vehicle as a single unit to represent the movements of drivers in a traffic stream. Due to this very fact, the traditional car-following models have the driver behavior related parameters, but ignore the vehicle related aspects. This approach is appropriate for homogeneous traffic conditions where car is the major vehicle type. However, in heterogeneous traffic conditions where multiple vehicle types are present, it becomes important to incorporate the vehicle related parameters exclusively to account for the varying dynamic and static characteristics. Thus, this paper presents a driver-vehicle integrated model hinged on the principles involved in physics-based spring-mass-damper mechanical system. While the spring constant represents the driver’s aggressiveness, the damping constant and the mass component take care of the stability and size/weight related aspects, respectively. The proposed model when tested, behaved pragmatically in representing the vehicle-type dependent longitudinal movements of vehicles.

The Evolution of Instructional Design Principles for Intelligent Computer-Assisted Instruction.

ERIC Educational Resources Information Center

Dede, Christopher; Swigger, Kathleen

1988-01-01

Discusses and compares the design and development of computer assisted instruction (CAI) and intelligent computer assisted instruction (ICAI). Topics discussed include instructional systems design (ISD), artificial intelligence, authoring languages, intelligent tutoring systems (ITS), qualitative models, and emerging issues in instructional…

Straight ahead running of a nonlinear car and driver model - new nonlinear behaviours highlighted

NASA Astrophysics Data System (ADS)

Della Rossa, Fabio; Mastinu, Giampiero

2018-05-01

The paper deals with the bifurcation analysis of a validated simple model describing a vehicle+driver running straight ahead. The mechanical model of the car has two degrees of freedom and the related equations of motion contain the nonlinear tyre characteristics. The driver is described by a very simple model. Bifurcation analysis is adopted for characterising straight ahead motion at different speeds for different drivers. A nonlinear sensitivity analysis is performed as a function of the driver's parameters and forward vehicle speed. A wealth of unreferenced bifurcations is discovered both for the understeering (UN) and for the oversteering (OV) vehicle. For the UN vehicle, a supercritical Hopf bifurcation may occur as the forward speed is increased. Also tangent (fold) bifurcations (saddle-node bifurcation of limit cycles) occur as the speed (or disturbance) is further increased. For the OV vehicle, a subcritical Hopf bifurcation occurs as the speed reaches a critical value. The preview distance (a driver's control parameter) plays a fundamental role in straight ahead driving. Either too short or too long preview distances are negative for straight ahead running.

A collision model for safety evaluation of autonomous intelligent cruise control.

PubMed

Touran, A; Brackstone, M A; McDonald, M

1999-09-01

This paper describes a general framework for safety evaluation of autonomous intelligent cruise control in rear-end collisions. Using data and specifications from prototype devices, two collision models are developed. One model considers a train of four cars, one of which is equipped with autonomous intelligent cruise control. This model considers the car in front and two cars following the equipped car. In the second model, none of the cars is equipped with the device. Each model can predict the possibility of rear-end collision between cars under various conditions by calculating the remaining distance between cars after the front car brakes. Comparing the two collision models allows one to evaluate the effectiveness of autonomous intelligent cruise control in preventing collisions. The models are then subjected to Monte Carlo simulation to calculate the probability of collision. Based on crash probabilities, an expected value is calculated for the number of cars involved in any collision. It is found that given the model assumptions, while equipping a car with autonomous intelligent cruise control can significantly reduce the probability of the collision with the car ahead, it may adversely affect the situation for the following cars.

Beyond a bigger brain: Multivariable structural brain imaging and intelligence

PubMed Central

Ritchie, Stuart J.; Booth, Tom; Valdés Hernández, Maria del C.; Corley, Janie; Maniega, Susana Muñoz; Gow, Alan J.; Royle, Natalie A.; Pattie, Alison; Karama, Sherif; Starr, John M.; Bastin, Mark E.; Wardlaw, Joanna M.; Deary, Ian J.

2015-01-01

People with larger brains tend to score higher on tests of general intelligence (g). It is unclear, however, how much variance in intelligence other brain measurements would account for if included together with brain volume in a multivariable model. We examined a large sample of individuals in their seventies (n = 672) who were administered a comprehensive cognitive test battery. Using structural equation modelling, we related six common magnetic resonance imaging-derived brain variables that represent normal and abnormal features—brain volume, cortical thickness, white matter structure, white matter hyperintensity load, iron deposits, and microbleeds—to g and to fluid intelligence. As expected, brain volume accounted for the largest portion of variance (~ 12%, depending on modelling choices). Adding the additional variables, especially cortical thickness (+~ 5%) and white matter hyperintensity load (+~ 2%), increased the predictive value of the model. Depending on modelling choices, all neuroimaging variables together accounted for 18–21% of the variance in intelligence. These results reveal which structural brain imaging measures relate to g over and above the largest contributor, total brain volume. They raise questions regarding which other neuroimaging measures might account for even more of the variance in intelligence. PMID:26240470

The Actualization of Literary Learning Model Based on Verbal-Linguistic Intelligence

ERIC Educational Resources Information Center

Hali, Nur Ihsan

2017-01-01

This article is inspired by Howard Gardner's concept of linguistic intelligence and also from some authors' previous writings. All of them became the authors' reference in developing ideas on constructing a literary learning model based on linguistic intelligence. The writing of this article is not done by collecting data empirically, but by…

Intelligent tutoring systems for systems engineering methodologies

NASA Technical Reports Server (NTRS)

Meyer, Richard J.; Toland, Joel; Decker, Louis

1991-01-01

The general goal is to provide the technology required to build systems that can provide intelligent tutoring in IDEF (Integrated Computer Aided Manufacturing Definition Method) modeling. The following subject areas are covered: intelligent tutoring systems for systems analysis methodologies; IDEF tutor architecture and components; developing cognitive skills for IDEF modeling; experimental software; and PC based prototype.

An Analytical Model / Emotional Intelligence Quotient and QOL in Mothers with Infants in Japan.

PubMed

Ohashi, Junko; Katsura, Toshiki; Hoshino, Akiko; Usui, Kanae

2013-01-01

The purpose of this study was to examine the relationship between the emotional intelligence quotient and health-related quality of life using structural equation modeling. A self-administered questionnaire survey was conducted among 1,911 mothers who visited the Health Center for an infant medical examination. A hypothetical model was constructed using variables of the emotional intelligence quotient, social support, coping, parenting stress, and perceived health competence. There were a total of 1,104 valid responses (57.8%). Significant standardized estimates were obtained, confirming the goodness of fit issues with the model. The emotional intelligence quotient had a strong impact on physical and psychological quality of life, and showed the greatest association with coping. This study differed from previous studies in that, due to the inclusion of social support and explanatory variables in coping, an increase in coping strategies was more highly associated with emotional intelligence quotient levels than with social support. An enhanced emotional intelligence quotient should be considered a primary objective to promote the health of mothers with infant children.

OFMTutor: An operator function model intelligent tutoring system

NASA Technical Reports Server (NTRS)

Jones, Patricia M.

1989-01-01

The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described.

Association of climate drivers with rainfall in New South Wales, Australia, using Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Duc, Hiep Nguyen; Rivett, Kelly; MacSween, Katrina; Le-Anh, Linh

2017-01-01

Rainfall in New South Wales (NSW), located in the southeast of the Australian continent, is known to be influenced by four major climate drivers: the El Niño/Southern Oscillation (ENSO), the Interdecadal Pacific Oscillation (IPO), the Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD). Many studies have shown the influences of ENSO, IPO modulation, SAM and IOD on rainfall in Australia and on southeast Australia in particular. However, only limited work has been undertaken using a multiple regression framework to examine the extent of the combined effect of these climate drivers on rainfall. This paper analysed the role of these combined climate drivers and their interaction on the rainfall in NSW using Bayesian Model Averaging (BMA) to account for model uncertainty by considering each of the linear models across the whole model space which is equal to the set of all possible combinations of predictors to find the model posterior probabilities and their expected predictor coefficients. Using BMA for linear regression models, we are able to corroborate and confirm the results from many previous studies. In addition, the method gives the ranking order of importance and the probability of the association of each of the climate drivers and their interaction on the rainfall at a site. The ability to quantify the relative contribution of the climate drivers offers the key to understand the complex interaction of drivers on rainfall, or lack of rainfall in a region, such as the three big droughts in southeastern Australia which have been the subject of discussion and debate recently on their causes.

Predicting speech intelligibility based on the signal-to-noise envelope power ratio after modulation-frequency selective processing.

PubMed

Jørgensen, Søren; Dau, Torsten

2011-09-01

A model for predicting the intelligibility of processed noisy speech is proposed. The speech-based envelope power spectrum model has a similar structure as the model of Ewert and Dau [(2000). J. Acoust. Soc. Am. 108, 1181-1196], developed to account for modulation detection and masking data. The model estimates the speech-to-noise envelope power ratio, SNR(env), at the output of a modulation filterbank and relates this metric to speech intelligibility using the concept of an ideal observer. Predictions were compared to data on the intelligibility of speech presented in stationary speech-shaped noise. The model was further tested in conditions with noisy speech subjected to reverberation and spectral subtraction. Good agreement between predictions and data was found in all cases. For spectral subtraction, an analysis of the model's internal representation of the stimuli revealed that the predicted decrease of intelligibility was caused by the estimated noise envelope power exceeding that of the speech. The classical concept of the speech transmission index fails in this condition. The results strongly suggest that the signal-to-noise ratio at the output of a modulation frequency selective process provides a key measure of speech intelligibility. © 2011 Acoustical Society of America

Vehicle Steering control: A model of learning

NASA Technical Reports Server (NTRS)

Smiley, A.; Reid, L.; Fraser, M.

1978-01-01

A hierarchy of strategies were postulated to describe the process of learning steering control. Vehicle motion and steering control data were recorded for twelve novices who drove an instrumented car twice a week during and after a driver training course. Car-driver describing functions were calculated, the probable control structure determined, and the driver-alone transfer function modelled. The data suggested that the largest changes in steering control with learning were in the way the driver used the lateral position cue.

Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation

NASA Astrophysics Data System (ADS)

McGillivray, Max Falkenberg; Cheng, William; Peters, Nicholas S.; Christensen, Kim

2018-04-01

Mapping resolution has recently been identified as a key limitation in successfully locating the drivers of atrial fibrillation (AF). Using a simple cellular automata model of AF, we demonstrate a method by which re-entrant drivers can be located quickly and accurately using a collection of indirect electrogram measurements. The method proposed employs simple, out-of-the-box machine learning algorithms to correlate characteristic electrogram gradients with the displacement of an electrogram recording from a re-entrant driver. Such a method is less sensitive to local fluctuations in electrical activity. As a result, the method successfully locates 95.4% of drivers in tissues containing a single driver, and 95.1% (92.6%) for the first (second) driver in tissues containing two drivers of AF. Additionally, we demonstrate how the technique can be applied to tissues with an arbitrary number of drivers. In its current form, the techniques presented are not refined enough for a clinical setting. However, the methods proposed offer a promising path for future investigations aimed at improving targeted ablation for AF.

Architecture of fluid intelligence and working memory revealed by lesion mapping.

PubMed

Barbey, Aron K; Colom, Roberto; Paul, Erick J; Grafman, Jordan

2014-03-01

Although cognitive neuroscience has made valuable progress in understanding the role of the prefrontal cortex in human intelligence, the functional networks that support adaptive behavior and novel problem solving remain to be well characterized. Here, we studied 158 human brain lesion patients to investigate the cognitive and neural foundations of key competencies for fluid intelligence and working memory. We administered a battery of neuropsychological tests, including the Wechsler Adult Intelligence Scale (WAIS) and the N-Back task. Latent variable modeling was applied to obtain error-free scores of fluid intelligence and working memory, followed by voxel-based lesion-symptom mapping to elucidate their neural substrates. The observed latent variable modeling and lesion results support an integrative framework for understanding the architecture of fluid intelligence and working memory and make specific recommendations for the interpretation and application of the WAIS and N-Back task to the study of fluid intelligence in health and disease.

Changing drivers' minds: the evaluation of an advanced driver coaching system.

PubMed

Stanton, N A; Walker, G H; Young, M S; Kazi, T; Salmon, P M

2007-08-01

This paper reports on the study of an advanced driver coaching system. The study distinguishes between different types of post-licensure programmes in order to explore a system based on a model of identifying and responding to hazards, called 'information, position, speed, gear and acceleration' (IPSGA). Previous literature has been sceptical about the benefits of advanced driver education; thus, the current study was designed to control for the effects of coaching drivers in the 'IPSGA' system (the treatment group) against the effects of being accompanied (control group 1), as well as the mere effects of time (control group 2). Measures were taken before the driver coaching began (as a baseline measure) and again after 8 weeks (to see if any changes had occurred). These measures included driver knowledge via a post-drive interview, observations of driving skill and driver attitude using a locus of control scale. The results suggest that advanced driver coaching using the IPSGA system had a beneficial effect on all of these measures. Drivers in the coaching condition improved their situation awareness, driving skills and reduced attributions of external locus of control. The study lends support to the case for one-to-one individualized driver coaching using a systematic model of driving.

Emotions, Intelligence, and Performance. Symposium 45. [Concurrent Symposium Session at AHRD Annual Conference, 2000.

ERIC Educational Resources Information Center

Bryant, Doug

This paper, titled "The Components of Emotional Intelligence and the Relationship to Sales Performance," presents two general approaches to studying emotional intelligence. The first is a broad model approach that considers abilities as well as a series of personality traits. The second is based on ability models. The possible correlation between…

Comparing the Construct and Criterion-Related Validity of Ability-Based and Mixed-Model Measures of Emotional Intelligence

ERIC Educational Resources Information Center

Livingstone, Holly A.; Day, Arla L.

2005-01-01

Despite the popularity of the concept of emotional intelligence(EI), there is much controversy around its definition, measurement, and validity. Therefore, the authors examined the construct and criterion-related validity of an ability-based EI measure (Mayer Salovey Caruso Emotional Intelligence Test [MSCEIT]) and a mixed-model EI measure…

The Role of Human Intelligence in Computer-Based Intelligent Tutoring Systems.

ERIC Educational Resources Information Center

Epstein, Kenneth; Hillegeist, Eleanor

An Intelligent Tutoring System (ITS) consists of an expert problem-solving program in a subject domain, a tutoring model capable of remediation or primary instruction, and an assessment model that monitors student understanding. The Geometry Proof Tutor (GPT) is an ITS which was developed at Carnegie Mellon University and field tested in the…

Development and Application of a Multi-Modal Task Analysis to Support Intelligent Tutoring of Complex Skills

ERIC Educational Resources Information Center

Skinner, Anna; Diller, David; Kumar, Rohit; Cannon-Bowers, Jan; Smith, Roger; Tanaka, Alyssa; Julian, Danielle; Perez, Ray

2018-01-01

Background: Contemporary work in the design and development of intelligent training systems employs task analysis (TA) methods for gathering knowledge that is subsequently encoded into task models. These task models form the basis of intelligent interpretation of student performance within education and training systems. Also referred to as expert…

Emotional Intelligence: The MSCEIT from the Perspective of Generalizability Theory

ERIC Educational Resources Information Center

Follesdal, Hallvard; Hagtvet, Knut A.

2009-01-01

The Mayer, Salovey, & Caruso Emotional Intelligence Test (MSCEIT) has been reported to provide reliable scores for the four-branch ability model of emotional intelligence [Mayer, J. D., Salovey, P., & Caruso, D. R. (2002). "Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). User's manual." Toronto, Canada: Multi-Health…

Development of Velocity Guidance Assistance System by Haptic Accelerator Pedal Reaction Force Control

NASA Astrophysics Data System (ADS)

Yin, Feilong; Hayashi, Ryuzo; Raksincharoensak, Pongsathorn; Nagai, Masao

This research proposes a haptic velocity guidance assistance system for realizing eco-driving as well as enhancing traffic capacity by cooperating with ITS (Intelligent Transportation Systems). The proposed guidance system generates the desired accelerator pedal (abbreviated as pedal) stroke with respect to the desired velocity obtained from ITS considering vehicle dynamics, and provides the desired pedal stroke to the driver via a haptic pedal whose reaction force is controllable and guides the driver in order to trace the desired velocity in real time. The main purpose of this paper is to discuss the feasibility of the haptic velocity guidance. A haptic velocity guidance system for research is developed on the Driving Simulator of TUAT (DS), by attaching a low-inertia, low-friction motor to the pedal, which does not change the original characteristics of the original pedal when it is not operated, implementing an algorithm regarding the desired pedal stroke calculation and the reaction force controller. The haptic guidance maneuver is designed based on human pedal stepping experiments. A simple velocity profile with acceleration, deceleration and cruising is synthesized according to naturalistic driving for testing the proposed system. The experiment result of 9 drivers shows that the haptic guidance provides high accuracy and quick response in velocity tracking. These results prove that the haptic guidance is a promising velocity guidance method from the viewpoint of HMI (Human Machine Interface).

Testing a structural model of young driver willingness to uptake Smartphone Driver Support Systems.

PubMed

Kervick, Aoife A; Hogan, Michael J; O'Hora, Denis; Sarma, Kiran M

2015-10-01

There is growing interest in the potential value of using phone applications that can monitor driver behaviour (Smartphone Driver Support Systems, 'SDSSs') in mitigating risky driving by young people. However, their value in this regard will only be realised if young people are willing to use this technology. This paper reports the findings of a study in which a novel structural model of willingness to use SDSSs was tested. Grounded in the driver monitoring and Technology Acceptance (TA) research literature, the model incorporates the perceived risks and gains associated with potential SDSS usage and additional social cognitive factors, including perceived usability and social influences. A total of 333 smartphone users, aged 18-24, with full Irish driving licenses completed an online questionnaire examining willingness or Behavioural Intention (BI) to uptake a SDSS. Following exploratory and confirmatory factor analyses, structural equation modelling indicated that perceived gains and social influence factors had significant direct effects on BI. Perceived risks and social influence also had significant indirect effects on BI, as mediated by perceived gains. Overall, this model accounted for 72.5% of the variance in willingness to uptake SDSSs. Multi-group structural models highlighted invariance of effects across gender, high and low risk drivers, and those likely or unlikely to adopt novel phone app technologies. These findings have implications for our understanding of the willingness of young drivers to adopt and use SDSSs, and highlight potential factors that could be targeted in behavioural change interventions seeking to improve usage rates. Copyright © 2015 Elsevier Ltd. All rights reserved.

Driver-vehicle effectiveness model : volume II : appendices

DOT National Transportation Integrated Search

1978-12-01

The Driver-Vehicle Effectiveness Model (DRIVEM) is a Monte Carlo simulation model intended for use by NHTSA to evaluate alternative vehicle subsystems or effects of legislative actions proposed to reduce the probability and severity of highway traffi...

Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces.

PubMed

Zhang, Sihai; Wang, Zhiyang

2016-01-01

How to understand individual human actions is a fundamental question to modern science, which drives and incurs many social, technological, racial, religious and economic phenomena. Human dynamics tries to reveal the temporal pattern and internal mechanism of human actions in letter or electronic communications, from the perspective of continuous interactions among friends or acquaintances. For interactions between stranger to stranger, taxi industry provide fruitful phenomina and evidence to investigate the action decisions. In fact, one striking disturbing events commonly reported in taxi industry is passenger refusing or denial, whose reasons vary, including skin color, blind passenger, being a foreigner or too close destination, religion reasons and anti specific nationality, so that complaints about taxi passenger refusing have to be concerned and processed carefully by local governments. But more universal factors for this phenomena are of great significance, which might be fulfilled by big data research to obtain novel insights in this question. In this paper, we demonstrate the big data analytics application in revealing novel insights from massive taxi trace data, which, for the first time, validates the passengers denial in taxi industry and estimates the denial ratio in Beijing city. We first quantify the income differentiation facts among taxi drivers. Then we find out that choosing the drop-off places also contributes to the high income for taxi drivers, compared to the previous explanation of mobility intelligence. Moreover, we propose the pick-up, drop-off and grid diversity concepts and related diversity analysis suggest that, high income taxi drivers will deny passengers in some situations, so as to choose the passengers' destination they prefer. Finally we design an estimation method for denial ratio and infer that high income taxi drivers will deny passengers with 8.52% likelihood in Beijing. Our work exhibits the power of big data analysis in revealing some dark side investigation.

Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces

PubMed Central

Zhang, Sihai; Wang, Zhiyang

2016-01-01

How to understand individual human actions is a fundamental question to modern science, which drives and incurs many social, technological, racial, religious and economic phenomena. Human dynamics tries to reveal the temporal pattern and internal mechanism of human actions in letter or electronic communications, from the perspective of continuous interactions among friends or acquaintances. For interactions between stranger to stranger, taxi industry provide fruitful phenomina and evidence to investigate the action decisions. In fact, one striking disturbing events commonly reported in taxi industry is passenger refusing or denial, whose reasons vary, including skin color, blind passenger, being a foreigner or too close destination, religion reasons and anti specific nationality, so that complaints about taxi passenger refusing have to be concerned and processed carefully by local governments. But more universal factors for this phenomena are of great significance, which might be fulfilled by big data research to obtain novel insights in this question. In this paper, we demonstrate the big data analytics application in revealing novel insights from massive taxi trace data, which, for the first time, validates the passengers denial in taxi industry and estimates the denial ratio in Beijing city. We first quantify the income differentiation facts among taxi drivers. Then we find out that choosing the drop-off places also contributes to the high income for taxi drivers, compared to the previous explanation of mobility intelligence. Moreover, we propose the pick-up, drop-off and grid diversity concepts and related diversity analysis suggest that, high income taxi drivers will deny passengers in some situations, so as to choose the passengers’ destination they prefer. Finally we design an estimation method for denial ratio and infer that high income taxi drivers will deny passengers with 8.52% likelihood in Beijing. Our work exhibits the power of big data analysis in revealing some dark side investigation. PMID:27812121

The interactive effect on injury severity of driver-vehicle units in two-vehicle crashes.

PubMed

Zeng, Qiang; Wen, Huiying; Huang, Helai

2016-12-01

This study sets out to investigate the interactive effect on injury severity of driver-vehicle units in two-vehicle crashes. A Bayesian hierarchical ordered logit model is proposed to relate the variation and correlation of injury severity of drivers involved in two-vehicle crashes to the factors of both driver-vehicle units and the crash configurations. A total of 6417 crash records with 12,834 vehicles involved in Florida are used for model calibration. The results show that older, female and not-at-fault drivers and those without use of safety equipment are more likely to be injured but less likely to injure the drivers in the other vehicles. New vehicles and lower speed ratios are associated with lower injury degree of both drivers involved. Compared with automobiles, vans, pick-ups, light trucks, median trucks, and heavy trucks possess better self-protection and stronger aggressivity. The points of impact closer to the driver's seat in general indicate a higher risk to the own drivers while engine cover and vehicle rear are the least hazardous to other drivers. Head-on crashes are significantly more severe than angle and rear-end crashes. We found that more severe crashes occurred on roadways than on shoulders or safety zones. Based on these results, some suggestions for traffic safety education, enforcement and engineering are made. Moreover, significant within-crash correlation is found in the crash data, which demonstrates the applicability of the proposed model. Copyright © 2016 Elsevier Ltd and National Safety Council. All rights reserved.

A two-lane cellular automaton traffic flow model with the influence of driver, vehicle and road

NASA Astrophysics Data System (ADS)

Zhao, Han-Tao; Nie, Cen; Li, Jing-Ru; Wei, Yu-Ao

2016-07-01

On the basis of one-lane comfortable driving model, this paper established a two-lane traffic cellular automata model, which improves the slow randomization effected by brake light. Considering the driver psychological characteristics and mixed traffic, we studied the lateral influence between vehicles on adjacent lanes. Through computer simulation, the space-time diagram and the fundamental figure under different conditions are obtained. The study found that aggressive driver makes a slight congestion in low-density traffic and improves the capacity of high-density traffic, when the density exceeds 20pcu/km the more aggressive drivers the greater the flow, when the density below 40pcu/km driver character makes an effect, the more cautious driver, the lower the flow. The ratio of big cars has the same effect as the ratio of aggressive drivers. Brake lights have the greatest impact on traffic flow and when the density exceeds 10pcu/km the traffic flow fluctuates. Under periodic boundary conditions, the disturbance of road length on traffic is minimal. The lateral influence only play a limited role in the medium-density conditions, and only affect the average speed of traffic at low density.

Personality and attitudes as predictors of risky driving among older drivers.

PubMed

Lucidi, Fabio; Mallia, Luca; Lazuras, Lambros; Violani, Cristiano

2014-11-01

Although there are several studies on the effects of personality and attitudes on risky driving among young drivers, related research in older drivers is scarce. The present study assessed a model of personality-attitudes-risky driving in a large sample of active older drivers. A cross-sectional design was used, and structured and anonymous questionnaires were completed by 485 older Italian drivers (Mean age=68.1, SD=6.2, 61.2% males). The measures included personality traits, attitudes toward traffic safety, risky driving (errors, lapses, and traffic violations), and self-reported crash involvement and number of issued traffic tickets in the last 12 months. Structural equation modeling showed that personality traits predicted both directly and indirectly traffic violations, errors, and lapses. More positive attitudes toward traffic safety negatively predicted risky driving. In turn, risky driving was positively related to self-reported crash involvement and higher number of issued traffic tickets. Our findings suggest that theoretical models developed to account for risky driving of younger drivers may also apply in the older drivers, and accordingly be used to inform safe driving interventions for this age group. Copyright © 2014 Elsevier Ltd. All rights reserved.

Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.

PubMed

Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla

2014-12-01

This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

NASA Astrophysics Data System (ADS)

Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

2009-11-01

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

Editorial: Cognitive Architectures, Model Comparison and AGI

NASA Astrophysics Data System (ADS)

Lebiere, Christian; Gonzalez, Cleotilde; Warwick, Walter

2010-12-01

Cognitive Science and Artificial Intelligence share compatible goals of understanding and possibly generating broadly intelligent behavior. In order to determine if progress is made, it is essential to be able to evaluate the behavior of complex computational models, especially those built on general cognitive architectures, and compare it to benchmarks of intelligent behavior such as human performance. Significant methodological challenges arise, however, when trying to extend approaches used to compare model and human performance from tightly controlled laboratory tasks to complex tasks involving more open-ended behavior. This paper describes a model comparison challenge built around a dynamic control task, the Dynamic Stocks and Flows. We present and discuss distinct approaches to evaluating performance and comparing models. Lessons drawn from this challenge are discussed in light of the challenge of using cognitive architectures to achieve Artificial General Intelligence.

Computational Foundations of Natural Intelligence

PubMed Central

van Gerven, Marcel

2017-01-01

New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong AI. After reviewing basic principles, a variety of computational modeling approaches is discussed. Subsequently, I concentrate on the use of artificial neural networks as a framework for modeling cognitive processes. This paper ends by outlining some of the challenges that remain to fulfill the promise of machines that show human-like intelligence. PMID:29375355

Misbehaving Peer Models in the Classroom: An Investigation of the Effects of Social Class and Intelligence.

ERIC Educational Resources Information Center

Kniveton, Bromley H.

1987-01-01

Investigates the effects on young male students of differing social backgrounds and varying levels of intelligence, of seeing a peer misbehave. Notes that working class boys imitated the misbehaving model significantly more than middle-class boys. Level of intelligence was not found to relate to the amount a student imitated a misbehaving peer.…

Integrated driver modelling considering state transition feature for individual adaptation of driver assistance systems

NASA Astrophysics Data System (ADS)

Raksincharoensak, Pongsathorn; Khaisongkram, Wathanyoo; Nagai, Masao; Shimosaka, Masamichi; Mori, Taketoshi; Sato, Tomomasa

2010-12-01

This paper describes the modelling of naturalistic driving behaviour in real-world traffic scenarios, based on driving data collected via an experimental automobile equipped with a continuous sensing drive recorder. This paper focuses on the longitudinal driving situations which are classified into five categories - car following, braking, free following, decelerating and stopping - and are referred to as driving states. Here, the model is assumed to be represented by a state flow diagram. Statistical machine learning of driver-vehicle-environment system model based on driving database is conducted by a discriminative modelling approach called boosting sequential labelling method.

Assessment of suturing in the vertical plane shows the efficacy of the multi-degree-of-freedom needle driver for neonatal laparoscopy.

PubMed

Takazawa, Shinya; Ishimaru, Tetsuya; Fujii, Masahiro; Harada, Kanako; Sugita, Naohiko; Mitsuishi, Mamoru; Iwanaka, Tadashi

2013-11-01

We have developed a thin needle driver with multiple degrees-of-freedom (DOFs) for neonatal laparoscopic surgery. The tip of this needle driver has three DOFs for grasp, deflection and rotation. Our aim was to evaluate the performance of the multi-DOF needle driver in vertical plane suturing. Six pediatric surgeons performed four directional suturing tasks in the vertical plane using the multi-DOF needle driver and a conventional one. Assessed parameters were the accuracy of insertion and exit, the depth of suture, the inclination angle of the needle and the force applied on the model. In left and right direction sutures, the inclination angle of the needle with the multi-DOF needle driver was significantly smaller than that with the conventional one (p = 0.014, 0.042, respectively). In left and right direction sutures, the force for pulling the model with the multi-DOF needle driver was smaller than that with the conventional one (p = 0.036, 0.010, respectively). This study showed that multi-directional suturing on a vertical plane using the multi-DOF needle driver had better needle trajectories and was less invasive as compared to a conventional needle driver.

Automobile driver fatalities in frontal impacts: air bags compared with manual belts.

PubMed Central

Zador, P L; Ciccone, M A

1993-01-01

OBJECTIVES. The effectiveness of air bags was estimated in this study by comparing driver fatalities in frontal crashes with driver fatalities in nonfrontal crashes, for cars with air bags and manual belts and cars with manual belts only. METHODS. Fatal Accident Reporting System data for drivers fatally injured during 1985 to 1991 in 1985 to 1991 model year cars that were equipped with air bags in or before model year 1991 were analyzed. RESULTS. Driver fatalities in frontal crashes in air bag cars were 28% lower than those in comparable cars with manual belts only. This percentage was used for estimating the overall fatality reduction in air bag cars. The reduction was greater in large cars (50%) than in midsize cars (19%) or in small cars (14%). Air bags reduced driver fatalities in frontal crashes involving ejection by about 9%. Fatalities in frontal crashes among drivers who were reportedly using manual belts at the time of the crash were reduced by about 15%. The comparable reduction among drivers who were reportedly not using manual belts was 31%. CONCLUSION. It was estimated that air bags reduced the total number of all driver fatalities by about 19%. PMID:8484445

Exploring the impact of signal types and adjacent vehicles on drivers' choices after the onset of yellow

NASA Astrophysics Data System (ADS)

Bao, Ji; Chen, Qun; Luo, Dandan; Wu, Yuli; Liang, Zuli

2018-06-01

Drivers' choices at signalized intersections may be made in great uncertainty after the onset of yellow, which creates potential hazards for road safety. These choices are analyzed and modeled based on field observations at three comparable signalized intersections in Changsha, China. The results show that intersections without monitoring devices widen the indecision zone, which can increase the risk of rear-end collisions and the uncertainty of drivers' decision-making. In addition, drivers are more likely to stop during the yellow interval at intersections equipped with a green signal countdown device (GSCD) than at those with a green signal flashing device (GSFD). Subsequently, according to the results of a binary logistic regression model (BLRM), drivers' decision making at the onset of the yellow indication is greatly influenced by the vehicle's spot speed, the distance to the stop line, and signal and monitoring devices. The presence of an adjacent vehicle with a short space headway can particularly motivate the following driver to make a go-decision after the first driver chooses to pass the intersection. However, a stop-decision by a driver in an adjacent lane can also prompt the following driver to stop.

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Driver-centred vehicle automation: using network analysis for agent-based modelling of the driver in highly automated driving systems.

PubMed

Banks, Victoria A; Stanton, Neville A

2016-11-01

To the average driver, the concept of automation in driving infers that they can become completely 'hands and feet free'. This is a common misconception, however, one that has been shown through the application of Network Analysis to new Cruise Assist technologies that may feature on our roads by 2020. Through the adoption of a Systems Theoretic approach, this paper introduces the concept of driver-initiated automation which reflects the role of the driver in highly automated driving systems. Using a combination of traditional task analysis and the application of quantitative network metrics, this agent-based modelling paper shows how the role of the driver remains an integral part of the driving system implicating the need for designers to ensure they are provided with the tools necessary to remain actively in-the-loop despite giving increasing opportunities to delegate their control to the automated subsystems. Practitioner Summary: This paper describes and analyses a driver-initiated command and control system of automation using representations afforded by task and social networks to understand how drivers remain actively involved in the task. A network analysis of different driver commands suggests that such a strategy does maintain the driver in the control loop.

Automated Driving System Architecture to Ensure Safe Delegation of Driving Authority

NASA Astrophysics Data System (ADS)

YUN, Sunkil; NISHIMURA, Hidekazu

2016-09-01

In this paper, the architecture of an automated driving system (ADS) is proposed to ensure safe delegation of driving authority between the ADS and a driver. Limitations of the ADS functions may activate delegation of driving authority to a driver. However, it leads to severe consequences in emergency situations where a driver may be drowsy or distracted. To address these issues, first, the concept model for the ADS in the situation for delegation of driving authority is described taking the driver's behaviour and state into account. Second, the behaviour / state of a driver and functional flow / state of ADS and the interactions between them are modelled to understand the context where the ADS requests to delegate the driving authority to a driver. Finally, the proposed architecture of the ADS is verified under the simulations based on the emergency braking scenarios. In the verification process using simulation, we have derived the necessary condition for safe delegation of driving authority is that the ADS should assist s driver even after delegating driving authority to a driver who has not enough capability to regain control of the driving task.

Intelligent Diagnosis of Degradation State under Corrosion

NASA Astrophysics Data System (ADS)

Isoc, Dorin; Ignat-Coman, Aurelian; Joldiş, Adrian

2008-06-01

The work presents an inter- and multi-disciplinary research where the diagnosis is treated by using the artificial intelligence means and the application the degradation state of buildings and urban power networks. A possible model of degradation process caused by the corrosion and the technical achievement manner is given. The notions of micro- and macro-modeling and model granularity are introduced and applied. For resulting model the specification of intelligent processing of information and further the knowledge for suggested model are prepared. As concluding remarks the results are analysed and interpreted and a generalized approach is suggested and argued.

Resilience moderates the relationship between emotional intelligence and clinical communication ability among Chinese practice nursing students: A structural equation model analysis.

PubMed

Kong, Linghua; Liu, Yun; Li, Guopeng; Fang, Yueyan; Kang, Xiaofei; Li, Ping

2016-11-01

To examine the positive association between emotional intelligence and clinical communication ability among practice nursing students, and to determine whether resilience plays a moderating role in the relationship between emotional intelligence and clinical communication ability among Chinese practice nursing students. Three hundred and seventy-seven practice nursing students from three hospitals participated in this study. They completed questionnaires including the Emotional Intelligence Inventory (EII), Connor-Davidson Resilience Scale (CD-RISC-10), and Clinical Communication Ability Scale (CCAS). Structural equation modeling was used to analyze the relationships among emotional intelligence, resilience, and clinical communication ability. Emotional intelligence was positively associated with clinical communication ability (P<0.01). Resilience significantly affected clinical communication ability (P<0.01) and moderated the relationship between emotional intelligence and clinical communication ability (P<0.01). Emotional intelligence is positively related to clinical communication ability among Chinese practice nursing students, and resilience moderates the relationship between emotional intelligence and clinical communication ability, which may provide scientific evidence to aid in developing intervention strategies to improve clinical communication ability. Copyright © 2016 Elsevier Ltd. All rights reserved.

A decision model applied to alcohol effects on driver signal light behavior

NASA Technical Reports Server (NTRS)

Schwartz, S. H.; Allen, R. W.

1978-01-01

A decision model including perceptual noise or inconsistency is developed from expected value theory to explain driver stop and go decisions at signaled intersections. The model is applied to behavior in a car simulation and instrumented vehicle. Objective and subjective changes in driver decision making were measured with changes in blood alcohol concentration (BAC). Treatment levels averaged 0.00, 0.10 and 0.14 BAC for a total of 26 male subjects. Data were taken for drivers approaching signal lights at three timing configurations. The correlation between model predictions and behavior was highly significant. In contrast to previous research, analysis indicates that increased BAC results in increased perceptual inconsistency, which is the primary cause of increased risk taking at low probability of success signal lights.

A method to model anticipatory postural control in driver braking events.

PubMed

Östh, Jonas; Eliasson, Erik; Happee, Riender; Brolin, Karin

2014-09-01

Human body models (HBMs) for vehicle occupant simulations have recently been extended with active muscles and postural control strategies. Feedback control has been used to model occupant responses to autonomous braking interventions. However, driver postural responses during driver initiated braking differ greatly from autonomous braking. In the present study, an anticipatory postural response was hypothesized, modelled in a whole-body HBM with feedback controlled muscles, and validated using existing volunteer data. The anticipatory response was modelled as a time dependent change in the reference value for the feedback controllers, which generates correcting moments to counteract the braking deceleration. The results showed that, in 11 m/s(2) driver braking simulations, including the anticipatory postural response reduced the peak forward displacement of the head by 100mm, of the shoulder by 30 mm, while the peak head flexion rotation was reduced by 18°. The HBM kinematic response was within a one standard deviation corridor of corresponding test data from volunteers performing maximum braking. It was concluded that the hypothesized anticipatory responses can be modelled by changing the reference positions of the individual joint feedback controllers that regulate muscle activation levels. The addition of anticipatory postural control muscle activations appears to explain the difference in occupant kinematics between driver and autonomous braking. This method of modelling postural reactions can be applied to the simulation of other driver voluntary actions, such as emergency avoidance by steering. Copyright © 2014. Published by Elsevier B.V.

The Influence of Individual Driver Characteristics on Congestion Formation

NASA Astrophysics Data System (ADS)

Wang, Lanjun; Zhang, Hao; Meng, Huadong; Wang, Xiqin

Previous works have pointed out that one of the reasons for the formation of traffic congestion is instability in traffic flow. In this study, we investigate theoretically how the characteristics of individual drivers influence the instability of traffic flow. The discussions are based on the optimal velocity model, which has three parameters related to individual driver characteristics. We specify the mappings between the model parameters and driver characteristics in this study. With linear stability analysis, we obtain a condition for when instability occurs and a constraint about how the model parameters influence the unstable traffic flow. Meanwhile, we also determine how the region of unstable flow densities depends on these parameters. Additionally, the Langevin approach theoretically validates that under the constraint, the macroscopic characteristics of the unstable traffic flow becomes a mixture of free flows and congestions. All of these results imply that both overly aggressive and overly conservative drivers are capable of triggering traffic congestion.

Modeling The Frontal Collison In Vehicles And Determining The Degree Of Injury On The Driver

NASA Astrophysics Data System (ADS)

Oţăt, Oana Victoria

2015-09-01

The present research study aims at analysing the kinematic and the dynamic behaviour of the vehicle's driver in a frontal collision. Hence, a subsequent objective of the research paper is to establish the degree of injury suffered by the driver. Therefore, in order to achieve the objectives set, first, we had to define the type of the dummy placed in the position of the driver, and then to design the three-element assembly, i.e. the chair-steering wheel-dashboard assembly. Based on this model, the following step focused on the positioning of the dummy, which has also integrated the defining of the contacts between the components of the dummy and the seat elements. Seeking to model such a behaviour that would highly accurately reflect the driver's movements in a frontal collision, passive safety systems have also been defined and simulated, namely the seatbelt and the frontal airbag.

Decay of Iconic Memory Traces Is Related to Psychometric Intelligence: A Fixed-Links Modeling Approach

ERIC Educational Resources Information Center

Miller, Robert; Rammsayer, Thomas H.; Schweizer, Karl; Troche, Stefan J.

2010-01-01

Several memory processes have been examined regarding their relation to psychometric intelligence with the exception of sensory memory. This study examined the relation between decay of iconic memory traces, measured with a partial-report task, and psychometric intelligence, assessed with the Berlin Intelligence Structure test, in 111…

Effect of calcium chloride concentration on output force in electrical actuator made of sodium alginate gel

NASA Astrophysics Data System (ADS)

Wu, Yuda; Zhao, Gang; Wei, Chengye; Liu, Shuang; Fu, Yu; Liu, Xvxiong

2018-01-01

As a kind of artificial muscle intelligent material, the biological gel electric driver has the advantages of low driving voltage, large strain, good biological compatibility, good flexibility, low price, etc. The application prospect is broad and it has high academic value. Alginate, as a common substance in sea, has characteristics of low cost, green and pollution-free. Therefore,this paper obtains biological gel electric actuator by sodium alginate and calcium chloride. Effects on output force of the electric actuator is researched by changing the crosslinking of calcium chloride concentration and the output force enhancement mechanism is analyzed in this paper.

Action prediction based on anticipatory brain potentials during simulated driving.

PubMed

Khaliliardali, Zahra; Chavarriaga, Ricardo; Gheorghe, Lucian Andrei; Millán, José del R

2015-12-01

The ability of an automobile to infer the driver's upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain-computer interface able to decode the driver's intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating. We investigated anticipatory slow cortical potentials in electroencephalogram recorded from 18 healthy participants in a driving simulator using a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions: count-down numbers followed by 'Start'/'Stop' cue. We report decoding performance before the action onset using a quadratic discriminant analysis classifier based on temporal features. (i) Despite the visual and driving related cognitive distractions, we show the presence of anticipatory event related potentials locked to the stimuli onset similar to the widely reported CNV signal (with an average peak value of -8 μV at electrode Cz). (ii) We demonstrate the discrimination between cases requiring to perform an action upon imperative subsequent stimulus (Go condition, e.g. a 'Red' traffic light) versus events that do not require such action (No-go condition; e.g. a 'Yellow' light); with an average single trial classification performance of 0.83 ± 0.13 for braking and 0.79 ± 0.12 for accelerating (area under the curve). (iii) We show that the centro-medial anticipatory potentials are observed as early as 320 ± 200 ms before the action with a detection rate of 0.77 ± 0.12 in offline analysis. We show for the first time the feasibility of predicting the driver's intention through decoding anticipatory related potentials during simulated car driving with high recognition rates.

NED-IIS: An Intelligent Information System for Forest Ecosystem Management

Treesearch

W.D. Potter; S. Somasekar; R. Kommineni; H.M. Rauscher

1999-01-01

We view Intelligent Information System (IIS) as composed of a unified knowledge base, database, and model base. The model base includes decision support models, forecasting models, and cvsualization models for example. In addition, we feel that the model base should include domain specific porblems solving modules as well as decision support models. This, then,...

On-road vehicle detection: a review.

PubMed

Sun, Zehang; Bebis, George; Miller, Ronald

2006-05-01

Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.

The impact of rainfall on the temporal and spatial distribution of taxi passengers

PubMed Central

Zhang, Yong; Gao, Liangpeng; Geng, Nana; Li, Xuefeng

2017-01-01

This paper focuses on the impact of rainfall on the temporal and spatial distribution of taxi passengers. The main objective is to provide guidance for taxi scheduling on rainy days. To this end, we take the occupied and empty states of taxis as units of analysis. By matching a taxi's GPS data to its taximeter data, we can obtain the taxi's operational time and the taxi driver's income from every unit of analysis. The ratio of taxi operation time to taxi drivers' income is used to measure the quality of taxi passengers. The research results show that the spatio-temporal evolution of urban taxi service demand differs based on rainfall conditions and hours of operation. During non-rush hours, taxi demand in peripheral areas is significantly reduced under increasing precipitation conditions, whereas during rush hours, the demand for highly profitable taxi services steadily increases. Thus, as an intelligent response for taxi operations and dispatching, taxi services should guide cruising taxis to high-demand regions to increase their service time and ride opportunities. PMID:28873430

Intelligent on-board system for driving assistance

NASA Astrophysics Data System (ADS)

Rombaut, Michele; Le Fort-Piat, N.

1995-09-01

We present in this paper, an electronic copilot embedded in a real car. The system objective is to help the driver by sending alarms or warnings in order to avoid dangerous situtations. An onboard perception system based on CCD cameras and proprioceptive sensors is used ot provide information concerning the environment and the internal state of the vehicle. From this set of information, the copilot is able to analyze the situation and to generate adequate warnings to the driver according to the circumstances. The definition and the development of such a system deal with multisensor data fusion and supervision strategies. The framework of this work was the European Prometheus Pro-Art program. The electronic copilot has been integrated in a prototype vehicle called Prolab2. This French demonstrator integrates the works of nine research laboratories and two car companies: PSA and RENAULT. After a brief presentation of the global demonstrator, we present the two principal parts developed in our laboratory corresponding to the high level modules of the system: the dynamic data manager and the situation supervision.

Study on road sign recognition in LabVIEW

NASA Astrophysics Data System (ADS)

Panoiu, M.; Rat, C. L.; Panoiu, C.

2016-02-01

Road and traffic sign identification is a field of study that can be used to aid the development of in-car advisory systems. It uses computer vision and artificial intelligence to extract the road signs from outdoor images acquired by a camera in uncontrolled lighting conditions where they may be occluded by other objects, or may suffer from problems such as color fading, disorientation, variations in shape and size, etc. An automatic means of identifying traffic signs, in these conditions, can make a significant contribution to develop an Intelligent Transport Systems (ITS) that continuously monitors the driver, the vehicle, and the road. Road and traffic signs are characterized by a number of features which make them recognizable from the environment. Road signs are located in standard positions and have standard shapes, standard colors, and known pictograms. These characteristics make them suitable for image identification. Traffic sign identification covers two problems: traffic sign detection and traffic sign recognition. Traffic sign detection is meant for the accurate localization of traffic signs in the image space, while traffic sign recognition handles the labeling of such detections into specific traffic sign types or subcategories [1].

Emotional Intelligence Mediates the Relationship between Age and Subjective Well-Being.

PubMed

Chen, Yiwei; Peng, Yisheng; Fang, Ping

2016-07-01

Individuals' Subjective Well-being (SWB) increases as they grow older. Past literature suggests that emotional intelligence may increase with age and lead to higher levels of SWB in older adults. The primary purpose of the present study was to test whether emotional intelligence would mediate the relationship between age and SWB. A total of 360 Chinese adults (age range: 20 to 79 years old) participated in this study. They filled out questionnaires that assessed their age, life satisfaction (The Satisfaction with Life Scale), affective well-being (The Positive and Negative Affect Schedule), and emotional intelligence (The Wong and Law Emotional Intelligence Scale). Using Structural Equation Modeling, the mediation model was supported, χ(2) (75) = 194.21, p < .01; RMSEA = .07; CFI = .91. Emotional intelligence partially mediated the relationship between age and life satisfaction, and fully mediated the relationship between age and affective well-being. The findings suggest that older adults may use their increased emotional intelligence to enhance their SWB. © The Author(s) 2016.

Emotional Intelligence Mediates the Relationship between Age and Subjective Well-Being

PubMed Central

Chen, Yiwei; Peng, Yisheng; Fang, Ping

2017-01-01

Individuals’ Subjective Well-being (SWB) increases as they grow older. Past literature suggests that emotional intelligence may increase with age and lead to higher levels of SWB in older adults. The primary purpose of the present study was to test whether emotional intelligence would mediate the relationship between age and SWB. A total of 360 Chinese adults (age range: 20 to 79 years old) participated in this study. They filled out questionnaires that assessed their age, life satisfaction (The Satisfaction with Life Scale), affective well-being (The Positive and Negative Affect Schedule), and emotional intelligence (The Wong and Law Emotional Intelligence Scale). Using Structural Equation Modeling, the mediation model was supported, χ2 (75) =194.21, p < .01; RMSEA =.07; CFI = .91. Emotional intelligence partially mediated the relationship between age and life satisfaction, and fully mediated the relationship between age and affective well-being. The findings suggest that older adults may use their increased emotional intelligence to enhance their SWB. PMID:27199490

Color regeneration from reflective color sensor using an artificial intelligent technique.

PubMed

Saracoglu, Ömer Galip; Altural, Hayriye

2010-01-01

A low-cost optical sensor based on reflective color sensing is presented. Artificial neural network models are used to improve the color regeneration from the sensor signals. Analog voltages of the sensor are successfully converted to RGB colors. The artificial intelligent models presented in this work enable color regeneration from analog outputs of the color sensor. Besides, inverse modeling supported by an intelligent technique enables the sensor probe for use of a colorimetric sensor that relates color changes to analog voltages.

Nonlinear differential system applied of a mechanical plan model of the automotives used for the nonlinear stability analysis

NASA Astrophysics Data System (ADS)

Simniceanu, Loreta; Mihaela, Bogdan; Otat, Victor; Trotea, Mario

2017-10-01

This paper proposes a plan mechanical model for the vehicles with two axles, taking into account the lateral deflection of the tire. For this mechanical model are determined two mathematical models under the nonlinear differential equations systems form without taking into account the action of the driver and taking into account. The analysis of driver-vehicle system consists in the mathematical description of vehicle dynamics, coupled with the possibilities and limits of the human factor. Description seeks to emphasize the significant influence of the driver in handling and stability analyzes of vehicles and vehicle-driver system stability until the advent of skidding. These mathematical models are seen as very useful tools to analyzing the vehicles stability. The paper analyzes the influence of some parameters of the vehicle on its behavior in terms of stability of dynamic systems.

Intelligent judgements over health risks in a spatial agent-based model.

PubMed

Abdulkareem, Shaheen A; Augustijn, Ellen-Wien; Mustafa, Yaseen T; Filatova, Tatiana

2018-03-20

Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed. We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of several macro metrics of interest: an epidemic curve, a risk perception curve, and a distribution of different types of coping strategies over time. Our results emphasize the importance of integrating behavioral aspects of decision making under risk into spatial disease ABMs using machine learning algorithms. This is especially relevant when studying cumulative impacts of behavioral changes and possible intervention strategies.

Alternative Fuel Vehicles: What Do the Drivers Say?

Science.gov Websites

); dedicated compressed natural gas (CNG) models; CNG after-market conversions; flexible-fuel methanol models ; flexible-fuel ethanol models, and gasoline models. Overall, drivers reported positive experiences, with primary concerns being lack of range (particularly for the CNG models) and lack of convenient fueling

Evaluation of driver fatigue on two channels of EEG data.

PubMed

Li, Wei; He, Qi-chang; Fan, Xiu-min; Fei, Zhi-min

2012-01-11

Electroencephalogram (EEG) data is an effective indicator to evaluate driver fatigue. The 16 channels of EEG data are collected and transformed into three bands (θ, α, and β) in the current paper. First, 12 types of energy parameters are computed based on the EEG data. Then, Grey Relational Analysis (GRA) is introduced to identify the optimal indicator of driver fatigue, after which, the number of significant electrodes is reduced using Kernel Principle Component Analysis (KPCA). Finally, the evaluation model for driver fatigue is established with the regression equation based on the EEG data from two significant electrodes (Fp1 and O1). The experimental results verify that the model is effective in evaluating driver fatigue. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Latent class analysis of accident risks in usage-based insurance: Evidence from Beijing.

PubMed

Jin, Wen; Deng, Yinglu; Jiang, Hai; Xie, Qianyan; Shen, Wei; Han, Weijian

2018-06-01

Car insurance is quickly becoming a big data industry, with usage-based insurance (UBI) poised to potentially change the business of insurance. Telematics data, which are transmitted from wireless devices in car, are widely used in UBI to obtain individual-level travel and driving characteristics. While most existing studies have introduced telematics data into car insurance pricing, the telematics-related characteristics are directly obtained from the raw data. In this study, we propose to quantify drivers' familiarity with their driving routes and develop models to quantify drivers' accident risks using the telematics data. In addition, we build a latent class model to study the heterogeneity in travel and driving styles based on the telematics data, which has not been investigated in literature. Our main results include: (1) the improvement to the model fit is statistically significant by adding telematics-related characteristics; (2) drivers' familiarity with their driving trips is critical to identify high risk drivers, and the relationship between drivers' familiarity and accident risks is non-linear; (3) the drivers can be classified into two classes, where the first class is the low risk class with 0.54% of its drivers reporting accidents, and the second class is the high risk class with 20.66% of its drivers reporting accidents; and (4) for the low risk class, drivers with high probability of reporting accidents can be identified by travel-behavior-related characteristics, while for the high risk class, they can be identified by driving-behavior-related characteristics. The driver's familiarity will affect the probability of reporting accidents for both classes. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Impact of Drivers' Race, Gender, and Age during Traffic Stops: Assessing Interaction Terms and the Social Conditioning Model

ERIC Educational Resources Information Center

Tillyer, Rob; Engel, Robin S.

2013-01-01

Recent research has demonstrated that minority drivers receive disparate traffic stop outcomes compared with similarly situated White drivers. This research, however, is often not grounded within a theoretical framework and fails to examine specific combinations of driver demographics. This study addresses those shortcomings by examining research…

49 CFR 599.302 - Dealer application for reimbursement-submission, contents.

Code of Federal Regulations, 2013 CFR

2013-10-01

... vehicle. (C) Model year. The model year of the vehicle. (D) Vehicle identification number (VIN). The 17... vehicle. (C) Model year. The model year of the vehicle. (D) Vehicle identification number (VIN). The 17... address of each purchaser. (C) Driver's license or State identification number. The State driver's license...

49 CFR 599.302 - Dealer application for reimbursement-submission, contents.

Code of Federal Regulations, 2012 CFR

2012-10-01

... vehicle. (C) Model year. The model year of the vehicle. (D) Vehicle identification number (VIN). The 17... vehicle. (C) Model year. The model year of the vehicle. (D) Vehicle identification number (VIN). The 17... address of each purchaser. (C) Driver's license or State identification number. The State driver's license...

49 CFR 599.302 - Dealer application for reimbursement-submission, contents.

Code of Federal Regulations, 2014 CFR

2014-10-01

... vehicle. (C) Model year. The model year of the vehicle. (D) Vehicle identification number (VIN). The 17... vehicle. (C) Model year. The model year of the vehicle. (D) Vehicle identification number (VIN). The 17... address of each purchaser. (C) Driver's license or State identification number. The State driver's license...

Artificial intelligence support for scientific model-building

NASA Technical Reports Server (NTRS)

Keller, Richard M.

1992-01-01

Scientific model-building can be a time-intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot easily be distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientific development team to understand. We believe that artificial intelligence techniques can facilitate both the model-building and model-sharing process. In this paper, we overview our effort to build a scientific modeling software tool that aids the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high-level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities.

The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.

PubMed

Han, Gaining; Fu, Weiping; Wang, Wen

2016-01-01

In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability.

The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm

PubMed Central

Han, Gaining; Fu, Weiping; Wang, Wen

2016-01-01

In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability. PMID:26880881

Sitting biomechanics, part II: optimal car driver's seat and optimal driver's spinal model.

PubMed

Harrison, D D; Harrison, S O; Croft, A C; Harrison, D E; Troyanovich, S J

2000-01-01

Driving has been associated with signs and symptoms caused by vibrations. Sitting causes the pelvis to rotate backwards and the lumbar lordosis to reduce. Lumbar support and armrests reduce disc pressure and electromyographically recorded values. However, the ideal driver's seat and an optimal seated spinal model have not been described. To determine an optimal automobile seat and an ideal spinal model of a driver. Information was obtained from peer-reviewed scientific journals and texts, automotive engineering reports, and the National Library of Medicine. Driving predisposes vehicle operators to low-back pain and degeneration. The optimal seat would have an adjustable seat back incline of 100 degrees from horizontal, a changeable depth of seat back to front edge of seat bottom, adjustable height, an adjustable seat bottom incline, firm (dense) foam in the seat bottom cushion, horizontally and vertically adjustable lumbar support, adjustable bilateral arm rests, adjustable head restraint with lordosis pad, seat shock absorbers to dampen frequencies in the 1 to 20 Hz range, and linear front-back travel of the seat enabling drivers of all sizes to reach the pedals. The lumbar support should be pulsating in depth to reduce static load. The seat back should be damped to reduce rebounding of the torso in rear-end impacts. The optimal driver's spinal model would be the average Harrison model in a 10 degrees posterior inclining seat back angle.

Assessing the impact of modeling limits on intelligent systems

NASA Technical Reports Server (NTRS)

Rouse, William B.; Hammer, John M.

1990-01-01

The knowledge bases underlying intelligent systems are validated. A general conceptual framework is provided for considering the roles in intelligent systems of models of physical, behavioral, and operational phenomena. A methodology is described for identifying limits in particular intelligent systems, and the use of the methodology is illustrated via an experimental evaluation of the pilot-vehicle interface within the Pilot's Associate. The requirements and functionality are outlined for a computer based knowledge engineering environment which would embody the approach advocated and illustrated in earlier discussions. Issues considered include the specific benefits of this functionality, the potential breadth of applicability, and technical feasibility.

Intelligent flight control systems

NASA Technical Reports Server (NTRS)

Stengel, Robert F.

1993-01-01

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms.

An extended continuum model accounting for the driver's timid and aggressive attributions

NASA Astrophysics Data System (ADS)

Cheng, Rongjun; Ge, Hongxia; Wang, Jufeng

2017-04-01

Considering the driver's timid and aggressive behaviors simultaneously, a new continuum model is put forwarded in this paper. By applying the linear stability theory, we presented the analysis of new model's linear stability. Through nonlinear analysis, the KdV-Burgers equation is derived to describe density wave near the neutral stability line. Numerical results verify that aggressive driving is better than timid act because the aggressive driver will adjust his speed timely according to the leading car's speed. The key improvement of this new model is that the timid driving deteriorates traffic stability while the aggressive driving will enhance traffic stability. The relationship of energy consumption between the aggressive and timid driving is also studied. Numerical results show that aggressive driver behavior can not only suppress the traffic congestion but also reduce the energy consumption.

Are Child Passengers Bringing Up the Rear? Evidence For Differential Improvements in Injury Risk Between Drivers and their Child Passengers

PubMed Central

Winston, Flaura K; Xie, Dawei; Durbin, Dennis R; Elliott, Michael R

2007-01-01

Since nearly half of children fatally injured in automobile crashes were restrained, optimizing occupant protection systems for children is essential to reducing morbidity and mortality. Data from the Partners for Child Passenger Safety study were used to compare the differential injury risk between drivers and their child passengers in the same crash, with a focus on vehicle model year. A matched cohort design and conditional logistic regression model were used in the analyses. Overall, injury risk for drivers was higher than for children, but the risk difference was largest for the oldest model year vehicles, particularly for children aged 4–8 in seat belts. While drivers experienced significant benefits in safety with increasing model years, children restrained by safety belts alone derived less safety benefit from newer vehicles. PMID:18184488

Crash risk and aberrant driving behaviors among bus drivers: the role of personality and attitudes towards traffic safety.

PubMed

Mallia, Luca; Lazuras, Lambros; Violani, Cristiano; Lucidi, Fabio

2015-06-01

Several studies have shown that personality traits and attitudes toward traffic safety predict aberrant driving behaviors and crash involvement. However, this process has not been adequately investigated in professional drivers, such as bus drivers. The present study used a personality-attitudes model to assess whether personality traits predicted aberrant self-reported driving behaviors (driving violations, lapses, and errors) both directly and indirectly, through the effects of attitudes towards traffic safety in a large sample of bus drivers. Additionally, the relationship between aberrant self-reported driving behaviors and crash risk was also assessed. Three hundred and one bus drivers (mean age=39.1, SD=10.7 years) completed a structured and anonymous questionnaire measuring personality traits, attitudes toward traffic safety, self-reported aberrant driving behaviors (i.e., errors, lapses, and traffic violations), and accident risk in the last 12 months. Structural equation modeling analysis revealed that personality traits were associated to aberrant driving behaviors both directly and indirectly. In particular altruism, excitement seeking, and normlessness directly predicted bus drivers' attitudes toward traffic safety which, in turn, were negatively associated with the three types of self-reported aberrant driving behaviors. Personality traits relevant to emotionality directly predicted bus drivers' aberrant driving behaviors, without any mediation of attitudes. Finally, only self-reported violations were related to bus drivers' accident risk. The present findings suggest that the hypothesized personality-attitudes model accounts for aberrant driving behaviors in bus drivers, and provide the empirical basis for evidence-based road safety interventions in the context of public transport. Copyright © 2015 Elsevier Ltd. All rights reserved.

A situation-response model for intelligent pilot aiding

NASA Technical Reports Server (NTRS)

Schudy, Robert; Corker, Kevin

1987-01-01

An intelligent pilot aiding system needs models of the pilot information processing to provide the computational basis for successful cooperation between the pilot and the aiding system. By combining artificial intelligence concepts with the human information processing model of Rasmussen, an abstraction hierarchy of states of knowledge, processing functions, and shortcuts are developed, which is useful for characterizing the information processing both of the pilot and of the aiding system. This approach is used in the conceptual design of a real time intelligent aiding system for flight crews of transport aircraft. One promising result was the tentative identification of a particular class of information processing shortcuts, from situation characterizations to appropriate responses, as the most important reliable pathway for dealing with complex time critical situations.

Predicting Speech Intelligibility with A Multiple Speech Subsystems Approach in Children with Cerebral Palsy

PubMed Central

Lee, Jimin; Hustad, Katherine C.; Weismer, Gary

2014-01-01

Purpose Speech acoustic characteristics of children with cerebral palsy (CP) were examined with a multiple speech subsystem approach; speech intelligibility was evaluated using a prediction model in which acoustic measures were selected to represent three speech subsystems. Method Nine acoustic variables reflecting different subsystems, and speech intelligibility, were measured in 22 children with CP. These children included 13 with a clinical diagnosis of dysarthria (SMI), and nine judged to be free of dysarthria (NSMI). Data from children with CP were compared to data from age-matched typically developing children (TD). Results Multiple acoustic variables reflecting the articulatory subsystem were different in the SMI group, compared to the NSMI and TD groups. A significant speech intelligibility prediction model was obtained with all variables entered into the model (Adjusted R-squared = .801). The articulatory subsystem showed the most substantial independent contribution (58%) to speech intelligibility. Incremental R-squared analyses revealed that any single variable explained less than 9% of speech intelligibility variability. Conclusions Children in the SMI group have articulatory subsystem problems as indexed by acoustic measures. As in the adult literature, the articulatory subsystem makes the primary contribution to speech intelligibility variance in dysarthria, with minimal or no contribution from other systems. PMID:24824584

Predicting speech intelligibility with a multiple speech subsystems approach in children with cerebral palsy.

PubMed

Lee, Jimin; Hustad, Katherine C; Weismer, Gary

2014-10-01

Speech acoustic characteristics of children with cerebral palsy (CP) were examined with a multiple speech subsystems approach; speech intelligibility was evaluated using a prediction model in which acoustic measures were selected to represent three speech subsystems. Nine acoustic variables reflecting different subsystems, and speech intelligibility, were measured in 22 children with CP. These children included 13 with a clinical diagnosis of dysarthria (speech motor impairment [SMI] group) and 9 judged to be free of dysarthria (no SMI [NSMI] group). Data from children with CP were compared to data from age-matched typically developing children. Multiple acoustic variables reflecting the articulatory subsystem were different in the SMI group, compared to the NSMI and typically developing groups. A significant speech intelligibility prediction model was obtained with all variables entered into the model (adjusted R2 = .801). The articulatory subsystem showed the most substantial independent contribution (58%) to speech intelligibility. Incremental R2 analyses revealed that any single variable explained less than 9% of speech intelligibility variability. Children in the SMI group had articulatory subsystem problems as indexed by acoustic measures. As in the adult literature, the articulatory subsystem makes the primary contribution to speech intelligibility variance in dysarthria, with minimal or no contribution from other systems.

Intelligent Elements for ISHM

NASA Technical Reports Server (NTRS)

Schmalzel, John L.; Morris, Jon; Turowski, Mark; Figueroa, Fernando; Oostdyk, Rebecca

2008-01-01

There are a number of architecture models for implementing Integrated Systems Health Management (ISHM) capabilities. For example, approaches based on the OSA-CBM and OSA-EAI models, or specific architectures developed in response to local needs. NASA s John C. Stennis Space Center (SSC) has developed one such version of an extensible architecture in support of rocket engine testing that integrates a palette of functions in order to achieve an ISHM capability. Among the functional capabilities that are supported by the framework are: prognostic models, anomaly detection, a data base of supporting health information, root cause analysis, intelligent elements, and integrated awareness. This paper focuses on the role that intelligent elements can play in ISHM architectures. We define an intelligent element as a smart element with sufficient computing capacity to support anomaly detection or other algorithms in support of ISHM functions. A smart element has the capabilities of supporting networked implementations of IEEE 1451.x smart sensor and actuator protocols. The ISHM group at SSC has been actively developing intelligent elements in conjunction with several partners at other Centers, universities, and companies as part of our ISHM approach for better supporting rocket engine testing. We have developed several implementations. Among the key features for these intelligent sensors is support for IEEE 1451.1 and incorporation of a suite of algorithms for determination of sensor health. Regardless of the potential advantages that can be achieved using intelligent sensors, existing large-scale systems are still based on conventional sensors and data acquisition systems. In order to bring the benefits of intelligent sensors to these environments, we have also developed virtual implementations of intelligent sensors.

Coordinating complex problem-solving among distributed intelligent agents

NASA Technical Reports Server (NTRS)

Adler, Richard M.

1992-01-01

A process-oriented control model is described for distributed problem solving. The model coordinates the transfer and manipulation of information across independent networked applications, both intelligent and conventional. The model was implemented using SOCIAL, a set of object-oriented tools for distributing computing. Complex sequences of distributed tasks are specified in terms of high level scripts. Scripts are executed by SOCIAL objects called Manager Agents, which realize an intelligent coordination model that routes individual tasks to suitable server applications across the network. These tools are illustrated in a prototype distributed system for decision support of ground operations for NASA's Space Shuttle fleet.

Assessing Chinese coach drivers' fitness to drive: The development of a toolkit based on cognition measurements.

PubMed

Wang, Huarong; Mo, Xian; Wang, Ying; Liu, Ruixue; Qiu, Peiyu; Dai, Jiajun

2016-10-01

Road traffic accidents resulting in group deaths and injuries are often related to coach drivers' inappropriate operations and behaviors. Thus, the evaluation of coach drivers' fitness to drive is an important measure for improving the safety of public transportation. Previous related research focused on drivers' age and health condition. Comprehensive studies about commercial drivers' cognitive capacities are limited. This study developed a toolkit consisting of nine cognition measurements across driver perception/sensation, attention, and reaction. A total of 1413 licensed coach drivers in Jiangsu Province, China were investigated and tested. Results indicated that drivers with accident history within three years performed overwhelmingly worse (p<0.001) on dark adaptation, dynamic visual acuity, depth perception, attention concentration, attention span, and significantly worse (p<0.05) on reaction to complex tasks compared with drivers with clear accident records. These findings supported that in the assessment of fitness to drive, cognitive capacities are sensitive to the detection of drivers with accident proneness. We first developed a simple evaluation model based on the percentile distribution of all single measurements, which defined the normal range of "fit-to-drive" by eliminating a 5% tail of each measurement. A comprehensive evaluation model was later constructed based on the kernel principal component analysis, in which the eliminated 5% tail was calculated from on integrated index. Methods to categorizing qualified, good, and excellent coach drivers and criteria for evaluating and training Chinese coach drivers' fitness to drive were also proposed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Model Selection for Solving Kinematic Problems

DTIC Science & Technology

1990-09-01

Bundy78] A. Bundy. Will it Reach the Top? Prediction in the Mechanics World. Aritificial Intelligence , 10:111-122, 1978. [Bundy,Luger,Mellish&Pamer78] A...ELEMENT. PfOJECT. TASK Artificial Intelligence Laboratory AREA 4 WORK UNIT NUMBERS 545 Technology Square Cambridge, MA 02139 It. CONTROLLiNG OFFICE...tificial Intelligence community, particularly in its application to diagnosis and trou- bleshooting. The core issue in this thesis, simply put, is, model

Multiple Intelligences in Action.

ERIC Educational Resources Information Center

Campbell, Bruce

1992-01-01

Describes the investigation of the effects of a four-step model program used with third through fifth grade students to implement Gardener's concepts of seven human intelligences--linguistic, logical/mathematical, visual/spatial, musical, kinesthetic, intrapersonal, and interpersonal intelligence--into daily learning. (BB)

Environment Modeling Using Runtime Values for JPF-Android

NASA Technical Reports Server (NTRS)

van der Merwe, Heila; Tkachuk, Oksana; Nel, Seal; van der Merwe, Brink; Visser, Willem

2015-01-01

Software applications are developed to be executed in a specific environment. This environment includes external native libraries to add functionality to the application and drivers to fire the application execution. For testing and verification, the environment of an application is simplified abstracted using models or stubs. Empty stubs, returning default values, are simple to generate automatically, but they do not perform well when the application expects specific return values. Symbolic execution is used to find input parameters for drivers and return values for library stubs, but it struggles to detect the values of complex objects. In this work-in-progress paper, we explore an approach to generate drivers and stubs based on values collected during runtime instead of using default values. Entry-points and methods that need to be modeled are instrumented to log their parameters and return values. The instrumented applications are then executed using a driver and instrumented libraries. The values collected during runtime are used to generate driver and stub values on- the-fly that improve coverage during verification by enabling the execution of code that previously crashed or was missed. We are implementing this approach to improve the environment model of JPF-Android, our model checking and analysis tool for Android applications.

Systems in Science: Modeling Using Three Artificial Intelligence Concepts.

ERIC Educational Resources Information Center

Sunal, Cynthia Szymanski; Karr, Charles L.; Smith, Coralee; Sunal, Dennis W.

2003-01-01

Describes an interdisciplinary course focusing on modeling scientific systems. Investigates elementary education majors' applications of three artificial intelligence concepts used in modeling scientific systems before and after the course. Reveals a great increase in understanding of concepts presented but inconsistent application. (Author/KHR)

Intelligent Model Management in a Forest Ecosystem Management Decision Support System

Treesearch

Donald Nute; Walter D. Potter; Frederick Maier; Jin Wang; Mark Twery; H. Michael Rauscher; Peter Knopp; Scott Thomasma; Mayukh Dass; Hajime Uchiyama

2002-01-01

Decision making for forest ecosystem management can include the use of a wide variety of modeling tools. These tools include vegetation growth models, wildlife models, silvicultural models, GIS, and visualization tools. NED-2 is a robust, intelligent, goal-driven decision support system that integrates tools in each of these categories. NED-2 uses a blackboard...

The Structure of Human Intelligence: It Is Verbal, Perceptual, and Image Rotation (VPR), Not Fluid and Crystallized

ERIC Educational Resources Information Center

Johnson, W.; Bouchard, T.J.

2005-01-01

In a heterogeneous sample of 436 adult individuals who completed 42 mental ability tests, we evaluated the relative statistical performance of three major psychometric models of human intelligence-the Cattell-Horn fluid-crystallized model, Vernon's verbal-perceptual model, and Carroll's three-strata model. The verbal-perceptual model fit…

The highly intelligent virtual agents for modeling financial markets

NASA Astrophysics Data System (ADS)

Yang, G.; Chen, Y.; Huang, J. P.

2016-02-01

Researchers have borrowed many theories from statistical physics, like ensemble, Ising model, etc., to study complex adaptive systems through agent-based modeling. However, one fundamental difference between entities (such as spins) in physics and micro-units in complex adaptive systems is that the latter are usually with high intelligence, such as investors in financial markets. Although highly intelligent virtual agents are essential for agent-based modeling to play a full role in the study of complex adaptive systems, how to create such agents is still an open question. Hence, we propose three principles for designing high artificial intelligence in financial markets and then build a specific class of agents called iAgents based on these three principles. Finally, we evaluate the intelligence of iAgents through virtual index trading in two different stock markets. For comparison, we also include three other types of agents in this contest, namely, random traders, agents from the wealth game (modified on the famous minority game), and agents from an upgraded wealth game. As a result, iAgents perform the best, which gives a well support for the three principles. This work offers a general framework for the further development of agent-based modeling for various kinds of complex adaptive systems.

Forecasting rain events - Meteorological models or collective intelligence?

NASA Astrophysics Data System (ADS)

Arazy, Ofer; Halfon, Noam; Malkinson, Dan

2015-04-01

Collective intelligence is shared (or group) intelligence that emerges from the collective efforts of many individuals. Collective intelligence is the aggregate of individual contributions: from simple collective decision making to more sophisticated aggregations such as in crowdsourcing and peer-production systems. In particular, collective intelligence could be used in making predictions about future events, for example by using prediction markets to forecast election results, stock prices, or the outcomes of sport events. To date, there is little research regarding the use of collective intelligence for prediction of weather forecasting. The objective of this study is to investigate the extent to which collective intelligence could be utilized to accurately predict weather events, and in particular rainfall. Our analyses employ metrics of group intelligence, as well as compare the accuracy of groups' predictions against the predictions of the standard model used by the National Meteorological Services. We report on preliminary results from a study conducted over the 2013-2014 and 2014-2015 winters. We have built a web site that allows people to make predictions on precipitation levels on certain locations. During each competition participants were allowed to enter their precipitation forecasts (i.e. 'bets') at three locations and these locations changed between competitions. A precipitation competition was defined as a 48-96 hour period (depending on the expected weather conditions), bets were open 24-48 hours prior to the competition, and during betting period participants were allowed to change their bets with no limitation. In order to explore the effect of transparency, betting mechanisms varied across study's sites: full transparency (participants able to see each other's bets); partial transparency (participants see the group's average bet); and no transparency (no information of others' bets is made available). Several interesting findings emerged from this study. First, we found evidence for the emergence of collective intelligence, as the group's mean prediction was superior to individuals' predictions (using the metrics of Collective Intelligence Quality and Win Ratio). Second, we found that overall the group's collective intelligence was not very different from the accuracy of the meteorological model (ECMWF): in 6 out of the 12 competition the results were almost indistinguishable (error differences of less than 2 mm); in 4 cases the model clearly outperformed the group; and in 2 cases the group outperformed the model. Third, the design of the bidding mechanism - namely transparency - seems to affect collective intelligence. Fourth, an analysis of individuals' predictions suggests that local knowledge (measured by the distance between home address and the site of competition) and the level of meteorological knowledge (assessed by a short quiz) were not correlated with prediction accuracy. Although, the findings reported here present only preliminary results from a long-term project and while we acknowledge that it is not possible to draw statistically significant conclusions from a study of 12 cases, our findings do reveal some important insights. Our results inform research on collective intelligence and meteorology, as well as have implications for practice (e.g. possibly incorporating collective intelligence into weather forecasting models).

Intelligence, personality, and interests: evidence for overlapping traits.

PubMed

Ackerman, P L; Heggestad, E D

1997-03-01

The authors review the development of the modern paradigm for intelligence assessment and application and consider the differentiation between intelligence-as-maximal performance and intelligence-as-typical performance. They review theories of intelligence, personality, and interest as a means to establish potential overlap. Consideration of intelligence-as-typical performance provides a basis for evaluation of intelligence-personality and intelligence-interest relations. Evaluation of relations among personality constructs, vocational interests, and intellectual abilities provides evidence for communality across the domains of personality of J. L. Holland's (1959) model of vocational interests. The authors provide an extensive meta-analysis of personality-intellectual ability correlations, and a review of interest-intellectual ability associations. They identify 4 trait complexes: social, clerical/conventional, science/math, and intellectual/cultural.

The Use of Technology in the Promotion of Children's Emotional Intelligence: The Multimedia Program "Developing Emotional Intelligence"

ERIC Educational Resources Information Center

D'Amico, Antonella

2018-01-01

"Developing Emotional Intelligence" is an Italian language multimedia tool created for children between 8 and 12 years of age. The software is based on the four 'branches' of model of emotional intelligence proposed by Mayer and Salovey and aims to evaluate and improve abilities in perception of emotions; using emotion to facilitate…

Practised Intelligence Testing Based on a Modern Test Conceptualization and Its Reference to the Common Intelligence Theories

ERIC Educational Resources Information Center

Kubinger, Klaus D.; Litzenberger, Margarete; Mrakotsky, Christine

2006-01-01

The question is to what extent intelligence test-batteries prove any kind of empirical reference to common intelligence theories. Of particular interest are conceptualized tests that are of a high psychometric standard--those that fit the Rasch model--and hence are not exposed to fundamental critique. As individualized testing, i.e., a…

Role of theory of mind and executive function in explaining social intelligence: a structural equation modeling approach.

PubMed

Yeh, Zai-Ting

2013-01-01

Social intelligence is the ability to understand others and the social context effectively and thus to interact with people successfully. Research has suggested that the theory of mind (ToM) and executive function may play important roles in explaining social intelligence. The specific aim of the present study was to test with structural equation modeling (SEM) the hypothesis that performance on ToM tasks is more associated with social intelligence in the elderly than is performance on executive functions. One hundred and seventy-seven participants (age 56-96) completed ToM, executive function, and other basic cognition tasks, and were rated with social intelligence scales. The SEM results showed that ToM and executive function were strongly correlated (0.54); however, only the path coefficient from ToM to social intelligence, and not from executive function, was significant (0.37). ToM performance, but not executive function, was strongly correlated with social intelligence among elderly individuals. ToM and executive function might play different roles in social behavior during normal aging; however, based on the present results, it is possible that ToM might play an important role in social intelligence.

Statistical Models for Predicting Automobile Driving Postures for Men and Women Including Effects of Age.

PubMed

Park, Jangwoon; Ebert, Sheila M; Reed, Matthew P; Hallman, Jason J

2016-03-01

Previously published statistical models of driving posture have been effective for vehicle design but have not taken into account the effects of age. The present study developed new statistical models for predicting driving posture. Driving postures of 90 U.S. drivers with a wide range of age and body size were measured in laboratory mockup in nine package conditions. Posture-prediction models for female and male drivers were separately developed by employing a stepwise regression technique using age, body dimensions, vehicle package conditions, and two-way interactions, among other variables. Driving posture was significantly associated with age, and the effects of other variables depended on age. A set of posture-prediction models is presented for women and men. The results are compared with a previously developed model. The present study is the first study of driver posture to include a large cohort of older drivers and the first to report a significant effect of age. The posture-prediction models can be used to position computational human models or crash-test dummies for vehicle design and assessment. © 2015, Human Factors and Ergonomics Society.

Extending Galactic Habitable Zone Modeling to Include the Emergence of Intelligent Life.

PubMed

Morrison, Ian S; Gowanlock, Michael G

2015-08-01

Previous studies of the galactic habitable zone have been concerned with identifying those regions of the Galaxy that may favor the emergence of complex life. A planet is deemed habitable if it meets a set of assumed criteria for supporting the emergence of such complex life. In this work, we extend the assessment of habitability to consider the potential for life to further evolve to the point of intelligence--termed the propensity for the emergence of intelligent life, φI. We assume φI is strongly influenced by the time durations available for evolutionary processes to proceed undisturbed by the sterilizing effects of nearby supernovae. The times between supernova events provide windows of opportunity for the evolution of intelligence. We developed a model that allows us to analyze these window times to generate a metric for φI, and we examine here the spatial and temporal variation of this metric. Even under the assumption that long time durations are required between sterilizations to allow for the emergence of intelligence, our model suggests that the inner Galaxy provides the greatest number of opportunities for intelligence to arise. This is due to the substantially higher number density of habitable planets in this region, which outweighs the effects of a higher supernova rate in the region. Our model also shows that φI is increasing with time. Intelligent life emerged at approximately the present time at Earth's galactocentric radius, but a similar level of evolutionary opportunity was available in the inner Galaxy more than 2 Gyr ago. Our findings suggest that the inner Galaxy should logically be a prime target region for searches for extraterrestrial intelligence and that any civilizations that may have emerged there are potentially much older than our own.

Generative Computer-Assisted Instruction and Artificial Intelligence. Report No. 5.

ERIC Educational Resources Information Center

Sinnott, Loraine T.

This paper reviews the state-of-the-art in generative computer-assisted instruction and artificial intelligence. It divides relevant research into three areas of instructional modeling: models of the subject matter; models of the learner's state of knowledge; and models of teaching strategies. Within these areas, work sponsored by Advanced…

Emotional Intelligence: What the Research Says.

ERIC Educational Resources Information Center

Cobb, Casey D.; Mayer, John D.

2000-01-01

Educational practices involving emotional intelligence should be based on solid research, not sensationalistic claims. There are two emotional-intelligence models based on ability and an ability/social-competence mixture. Emphasizing cooperative behavior could stifle creativity, healthy skepticism, or spontaneity. Teaching emotional reasoning pays…

A new modelling approach for zooplankton behaviour

NASA Astrophysics Data System (ADS)

Keiyu, A. Y.; Yamazaki, H.; Strickler, J. R.

We have developed a new simulation technique to model zooplankton behaviour. The approach utilizes neither the conventional artificial intelligence nor neural network methods. We have designed an adaptive behaviour network, which is similar to BEER [(1990) Intelligence as an adaptive behaviour: an experiment in computational neuroethology, Academic Press], based on observational studies of zooplankton behaviour. The proposed method is compared with non- "intelligent" models—random walk and correlated walk models—as well as observed behaviour in a laboratory tank. Although the network is simple, the model exhibits rich behavioural patterns similar to live copepods.

TIE: an ability test of emotional intelligence.

PubMed

Śmieja, Magdalena; Orzechowski, Jarosław; Stolarski, Maciej S

2014-01-01

The Test of Emotional Intelligence (TIE) is a new ability scale based on a theoretical model that defines emotional intelligence as a set of skills responsible for the processing of emotion-relevant information. Participants are provided with descriptions of emotional problems, and asked to indicate which emotion is most probable in a given situation, or to suggest the most appropriate action. Scoring is based on the judgments of experts: professional psychotherapists, trainers, and HR specialists. The validation study showed that the TIE is a reliable and valid test, suitable for both scientific research and individual assessment. Its internal consistency measures were as high as .88. In line with theoretical model of emotional intelligence, the results of the TIE shared about 10% of common variance with a general intelligence test, and were independent of major personality dimensions.

Investigation on occupant injury severity in rear-end crashes involving trucks as the front vehicle in Beijing area, China.

PubMed

Yuan, Quan; Lu, Meng; Theofilatos, Athanasios; Li, Yi-Bing

2017-02-01

Rear-end crashes attribute to a large portion of total crashes in China, which lead to many casualties and property damage, especially when involving commercial vehicles. This paper aims to investigate the critical factors for occupant injury severity in the specific rear-end crash type involving trucks as the front vehicle (FV). This paper investigated crashes occurred from 2011 to 2013 in Beijing area, China and selected 100 qualified cases i.e., rear-end crashes involving trucks as the FV. The crash data were supplemented with interviews from police officers and vehicle inspection. A binary logistic regression model was used to build the relationship between occupant injury severity and corresponding affecting factors. Moreover, a multinomial logistic model was used to predict the likelihood of fatal or severe injury or no injury in a rear-end crash. The results provided insights on the characteristics of driver, vehicle and environment, and the corresponding influences on the likelihood of a rear-end crash. The binary logistic model showed that drivers' age, weight difference between vehicles, visibility condition and lane number of road significantly increased the likelihood for severe injury of rear-end crash. The multinomial logistic model and the average direct pseudo-elasticity of variables showed that night time, weekdays, drivers from other provinces and passenger vehicles as rear vehicles significantly increased the likelihood of rear drivers being fatal. All the abovementioned significant factors should be improved, such as the conditions of lighting and the layout of lanes on roads. Two of the most common driver factors are drivers' age and drivers' original residence. Young drivers and outsiders have a higher injury severity. Therefore it is imperative to enhance the safety education and management on the young drivers who steer heavy duty truck from other cities to Beijing on weekdays. Copyright © 2016 Daping Hospital and the Research Institute of Surgery of the Third Military Medical University. Production and hosting by Elsevier B.V. All rights reserved.

A Multidisciplinary Model for Development of Intelligent Computer-Assisted Instruction.

ERIC Educational Resources Information Center

Park, Ok-choon; Seidel, Robert J.

1989-01-01

Proposes a schematic multidisciplinary model to help developers of intelligent computer-assisted instruction (ICAI) identify the types of required expertise and integrate them into a system. Highlights include domain types and expertise; knowledge acquisition; task analysis; knowledge representation; student modeling; diagnosis of learning needs;…

Effect of climate change, CO2 trends, nitrogen addition, and land-cover and management intensity changes on the carbon balance of European grasslands.

PubMed

Chang, Jinfeng; Ciais, Philippe; Viovy, Nicolas; Vuichard, Nicolas; Herrero, Mario; Havlík, Petr; Wang, Xuhui; Sultan, Benjamin; Soussana, Jean-François

2016-01-01

Several lines of evidence point to European managed grassland ecosystems being a sink of carbon. In this study, we apply ORCHIDEE-GM a process-based carbon cycle model that describes specific management practices of pastures and the dynamics of carbon cycling in response to changes in climatic and biogeochemical drivers. The model is used to simulate changes in the carbon balance [i.e., net biome production (NBP)] of European grasslands over 1991-2010 on a 25 km × 25 km grid. The modeled average trend in NBP is 1.8-2.0 g C m(-2)  yr(-2) during the past two decades. Attribution of this trend suggests management intensity as the dominant driver explaining NBP trends in the model (36-43% of the trend due to all drivers). A major change in grassland management intensity has occurred across Europe resulting from reduced livestock numbers. This change has 'inadvertently' enhanced soil C sequestration and reduced N2 O and CH4 emissions by 1.2-1.5 Gt CO2 -equivalent, offsetting more than 7% of greenhouse gas emissions in the whole European agricultural sector during the period 1991-2010. Land-cover change, climate change and rising CO2 also make positive and moderate contributions to the NBP trend (between 24% and 31% of the trend due to all drivers). Changes in nitrogen addition (including fertilization and atmospheric deposition) are found to have only marginal net effect on NBP trends. However, this may not reflect reality because our model has only a very simple parameterization of nitrogen effects on photosynthesis. The sum of NBP trends from each driver is larger than the trend obtained when all drivers are varied together, leaving a residual - nonattributed - term (22-26% of the trend due to all drivers) indicating negative interactions between drivers. © 2015 John Wiley & Sons Ltd.

A control theoretic model of driver steering behavior

NASA Technical Reports Server (NTRS)

Donges, E.

1977-01-01

A quantitative description of driver steering behavior such as a mathematical model is presented. The steering task is divided into two levels: (1) the guidance level involving the perception of the instantaneous and future course of the forcing function provided by the forward view of the road, and the response to it in an anticipatory open-loop control mode; (2) the stabilization level whereby any occuring deviations from the forcing function are compensated for in a closed-loop control mode. This concept of the duality of the driver's steering activity led to a newly developed two-level model of driver steering behavior. Its parameters are identified on the basis of data measured in driving simulator experiments. The parameter estimates of both levels of the model show significant dependence on the experimental situation which can be characterized by variables such as vehicle speed and desired path curvature.

A new data assimilation engine for physics-based thermospheric density models

NASA Astrophysics Data System (ADS)

Sutton, E. K.; Henney, C. J.; Hock-Mysliwiec, R.

2017-12-01

The successful assimilation of data into physics-based coupled Ionosphere-Thermosphere models requires rethinking the filtering techniques currently employed in fields such as tropospheric weather modeling. In the realm of Ionospheric-Thermospheric modeling, the estimation of system drivers is a critical component of any reliable data assimilation technique. How to best estimate and apply these drivers, however, remains an open question and active area of research. The recently developed method of Iterative Re-Initialization, Driver Estimation and Assimilation (IRIDEA) accounts for the driver/response time-delay characteristics of the Ionosphere-Thermosphere system relative to satellite accelerometer observations. Results from two near year-long simulations are shown: (1) from a period of elevated solar and geomagnetic activity during 2003, and (2) from a solar minimum period during 2007. This talk will highlight the challenges and successes of implementing a technique suited for both solar min and max, as well as expectations for improving neutral density forecasts.

(YIP-09) Improving Synthesis and Recognition of Crowded Scenes using Statistical Models of Group Behavior

DTIC Science & Technology

2013-05-01

prisoner’s dilemma. In Proceedings of Florida Artifical Intelligence Research Society, pages 2–7, Day- tona Beach, FL, May 2010. [6] M. Maghami* and...A. Shah*, P. Bell*, and G. Sukthankar. A destination recommendation system for virtual worlds. In Proceedings of Florida Artifical Intelligence ...question convey? Leveraging help-seeking behavior for improved modeling in a simulation- based intelligent tutor. In Proceedings of SpringSim Military

A Novel GMM-Based Behavioral Modeling Approach for Smartwatch-Based Driver Authentication.

PubMed

Yang, Ching-Han; Chang, Chin-Chun; Liang, Deron

2018-03-28

All drivers have their own distinct driving habits, and usually hold and operate the steering wheel differently in different driving scenarios. In this study, we proposed a novel Gaussian mixture model (GMM)-based method that can improve the traditional GMM in modeling driving behavior. This new method can be applied to build a better driver authentication system based on the accelerometer and orientation sensor of a smartwatch. To demonstrate the feasibility of the proposed method, we created an experimental system that analyzes driving behavior using the built-in sensors of a smartwatch. The experimental results for driver authentication-an equal error rate (EER) of 4.62% in the simulated environment and an EER of 7.86% in the real-traffic environment-confirm the feasibility of this approach.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

PubMed

Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jian; Wang, Chunbai

2016-02-19

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

The application of connectionism to query planning/scheduling in intelligent user interfaces

NASA Technical Reports Server (NTRS)

Short, Nicholas, Jr.; Shastri, Lokendra

1990-01-01

In the mid nineties, the Earth Observing System (EOS) will generate an estimated 10 terabytes of data per day. This enormous amount of data will require the use of sophisticated technologies from real time distributed Artificial Intelligence (AI) and data management. Without regard to the overall problems in distributed AI, efficient models were developed for doing query planning and/or scheduling in intelligent user interfaces that reside in a network environment. Before intelligent query/planning can be done, a model for real time AI planning and/or scheduling must be developed. As Connectionist Models (CM) have shown promise in increasing run times, a connectionist approach to AI planning and/or scheduling is proposed. The solution involves merging a CM rule based system to a general spreading activation model for the generation and selection of plans. The system was implemented in the Rochester Connectionist Simulator and runs on a Sun 3/260.

Food, health, and complexity: towards a conceptual understanding to guide collaborative public health action.

PubMed

Majowicz, Shannon E; Meyer, Samantha B; Kirkpatrick, Sharon I; Graham, Julianne L; Shaikh, Arshi; Elliott, Susan J; Minaker, Leia M; Scott, Steffanie; Laird, Brian

2016-06-08

What we eat simultaneously impacts our exposure to pathogens, allergens, and contaminants, our nutritional status and body composition, our risks for and the progression of chronic diseases, and other outcomes. Furthermore, what we eat is influenced by a complex web of drivers, including culture, politics, economics, and our built and natural environments. To date, public health initiatives aimed at improving food-related population health outcomes have primarily been developed within 'practice silos', and the potential for complex interactions among such initiatives is not well understood. Therefore, our objective was to develop a conceptual model depicting how infectious foodborne illness, food insecurity, dietary contaminants, obesity, and food allergy can be linked via shared drivers, to illustrate potential complex interactions and support future collaboration across public health practice silos. We developed the conceptual model by first conducting a systematic literature search to identify review articles containing schematics that depicted relationships between drivers and the issues of interest. Next, we synthesized drivers into a common model using a modified thematic synthesis approach that combined an inductive thematic analysis and mapping to synthesize findings. The literature search yielded 83 relevant references containing 101 schematics. The conceptual model contained 49 shared drivers and 227 interconnections. Each of the five issues was connected to all others. Obesity and food insecurity shared the most drivers (n = 28). Obesity shared several drivers with food allergy (n = 11), infectious foodborne illness (n = 7), and dietary contamination (n = 6). Food insecurity shared several drivers with infectious foodborne illness (n = 9) and dietary contamination (n = 9). Infectious foodborne illness shared drivers with dietary contamination (n = 8). Fewer drivers were shared between food allergy and: food insecurity (n = 4); infectious foodborne illness (n = 2); and dietary contamination (n = 1). Our model explicates potential interrelationships between five population health issues for which public health interventions have historically been siloed, suggesting that interventions targeted towards these issues have the potential to interact and produce unexpected consequences. Public health practitioners working in infectious foodborne illness, food insecurity, dietary contaminants, obesity, and food allergy should actively consider how their seemingly targeted public health actions may produce unintended positive or negative population health impacts.

Modelling how drivers respond to a bicyclist crossing their path at an intersection: How do test track and driving simulator compare?

PubMed

Boda, Christian-Nils; Dozza, Marco; Bohman, Katarina; Thalya, Prateek; Larsson, Annika; Lubbe, Nils

2018-02-01

Bicyclist fatalities are a great concern in the European Union. Most of them are due to crashes between motorized vehicles and bicyclists at unsignalised intersections. Different countermeasures are currently being developed and implemented in order to save lives. One type of countermeasure, active safety systems, requires a deep understanding of driver behaviour to be effective without being annoying. The current study provides new knowledge about driver behaviour which can inform assessment programmes for active safety systems such as Euro NCAP. This study investigated how drivers responded to bicyclists crossing their path at an intersection. The influences of car speed and cyclist speed on the driver response process were assessed for three different crossing configurations. The same experimental protocol was tested in a fixed-base driving simulator and on a test track. A virtual model of the test track was used in the driving simulator to keep the protocol as consistent as possible across testing environments. Results show that neither car speed nor bicycle speed directly influenced the response process. The crossing configuration did not directly influence the braking response process either, but it did influence the strategy chosen by the drivers to approach the intersection. The point in time when the bicycle became visible (which depended on the car speed, the bicycle speed, and the crossing configuration) and the crossing configuration alone had the largest effects on the driver response process. Dissimilarities between test-track and driving-simulator studies were found; however, there were also interesting similarities, especially in relation to the driver braking behaviour. Drivers followed the same strategy to initiate braking, independent of the test environment. On the other hand, the test environment affected participants' strategies for releasing the gas pedal and regulating deceleration. Finally, a mathematical model, based on both experiments, is proposed to characterize driver braking behaviour in response to bicyclists crossing at intersections. This model has direct implications on what variables an in-vehicle safety system should consider and how tests in evaluation programs should be designed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Domain-specific knowledge as the "dark matter" of adult intelligence: Gf/Gc, personality and interest correlates.

PubMed

Ackerman, P L

2000-03-01

An enduring controversy in intelligence theory and assessment, the argument that middle-aged adults are, on average, less intelligent than young adults, is addressed in this study. A sample of 228 educated adults between ages 21 and 62 years was given an array of tests that focused on a broad assessment of intelligence-as-knowledge, traditional estimates of fluid intelligence (Gf) and crystallized intelligence (Gc), personality, and interests. The results indicate that middle-aged adults are more knowledgeable in many domains, compared with younger adults. A coherent pattern of ability, personality, and interest relations is found. The results are consistent with a developmental perspective of intelligence that includes both traditional ability and non-ability determinants of intelligence during adulthood. A reassessment of the nature of intelligence in adulthood is provided, in the context of a lifelong learning and investment model, called PPIK, for intelligence-as-Process, Personality, Interests, and intelligence-as-Knowledge (Ackerman, 1996).

Information and redundancy: key concepts in understanding the genetic control of health and intelligence.

PubMed

Morris, J A

1999-08-01

A model is proposed in which information from the environment is analysed by complex biological decision-making systems which are highly redundant. A correct response is intelligent behaviour which preserves health; incorrect responses lead to disease. Mutations in genes which code for the redundant systems will accumulate in the genome and impair decision-making. The number of mutant genes will depend upon a balance between the new mutation rate per generation and systems of elimination based on synergistic interaction in redundant systems. This leads to a polygenic pattern of inheritance for intelligence and the common diseases. The model also gives a simple explanation for some of the hitherto puzzling aspects of work on the genetic basis of intelligence including the recorded rise in IQ this century. There is a prediction that health, intelligence and socio-economic position will be correlated generating a health differential in the social hierarchy. Furthermore, highly competitive societies will place those least able to cope in the harshest environment and this will impair health overall. The model points to a need for population monitoring of somatic mutation in order to preserve the health and intelligence of future generations.

On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum.

PubMed

Mayne, Richard; Adamatzky, Andrew; Jones, Jeff

2015-01-01

The plasmodium of slime mold Physarum polycephalum behaves as an amorphous reaction-diffusion computing substrate and is capable of apparently 'intelligent' behavior. But how does intelligence emerge in an acellular organism? Through a range of laboratory experiments, we visualize the plasmodial cytoskeleton-a ubiquitous cellular protein scaffold whose functions are manifold and essential to life-and discuss its putative role as a network for transducing, transmitting and structuring data streams within the plasmodium. Through a range of computer modeling techniques, we demonstrate how emergent behavior, and hence computational intelligence, may occur in cytoskeletal communications networks. Specifically, we model the topology of both the actin and tubulin cytoskeletal networks and discuss how computation may occur therein. Furthermore, we present bespoke cellular automata and particle swarm models for the computational process within the cytoskeleton and observe the incidence of emergent patterns in both. Our work grants unique insight into the origins of natural intelligence; the results presented here are therefore readily transferable to the fields of natural computation, cell biology and biomedical science. We conclude by discussing how our results may alter our biological, computational and philosophical understanding of intelligence and consciousness.

Driver injury severity related to inclement weather at highway-rail grade crossings in the United States.

PubMed

Hao, Wei; Daniel, Janice

2016-01-01

Previous studies on crash modeling at highway-rail grade crossings were aimed at exploring the factors that are likely to increase the crash frequencies at highway-rail grade crossings. In recent years, modeling driver's injury severity at highway-rail grade crossings has received interest. Because there were substantial differences among different weather conditions for driver's injury severity, this study attempts to explore the impact of weather influence on driver injury at highway-rail grade crossing. Utilizing the most recent 10 years (2002-2011) of highway-rail grade crossing accident data, this study applied a mixed logit model to explore the determinants of driver injury severity under different weather conditions at highway-rail grade crossing. Analysis results indicate that drivers' injury severity at highway-rail grade crossings is strongly different for different weather conditions. It was found that the factors significantly impacting driver injury severity at highway-rail grade crossings include motor vehicle speed, train speed, driver's age, gender, area type, lighting condition, highway pavement, traffic volume, and time of day. The findings of this study indicate that crashes are more prevalent if vehicle drivers are driving at high speed or the oncoming trains are high speed. Hence, a reduction in speed limit during inclement weather conditions could be particularly effective in moderating injury severity, allowing more reaction time for last-minute maneuvering and braking in moments before impacts. In addition, inclement weather-related crashes were more likely to occur in open areas and highway-rail grade crossings without pavement and lighting. Paved highway-rail grade crossings with installation of lights could be particularly effective in moderating injury severity.

Center for Artificial Intelligence

DTIC Science & Technology

1992-03-14

builder’s intelligent assistant. The basic approach of IGOR is to integrate the complementary strategies of exploratory and confirmatory data analysis...Recovery: A Model and Experiments," in Proceedings of the Ninth National Conference on Artifcial Intelligence , Anaheim, CA, July 1991, pp. 801-808. Howe...Lehnert University of Massachusetts, Amherst, MAJ (413) 545-1322 Lessei•:s.umass.edu Title: Center for Artificial Intelligence Contract #: N00014-86-K

Artificial intelligence in process control: Knowledge base for the shuttle ECS model

NASA Technical Reports Server (NTRS)

Stiffler, A. Kent

1989-01-01

The general operation of KATE, an artificial intelligence controller, is outlined. A shuttle environmental control system (ECS) demonstration system for KATE is explained. The knowledge base model for this system is derived. An experimental test procedure is given to verify parameters in the model.

How Can Intelligent CAL Better Adapt to Learners?

ERIC Educational Resources Information Center

Boyd, Gary McI.; Mitchell, P. David

1992-01-01

Discusses intelligent computer-aided learning (ICAL) support systems and considers learner characteristics as elements of ICAL student models. Cybernetic theory and attribute-treatment results are discussed, six components of a student model for tutoring are described, and methods for determining the student's model of the tutor are examined. (22…

From Interactive Open Learner Modelling to Intelligent Mentoring: STyLE-OLM and Beyond

ERIC Educational Resources Information Center

Dimitrova, Vania; Brna, Paul

2016-01-01

STyLE-OLM (Dimitrova 2003 "International Journal of Artificial Intelligence in Education," 13, 35-78) presented a framework for interactive open learner modelling which entails the development of the means by which learners can "inspect," "discuss" and "alter" the learner model that has been jointly…

What determines the take-over time? An integrated model approach of driver take-over after automated driving.

PubMed

Zeeb, Kathrin; Buchner, Axel; Schrauf, Michael

2015-05-01

In recent years the automation level of driver assistance systems has increased continuously. One of the major challenges for highly automated driving is to ensure a safe driver take-over of the vehicle guidance. This must be ensured especially when the driver is engaged in non-driving related secondary tasks. For this purpose it is essential to find indicators of the driver's readiness to take over and to gain more knowledge about the take-over process in general. A simulator study was conducted to explore how drivers' allocation of visual attention during highly automated driving influences a take-over action in response to an emergency situation. Therefore we recorded drivers' gaze behavior during automated driving while simultaneously engaging in a visually demanding secondary task, and measured their reaction times in a take-over situation. According to their gaze behavior the drivers were categorized into "high", "medium" and "low-risk". The gaze parameters were found to be suitable for predicting the readiness to take-over the vehicle, in such a way that high-risk drivers reacted late and more often inappropriately in the take-over situation. However, there was no difference among the driver groups in the time required by the drivers to establish motor readiness to intervene after the take-over request. An integrated model approach of driver behavior in emergency take-over situations during automated driving is presented. It is argued that primarily cognitive and not motor processes determine the take-over time. Given this, insights can be derived for further research and the development of automated systems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Envelope and intensity based prediction of psychoacoustic masking and speech intelligibility.

PubMed

Biberger, Thomas; Ewert, Stephan D

2016-08-01

Human auditory perception and speech intelligibility have been successfully described based on the two concepts of spectral masking and amplitude modulation (AM) masking. The power-spectrum model (PSM) [Patterson and Moore (1986). Frequency Selectivity in Hearing, pp. 123-177] accounts for effects of spectral masking and critical bandwidth, while the envelope power-spectrum model (EPSM) [Ewert and Dau (2000). J. Acoust. Soc. Am. 108, 1181-1196] has been successfully applied to AM masking and discrimination. Both models extract the long-term (envelope) power to calculate signal-to-noise ratios (SNR). Recently, the EPSM has been applied to speech intelligibility (SI) considering the short-term envelope SNR on various time scales (multi-resolution speech-based envelope power-spectrum model; mr-sEPSM) to account for SI in fluctuating noise [Jørgensen, Ewert, and Dau (2013). J. Acoust. Soc. Am. 134, 436-446]. Here, a generalized auditory model is suggested combining the classical PSM and the mr-sEPSM to jointly account for psychoacoustics and speech intelligibility. The model was extended to consider the local AM depth in conditions with slowly varying signal levels, and the relative role of long-term and short-term SNR was assessed. The suggested generalized power-spectrum model is shown to account for a large variety of psychoacoustic data and to predict speech intelligibility in various types of background noise.

A Binaural Grouping Model for Predicting Speech Intelligibility in Multitalker Environments

PubMed Central

Colburn, H. Steven

2016-01-01

Spatially separating speech maskers from target speech often leads to a large intelligibility improvement. Modeling this phenomenon has long been of interest to binaural-hearing researchers for uncovering brain mechanisms and for improving signal-processing algorithms in hearing-assistive devices. Much of the previous binaural modeling work focused on the unmasking enabled by binaural cues at the periphery, and little quantitative modeling has been directed toward the grouping or source-separation benefits of binaural processing. In this article, we propose a binaural model that focuses on grouping, specifically on the selection of time-frequency units that are dominated by signals from the direction of the target. The proposed model uses Equalization-Cancellation (EC) processing with a binary decision rule to estimate a time-frequency binary mask. EC processing is carried out to cancel the target signal and the energy change between the EC input and output is used as a feature that reflects target dominance in each time-frequency unit. The processing in the proposed model requires little computational resources and is straightforward to implement. In combination with the Coherence-based Speech Intelligibility Index, the model is applied to predict the speech intelligibility data measured by Marrone et al. The predicted speech reception threshold matches the pattern of the measured data well, even though the predicted intelligibility improvements relative to the colocated condition are larger than some of the measured data, which may reflect the lack of internal noise in this initial version of the model. PMID:27698261

A Binaural Grouping Model for Predicting Speech Intelligibility in Multitalker Environments.

PubMed

Mi, Jing; Colburn, H Steven

2016-10-03

Spatially separating speech maskers from target speech often leads to a large intelligibility improvement. Modeling this phenomenon has long been of interest to binaural-hearing researchers for uncovering brain mechanisms and for improving signal-processing algorithms in hearing-assistive devices. Much of the previous binaural modeling work focused on the unmasking enabled by binaural cues at the periphery, and little quantitative modeling has been directed toward the grouping or source-separation benefits of binaural processing. In this article, we propose a binaural model that focuses on grouping, specifically on the selection of time-frequency units that are dominated by signals from the direction of the target. The proposed model uses Equalization-Cancellation (EC) processing with a binary decision rule to estimate a time-frequency binary mask. EC processing is carried out to cancel the target signal and the energy change between the EC input and output is used as a feature that reflects target dominance in each time-frequency unit. The processing in the proposed model requires little computational resources and is straightforward to implement. In combination with the Coherence-based Speech Intelligibility Index, the model is applied to predict the speech intelligibility data measured by Marrone et al. The predicted speech reception threshold matches the pattern of the measured data well, even though the predicted intelligibility improvements relative to the colocated condition are larger than some of the measured data, which may reflect the lack of internal noise in this initial version of the model. © The Author(s) 2016.

Extraordinary intelligence and the care of infants

PubMed Central

Piantadosi, Steven T.; Kidd, Celeste

2016-01-01

We present evidence that pressures for early childcare may have been one of the driving factors of human evolution. We show through an evolutionary model that runaway selection for high intelligence may occur when (i) altricial neonates require intelligent parents, (ii) intelligent parents must have large brains, and (iii) large brains necessitate having even more altricial offspring. We test a prediction of this account by showing across primate genera that the helplessness of infants is a particularly strong predictor of the adults’ intelligence. We discuss related implications, including this account’s ability to explain why human-level intelligence evolved specifically in mammals. This theory complements prior hypotheses that link human intelligence to social reasoning and reproductive pressures and explains how human intelligence may have become so distinctive compared with our closest evolutionary relatives. PMID:27217560

Industry Cluster's Adaptive Co-competition Behavior Modeling Inspired by Swarm Intelligence

NASA Astrophysics Data System (ADS)

Xiang, Wei; Ye, Feifan

Adaptation helps the individual enterprise to adjust its behavior to uncertainties in environment and hence determines a healthy growth of both the individuals and the whole industry cluster as well. This paper is focused on the study on co-competition adaptation behavior of industry cluster, which is inspired by swarm intelligence mechanisms. By referencing to ant cooperative transportation and ant foraging behavior and their related swarm intelligence approaches, the cooperative adaptation and competitive adaptation behavior are studied and relevant models are proposed. Those adaptive co-competition behaviors model can be integrated to the multi-agent system of industry cluster to make the industry cluster model more realistic.

STANFORD ARTIFICIAL INTELLIGENCE PROJECT.

DTIC Science & Technology

ARTIFICIAL INTELLIGENCE , GAME THEORY, DECISION MAKING, BIONICS, AUTOMATA, SPEECH RECOGNITION, GEOMETRIC FORMS, LEARNING MACHINES, MATHEMATICAL MODELS, PATTERN RECOGNITION, SERVOMECHANISMS, SIMULATION, BIBLIOGRAPHIES.

An examination of the impact of five grade crossing safety factors on driver decision making

DOT National Transportation Integrated Search

2014-04-01

The authors applied signal detection theory to model the impact : of five grade-crossing safety factors to understand their impact : on driver decision making. The safety factors were improving : commercial motor vehicle (CMV) driver safety through f...

Assessing characteristics related to the use of seatbelts and cell phones by drivers: application of a bivariate probit model.

PubMed

Russo, Brendan J; Kay, Jonathan J; Savolainen, Peter T; Gates, Timothy J

2014-06-01

The effects of cell phone use and safety belt use have been an important focus of research related to driver safety. Cell phone use has been shown to be a significant source of driver distraction contributing to substantial degradations in driver performance, while safety belts have been demonstrated to play a vital role in mitigating injuries to crash-involved occupants. This study examines the prevalence of cell phone use and safety belt non-use among the driving population through direct observation surveys. A bivariate probit model is developed to simultaneously examine the factors that affect cell phone and safety belt use among motor vehicle drivers. The results show that several factors may influence drivers' decision to use cell phones and safety belts, and that these decisions are correlated. Understanding the factors that affect both cell phone use and safety belt non-use is essential to targeting policy and programs that reduce such behavior. Copyright © 2014 Elsevier Ltd. All rights reserved.

[The use of biological age on mental work capacity model in accelerated aging assessment of professional lorry-drivers].

PubMed

Bashkireva, A S

2012-01-01

The studies of biological age, aging rate, mental work capacity in professional drivers were conducted. The examination revealed peculiarities of system organization of functions determining the mental work capacity levels. Dynamics of the aging process of professional driver's organism in relation with calendar age and driving experience were shown using the biological age model. The results point at the premature decrease of the mental work capacity in professional drivers. It was proved, that premature age-related changes of physiologic and psychophysiologic indices in drivers are just "risk indicators", while long driving experience is a real risk factor, accelerating the aging process. The "risk group" with manifestations of accelerating aging was observed in 40-49-year old drivers with 15-19 years of professional experience. The expediency of using the following methods for the age rate estimation according to biologic age indices and necessity of prophylactic measures for premature and accelerated aging prevention among working population was demonstrated.

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa

2012-01-01

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.

Emotional Intelligence Competencies and the Army Leadership Requirements Model

DTIC Science & Technology

2015-06-12

comprised of five dimensions : knowing one’s emotions , managing emotions , motivation, recognizing emotions in others, and handling relationships. Emotional ...... EMOTIONAL INTELLIGENCE COMPETENCIES AND THE ARMY LEADERSHIP REQUIREMENTS MODEL A thesis presented to the Faculty of the U.S

The association between intelligence and lifespan is mostly genetic.

PubMed

Arden, Rosalind; Luciano, Michelle; Deary, Ian J; Reynolds, Chandra A; Pedersen, Nancy L; Plassman, Brenda L; McGue, Matt; Christensen, Kaare; Visscher, Peter M

2016-02-01

Several studies in the new field of cognitive epidemiology have shown that higher intelligence predicts longer lifespan. This positive correlation might arise from socioeconomic status influencing both intelligence and health; intelligence leading to better health behaviours; and/or some shared genetic factors influencing both intelligence and health. Distinguishing among these hypotheses is crucial for medicine and public health, but can only be accomplished by studying a genetically informative sample. We analysed data from three genetically informative samples containing information on intelligence and mortality: Sample 1, 377 pairs of male veterans from the NAS-NRC US World War II Twin Registry; Sample 2, 246 pairs of twins from the Swedish Twin Registry; and Sample 3, 784 pairs of twins from the Danish Twin Registry. The age at which intelligence was measured differed between the samples. We used three methods of genetic analysis to examine the relationship between intelligence and lifespan: we calculated the proportion of the more intelligent twins who outlived their co-twin; we regressed within-twin-pair lifespan differences on within-twin-pair intelligence differences; and we used the resulting regression coefficients to model the additive genetic covariance. We conducted a meta-analysis of the regression coefficients across the three samples. The combined (and all three individual samples) showed a small positive phenotypic correlation between intelligence and lifespan. In the combined sample observed r = .12 (95% confidence interval .06 to .18). The additive genetic covariance model supported a genetic relationship between intelligence and lifespan. In the combined sample the genetic contribution to the covariance was 95%; in the US study, 84%; in the Swedish study, 86%, and in the Danish study, 85%. The finding of common genetic effects between lifespan and intelligence has important implications for public health, and for those interested in the genetics of intelligence, lifespan or inequalities in health outcomes including lifespan. © The Author 2015; Published by Oxford University Press on behalf of the International Epidemiological Association.

The association between intelligence and lifespan is mostly genetic

PubMed Central

Arden, Rosalind; Deary, Ian J; Reynolds, Chandra A; Pedersen, Nancy L; Plassman, Brenda L; McGue, Matt; Christensen, Kaare; Visscher, Peter M

2016-01-01

Abstract Background: Several studies in the new field of cognitive epidemiology have shown that higher intelligence predicts longer lifespan. This positive correlation might arise from socioeconomic status influencing both intelligence and health; intelligence leading to better health behaviours; and/or some shared genetic factors influencing both intelligence and health. Distinguishing among these hypotheses is crucial for medicine and public health, but can only be accomplished by studying a genetically informative sample. Methods: We analysed data from three genetically informative samples containing information on intelligence and mortality: Sample 1, 377 pairs of male veterans from the NAS-NRC US World War II Twin Registry; Sample 2, 246 pairs of twins from the Swedish Twin Registry; and Sample 3, 784 pairs of twins from the Danish Twin Registry. The age at which intelligence was measured differed between the samples. We used three methods of genetic analysis to examine the relationship between intelligence and lifespan: we calculated the proportion of the more intelligent twins who outlived their co-twin; we regressed within-twin-pair lifespan differences on within-twin-pair intelligence differences; and we used the resulting regression coefficients to model the additive genetic covariance. We conducted a meta-analysis of the regression coefficients across the three samples. Results: The combined (and all three individual samples) showed a small positive phenotypic correlation between intelligence and lifespan. In the combined sample observed r  = .12 (95% confidence interval .06 to .18). The additive genetic covariance model supported a genetic relationship between intelligence and lifespan. In the combined sample the genetic contribution to the covariance was 95%; in the US study, 84%; in the Swedish study, 86%, and in the Danish study, 85%. Conclusions: The finding of common genetic effects between lifespan and intelligence has important implications for public health, and for those interested in the genetics of intelligence, lifespan or inequalities in health outcomes including lifespan. PMID:26213105

Leading to Learning and Competitive Intelligence

ERIC Educational Resources Information Center

Luu, Trong Tuan

2013-01-01

Purpose: This research aims to examine whether there is the chain effect from corporate social responsibility (CSR) and emotional intelligence (EI) to organizational learning and competitive intelligence in chemical companies in a Vietnam business setting. Design/methodology/approach: Structural equation modeling (SEM) approach was used to analyze…

Emotional Intelligence and Nursing Student Retention

ERIC Educational Resources Information Center

Wilson, Victoria Jane

2013-01-01

The study examined the constructs of a Multi-Intelligence Model of Retention with four constructs: cognitive and emotional-social intelligence, student characteristics, and environmental factors. Data were obtained from sophomore students entering two diploma, nine associate, and five baccalaureate nursing programs. One year later, retention and…

Spatial-Temporal Intelligence: Original Thinking Processes of Gifted Inventors.

ERIC Educational Resources Information Center

Cooper, Eileen E.

2000-01-01

This psychological phenomenological research analyzed cognition of 7 adult inventors and proposes a theory of original, creative thinking. Spatial intelligence is reviewed. Results provide 7 findings, including cognitive, motivational, affective, and psychokinesthetic factors. Spatial-temporal intelligence is theorized as an abstract model of…

[Epidemiological intelligence as a model of organization in health].

PubMed

Rodrigues-Júnior, Antonio Luiz

2012-03-01

The concept of epidemiological intelligence, as a construction of information societies, goes beyond monitoring a list of diseases and the ability to elicit rapid responses. The concept should consider the complexity of the definition of epidemiology in the identification of this object of study without being limited to a set of actions in a single government sector. The activities of epidemiological intelligence include risk assessment, strategies for prevention and protection, subsystems of information, crisis management rooms, geographical analysis, etc. This concept contributes to the understanding of policies in health, in multisectorial and geopolitical dimensions, as regards the organization of services around public health emergencies, primary healthcare, as well as disasters. The activities of epidemiological intelligence should not be restricted to scientific research, but the researchers must beware of threats to public health. Lalonde's model enabled consideration of epidemiological intelligence as a way to restructure policies and share resources by creating communities of intelligence, whose purpose is primarily to deal with public health emergencies and disasters.

Decomposing self-estimates of intelligence: structure and sex differences across 12 nations.

PubMed

von Stumm, Sophie; Chamorro-Premuzic, Tomas; Furnham, Adrian

2009-05-01

This study examines the structure of self-estimates of intelligence (SEI) across 12 nations (Australia, Austria, Brazil, France, Iran, Israel, Malaysia, South Africa, Spain, Turkey, UK and US). Participants rated themselves on general and specific abilities from three popular models of intelligence: Gardner's multiple intelligences, Sternberg's triarchic theory of intelligence, and Goleman's emotional intelligence. The results showed that (a) laypeople across nations have similar and invariant concepts of intelligence, (b) concepts of intelligence are cross-culturally closely related to academic notions of intellectual ability and (c) sex differences in general and specific SEI favouring men are consistent across countries. Male hubris and female humility in SEI seem independent of sex differences in actual cognitive ability and national levels of masculinity-femininity. Furthermore, international mean differences in general SEI could not be attributed to discrepancies in national intelligence quotient (IQ) levels or to cultural variations.

TIE: An Ability Test of Emotional Intelligence

PubMed Central

Śmieja, Magdalena; Orzechowski, Jarosław; Stolarski, Maciej S.

2014-01-01

The Test of Emotional Intelligence (TIE) is a new ability scale based on a theoretical model that defines emotional intelligence as a set of skills responsible for the processing of emotion-relevant information. Participants are provided with descriptions of emotional problems, and asked to indicate which emotion is most probable in a given situation, or to suggest the most appropriate action. Scoring is based on the judgments of experts: professional psychotherapists, trainers, and HR specialists. The validation study showed that the TIE is a reliable and valid test, suitable for both scientific research and individual assessment. Its internal consistency measures were as high as .88. In line with theoretical model of emotional intelligence, the results of the TIE shared about 10% of common variance with a general intelligence test, and were independent of major personality dimensions. PMID:25072656

Career Corner: Pitching Your Contributions at the Right Level

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

Anderson-Cook, Christine Michaela

Whether it is in a job interview, a presentation or in collaborations with colleagues with differing technical backgrounds, effectively conveying your ideas and contributions is at least as important as the content. Daniel Goleman speaks to the importance of emotional intelligence being a key driver of success and advancement. Why does this matter so much? If you dive right into technical details without providing a broader context and motivation for the problem, then the people with whom you are communicating will not appreciate the contribution. If you talk only about your ideas at a high level with insufficient detail, thenmore » the weight of your contributions might be undervalued or misinterpreted.« less

Career Corner: Pitching Your Contributions at the Right Level

DOE PAGES

Anderson-Cook, Christine Michaela

2017-04-15

Whether it is in a job interview, a presentation or in collaborations with colleagues with differing technical backgrounds, effectively conveying your ideas and contributions is at least as important as the content. Daniel Goleman speaks to the importance of emotional intelligence being a key driver of success and advancement. Why does this matter so much? If you dive right into technical details without providing a broader context and motivation for the problem, then the people with whom you are communicating will not appreciate the contribution. If you talk only about your ideas at a high level with insufficient detail, thenmore » the weight of your contributions might be undervalued or misinterpreted.« less

An Intelligent Parking Management System for Urban Areas.

PubMed

Vera-Gómez, Juan A; Quesada-Arencibia, Alexis; García, Carmelo R; Suárez Moreno, Raúl; Guerra Hernández, Fernando

2016-06-21

In this article we describe a low-cost, minimally-intrusive system for the efficient management of parking spaces on both public roads and controlled zones. This system is based on wireless networks of photoelectric sensors that are deployed on the access roads into and out of these areas. The sensors detect the passage of vehicles on these roads and communicate this information to a data centre, thus making it possible to know the number of vehicles in the controlled zone and the occupancy levels in real-time. This information may be communicated to drivers to facilitate their search for a parking space and to authorities so that they may take steps to control traffic when congestion is detected.

Effects of motivation on car-following

NASA Technical Reports Server (NTRS)

Boesser, T.

1982-01-01

Speed- and distance control by automobile-drivers is described best by linear models when the leading vehicles speed varies randomly and when the driver is motivated to keep a large distance. A car-following experiment required subjects to follow at 'safe' or at 'close' distance. Transfer-characteristics of the driver were extended by 1 octave when following 'closely'. Nonlinear properties of drivers control-movements are assumed to reflect different motivation-dependent control strategies.

Development of the physics driver in NOAA Environmental Modeling System (NEMS)

NASA Astrophysics Data System (ADS)

Lei, H.; Iredell, M.; Tripp, P.

2016-12-01

As a key component of the Next Generation Global Prediction System (NGGPS), a physics driver is developed in the NOAA Environmental Modeling System (NEMS) in order to facilitate the research, development, and transition to operations of innovations in atmospheric physical parameterizations. The physics driver connects the atmospheric dynamic core, the Common Community Physics Package and the other NEMS-based forecast components (land, ocean, sea ice, wave, and space weather). In current global forecasting system, the physics driver has incorporated major existing physics packages including radiation, surface physics, cloud and microphysics, ozone, and stochastic physics. The physics driver is also applicable to external physics packages. The structure adjustment in NEMS by separating the PHYS trunk is to create an open physics package pool. This open platform is beneficial to the enhancement of U.S. weather forecast ability. In addition, with the universal physics driver, the NEMS can also be used for specific functions by connecting external target physics packages through physics driver. The test of its function is to connect a physics dust-radiation model in the system. Then the modified system can be used for dust storm prediction and forecast. The physics driver is also developed into a standalone form. This is to facilitate the development works on physics packages. The developers can save instant fields of meteorology data and snapshots from the running system , and then used them as offline driving data fields to test the new individual physics modules or small modifications to current modules. This prevents the run of whole system for every test.

Mathematical model to predict drivers' reaction speeds.

PubMed

Long, Benjamin L; Gillespie, A Isabella; Tanaka, Martin L

2012-02-01

Mental distractions and physical impairments can increase the risk of accidents by affecting a driver's ability to control the vehicle. In this article, we developed a linear mathematical model that can be used to quantitatively predict drivers' performance over a variety of possible driving conditions. Predictions were not limited only to conditions tested, but also included linear combinations of these tests conditions. Two groups of 12 participants were evaluated using a custom drivers' reaction speed testing device to evaluate the effect of cell phone talking, texting, and a fixed knee brace on the components of drivers' reaction speed. Cognitive reaction time was found to increase by 24% for cell phone talking and 74% for texting. The fixed knee brace increased musculoskeletal reaction time by 24%. These experimental data were used to develop a mathematical model to predict reaction speed for an untested condition, talking on a cell phone with a fixed knee brace. The model was verified by comparing the predicted reaction speed to measured experimental values from an independent test. The model predicted full braking time within 3% of the measured value. Although only a few influential conditions were evaluated, we present a general approach that can be expanded to include other types of distractions, impairments, and environmental conditions.

Development of Driver/Vehicle Steering Interaction Models for Dynamic Analysis

DTIC Science & Technology

1988-12-01

Figure 5-10. The Linearized Single-Unit Vehicle Model ............................... 41 Figure 5-11. Interpretation of the Single-Unit Model...The starting point for the driver modelling research conducted under this project was a linear preview control model originally proposed by MacAdam 1...regardless of its origin, can pass at least the elementary validation test of exhibiting "cross-over model"-like- behavior in the vicinity of its

Reliability Prediction Approaches For Domestic Intelligent Electric Energy Meter Based on IEC62380

NASA Astrophysics Data System (ADS)

Li, Ning; Tong, Guanghua; Yang, Jincheng; Sun, Guodong; Han, Dongjun; Wang, Guixian

2018-01-01

The reliability of intelligent electric energy meter is a crucial issue considering its large calve application and safety of national intelligent grid. This paper developed a procedure of reliability prediction for domestic intelligent electric energy meter according to IEC62380, especially to identify the determination of model parameters combining domestic working conditions. A case study was provided to show the effectiveness and validation.

Knowledge-Based Software Development Tools

DTIC Science & Technology

1993-09-01

GREEN, C., AND WESTFOLD, S. Knowledge-based programming self-applied. In Machine Intelligence 10, J. E. Hayes, D. Mitchie, and Y. Pao, Eds., Wiley...Technical Report KES.U.84.2, Kestrel Institute, April 1984. [181 KORF, R. E. Toward a model of representation changes. Artificial Intelligence 14, 1...Artificial Intelligence 27, 1 (February 1985), 43-96. Replinted in Readings in Artificial Intelligence and Software Engineering, C. Rich •ad R. Waters

Driver performance and attention allocation in use of logo signs on freeway exit ramps.

PubMed

Zahabi, Maryam; Machado, Patricia; Lau, Mei Ying; Deng, Yulin; Pankok, Carl; Hummer, Joseph; Rasdorf, William; Kaber, David B

2017-11-01

The objective of this research was to quantify the effects of driver age, ramp signage configuration, including number of panels, logo format and sign familiarity, on driver performance and attention allocation when exiting freeways. Sixty drivers participated in a simulator study and analysis of variance models were used to assess response effects of the controlled manipulations. Results revealed elderly drivers to demonstrate worse performance and conservative control strategies as compared to middle-aged and young drivers. Elderly drivers also exhibited lower off-road fixation frequency and shorter off-road glance durations compared to middle-aged and young drivers. In general, drivers adopted a more conservative strategy when exposed to nine-panel signs as compared to six-panel signs and were more accurate in target detection when searching six-panels vs. nine and with familiar vs. unfamiliar logos. These findings provide an applicable guide for agency design of freeway ramp signage accounting for driver demographics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Use of artificial intelligence in supervisory control

NASA Technical Reports Server (NTRS)

Cohen, Aaron; Erickson, Jon D.

1989-01-01

Viewgraphs describing the design and testing of an intelligent decision support system called OFMspert are presented. In this expert system, knowledge about the human operator is represented through an operator/system model referred to as the OFM (Operator Function Model). OFMspert uses the blackboard model of problem solving to maintain a dynamic representation of operator goals, plans, tasks, and actions given previous operator actions and current system state. Results of an experiment to assess OFMspert's intent inferencing capability are outlined. Finally, the overall design philosophy for an intelligent tutoring system (OFMTutor) for operators of complex dynamic systems is summarized.

A new intrusion prevention model using planning knowledge graph

NASA Astrophysics Data System (ADS)

Cai, Zengyu; Feng, Yuan; Liu, Shuru; Gan, Yong

2013-03-01

Intelligent plan is a very important research in artificial intelligence, which has applied in network security. This paper proposes a new intrusion prevention model base on planning knowledge graph and discuses the system architecture and characteristics of this model. The Intrusion Prevention based on plan knowledge graph is completed by plan recognition based on planning knowledge graph, and the Intrusion response strategies and actions are completed by the hierarchical task network (HTN) planner in this paper. Intrusion prevention system has the advantages of intelligent planning, which has the advantage of the knowledge-sharing, the response focused, learning autonomy and protective ability.

Emotional intelligence education in pre-registration nursing programmes: an integrative review.

PubMed

Foster, Kim; McCloughen, Andrea; Delgado, Cynthia; Kefalas, Claudia; Harkness, Emily

2015-03-01

To investigate the state of knowledge on emotional intelligence (EI) education in pre-registration nursing programmes. Integrative literature review. CINAHL, Medline, Scopus, ERIC, and Web of Knowledge electronic databases were searched for abstracts published in English between 1992-2014. Data extraction and constant comparative analysis of 17 articles. Three categories were identified: Constructs of emotional intelligence; emotional intelligence curricula components; and strategies for emotional intelligence education. A wide range of emotional intelligence constructs were found, with a predominance of trait-based constructs. A variety of strategies to enhance students' emotional intelligence skills were identified, but limited curricula components and frameworks reported in the literature. An ability-based model for curricula and learning and teaching approaches is recommended. Copyright © 2014. Published by Elsevier Ltd.

Early prediction of student goals and affect in narrative-centered learning environments

NASA Astrophysics Data System (ADS)

Lee, Sunyoung

Recent years have seen a growing recognition of the role of goal and affect recognition in intelligent tutoring systems. Goal recognition is the task of inferring users' goals from a sequence of observations of their actions. Because of the uncertainty inherent in every facet of human computer interaction, goal recognition is challenging, particularly in contexts in which users can perform many actions in any order, as is the case with intelligent tutoring systems. Affect recognition is the task of identifying the emotional state of a user from a variety of physical cues, which are produced in response to affective changes in the individual. Accurately recognizing student goals and affect states could contribute to more effective and motivating interactions in intelligent tutoring systems. By exploiting knowledge of student goals and affect states, intelligent tutoring systems can dynamically modify their behavior to better support individual students. To create effective interactions in intelligent tutoring systems, goal and affect recognition models should satisfy two key requirements. First, because incorrectly predicted goals and affect states could significantly diminish the effectiveness of interactive systems, goal and affect recognition models should provide accurate predictions of user goals and affect states. When observations of users' activities become available, recognizers should make accurate early" predictions. Second, goal and affect recognition models should be highly efficient so they can operate in real time. To address key issues, we present an inductive approach to recognizing student goals and affect states in intelligent tutoring systems by learning goals and affect recognition models. Our work focuses on goal and affect recognition in an important new class of intelligent tutoring systems, narrative-centered learning environments. We report the results of empirical studies of induced recognition models from observations of students' interactions in narrative-centered learning environments. Experimental results suggest that induced models can make accurate early predictions of student goals and affect states, and they are sufficiently efficient to meet the real-time performance requirements of interactive learning environments.

TEx-Sys Model for Building Intelligent Tutoring Systems

ERIC Educational Resources Information Center

Stankov, Slavomir; Rosic, Marko; Zitko, Branko; Grubisic, Ani

2008-01-01

Special classes of asynchronous e-learning systems are the intelligent tutoring systems which represent an advanced learning and teaching environment adaptable to individual student's characteristics. Authoring shells have an environment that enables development of the intelligent tutoring systems. In this paper we present, in entirety, for the…

The Relationship between Principals' Cultural Intelligence Levels and Their Cultural Leadership Behaviors

ERIC Educational Resources Information Center

Göksoy, Süleyman

2017-01-01

This study aimed to identify school administrators' views on school administrators' cultural intelligence and cultural leadership behaviors. The study employed relational screening model, a descriptive research method, since it set out to determine the existing situation. "Cultural Intelligence Scale" and "Cultural Leadership…

Modeling and analyses for an extended car-following model accounting for drivers' situation awareness from cyber physical perspective

NASA Astrophysics Data System (ADS)

Chen, Dong; Sun, Dihua; Zhao, Min; Zhou, Tong; Cheng, Senlin

2018-07-01

In fact, driving process is a typical cyber physical process which couples tightly the cyber factor of traffic information with the physical components of the vehicles. Meanwhile, the drivers have situation awareness in driving process, which is not only ascribed to the current traffic states, but also extrapolates the changing trend. In this paper, an extended car-following model is proposed to account for drivers' situation awareness. The stability criterion of the proposed model is derived via linear stability analysis. The results show that the stable region of proposed model will be enlarged on the phase diagram compared with previous models. By employing the reductive perturbation method, the modified Korteweg de Vries (mKdV) equation is obtained. The kink-antikink soliton of mKdV equation reveals theoretically the evolution of traffic jams. Numerical simulations are conducted to verify the analytical results. Two typical traffic Scenarios are investigated. The simulation results demonstrate that drivers' situation awareness plays a key role in traffic flow oscillations and the congestion transition.

Intelligent controller of novel design

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

Zhou Qi Jian; Bai Jian Kuo

1983-01-01

This paper presents the authors attempt to combine the control engineering principle with human intelligence to form a new control algorithm. The hybrid system thus formed is both analogous and logical in actions and is called the intelligent controller (IC). With the help of cybernetics princple, the operator's intelligent action of control is programmed into the controller and the system is thus taught to act like an intelligent being within the prescribed range. Remarkable results were obtained from experiments conducted on an electronic model simulating the above mentioned system. Stability studies and system analysis are presented. 12 references.

Intelligent policy making? Key actors' perspectives on the development and implementation of an early years' initiative in Scotland's public health arena.

PubMed

Deas, L; Mattu, L; Gnich, W

2013-11-01

Increased political enthusiasm for evidence-based policy and action has re-ignited interest in the use of evidence within political and practitioner networks. Theories of evidence-based policy making and practice are being re-considered in an attempt to better understand the processes through which knowledge translation occurs. Understanding how policy develops, and practice results, has the potential to facilitate effective evidence use. Further knowledge of the factors which shape healthcare delivery and their influence in different contexts is needed. This paper explores the processes involved in the development of a complex intervention in Scotland's National Health Service (NHS). It uses a national oral health programme for children (Childsmile) as a case study, drawing upon key actors' perceptions of the influence of different drivers (research evidence, practitioner knowledge and values, policy, and political and local context) to programme development. Framework analysis is used to analyse stakeholder accounts from in-depth interviews. Documentary review is also undertaken. Findings suggest that Childsmile can be described as an 'evidence-informed' intervention, blending available research evidence with knowledge from practitioner experience and continual learning through evaluation, to plan delivery. The importance of context was underscored, in terms of the need to align with prevailing political ideology and in the facilitative strength of networks within the relatively small public health community in Scotland. Respondents' perceptions support several existing theoretical models of translation, however no single theory offered a comprehensive framework covering all aspects of the complex processes reported. Childsmile's use of best available evidence and on-going contribution to knowledge suggest that the programme is an example of intelligent policy making with international relevance. Copyright © 2013 Elsevier Ltd. All rights reserved.

Effects of cognitive training on the structure of intelligence.

PubMed

Protzko, John

2017-08-01

Targeted cognitive training, such as n-back or speed of processing training, in the hopes of raising intelligence is of great theoretical and practical importance. The most important theoretical contribution, however, is not about the malleability of intelligence. Instead, I argue the most important and novel theoretical contribution is understanding the causal structure of intelligence. The structure of intelligence, most often taken as a hierarchical factor structure, necessarily prohibits transfer from subfactors back up to intelligence. If this is the true structure, targeted cognitive training interventions will fail to increase intelligence not because intelligence is immutable, but simply because there is no causal connection between, say, working memory and intelligence. Seeing the structure of intelligence for what it is, a causal measurement model, allows us to focus testing on the presence and absence of causal links. If we can increase subfactors without transfer to other facets, we may be confirming the correct causal structure more than testing malleability. Such a blending into experimental psychometrics is a strong theoretical pursuit.

Effective Stress Management: A Model of Emotional Intelligence, Self-Leadership, and Student Stress Coping

ERIC Educational Resources Information Center

Houghton, Jeffery D.; Wu, Jinpei; Godwin, Jeffrey L.; Neck, Christopher P.; Manz, Charles C.

2012-01-01

This article develops and presents a model of the relationships among emotional intelligence, self-leadership, and stress coping among management students. In short, the authors' model suggests that effective emotion regulation and self-leadership, as mediated through positive affect and self-efficacy, has the potential to facilitate stress coping…

Developing Talents: A Longitudinal Examination of Intellectual Ability and Academic Achievement

ERIC Educational Resources Information Center

McCoach, D. Betsy; Yu, Huihui; Gottfried, Allen W.; Gottfried, Adele Eskeles

2017-01-01

The Fullerton Longitudinal Study offers a unique opportunity to model the stability of intelligence and achievement and their relations from elementary through secondary school. Using latent variable modeling, we fit a cross-lagged panel model to examine the relations between intelligence and achievement in two academic domains: mathematics and…

The Application of Multiple Intelligences Theory to Reading Instruction.

ERIC Educational Resources Information Center

Alexander, James C.

Top-down and bottom-up theories have long dominated the field of reading. Recently, interactive models have been proposed by some researchers. One model, the interactive-compensatory model, hypothesizes that a deficiency in one processing area is compensated for by a relative strength in another area. The concept of multiple intelligences is one…

Agent autonomy approach to probabilistic physics-of-failure modeling of complex dynamic systems with interacting failure mechanisms

NASA Astrophysics Data System (ADS)

Gromek, Katherine Emily

A novel computational and inference framework of the physics-of-failure (PoF) reliability modeling for complex dynamic systems has been established in this research. The PoF-based reliability models are used to perform a real time simulation of system failure processes, so that the system level reliability modeling would constitute inferences from checking the status of component level reliability at any given time. The "agent autonomy" concept is applied as a solution method for the system-level probabilistic PoF-based (i.e. PPoF-based) modeling. This concept originated from artificial intelligence (AI) as a leading intelligent computational inference in modeling of multi agents systems (MAS). The concept of agent autonomy in the context of reliability modeling was first proposed by M. Azarkhail [1], where a fundamentally new idea of system representation by autonomous intelligent agents for the purpose of reliability modeling was introduced. Contribution of the current work lies in the further development of the agent anatomy concept, particularly the refined agent classification within the scope of the PoF-based system reliability modeling, new approaches to the learning and the autonomy properties of the intelligent agents, and modeling interacting failure mechanisms within the dynamic engineering system. The autonomous property of intelligent agents is defined as agent's ability to self-activate, deactivate or completely redefine their role in the analysis. This property of agents and the ability to model interacting failure mechanisms of the system elements makes the agent autonomy fundamentally different from all existing methods of probabilistic PoF-based reliability modeling. 1. Azarkhail, M., "Agent Autonomy Approach to Physics-Based Reliability Modeling of Structures and Mechanical Systems", PhD thesis, University of Maryland, College Park, 2007.

The Design and Development of the Dragoon Intelligent Tutoring System for Model Construction: Lessons Learned

ERIC Educational Resources Information Center

Wetzel, Jon; VanLehn, Kurt; Butler, Dillan; Chaudhari, Pradeep; Desai, Avaneesh; Feng, Jingxian; Grover, Sachin; Joiner, Reid; Kong-Sivert, Mackenzie; Patade, Vallabh; Samala, Ritesh; Tiwari, Megha; van de Sande, Brett

2017-01-01

This paper describes Dragoon, a simple intelligent tutoring system which teaches the construction of models of dynamic systems. Modelling is one of seven practices dictated in two new sets of educational standards in the U.S.A., and Dragoon is one of the first systems for teaching model construction for dynamic systems. Dragoon can be classified…

Videos | Argonne National Laboratory

Science.gov Websites

science --Agent-based modeling --Applied mathematics --Artificial intelligence --Cloud computing management -Intelligence & counterterrorrism -Vulnerability assessment -Sensors & detectors Programs

Sequentially Executed Model Evaluation Framework

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

2015-10-20

Provides a message passing framework between generic input, model and output drivers, and specifies an API for developing such drivers. Also provides batch and real-time controllers which step the model and I/O through the time domain (or other discrete domain), and sample I/O drivers. This is a library framework, and does not, itself, solve any problems or execute any modeling. The SeMe framework aids in development of models which operate on sequential information, such as time-series, where evaluation is based on prior results combined with new data for this iteration. Has applications in quality monitoring, and was developed as partmore » of the CANARY-EDS software, where real-time water quality data is being analyzed for anomalies.« less

Weak hydrological sensitivity to temperature change over land, independent of climate forcing

NASA Astrophysics Data System (ADS)

Samset, Bjorn H.

2017-04-01

As the global surface temperature changes, so will patterns and rates of precipitation. Theoretically, these changes can be understood in terms of changes to the energy balance of the atmosphere, caused by introducing drivers of climate change such as greenhouse gases, aerosols and altered insolation. Climate models, however, disagree strongly in their prediction of precipitation changes, both for historical and future emission pathways, and per degree of surface warming in idealized experiments. The latter value, often termed the apparent hydrological sensitivity, has also been found to differ substantially between climate drivers. Here, we present the global and regional hydrological sensitivity (HS) to surface temperature changes, for perturbations to CO2, CH4, sulfate and black carbon concentrations, and solar irradiance. Based on results from 10 climate models participating in the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), we show how modeled global mean precipitation increases by 2-3 % per kelvin of global mean surface warming, independent of driver, when the effects of rapid adjustments are removed. Previously reported differences in response between drivers are therefore mainly ascribable to rapid atmospheric adjustment processes. All models show a sharp contrast in behavior over land and over ocean, with a strong surface temperature driven (slow) ocean HS of 3-5 %/K, while the slow land HS is only 0-2 %/K. Separating the response into convective and large-scale cloud processes, we find larger inter-model differences, in particular over land regions. Large-scale precipitation changes are most relevant at high latitudes, while the equatorial HS is dominated by convective precipitation changes. Black carbon stands out as the driver with the largest inter-model slow HS variability, and also the strongest contrast between a weak land and strong sea response. Convective precipitation in the Arctic and large scale precipitation around the Equator are found to be topics where further model investigations and observational constraints may provide rapid improvements to modelling of the precipitation response to future, CO2 dominated climate change.

Modeling the dynamics of driver's dilemma zone perception using machine learning methods for safer intersection control.

DOT National Transportation Integrated Search

2014-04-01

The "dilemma zone" (DZ) is defined as the area where drivers approaching a signalized intersection must decide to either proceed or stop at the onset of the yellow indication. Drivers that might perceive themselves to be too close to an intersection ...

Speeding by young novice drivers: What can personal characteristics and psychosocial theory add to our understanding?

PubMed

Scott-Parker, Bridie; Hyde, Melissa K; Watson, Barry; King, Mark J

2013-01-01

Young novice drivers continue to be overrepresented in fatalities and injuries arising from crashes even with the introduction of countermeasures such as graduated driver licensing (GDL). Enhancing countermeasures requires a better understanding of the variables influencing risky driving. One of the most common risky behaviours performed by drivers of all ages is speeding, which is particularly risky for young novice drivers who, due to their driving inexperience, have difficulty in identifying and responding appropriately to road hazards. Psychosocial theory can improve our understanding of contributors to speeding, thereby informing countermeasure development and evaluation. This paper reports an application of Akers' social learning theory (SLT), augmented by Gerrard and Gibbons' prototype/willingness model (PWM), in addition to personal characteristics of age, gender, car ownership, and psychological traits/states of anxiety, depression, sensation seeking propensity and reward sensitivity, to examine the influences on self-reported speeding of young novice drivers with a Provisional (intermediate) licence in Queensland, Australia. Young drivers (n=378) recruited in 2010 for longitudinal research completed two surveys containing the Behaviour of Young Novice Drivers Scale, and reported their attitudes and behaviours as pre-Licence/Learner (Survey 1) and Provisional (Survey 2) drivers and their sociodemographic characteristics. An Akers' measurement model was created. Hierarchical multiple regressions revealed that (1) personal characteristics (PC) explained 20.3%; (2) the combination of PC and SLT explained 41.1%; (3) the combination of PC, SLT and PWM explained 53.7% of variance in self-reported speeding. Whilst there appeared to be considerable shared variance, the significant predictors in the final model included gender, car ownership, reward sensitivity, depression, personal attitudes, and Learner speeding. These results highlight the capacity for psychosocial theory to improve our understanding of speeding by young novice drivers, revealing relationships between previous behaviour, attitudes, psychosocial characteristics and speeding. The findings suggest multi-faceted countermeasures should target the risky behaviour of Learners, and Learner supervisors should be encouraged to monitor their Learners' driving speed. Novice drivers should be discouraged from developing risky attitudes towards speeding. Copyright © 2012 Elsevier Ltd. All rights reserved.

The role of empathy and emotional intelligence in nurses' communication attitudes using regression models and fuzzy-set qualitative comparative analysis models.

PubMed

Giménez-Espert, María Del Carmen; Prado-Gascó, Vicente Javier

2018-03-01

To analyse link between empathy and emotional intelligence as a predictor of nurses' attitudes towards communication while comparing the contribution of emotional aspects and attitudinal elements on potential behaviour. Nurses' attitudes towards communication, empathy and emotional intelligence are key skills for nurses involved in patient care. There are currently no studies analysing this link, and its investigation is needed because attitudes may influence communication behaviours. Correlational study. To attain this goal, self-reported instruments (attitudes towards communication of nurses, trait emotional intelligence (Trait Emotional Meta-Mood Scale) and Jefferson Scale of Nursing Empathy (Jefferson Scale Nursing Empathy) were collected from 460 nurses between September 2015-February 2016. Two different analytical methodologies were used: traditional regression models and fuzzy-set qualitative comparative analysis models. The results of the regression model suggest that cognitive dimensions of attitude are a significant and positive predictor of the behavioural dimension. The perspective-taking dimension of empathy and the emotional-clarity dimension of emotional intelligence were significant positive predictors of the dimensions of attitudes towards communication, except for the affective dimension (for which the association was negative). The results of the fuzzy-set qualitative comparative analysis models confirm that the combination of high levels of cognitive dimension of attitudes, perspective-taking and emotional clarity explained high levels of the behavioural dimension of attitude. Empathy and emotional intelligence are predictors of nurses' attitudes towards communication, and the cognitive dimension of attitude is a good predictor of the behavioural dimension of attitudes towards communication of nurses in both regression models and fuzzy-set qualitative comparative analysis. In general, the fuzzy-set qualitative comparative analysis models appear to be better predictors than the regression models are. To evaluate current practices, establish intervention strategies and evaluate their effectiveness. The evaluation of these variables and their relationships are important in creating a satisfied and sustainable workforce and improving quality of care and patient health. © 2018 John Wiley & Sons Ltd.

Understanding the Impact of a Half Day Learning Intervention on Emotional Intelligence Competencies: An Exploratory Study

ERIC Educational Resources Information Center

Carrick, Laurie Ann

2010-01-01

Empirical evidence has identified emotional intelligence competencies as part of the transformational leadership style. The development of emotional intelligence competencies has been reviewed in the context of a leadership development learning intervention encompassing the model of assessment, challenge and support. The exploratory study…

Expertise, Task Complexity, and Artificial Intelligence: A Conceptual Framework.

ERIC Educational Resources Information Center

Buckland, Michael K.; Florian, Doris

1991-01-01

Examines the relationship between users' expertise, task complexity of information system use, and artificial intelligence to provide the basis for a conceptual framework for considering the role that artificial intelligence might play in information systems. Cognitive and conceptual models are discussed, and cost effectiveness is considered. (27…

Development of a Real-Time Intelligent Network Environment.

ERIC Educational Resources Information Center

Gordonov, Anatoliy; Kress, Michael; Klibaner, Roberta

This paper presents a model of an intelligent computer network that provides real-time evaluation of students' performance by incorporating intelligence into the application layer protocol. Specially designed drills allow students to independently solve a number of problems based on current lecture material; students are switched to the most…

Emotional Intelligence and Cognitive Moral Development in Undergraduate Business Students

ERIC Educational Resources Information Center

McBride, Elizabeth A.

2010-01-01

This study examines relationships between emotional intelligence (EI) and cognitive moral development (CMD) in undergraduate business students. The ability model of emotional intelligence was used in this study, which evaluated possible relationships between EI and CMD in a sample of 82 undergraduate business students. The sample population was…

The Nature of Reflective Practice and Emotional Intelligence in Tutorial Settings

ERIC Educational Resources Information Center

Gill, Gobinder Singh

2014-01-01

The purpose of this paper was to assess the nature of reflective practice and emotional intelligence in tutorial settings. Following the completion of a self-report measure of emotional intelligence, practitioners incorporated a model of reflective practice into their tutorial sessions. Practitioners were instructed to utilise reflective practice…

The Relationship between the Emotional Intelligence of Secondary Public School Principals and School Performance

ERIC Educational Resources Information Center

Ashworth, Stephanie R.

2013-01-01

The study examined the relationship between secondary public school principals' emotional intelligence and school performance. The correlational study employed an explanatory sequential mixed methods model. The non-probability sample consisted of 105 secondary public school principals in Texas. The emotional intelligence characteristics of the…

A Note on Systems Intelligence in Knowledge Management

ERIC Educational Resources Information Center

Sasaki, Yasuo

2017-01-01

Purpose: This paper aims to show that systems intelligence (SI) can be a useful perspective in knowledge management, particularly in the context of the socialization, externalization, combination and internalization (SECI) model. SI is a recently developed systemic concept, a certain kind of human intelligence based on a systems thinking…

Applications of Artificial Intelligence in Education--A Personal View.

ERIC Educational Resources Information Center

Richer, Mark H.

1985-01-01

Discusses: how artificial intelligence (AI) can advance education; if the future of software lies in AI; the roots of intelligent computer-assisted instruction; protocol analysis; reactive environments; LOGO programming language; student modeling and coaching; and knowledge-based instructional programs. Numerous examples of AI programs are cited.…

Developing Students' Cultural Intelligence through an Experiential Learning Activity: A Cross-Cultural Consumer Behavior Interview

ERIC Educational Resources Information Center

Kurpis, Lada Helen; Hunter, James

2017-01-01

Business schools can increase their competitiveness by offering students intercultural skills development opportunities integrated into the traditional curricula. This article makes a contribution by proposing an approach to developing students' cultural intelligence that is based on the cultural intelligence (CQ) model, experiential learning…

Stupid Tutoring Systems, Intelligent Humans

ERIC Educational Resources Information Center

Baker, Ryan S.

2016-01-01

The initial vision for intelligent tutoring systems involved powerful, multi-faceted systems that would leverage rich models of students and pedagogies to create complex learning interactions. But the intelligent tutoring systems used at scale today are much simpler. In this article, I present hypotheses on the factors underlying this development,…

The Homeland Security Ecosystem: An Analysis of Hierarchical and Ecosystem Models and Their Influence on Decision Makers

DTIC Science & Technology

2012-12-01

flows, diversity, emergence, networks, fusion, strategic planning, information sharing, ecosystem, hierarchy, NJ Regional Operations Intelligence ...Related Information...........................................................................79 viii 3. Production of Disaster Intelligence for... Intelligence for Field Personnel .................80 5. Focused Collection Efforts to Support FEMA and NJ OEM Operations

The use of artificially intelligent agents with bounded rationality in the study of economic markets

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

Rajan, V.; Slagle, J.R.

The concepts of {open_quote}knowledge{close_quote} and {open_quote}rationality{close_quote} are of central importance to fields of science that are interested in human behavior and learning, such as artificial intelligence, economics, and psychology. The similarity between artificial intelligence and economics - both are concerned with intelligent thought, rational behavior, and the use and acquisition of knowledge - has led to the use of economic models as a paradigm for solving problems in distributed artificial intelligence (DAI) and multi agent systems (MAS). What we propose is the opposite; the use of artificial intelligence in the study of economic markets. Over the centuries various theories ofmore » market behavior have been advanced. The prevailing theory holds that an asset`s current price converges to the risk adjusted value of the rationally expected dividend stream. While this rational expectations model holds in equilibrium or near-equilibrium conditions, it does not sufficiently explain conditions of market disequilibrium. An example of market disequilibrium is the phenomenon of a speculative bubble. We present an example of using artificially intelligent agents with bounded rationality in the study of speculative bubbles.« less

Distributed neural system for emotional intelligence revealed by lesion mapping.

PubMed

Barbey, Aron K; Colom, Roberto; Grafman, Jordan

2014-03-01

Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease.

Distributed neural system for emotional intelligence revealed by lesion mapping

PubMed Central

Colom, Roberto; Grafman, Jordan

2014-01-01

Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease. PMID:23171618

The Brain as a Distributed Intelligent Processing System: An EEG Study

PubMed Central

da Rocha, Armando Freitas; Rocha, Fábio Theoto; Massad, Eduardo

2011-01-01

Background Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion The present results support these claims and the neural efficiency hypothesis. PMID:21423657

Development of Accomodation Models for Soldiers in Vehicles: Driver

DTIC Science & Technology

2014-09-01

human needs and performance. A small section of this standard addresses the design of vehicle seats and the layout of the driver workstation...drivers and passengers (squad). The study was designed to focus on tactical vehicle (truck) designs with fixed driver heel points and H30 values...fore-aft and vertically, along with adjusting the seat back angle, to obtain a comfortable driving position. The Soldier’s posture and seat adjustments

Multilevel analysis of the role of human factors in regional disparities in crash outcomes.

PubMed

Adanu, Emmanuel Kofi; Smith, Randy; Powell, Lars; Jones, Steven

2017-12-01

A growing body of research has examined the disparities in road traffic safety among population groups and geographic regions. These studies reveal disparities in crash outcomes between people and regions with different socioeconomic characteristics. A critical aspect of the road traffic crash epidemic that has received limited attention is the influence of local characteristics on human elements that increase the risk of getting into a crash. This paper applies multilevel logistic regression modeling techniques to investigate the influence of driver residential factors on driver behaviors in an attempt to explain the area-based differences in the severity of road crashes across the State of Alabama. Specifically, the paper reports the effects of characteristics attributable to drivers and the geographic regions they reside on the likelihood of a crash resulting in serious injuries. Model estimation revealed that driver residence (postal code or region) accounted for about 7.3% of the variability in the probability of a driver getting into a serious injury crash, regardless of driver characteristics. The results also reveal disparities in serious injury crash rate as well as significant proportions of serious injury crashes involving no seatbelt usage, driving under influence (DUI), unemployed drivers, young drivers, distracted driving, and African American drivers among some regions. The average credit scores, average commute times, and populations of driver postal codes are shown to be significant predictors for risk of severe injury crashes. This approach to traffic crash analysis presented can serve as the foundation for evidence-based policies and also guide the implementation of targeted countermeasures. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modeling the safety impacts of driving hours and rest breaks on truck drivers considering time-dependent covariates.

PubMed

Chen, Chen; Xie, Yuanchang

2014-12-01

Driving hours and rest breaks are closely related to driver fatigue, which is a major contributor to truck crashes. This study investigates the effects of driving hours and rest breaks on commercial truck driver safety. A discrete-time logistic regression model is used to evaluate the crash odds ratios of driving hours and rest breaks. Driving time is divided into 11 one hour intervals. These intervals and rest breaks are modeled as dummy variables. In addition, a Cox proportional hazards regression model with time-dependent covariates is used to assess the transient effects of rest breaks, which consists of a fixed effect and a variable effect. Data collected from two national truckload carriers in 2009 and 2010 are used. The discrete-time logistic regression result indicates that only the crash odds ratio of the 11th driving hour is statistically significant. Taking one, two, and three rest breaks can reduce drivers' crash odds by 68%, 83%, and 85%, respectively, compared to drivers who did not take any rest breaks. The Cox regression result shows clear transient effects for rest breaks. It also suggests that drivers may need some time to adjust themselves to normal driving tasks after a rest break. Overall, the third rest break's safety benefit is very limited based on the results of both models. The findings of this research can help policy makers better understand the impact of driving time and rest breaks and develop more effective rules to improve commercial truck safety. Copyright © 2014 National Safety Council and Elsevier Ltd. All rights reserved.

Understanding young and older male drivers' willingness to drive while intoxicated: the predictive utility of constructs specified by the theory of planned behaviour and the prototype willingness model.

PubMed

Rivis, Amanda; Abraham, Charles; Snook, Sarah

2011-05-01

The present study examined the predictive utility of constructs specified by the theory of planned behaviour (TPB) and prototype willingness model (PWM) for young and older male drivers' willingness to drive while intoxicated. A cross-sectional questionnaire was employed. Two hundred male drivers, recruited via a street survey, voluntarily completed measures of attitude, subjective norm, perceived behavioural control, prototype perceptions, and willingness. Findings showed that the TPB and PWM variables explained 65% of the variance in young male drivers' willingness and 47% of the variance in older male drivers' willingness, with the interaction between prototype favourability and similarity contributing 7% to the variance explained in older males' willingness to drive while intoxicated. The findings possess implications for theory, research, and anti-drink driving campaigns. ©2010 The British Psychological Society.

Modeling of Steer-by-Wire System Used in New Braking Handwheel Concept

NASA Astrophysics Data System (ADS)

Messaoudène, K.; Oufroukh, N. Ait; Mammar, S.

2008-06-01

The handwheel is one of the primary control mechanisms of automobile thus interaction between the handwheel and the driver is critical to safety. The driver applies forces that direct the vehicle while the handwheel communicates feedback information to the driver of the forces experience by the car within its environment. The handwheel also provides a predictable mechanical feel to the driver to allow smooth and safe control. Many researchers tried to reproduce this feeling by creating steer-by-wire systems. This paper explores this new concept of handwheel and it describes the modeling steps of the components including the restitution mechanism for force feedback and its various links with the vehicle lateral dynamics and the pneumatic contacts. The aim is to explore the possibility to combine a braking device within the steer-by-wire system in order to provide a more suitable and ergonomic device to the driver.

Prevention of traffic accidents: the assessment of perceptual-motor alterations before obtaining a driving license. A longitudinal study of the first years of driving.

PubMed

Martín, Fermina Sánchez; Estévez, M Angeles Quiroga

2005-03-01

A longitudinal study was designed with two objectives: first, to provide a wide cognitive, personality and social description of new drivers before they started to drive cars. Second, to examine the relationship between cognitive and other characteristics drivers had before obtaining their driving license and the number and type of accidents they were involved in during the first years as drivers. The longitudinal study started in 1997 and ended in 2002. The first assessment was made up of 241 individuals at the time they enrolled on the driving course. The follow-up evaluation in the year 2002 was carried out on 144 components of the initial sample after five years driving. Age, gender and education level were matched to represent the population of Spain. Participants were assessed with the Bender Test for visual-motor ability, the B101 Test for practical intelligence, the B19 Test for visual-motor bi-manual coordination, and the TKK-1108 for speed anticipation. Personality was also evaluated with the Rorschach test and the PSY (Psychological Assessment Questionnaire). Five years later, a new examination of all those variables was made as well as a structured interview with each participant in order to collect data relating to significant life events during that time, driving habits, opinions in relation to certain traffic rules and information on accidents, incidents and/or sanctions. Serious and/or minor accidents are concentrated on a few drivers. Accidentality is not related to gender or age, but educational level is related to serious accidents. The number of accidents (severe or minor ones) cannot be predicted if considered as a continuous variable, but it is possible if considered as a discrete variable. In this case two different cognitive profiles account for the number and type of accidents. The number and type of accidents during their first years of driving are related to the cognitive profiles of drivers assessed before they obtained their driving license.

National Water Model: Providing the Nation with Actionable Water Intelligence

NASA Astrophysics Data System (ADS)

Aggett, G. R.; Bates, B.

2017-12-01

The National Water Model (NWM) provides national, street-level detail of water movement through time and space. Operating hourly, this flood of information offers enormous benefits in the form of water resource management, natural disaster preparedness, and the protection of life and property. The Geo-Intelligence Division at the NOAA National Water Center supplies forecasters and decision-makers with timely, actionable water intelligence through the processing of billions of NWM data points every hour. These datasets include current streamflow estimates, short and medium range streamflow forecasts, and many other ancillary datasets. The sheer amount of NWM data produced yields a dataset too large to allow for direct human comprehension. As such, it is necessary to undergo model data post-processing, filtering, and data ingestion by visualization web apps that make use of cartographic techniques to bring attention to the areas of highest urgency. This poster illustrates NWM output post-processing and cartographic visualization techniques being developed and employed by the Geo-Intelligence Division at the NOAA National Water Center to provide national actionable water intelligence.

Distributed intelligent monitoring and reporting facilities

NASA Astrophysics Data System (ADS)

Pavlou, George; Mykoniatis, George; Sanchez-P, Jorge-A.

1996-06-01

Distributed intelligent monitoring and reporting facilities are of paramount importance in both service and network management as they provide the capability to monitor quality of service and utilization parameters and notify degradation so that corrective action can be taken. By intelligent, we refer to the capability of performing the monitoring tasks in a way that has the smallest possible impact on the managed network, facilitates the observation and summarization of information according to a number of criteria and in its most advanced form and permits the specification of these criteria dynamically to suit the particular policy in hand. In addition, intelligent monitoring facilities should minimize the design and implementation effort involved in such activities. The ISO/ITU Metric, Summarization and Performance management functions provide models that only partially satisfy the above requirements. This paper describes our extensions to the proposed models to support further capabilities, with the intention to eventually lead to fully dynamically defined monitoring policies. The concept of distributing intelligence is also discussed, including the consideration of security issues and the applicability of the model in ODP-based distributed processing environments.

Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

PubMed

Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

2016-02-01

To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Microscopic prediction of speech intelligibility in spatially distributed speech-shaped noise for normal-hearing listeners.

PubMed

Geravanchizadeh, Masoud; Fallah, Ali

2015-12-01

A binaural and psychoacoustically motivated intelligibility model, based on a well-known monaural microscopic model is proposed. This model simulates a phoneme recognition task in the presence of spatially distributed speech-shaped noise in anechoic scenarios. In the proposed model, binaural advantage effects are considered by generating a feature vector for a dynamic-time-warping speech recognizer. This vector consists of three subvectors incorporating two monaural subvectors to model the better-ear hearing, and a binaural subvector to simulate the binaural unmasking effect. The binaural unit of the model is based on equalization-cancellation theory. This model operates blindly, which means separate recordings of speech and noise are not required for the predictions. Speech intelligibility tests were conducted with 12 normal hearing listeners by collecting speech reception thresholds (SRTs) in the presence of single and multiple sources of speech-shaped noise. The comparison of the model predictions with the measured binaural SRTs, and with the predictions of a macroscopic binaural model called extended equalization-cancellation, shows that this approach predicts the intelligibility in anechoic scenarios with good precision. The square of the correlation coefficient (r(2)) and the mean-absolute error between the model predictions and the measurements are 0.98 and 0.62 dB, respectively.

Machine listening intelligence

NASA Astrophysics Data System (ADS)

Cella, C. E.

2017-05-01

This manifesto paper will introduce machine listening intelligence, an integrated research framework for acoustic and musical signals modelling, based on signal processing, deep learning and computational musicology.

Driving in traffic: short-range sensing for urban collision avoidance

NASA Astrophysics Data System (ADS)

Thorpe, Chuck E.; Duggins, David F.; Gowdy, Jay W.; MacLaughlin, Rob; Mertz, Christoph; Siegel, Mel; Suppe, Arne; Wang, Chieh-Chih; Yata, Teruko

2002-07-01

Intelligent vehicles are beginning to appear on the market, but so far their sensing and warning functions only work on the open road. Functions such as runoff-road warning or adaptive cruise control are designed for the uncluttered environments of open highways. We are working on the much more difficult problem of sensing and driver interfaces for driving in urban areas. We need to sense cars and pedestrians and curbs and fire plugs and bicycles and lamp posts; we need to predict the paths of our own vehicle and of other moving objects; and we need to decide when to issue alerts or warnings to both the driver of our own vehicle and (potentially) to nearby pedestrians. No single sensor is currently able to detect and track all relevant objects. We are working with radar, ladar, stereo vision, and a novel light-stripe range sensor. We have installed a subset of these sensors on a city bus, driving through the streets of Pittsburgh on its normal runs. We are using different kinds of data fusion for different subsets of sensors, plus a coordinating framework for mapping objects at an abstract level.

Energy-Saving Tunnel Illumination System Based on LED's Intelligent Control

NASA Astrophysics Data System (ADS)

Guo, Shanshan; Gu, Hanting; Wu, Lan; Jiang, Shuixiu

2011-02-01

At present there is a lot of electric energy wastage in tunnel illumination, whose design is based on the maximum brightness outside and the maximum vehicle speed all year round. LED's energy consumption is low, and the control of its brightness is simple and effective. It can be quickly adjusted between 0-100% of its maximum brightness, and will not affect the service life. Therefore, using LED as tunnel's illumination source, we can achieve a good energy saving effect. According to real-time data acquisition of vehicle speed, traffic flow and brightness outside the tunnel, the auto real-time control of tunnel illumination can be achieved. And the system regulated the LED luminance by means of combination of LED power module and intelligent control module. The tunnel information was detected by inspection equipments, which included luminometer, vehicle detector, and received by RTU(Remote Terminal Unit), then synchronously transmitted to PC. After data processing, RTU emitted the dimming signal to the LED driver to adjust the brightness of LED. Despite the relatively high cost of high-power LED lights, the enormous energy-saving effect and the well-behaved controllability is beyond compare to other lighting devices.

Clinical sequencing in leukemia with the assistance of artificial intelligence.

PubMed

Tojo, Arinobu

2017-01-01

Next generation sequencing (NGS) of cancer genomes is now becoming a prerequisite for accurate diagnosis and proper treatment in clinical oncology. Because the genomic regions for NGS expand from a certain set of genes to the whole exome or whole genome, the resulting sequence data becomes incredibly enormous and makes it quite laborious to translate the genomic data into medicine, so-called annotation and curation. We organized a clinical sequencing team and established a bidirectional (bed-to-bench and bench-to-bed) system to integrate clinical and genomic data for hematological malignancies. We also started a collaborative research project with IBM Japan to adopt the artificial intelligence Watson for Genomics (WfG) to the pipeline of medical informatics. Genomic DNA was prepared from malignant as well as normal tissues in each patient and subjected to NGS. Sequence data was analyzed using an in-house semi-automated pipeline in combination with WfG, which was used to identify candidate driver mutations and relevant pathways from which applicable drug information was deduced. Currently, we have analyzed more than 150 patients with hematological disorders, including AML and ALL, and obtained many informative findings. In this presentation, I will introduce some of the achievements we have made so far.