Systems Features Analysis (SFA) and Analytic Hierarchy Process (AHP) in Systems Design and Development

This paper tries to address the problem of deriving the different features of a system and then having a way of making informed decisions about them based on their level of importance to the whole system as well as to each other depending on several given factors. The use of Systems Features Analysis (SFA) to derive the features and Applied Hierarchy Process (AHP) to decide on their importance fits the given situation and they are described in this paper. These tools are successfully applied to two system development cases, a whole system and some components of a system respectively, which showed their effectiveness and usefulness. An AHP-based software called SuperDecisions is utilized to immediately use AHP in the software design and development process in the shortest possible time

Navigation System Heading and Position Accuracy Improvement through GPS and INS Data Fusion

Commercial navigation systems currently in use have reduced position and heading error but are usually quite expensive. It is proposed that extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) be used in the integration of a global positioning system (GPS) with an inertial navigation system (INS). GPS and INS individually exhibit large errors but they do complement each other by maximizing the advantage of each in calculating the heading angle and position through EKF and UKF. The proposed method was tested using low cost GPS, a cheap electronic compass (EC), and an inertial management unit (IMU) which provided accurate heading and position information, verifying the efficacy of the proposed algorithm

Trajectory Tracking and Stabilization of a Quadrotor Using Model Predictive Control of Laguerre Functions

This paper presents a solution to stability and trajectory tracking of a quadrotor system using a model predictive controller designed using a type of orthonormal functions called Laguerre functions. A linear model of the quadrotor is derived and used. To check the performance of the controller we compare it with a linear quadratic regulator and a more traditional linear state space MPC. Simulations for trajectory tracking and stability are performed in MATLAB and results provided in this paper

DeePromoter: Robust Promoter Predictor Using Deep Learning

The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm

Design of feedforward and feedback position control for passive bilateral teleoperation with delays

Bilateral teleoperation systems connected to computer networks such as the internet must be able to operate with varying time delays since such systems can easily become unstable. A passivity concept has been used as the framework to solve the stability problem in the bilateral control of teleoperation systems. Passivity and tracking performance are recovered using a control architecture that incorporates time varying gains into the transmission path, feedforward, and feedback position control. The proposed architecture has an inner component that can accommodate any configuration but still remain stable and passive even with varying time delay. The simulation results for a single degree of freedom master/slave system demonstrate the performance of the proposed control architecture

Nonlinear dynamic system identification using recurrent neural networks

Vita.The objective of this research is to develop a nonlinear empirical model structure and an associated parameter estimation algorithm based on artificial neural networks (ANNs), and to further use it for the identification of a highly nonlinear process system component, namely a U-Tube Steam Generator (UTSG). The proposed model structure is called a Recurrent Multilayer Perceptron (RMLP). RMLP is a hybrid feedforward and feedback neural network, which in addition to the feedforward connections it exhibits local information feedback, through time delayed recurrency and cross-talk. A static and a dynamic learning algorithm is derived for parameter estimation (learning), and both algorithms are used to train the RMLPs. The capability of the RMLP to identify nonlinear systems using dynamic learning is demonstrated through several examples. Traditional and ANN based model structures are compared for their effectiveness to identify nonlinear systems. Comparisons of the chosen model structures is accomplished through a number of deterministic and stochastic examples. The responses of the identified models to different test signals, unknown during estimation, are presented for investigating their predictive performance. As expected, nonlinear model structures perform better than their linear counterparts. Furthermore, among the nonlinear structures, the RMLP based models exhibit improved predictive performance when identifying stochastic systems. For deterministic systems identification, however, feedforward multilayer perceptron (FMLP) and RMLP based empirical models reveal comparable accuracy. The effectiveness of the RMLP nonlinear empirical model structure with static learning is further demonstrated by developing two models for a UTSG, each valid in the vicinity of an operating power level. A significant drawback of the static learning algorithm has been the excessively long off-line training times required for the development of an even simplified model for a UTSG, hindering the further development of a single model valid in the entire UTSG normal operating envelope..

Nonlinear dynamic system identification using recurrent neural networks

Vita.The objective of this research is to develop a nonlinear empirical model structure and an associated parameter estimation algorithm based on artificial neural networks (ANNs), and to further use it for the identification of a highly nonlinear process system component, namely a U-Tube Steam Generator (UTSG). The proposed model structure is called a Recurrent Multilayer Perceptron (RMLP). RMLP is a hybrid feedforward and feedback neural network, which in addition to the feedforward connections it exhibits local information feedback, through time delayed recurrency and cross-talk. A static and a dynamic learning algorithm is derived for parameter estimation (learning), and both algorithms are used to train the RMLPs. The capability of the RMLP to identify nonlinear systems using dynamic learning is demonstrated through several examples. Traditional and ANN based model structures are compared for their effectiveness to identify nonlinear systems. Comparisons of the chosen model structures is accomplished through a number of deterministic and stochastic examples. The responses of the identified models to different test signals, unknown during estimation, are presented for investigating their predictive performance. As expected, nonlinear model structures perform better than their linear counterparts. Furthermore, among the nonlinear structures, the RMLP based models exhibit improved predictive performance when identifying stochastic systems. For deterministic systems identification, however, feedforward multilayer perceptron (FMLP) and RMLP based empirical models reveal comparable accuracy. The effectiveness of the RMLP nonlinear empirical model structure with static learning is further demonstrated by developing two models for a UTSG, each valid in the vicinity of an operating power level. A significant drawback of the static learning algorithm has been the excessively long off-line training times required for the development of an even simplified model for a UTSG, hindering the further development of a single model valid in the entire UTSG normal operating envelope..

Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network

Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, it is a challenging task due to the different scales and appearances of the objects. On the other hand, object detection task in VHR aerial images has improved remarkably in recent years due to the achieved advances in convolution neural networks (CNN). Most of the proposed methods depend on a two-stage approach, namely: a region proposal stage and a classification stage such as Faster R-CNN. Even though two-stage approaches outperform the traditional methods, their optimization is not easy and they are not suitable for real-time applications. In this paper, a uniform one-stage model for object detection in VHR aerial images has been proposed. In order to tackle the challenge of different scales, a densely connected feature pyramid network has been proposed by which high-level multi-scale semantic feature maps with high-quality information are prepared for object detection. This work has been evaluated on two publicly available datasets and outperformed the current state-of-the-art results on both in terms of mean average precision (mAP) and computation time