On platform-based sensor management

Strömberg, Dan; Andersson, Maria; Lantz, Fredrik

doi:10.1109/icif.2002.1021209

Cited by 14 publications

(7 citation statements)

References 16 publications

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“…This paper takes the greenhouse production of common leafy vegetables --lettuce for example [5] . Lettuce is suitable for growing in cool place, studies showing that the most suitable for the growth temperature is 15-25°C.…”

Section: Simulation Researchmentioning

confidence: 99%

A Method for the Measurement of Temperature Based on Neural Network PID

Chen¹

2018

Proceedings of the 2018 3rd International Workshop on Materials Engineering and Computer Sciences (IWMECS 2018)

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Environment for plant growth is difficult to establish precise mathematical model. The conventional control methods are difficult to be well controlled, and put forward a neural network PID control temperature on the growth environment of plant. In this paper, taking the lettuce as an example, using MATLAB to simulate the PID control and PID control of BP neural network, the results proved that PID control of BP neural network has small overshoot, fast response speed and good stability compared with the traditional PID control, and better controlled temperature changing with the target temperature. Temperature sensor Humidity sensor Light sensor CO 2 sensor data acquisition unit communication interface CPU communication connection actuator Warming-up device Cooling equipment CO 2 generator LED clearstory 3rd International Workshop on Materials Engineering and Computer Sciences (IWMECS 2018

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Section: Simulation Researchmentioning

confidence: 99%

A Method for the Measurement of Temperature Based on Neural Network PID

Chen¹

2018

Proceedings of the 2018 3rd International Workshop on Materials Engineering and Computer Sciences (IWMECS 2018)

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“…The scheduling of sensors has received considerable attention over the last years, especially in the fields of military (Stromberg, Andersson, Lantz, 2002) and robotics (Fox, 1994). This is due to the fact that in both fields multiple sensors provide object state observations for one or multiple feature services under a dynamically changing environment.…”

Section: Sensor Schedulingmentioning

confidence: 99%

A time-triggered object tracking subsystem for advanced driver assistance systems

Elmenreich

Koplin

2011

Elektrotech. Inftech.

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Multi-sensor object tracking is an important feature for advanced driver assistance systems in future automobiles. Most state-of-the-art systems cannot guarantee deterministic processing of the sensor values due to unsynchronized sensing and processing units. To overcome this shortcoming we propose a paradigm shift towards a time-triggered system architecture providing a deterministic bus system, synchronized nodes, and a global time-base. The paradigm shift is supported by results of a simulation of different synchronization and scheduling approaches which suggest that although non-time-triggered approaches perform well in scenarios with low process noise, the time-triggered model becomes advantageous in potentially dangerous scenarios with high dynamics. In order to validate the results of the simulation for real life scenarios, we analyzed test drives derived from a testbed featuring a Volkswagen Touran being equipped with a laser scanner, a stereo camera system, a FlexRay communication system, an object tracking subsystem and a differential GPS system as reference. Zeitgesteuerte Objekterkennung in Fahrerassistenzsystemen.Im Automobil der Zukunft spielen Fahrerassistenzsysteme eine wichtige Rolle. Ein wichtiges Untersystem sind dabei Objektverfolgungssysteme, welche andere Fahrzeuge mit mehreren Sensoren erfassen und deren Position berechnen. Die Architektur der derzeitigen Systeme kann jedoch oft weder Echtzeiteigenschaften noch Determinismus oder synchronisierte Verarbeitung garantieren. Um dieses Problem zu l€ osen, schlagen die Autoren einen Pradigmenwechsel zu einer zeitgesteuerten Architektur vor. Ein simulationsgest€ utzter Vergleich verschiedener Ans€ atze legte die Vermutung nahe, dass die eventgesteuerten Modelle in Szenarien mit niedriger Dynamik bessere Ergebnisse liefern, in potentiell gef€ ahrlichen Szenarien mit hoher Dynamik aber das zeitgesteuerte Modell von Vorteil ist. Um die Realit€ atsn€ ahe der Simulationsergebnisse zu € uberpr€ ufen, wurden beide Ans€ atze in einer Testumgebung mit einem Volkswagen Touran evaluiert. Das Testfahrzeug war hierf€ ur mit einem Laser-Scanner, einem Stereo-Kamera-System, einem FlexRay-Kommunikationssystem, einem Objektverfolgungssystem und einem Differential-GPS-System als Referenz ausgestattet.

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“…If an agent pursues multiple goals then the rules should automatically detect it as conflict situation. For the SRA, let the mutually exclusive goals (or plans) are p 1 : RadarIsNotJammed, p 2 : RadarIsJammed, p 3 : SendTheMessageToADDCA, p 4 : Frequency Hopping. The rules are defined as:…”

Section: A Logical Evaluationmentioning

confidence: 99%

Modeling Intelligent Decision-Making Command and Control Agents: An Application to Air Defense

Das

2014

IEEE Intell. Syst.

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The paper is a half-way between the agent technology and the mathematical reasoning to model tactical decision making tasks. These models are applied to air defense (AD) domain for command and control (C2). It also addresses the issues related to evaluation of agents. The agents are designed and implemented using the agent-programming paradigm. The agents are deployed in an air combat simulated environment for performing the tasks of C2 like electronic counter counter measures, threat assessment, and weapon allocation. The simulated AD system runs without any human intervention, and represents state-of-the-art model for C2 autonomy. The use of agents as autonomous decision making entities is particularly useful in view of futuristic network centric warfare.

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On platform-based sensor management

Cited by 14 publications

References 16 publications

A Method for the Measurement of Temperature Based on Neural Network PID

A Method for the Measurement of Temperature Based on Neural Network PID

A time-triggered object tracking subsystem for advanced driver assistance systems

Modeling Intelligent Decision-Making Command and Control Agents: An Application to Air Defense

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