First MEM task IRMEO in IRAF

The first task for image restoration using the Maximum Entropy Method (MEM) in IRAF, called IRMEO, is described. The underlining algorithm is the approximate Newton method for optimization. The basic input images and parameters for deconvolution are described in some detail. Results of preliminary tests, including the number of iterations, required CPU time on a variety of computers, and deconvolved images are reported and compared with those from other deconvolution methods. The merits and limitations of this task are pointed out. The possible development of better MEM tasks on the basis of IRMEO is also discussed

The maximum entropy method and its application in radio astronomy

This thesis consists of seven chapters, two appendices and a bibliography. In addition, three publications are included. The first digit in the page numbers refers to the chapter or appendix, and the second one or two digits are the serial numbers of the page in the chapter or appendix. For equations, figures and tables, the first digit and the second one or two digits refer respectively to the chapter and section; the third one or two digits refer to its serial occurrence in the chapter. The references in the bibliography are listed in alphabetical order by the author. Mathematical symbols are defined when they are first introduced. All abbreviated terms in the text are given in full at the first mention, with the abbreviations in parentheses; thereafter, the abbreviations are used. However, words in full may be repeated for readability

Pitch Channel Control of a REMUS AUV with Input Saturation and Coupling Disturbances

The motion of an underwater vehicle is prone to be affected by time-varying model parameters and the actuator limitation in control practice. Adaptive control is an effective method to deal with the general system dynamic uncertainties and disturbances. However, the effect of disturbances control on transient dynamics is not prominent. In this paper, we redesign the L 1 adaptive control architecture (L1AC) with anti-windup (AW) compensator to guarantee robust and fast adaption of the underwater vehicle with input saturation and coupling disturbances. To reduce the fluctuation of vehicle states, the Riccati-based AW compensator is utilized to compensate the output signal from L1AC controller via taking proper modification. The proposed method is applied to the pitch channel of REMUS vehicle’s six Degrees Of Freedom (DOF) model with strong nonlinearities and compared with L1AC baseline controller. Simulations show the effectiveness of the proposed control strategy compared to the original L1AC. Besides, the fluctuation in roll channel coupled with pitch channel is suppressed according to the performances of control tests

Pitch Channel Control of a REMUS AUV with Input Saturation and Coupling Disturbances

The motion of an underwater vehicle is prone to be affected by time-varying model parameters and the actuator limitation in control practice. Adaptive control is an effective method to deal with the general system dynamic uncertainties and disturbances. However, the effect of disturbances control on transient dynamics is not prominent. In this paper, we redesign the L 1 adaptive control architecture (L1AC) with anti-windup (AW) compensator to guarantee robust and fast adaption of the underwater vehicle with input saturation and coupling disturbances. To reduce the fluctuation of vehicle states, the Riccati-based AW compensator is utilized to compensate the output signal from L1AC controller via taking proper modification. The proposed method is applied to the pitch channel of REMUS vehicle’s six Degrees Of Freedom (DOF) model with strong nonlinearities and compared with L1AC baseline controller. Simulations show the effectiveness of the proposed control strategy compared to the original L1AC. Besides, the fluctuation in roll channel coupled with pitch channel is suppressed according to the performances of control tests

Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dictionary learned by the training samples, including thedifferent classes, shift invariant sparse feature for the training as well as test samples can be formed through sparse codes and employed as the input of classifier. A support vector machine (SVM) with optimized parameters has been extensively used in intelligent diagnosis of machinery fault. Hence, in this study, a novel fault diagnosis method of rolling bearings using shift invariant sparse feature and optimized SVM is proposed. Firstly, dictionary learning by shift invariant K-SVD algorithm is conducted. Then, shift invariant sparse feature is constructed with the learned over-complete dictionary. Finally, optimized SVM is employed for classification of the shift invariant sparse feature corresponding to different classes, hence, bearing fault diagnosis is achieved. With regard to the optimized SVM, three methods including grid search, generic algorithm (GA), and particle swarm optimization (PSO) are respectively carried out. The experiment results show that the shift invariant sparse feature using shift invariant K-SVD can effectively distinguish the bearing vibration signals corresponding to different running states. Moreover, optimized SVM can significantly improve the diagnosis precision

YOLO-JD: A Deep Learning Network for Jute Diseases and Pests Detection from Images

Recently, disease prevention in jute plants has become an urgent topic as a result of the growing demand for finer quality fiber. This research presents a deep learning network called YOLO-JD for detecting jute diseases from images. In the main architecture of YOLO-JD, we integrated three new modules such as Sand Clock Feature Extraction Module (SCFEM), Deep Sand Clock Feature Extraction Module (DSCFEM), and Spatial Pyramid Pooling Module (SPPM) to extract image features effectively. We also built a new large-scale image dataset for jute diseases and pests with ten classes. Compared with other state-of-the-art experiments, YOLO-JD has achieved the best detection accuracy, with an average mAP of 96.63%

PDE Formation and Iterative Docking Control of USVs for the Straight-Line-Shaped Mission

In this paper, an intelligent control scheme of formation collision avoidance and iterative docking is proposed for full-actuated unmanned surface vehicles (USVs). The artificial potential field method is integrated into the partial differential equation (PDE) formation control approach, which can improve the collision-avoidance performance of the formation. During the docking process of the straight-line formation, the USV agent is expected to track the desired commands accurately. Considering the possibility of docking failure, an iterative learning model predictive control (ILMPC) scheme is introduced. Once the moving USV fails in docking on the stationary USV, the moving agent can return to the origin to re-execute the docking process. The ILMPC method has the advantages of model predictive control and the iterative learning, so it can consider the future process dynamics in the time domain and overcome periodic disturbances. Simulation results show that USVs can avoid collisions with each other in the straight-line-formation mission. Furthermore, the USV agent can dock one-by-one successfully when interference exists

PDE Formation and Iterative Docking Control of USVs for the Straight-Line-Shaped Mission

In this paper, an intelligent control scheme of formation collision avoidance and iterative docking is proposed for full-actuated unmanned surface vehicles (USVs). The artificial potential field method is integrated into the partial differential equation (PDE) formation control approach, which can improve the collision-avoidance performance of the formation. During the docking process of the straight-line formation, the USV agent is expected to track the desired commands accurately. Considering the possibility of docking failure, an iterative learning model predictive control (ILMPC) scheme is introduced. Once the moving USV fails in docking on the stationary USV, the moving agent can return to the origin to re-execute the docking process. The ILMPC method has the advantages of model predictive control and the iterative learning, so it can consider the future process dynamics in the time domain and overcome periodic disturbances. Simulation results show that USVs can avoid collisions with each other in the straight-line-formation mission. Furthermore, the USV agent can dock one-by-one successfully when interference exists