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Vehicle detection is the most basic and important technology in advanced driver assistant system. Conventional methods do not reflect characteristic information of vehicle images, so they were vulnerable to noise. In order to improve the performance of vehicle detection, this paper proposes a vehicle detection framework using selective multi-stage features in convolutional neural networks. We design...
In this paper we describe implementation of several step pattern recognition framework. Pattern recognition is the main aspect for different important areas such as video surveillance, biometrics, interactive game applications, human computer interaction and access control systems. These systems require fast real time detection and recognition with high recognition rate. In this paper we propose implementation...
In modern conditions, the functioning and management of electric power systems requires the creation of a computational model for large-scale schemes based on methods for estimating the state. Such schemes are not fully observable, data may be distorted, their synchronization is poor and, as a consequence, the adoption of incorrect decisions based on the calculation model. At present, the agent approach...
This work presents the implementation of a method for classification of wear particle contaminant present in industrial oil by using image processing and neural networks. It is based on morphological data obtained from a computer vision system and employs Self-Organizing Maps to classify particles' features intro different wear debris groups. The dataset used for training the neural network and further...
In this paper, a new formula called Zhang discretization 4-instant g-square with subtype Q (i.e., ZD4IgS_Q) is proposed, which is used for discretizing continuous-time zeroing neurodynamics (CTZN) model. Besides, in order to solve future nonlinear systems of equations (FNSoE), also termed discrete time-varying nonlinear systems of equations, a ZD4IgS-Q-type discrete-time zeroing neurodynamics (DTZN)...
The most important part of the heating, ventilation, and air conditioning technology is heating System. This part is used in smart buildings and provides the desired air quality and thermal comfort. The time delay and uncertainty in model parameters due to the several operation mode cause the main challenges in heating system control by the traditional PID approaches. To overcome these problems, this...
Sliding Mode Control is a nonlinear control methodology based on the use of a discontinuous control input which forces the controlled system to switch from one structure to another, evolving as a variable structure system. This structure variation makes the system state reach in a finite time a pre-specified subspace of the system state space where the desired dynamical properties are assigned to...
A multiple-step vehicle torque demand forecasting problem is investigated in this paper. Neural network method is applied to forecast toque demand in the future five steps based on the measurements of vehicle speed and torque demand in the past three steps. Neural networks for forecasting are trained based on history drive cycles' data and used to do multiple-step forecasting in another drive cycle...
This paper describes a clean-up robot that is composed of a mobile base and a manipulator. An object recognition algorithm that is based on an active stereo camera system is also proposed for the clean-up robot. In order for the robot to clear a dining table, the stereo camera system must identify the objects on the dining table and evaluate their positions. For localizing and detecting objects, a...
This paper proposes a side slip angle estimation method using a low-cost Global Positioning System (GPS)/Inertial Measurement Unit (IMU). To estimate the side slip angle, we use the yaw rate of IMU and the course angle of GPS. Planar lateral vehicle model-based estimator was designed to integrate the yaw rate of IMU and course angle of GPS. The proposed method was validated via an experiment.
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