This paper investigates the longitudinal flight dynamics and stability of flapping-wing micro air vehicles. Periodic external forces and moments due to the flapping motion characterize the dynamics of this system as NLTP (Non Linear Time Periodic). However, the averaging theorem can be applied to an NLTP system to obtain an NLTI (Non Linear Time Invariant) system which allows us to use a standard eigen value analysis to assess the stability of the system with linearization around a reference point. In this paper, we investigate the dynamics and stability of a hawkmoth-scale flapping-wing air vehicle by establishing an LTI (Linear Time Invariant) system model around a hovering condition. Also, a direct time integration of full nonlinear equations of motion of the flapping-wing micro air vehicle is conducted to see how the longitudinal flight dynamics appear in the time domain beyond the reference point, i.e. hovering condition. In the study, the flapping-wing air vehicle exhibited three distinct dynamic modes of motion in the longitudinal plane of motion: two stable subsidence modes and one unstable oscillatory mode. The unstable oscillatory mode is found to be a combination of a pitching velocity state and a forward/backward velocity state.

Unmanned air vehicles (UAVs) are getting popular not only as a private usage for the aerial photograph but military usage for the surveillance, reconnaissance and supply missions. For an UAV to successfully achieve these kind of missions, geolocation (localization) must be implied to track an interested target or fly by reference. In this research, we adopted multi-sensor fusion (MSF) algorithm to increase the accuracy of the geolocation and verified the algorithm using two multicopter UAVs. One UAV is equipped with an optical camera, and another UAV is equipped with an optical camera and a laser range finder. Throughout the experiment, we have obtained measurements about a fixed ground target and estimated the target position by a series of coordinate transformations and sequential Kalman filter. The result showed that the MSF has better performance in estimating target location than the case of using single sensor. Moreover, the experimental result implied that multi-sensor geolocation algorithm is able to have further improvements in localization accuracy and feasibility of other complicated applications such as moving target tracking and multiple target tracking.

This paper describes the design of a robust H-infinity attitude controller for a quad-rotor. The linear model of a quad-rotor was estimated using PEM (Prediction Error Minimization) method with experimental input and output data. To design an attitude controller, an extended plant was constructed by adjusting several uncertainties and weighting functions. An H-infinity controller was obtained by applying H-infinity methodology to the extended plant. Through frequency-domain analysis, it was shown that the designed controller can overcome uncertainties up to 75% of the plant model. The performance and robustness of the controller were verified through time-domain simulation.

A conventional fire detection has been developed based on images captured from a fixed camera. However, It is difficult to apply current algorithms to a flying Quad-rotor to detect fire. To solve this problem, we propose that the fire detection algorithm can be modified for Quad-rotor using Ego-motion compensation. The proposed fire detection algorithm consists of color detection, motion detection, and fire determination using a randomness test. Color detection and randomness test are adapted similarly from an existing algorithm. However, Ego-motion compensation is adapted on motion detection for compensating the degree of Quad-rotor's motion using Planar Projective Transformation based on Optical Flow, RANSAC Algorithm, and Homography. By adapting Ego-motion compensation on the motion detection step, it has been proven that the proposed algorithm has been able to detect fires 83% of the time in hovering mode.

In this paper, we propose a near optimal controller design method for permanent magnet synchronous generators (PMSGs) of MW-class direct-driven wind turbine systems based on SDRE (State Dependent Riccati Equation) approach. Using the solution matrix of an SDRE, we parameterize the optimal controller gain. We present a simple algorithm to compute the near optimal controller gain. The proposed optimal controller can enable PMSGs to precisely track the reference speed determined by the MPPT algorithm. Finally, numerical simulation results are given to verify the effectiveness of the proposed optimal controller.

In this paper, a finite impulse response (FIR) filter is proposed for discrete-time nonlinear systems. The proposed filter is designed by combining the estimate of the perturbation state and nominal state. The perturbation state is estimated by adapting the optimal time-varying FIR filter for the linearized perturbation model and the nominal state is directly obtained from the nonlinear nominal trajectory model. Since the FIR structured estimators use the finite horizon information on the most recent time interval, the proposed extended FIR filter satisfies the bounded input/bounded output (BIBO) stability, which can't be obtained from infinite impulse response (IIR) estimators. Thus, it can be expected that the proposed extended FIR filter is more robust than IIR structured estimators such as an extended Kalman filter for the round-of errors and the uncertainties from unknown initial states and uncertain system model parameters. The simulation results show that the proposed filter has better performance than the extended Kalman filter (EKF) in both robustness and fast convergency.

In this paper, we present an algorithm for the analysis of poses while playing table-tennis using action recognition. We use Kinect as the 3D sensor and 3D skeleton data provided by Kinect for further processing. We adopt a spherical coordinate system and feature selected using k-means clustering. We automatically detect the starting and ending frame and discriminate the action of table-tennis into two groups of forehand and backhand swing. Each swing is modeled using HMM(Hidden Markov Model) and we used a dataset composed of 200 sequences from two players. We can discriminate two types of table tennis swing in real-time. Also, it can provide analysis according to similarities found in good poses.

In this paper, we propose a vision-based method to recognize lane changes of an autonomous vehicle. The proposed method is based on six states of driving situations defined by the positional relationship between a vehicle and its nearest lane detected. With the combinations of these states, the lane change is detected. The proposed method yields 98% recognition accuracy of lane change even in poor situations with partially invisible lanes.

In this paper, The ET-MBEF algorithm is presented for CCD imaging seeker. At the imaging seeker, target size information is important factor for accurate tracking. The MBEF algorithm was proposed to estimate target size at IIR seeker. However, the MBEF algorithm can't be applied at CCD imaginary target size estimation. In order to overcome the problem, we propose ET-MBEF algorithm which based on ET (Edge Template) and MBEF algorithm. The performance of proposed method is tested at target intercept scenario. The experiment results show that the proposed algorithm has the accurate target intercept performance.

In this paper, we found the usefulness of the deep belief network (DBN) in the fields of brain-computer interface (BCI), especially in relation to imagined speech. In recent years, the growth of interest in the BCI field has led to the development of a number of useful applications, such as robot control, game interfaces, exoskeleton limbs, and so on. However, while imagined speech, which could be used for communication or military purpose devices, is one of the most exciting BCI applications, there are some problems in implementing the system. In the previous paper, we already handled some of the issues of imagined speech when using the International Phonetic Alphabet (IPA), although it required complementation for multi class classification problems. In view of this point, this paper could provide a suitable solution for vowel classification for imagined speech. We used the DBN algorithm, which is known as a deep learning algorithm for multi-class vowel classification, and selected four vowel pronunciations:, /a/, /i/, /o/, /u/ from IPA. For the experiment, we obtained the required 32 channel raw electroencephalogram (EEG) data from three male subjects, and electrodes were placed on the scalp of the frontal lobe and both temporal lobes which are related to thinking and verbal function. Eigenvalues of the covariance matrix of the EEG data were used as the feature vector of each vowel. In the analysis, we provided the classification results of the back propagation artificial neural network (BP-ANN) for making a comparison with DBN. As a result, the classification results from the BP-ANN were 52.04%, and the DBN was 87.96%. This means the DBN showed 35.92% better classification results in multi class imagined speech classification. In addition, the DBN spent much less time in whole computation time. In conclusion, the DBN algorithm is efficient in BCI system implementation.

A vision-based blank alignment unit for a press automation line is introduced in this paper. A press is a machine tool that changes the shape of a blank by applying pressure and is widely used in industries requiring mass production. In traditional press automation lines, a mechanical centering unit, which consists of guides and ball bearings, is employed to align a blank before a robot inserts it into the press. However it can only align limited sized and shaped of blanks. Moreover it cannot be applied to a process where more than two blanks are simultaneously inserted. To overcome these problems, we developed a press centering unit by means of vision sensors for press automation lines. The specification of the vision system is determined by considering information of the blank and the required accuracy. A vision application S/W with pattern recognition, camera calibration and monitoring functions is designed to successfully detect multiple blanks. Through real experiments with an industrial robot, we validated that the proposed system was able to align various sizes and shapes of blanks, and successfully detect more than two blanks which were simultaneously inserted.

With the development of communication technologies and smartphone, requirements of positioning accuracy for LBS (Location Based Service) are becoming increasingly important. LBS is a service which offers the information or entertainment based on a location to users. Therefore, positioning is very important for LBS. Among many positioning methods, IEEE 802.11 WLAN positioning measures the distance using the RSSI (Received Signal Strength Indicator) attenuation log model. Thus in order to enhance positioning, we modify the IEEE 802.11 RSSI attenuation log model by adaptive weighting method. In this paper, we propose improved IEEE 802.11 RSSI attenuation log model for enhanced indoor positioning.

Visible light communication (VLC) is a short range optical communication technology using light emitting diodes. In this paper, we implement variable pulse position modulation (VPPM) defined in the IEEE 802.15.7 standard with a Cortex-M processor, which is a new modulation scheme for VLC providing a dimming control mechanism for flicker-free optical communication. The synchronization procedure between the transmitter and receiver is not defined clearly in the VPPM standard. In order to synchronize the transmitter and receiver easily, two overhead bits are used including one start bit and one stop bit modulated by on-off keying (OOK). Experimental results under various environmental conditions show that the proposed VPPM can support a seamless 100 Kbps data rate with 10 levels in the dimming control.