Further Analysis of Machine Tool Dimensional Accuracy and Thermal Stability under Varying Floor Temperature

Machining is as old as humanity, and changes in temperature in both the machine’s internal and external environments can be of great concern as they affect the machine’s thermal stability and, thus, the machine’s dimensional accuracy. This paper is a continuation of our earlier work, which aimed to analyze the effect of the internal temperature of a machine tool as the machine is put into operation and vary the external temperature, the machine floor temperature. Some experiments are carried out under controlled conditions to study how machine tool components get heated up and how this heating up affects the machine’s accuracy due to thermally induced deviations. Additionally, another angle is added by varying the machine floor temperature. The parameters mentioned above are explored in line with the overall thermal stability of the machine tool and its dimensional accuracy. A Robodrill CNC machine tool is used. The CNC was first soaked with thermal energy by gradually raising the machine floor temperature to a certain level before putting the machine in operation. The machine was monitored, and analytical methods were deplored to evaluate thermal stability. Secondly, the machine was run idle for some time under raised floor temperature before it was put into operation. Data was also collected and analyzed. It is observed that machine thermal stability can be achieved in several ways depending on how the above parameters are joggled. This paper, in conclusion, reinforces the idea of machine tool warm-up process in conjunction with a carefully analyzed and established machine floor temperature variation for the approximation of the machine tool’s thermally stability to map the long-time behavior of the machine tool.

Fault detection for nonlinear networked control systems based on fuzzy observer

Security and reliability must be focused on control sys- tems firstly, and fault detection and diagnosis （FDD） is the main theory and technology. Now, there are many positive results in FDD for linear networked control systems （LNCSs）, but nonlinear networked control systems （NNCSs） are less involved. Based on the T-S fuzzy-modeling theory, NNCSs are modeled and network random time-delays are changed into the unknown bounded uncertain part without changing its structure. Then a fuzzy state observer is designed and an observer-based fault detection approach for an NNCS is presented. The main results are given and the relative theories are proved in detail. Finally, some simulation results are given and demonstrate the proposed method is effective.

Control of chaotic oscillations of a satellite

Analytical conditions and practical methods of their realization are proposed to solve a problem of a command signal tracking for a nonlinear disturbed system. Nonlinear disturbed plants consisting of linear dynamic block and nonlinear block in feedback are considered. Nonlinear part of the plant and disturbance are unknown and bounded. The paper illustrates a possibility of applications of proposed algorithms to control libration angle of satellite.

Type-2 Fuzzy Logic Controllers Based Genetic Algorithm for the Position Control of DC Motor

Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainty. This research article proposes the position control of (DC) motor. The proposed algorithm of this article lies in the application of a genetic algorithm interval type-2 fuzzy logic controller (GAIT2FLC) in the design of fuzzy controller for the position control of DC Motor. The entire system has been modeled using MATLAB R11a. The performance of the proposed GAIT2FLC is compared with that of its corresponding conventional genetic algorithm type-1 FLC in terms of several performance measures such as rise time, peak overshoot, settling time, integral absolute error (IAE) and integral of time multiplied absolute error (ITAE) and in each case, the proposed scheme shows improved performance over its conventional counterpart. Extensive simulation studies are conducted to compare the response of the given system with the conventional genetic algorithm type-1 fuzzy controller to the response given with the proposed GAIT2FLC scheme.

The Usage of the Digital Controller in Regulating Boost Converter

This paper proposes the modeling and simulation technique to analyze and design a Boost converter using generalized minimum variance method with discrete-time quasi-sliding mode to adjust the converter switch through a pulse width modulation (PWM), so as to enhance a stable output voltage. The control objective is to maintain the sensed output voltage stable, constant and equal to some constant reference voltage (8 volt) in the load resistance variation (24, 48, 240) Ω and input voltage variation (20, 24, 28) volt circumstances. This control strategy is very appropriate for the digitally controlled power converter and for the system requirement accomplishment, resulting high output voltage accuracy. The performance degradation in practical implementation can be expected due to noise, PWM nonlinearities, and components imperfection. The digital simulation using MATHLAB/Simulink is performed to validate the functionality of the system.

Optimal Multiperiodic Control for Inventory Coupled Systems: A Multifrequency Second-Order Test

A complex autonomous inventory coupled system is considered. It can take, for example, the form of a network of chemical or biochemical reactors, where the inventory interactions perform the recycling of by-products between the subsystems. Because of the flexible subsystems interactions, each of them can be operated with their own periods utilizing advantageously their dynamic properties. A multifrequency second-order test generalizing the p-test for single systems is described. It can be used to decide which kind of the operation (the static one, the periodic one or the multiperiodic one) will intensify the productivity of a complex system. An illustrative example of the multiperiodic optimization of a complex chemical production system is presented.

Comparative Analysis of Sonar Heads Drive Systems

This paper is dealing with a comparative analysis, from technical point of view of the solutions with the highest potentiality utilized in sonar heads drives. Even though the use of DC servomotors is a convenient solution for most customers, from some modem analysis criteria points of view, this type of drive system has a low reliability and a greater impact on the environment, compared to AC servomotors. From this class of AC servomotors, high behaviors, in such an application, have stepper motors and electronically commutated motor （brushless DC）. That is why, analysis in this paper, balances these two classes of AC servomotors. The systems performed are analyzed in Matlab/Simulink and PowerSim environments.

Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors:Challenges and Future Trends

Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other media.Deep learning(DL)methods are more successful than other traditional machine learning(ML)methods inCV.DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face recognition.In this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is presented.The sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV.This review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

Optimization of Continuous Airworthiness Problems

Recently aviation accident data shows that many fatal accidents in aviation are due to airworthiness issues despite the fact that all civil and private aircraft are required to comply with the airworthiness standards set by their national airworthiness authority.This paper presents a unique approach to continuous airworthiness problems optimization needed to reduce the risk associated with the gap between aircraft designers&manufacturing organization and continuing airworthiness(state of civil aviation authority and air operators).As a result of the paper summarizes these problems and searching of the possible solutions to be optimized,these problems are achieved to get more integration between(designers&manufacturing and air operators),finally the recommendations are drawn to address the safe operation of the aircraft and can be given to the International Civil Aviation Organization(ICAO),Federal Aviation Administration(FAA)and European Aviation Safety Agency(EASA)and Civil Aviation Authorities(CAAs)for more integration between all of them structure.

Low-rank constrained weighted discriminative regression for multi-view feature learning

In recent years,multi-view learning has attracted much attention in the fields of data mining,knowledge discovery and machine learning,and been widely used in classification,clustering and information retrieval,and so forth.A new supervised feature learning method for multi-view data,called low-rank constrained weighted discriminative regression(LWDR),is proposed.Different from previous methods handling each view separately,LWDR learns a discriminative projection matrix by fully exploiting the complementary information among all views from a unified perspective.Based on least squares regression model,the highdimensional multi-view data is mapped into a common subspace,in which different views have different weights in ptojection.The weights are adaptively updated to estimate the roles of all views.To improve the intra-class similarity of learned features,a low-rank constraint is designed and imposed on the multi-view features of each class,which improves the feature discrimination.An iterative optimization algorithm is designed to solve the LWDR model efficiently;Experiments on four popular datasets,including Handwritten,CaltechlOl,PIE and AwA,demonstrate the effectiveness of the proposed method.

NONPARAMETRIC IDENTIFICATION OF MISO HAMMERSTEIN SYSTEM FROM STRUCTURED DATA

The problem of nonparametric identification of a multivariate nonlinearity in a D-input Hammer- stein system is examined. It is demonstrated that if the input measurements are structured, in the sense that there exists some hidden relation between them, i.e. if they are distributed on some （unknown） d-dimensional space M in IRD, d 〈 D, then the system nonlinearity can be recovered at points on M with the convergence rate O（n-1/（2＋d）） dependent on d. This rate is thus faster than the generic rate O（n-1/（2＋D）） achieved by typical nonparametric algorithms and controlled solely by the number of inputs D.