31 research outputs found
NEURAL-NETWORK MULTIPLE MODELS FILTER (NMM)-BASED POSITION ESTIMATION SYSTEM FOR AUTONOMOUS VEHICLES
A highly accurate and reliable vehicle position estimation system is an important component of an autonomous driving system. In generally, a global positioning system (UPS) receiver is employed for the vehicle position estimation of autonomous vehicles. However, a stand-alone UPS does not always provide accurate and reliable information of the vehicle position due to frequent UPS blockages and multipath errors. In order to overcome these problems, a sensor fusion scheme that combines the data from the UPS receiver and several on-board sensors has been studied. In previous researches, a single model filter-based sensor fusion algorithm was used to integrate information from the UPS and on-board sensors. However, an estimate obtained from a single model is difficult to cover the various driving environments, including urban areas, off-road areas, and highways. Thus, a multiple models filter (MMF) has been introduced to address this limitation by adapting multiple models to a wide range of driving conditions. An adaptation of the multiple model is achieved through the use of the model probability. The MMF combines several vehicle models using the model probabilities, which indicate the suitability of the current driving condition. In this paper, we propose a vehicle position estimation algorithm for an autonomous vehicle that is based on a neural network (NN)-based MMF. The model probabilities are determined through the NN. The proposed position estimation system was evaluated through simulations and experiments. The experimental results show that the proposed position estimation algorithm is suitable for application in an autonomous driving system over a wide range of driving conditions.This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MEST) (No. 2011-0017495), Industrial Strategy Technology Development Program of Ministry of Knowledge Economy (MKE) (No. 10039673), Energy Resource R&D program(2006ETR11P091C) under the MKE, Science and Technology through the BK21 Program (201000000000173), and MKE and Korea Institute for Advancement in Technology (KIAT) through the Workforce Development Program in Strategic Technology
Viability study of all-IIIV SRAM for beyond-22-nm logic circuits
A physics-based compact model for IIIV FETs is developed for logic circuit applications. The model is applied to study sub-22-nm technology 6T-SRAM cells with InGaAs MOSFETs. The pull-down and pass gate combination is optimized for maximum cell stability. The drawbacks of having a weak IIIV PMOS as the pull-up device in a SRAM cell are investigated. In this letter, we propose a minimum requirement for PMOS strength for all-IIIV SRAM to be viable in a logic chip. Also, by assuming a high-performance PMOS, we observe a 26% higher static current noise margin and a two times faster write speed compared to conventional SRAM. �� 2011 IEEE.Manuscript received November 29, 2010; accepted April 4, 2011. Date of publication May 19, 2011; date of current version June 29, 2011. This work was supported in part by the NSF under Grant ECS 0501096, by the Focus Center Research Program (MSD and C2S2), and by the Intel Corporation. The work of S. Oh was supported in part by the Samsung Scholarship and in part by The Burt and Deedee McMurtry Stanford Graduate Fellowship Fund. The review of this letter was arranged by Editor A. Ortiz-Conde
Real-time combustion parameter estimation algorithm for light-duty diesel engines using in-cylinder pressure measurement
This paper proposes a real-time estimation algorithm of combustion parameters for the location of 50% of mass fraction burnt (MFB50), and indicated mean effective pressure (IMEP). The proposed estimation algorithm uses the difference pressure only instead of the in-cylinder pressure for calculation of the combustion parameters. Since the difference pressure is the pressure that is generated only by the combustion, it occurs between the start of combustion (SOC) and the end of combustion (EOC); this allows the proposed algorithm to estimate the combustion parameters with fewer cylinder pressure data samples and low computational load compared with the conventional method. The proposed algorithm estimates the IMEP with a result acquired during the MFB50 calculation and that can significantly reduce the computational load required to calculate the combustion parameters. Consequently, the proposed estimation algorithm requires only 51% of the execution time to calculate the combustion parameters compared to the conventional method. The proposed estimation algorithm is validated with an engine experiment under 131 operating conditions that showed high linear correlation with the original combustion parameters. In-cylinder pressure based combustion control using the estimated combustion parameters is introduced as a case study and the proposed estimation algorithm validated its significant potential for real-time applications. (C) 2013 Elsevier Ltd. All rights reserved.This work was financially supported by the Ministry of Education, Science and Technology through the BrainKorea 21 Program (201000000000173), the Ministry of Knowledge Economy (MKE) and the Korea Institute for Advancement in Technology (KIAT) through the Workforce Development Program in Strategic Technology, the Energy Resource R&D program (2006ETR11P091C) under the Ministry of Knowledge Economy (MKE), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0017495), the Industrial Strategy Technology Development Program of Ministry of Knowledge Economy (MKE) (No. 10039673), and the Industrial Strategy Technology Development Program of Ministry of Knowledge Economy (MKE) (No. 10042633)
Lane Detection and Tracking Using Morphology and Multiple ROI
Lane detection algorithm have been used for passenger safety systems of luxury vehicle such as the lane keeping assist system (LKAS) and the lane departure warning system (LDWS). In order to enhance the performance of passenger safety systems, robustness and low computation load is required for an effective lane detection algorithm. In previous studies, edge detection, pattern recognition, and probabilistic method have been applied for lane detection. However these approaches have some limitations such as high sensitivity to noise, non-uniform illumination, and high computation load. In this study, we proposed a robust lane detection algorithm using morphology and tracking methods. Morphology is used as a preprocessor for lane detection to reduce the influence of cracked road surfaces and shadows. Moreover, we applied tracking method using multiple regions of interest (ROI) windows to reduce the computation time. In particular, the sizes of multiple ROI were determined by considering the geometric scale effect of the lane mark width. The proposed lane detection and tracking algorithm was evaluated on various road conditions and large scale data. As a result, the proposed algorithm proved to be robust and fast enough to apply to real-time safety-critical systems
Observer design for exhaust gas recirculation rate estimation in a variable-geometry turbocharger diesel engine using a model reference identification scheme
Exhaust gas recirculation systems are used in diesel engines to reduce nitrogen oxide emissions. Since excessive recirculation in the cylinders may lead to an increase in generation of particulate matter and to unstable combustion, the exhaust gas recirculation rate should be measured correctly and should be controlled precisely. Unfortunately, the harsh conditions of the exhaust gas recirculation path make it difficult to measure the exhaust gas recirculation mass flow rate directly by using the relevant sensors. Therefore, precise control of the exhaust gas recirculation system depends on accurate estimation of the exhaust gas recirculation rate. To estimate accurately the exhaust gas recirculation rate in a turbocharged diesel engine, we propose an observer based on a model reference identification scheme. A linear parameter-varying model of the intake manifold pressure was derived to serve as the observer's reference model. An update rule of the observer was designed with the model reference identification scheme. The intake and exhaust temperature models were developed through an empirical approach. Convergence of the proposed observer was proven in terms of the Lyapunov stability criterion. The proposed observer was implemented on a real-time embedded system and validated successfully through experiments on the engine.This work was financially supported by the BK21 plus program under the Ministry of Education, Republic of Korea (grant number 22A20130000045), the National Research Foundation of Korea grant funded by the Korea Government (grant number 2011-0017495) and the Industrial Strategy Technology Development Program of the Ministry of Knowledge Economy (grant number 10039673)
Suppression of Light Influx Into the Channel Region of Photosensitive Thin-Film Transistors
Analysis on the light influx into a bottom-gate, etch-stopper structure thin-film transistor is presented. Reduction of the light influx by means of structural changes in the device can lead to a universal improvement in negative-bias temperature illumination stress instability of metal-oxide transistors. When the devices are illuminated by a fixed light source from below, the dominant light influx occurs in the channel-width direction, due to light reflections off the boundary between the passivation layer and the ambient. Wave propagation into the channel can be suppressed by using thinner dielectric layers or applying an overlying coating layer with a larger refractive index than that of the passivation dielectric material
A Physics-Based Compact Model of III-V FETs for Digital Logic Applications: Current-Voltage and Capacitance-Voltage Characteristics
A physics-based analytical compact model of InGaAs field-effect transistors (FETs) for digital logic applications is developed. This model neither heavily depends on parameter extraction nor requires any time-consuming computation while capturing the essential physics, enabling digital circuit design and circuit-level performance estimation for III-V FETs. The model captures short channel effects, trapezoidal-shape quantum-well energies, bias-dependent ballistic ratios, and capacitances including 2-D potential profile information. Each is verified via numerical calculations and 2-D electrostatic simulation, followed by a comparison of the model I-V characteristics with experiment data. Finally, the transient response of FO4 inverters demonstrates the use of the compact model for future technology circuit simulations.Manuscript received April 29, 2009; revised September 9, 2009. First published October 30, 2009; current version published November 20, 2009. This work was supported in part by NSF under Grant ECS 0501096, by the Focus Center Research Program (MSD), and by Intel Corporation. The work of S. Oh was supported in part by the Samsung Scholarship and in part by The Burt and Deedee McMurtry Stanford Graduate Fellowship Fund. The review of this paper was arranged by Editor M. Anwar
Physics-based compact model for IIIV digital logic FETs including gate tunneling leakage and parasitic capacitance
A physics-based compact model is developed for IIIV field-effect transistors for digital logic applications. Quasi-ballistic ratios, trapezoidal quantum-well subband energy levels, and 2-D source/drain influence on both electrostatics and capacitance are considered. Furthermore, gate tunneling leakage current and parasitic capacitance models are included. These latter effects are important in future technology logic applications, particularly in circuits such as high-density cache arrays. In this paper, we describe the IIIV compact model including the gate leakage current and parasitic capacitance analytical models. The efficacy of the compact model in a practical circuit environment is demonstrated using transient simulations of a 6T-static random access memory cell. In addition, we provide design guidelines for optimization of the intrinsic and the extrinsic structure with regard to the parasitic effects. �� 2011 IEEE.Manuscript received May 13, 2010; revised September 30, 2010 and December 4, 2010; accepted January 2, 2011. Date of current version March 23, 2011. This work was supported in part by the National Science Foundation under Grant ECS 0501096, by the Focus Center Research Program (Materials, Structures, and Devices and Center for Circuits and Systems Solutions), and by Intel Corporation. The work of S. Oh was supported in part by Samsung Scholarship and The Burt and Deedee McMurtry Stanford Graduate Fellowship Fund. The review of this paper was arranged by Editor S. Bandyopadhyay
Alternator Torque Model Based on Equivalent Circuit of Synchronous Generator for Electric Power Management
This paper presents an alternator model suitable for electric power management research. The proposed model estimates the alternator driving torque under various driving conditions, such as engine speed, output current, and generation voltage. The base equations of the proposed model are derived from the equivalent circuit and the phasor diagram of the field-wound-type synchronous generator. Model parameters that affect power conversion efficiency were defined and identified through open and short load tests on a bench. Validation tests were also performed to evaluate model accuracy under several representative driving conditions. Through a case study, we show that the proposed model is the effective way to research power management.This work was supported in part by the Ministry of Knowledge Economy, Korea, through the Energy Resource R&D Program under Grant 2006ETR11P091C; by the National Research Foundation of Korea funded by the Korean government under Grant 2011-0017495; and by the Ministry of Knowledge Economy through the Industrial Strategy Technology Development Program under Grant 10039673 and Grant 10042633. The review of this paper was coordinated by Dr. C. C. Mi. (Corresponding author: M. Sunwoo.
Information Fusion of Cameras and Laser Radars for Perception Systems of Autonomous Vehicles
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