The optimal control system of the ship based on the linear quadratic regular algorithm

In this paper, the authors propose an optimal controller for the ship motion. Firstly, the model and dynamic equations of the ship motion are presented. Based on the model of the ship motion, the authors build the linear quadratic regular algorithm-based control system of ship motion to minimize the error between the desired trajectory and the response trajectory. The task of the controller is to control the trajectory of the ship to coincide with the desired trajectory. The ship model and controller are built to investigate the system quality through Matlab-Simulink software. The results show that the quality of the hold control system is very high. The trajectory of a ship always follows the desired trajectory with very small errors

Fuzzy-proportional-integral-derivative-based controller for object tracking in mobile robots

This paper aims at designing and implementing an intelligent controller for the orientation control of a two-wheeled mobile robot. The controller is designed in LabVIEW and based on analyzed image parameters from cameras. The image program calculates the distance and angle from the camera to the object. The fuzzy controller will get these parameters as crisp input data and send the calculated velocity as crisp output data to the right and left wheel motor for the robot tracking the target object. The results show that the controller gives a fast response and high reliability and quickly carries out data recovery from system faults. The system also works well in the uncertainties of process variables and without mathematical modeling

Determinants of students’ satisfaction with distance education programs at Ho Chi Minh City Open University

This study was implemented to identify factors affecting students’ satisfaction with the distance learning programs offered by Ho Chi Minh City Open University. A system of econometric equations was simultaneously regressed by SUR procedure in SAS program with dependent variables representing three components of students’ satisfaction with instructors, their satisfaction with curriculums and their satisfaction with program administration. Data for the regression were collected from a survey in two years 2009 and 2010 with structured questionnaires responded by 361 students following distance learning programs in five majors: Business Administration, Economics, Finance, Sociology and Construction. The regression results confirmed that there existed positive interrelationship among students’ satisfaction with the three components. A diagram was constructed to exhibit magnitude of the interrelationship as well as determinants of students’ satisfaction with each component. Besides the interrelationship, the satisfaction for instructors was significantly determined by other factors such as age, marital status, communication (directly or indirectly) with instructors. Whilst, other factors affecting students’ satisfaction with distance learning curriculums include the frequency of students’ Internet use and their income

A Probabilistic Shaping Approach for Optical Region-of-Interest Signaling

We propose a probabilistic shaping approach for region-of-interest signaling, where a low-rate signal controls the desired probabilistic ranges of a high-rate data stream using a flexible distribution controller. In addition, we introduce run-length-aware values for frozen bit indices in systematic polar code to minimize the run-length without using run-length-limited code. Our compact system can support soft-decision forward-error-correction decoding with excellent spectral efficiency compared with related work based on hybrid modulation schemes.Comment: Cite to this paper as: Nguyen, Duc-Phuc, Yoshifumi Shiraki, Jun Muramatsu, and Takehiro Moriya. "A Probabilistic Shaping Approach for Optical Region-of-Interest Signaling." IEEE Photonics Technology Letters 34, no. 6 (2022): 309-31

Transfer AdaBoost SVM for Link Prediction in Newly Signed Social Networks using Explicit and PNR Features

AbstractIn signed social network, the user-generated content and interactions have overtaken the web. Questions of whom and what to trust has become increasingly important. We must have methods which predict the signs of links in the social network to solve this problem. We study signed social networks with positive links (friendship, fan, like, etc) and negative links (opposition, anti-fan, dislike, etc). Specifically, we focus how to effectively predict positive and negative links in newly signed social networks. With SVM model, the small amount of edge sign information in newly signed network is not adequate to train a good classifier. In this paper, we introduce an effective solution to this problem. We present a novel transfer learning framework is called Transfer AdaBoost with SVM (TAS) which extends boosting-based learning algorithms and incorporates properly designed RBFSVM (SVM with the RBF kernel) component classifiers. With our framework, we use explicit topological features and Positive Negative Ratio (PNR) features which are based on decision-making theory. Experimental results on three networks (Epinions, Slashdot and Wiki) demonstrate our method that can improve the prediction accuracy by 40% over baseline methods. Additionally, our method has faster performance time