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The micro-vision system is designed for micro-fabrication. This system with force sensor controls micro-fabrication operation. The focus is calculated by one order moment. Gradient evaluation function and frequency domain evaluation function are combined as evaluation function in auto-focus technology. Image definition evaluation function based on gradient is adopted in large step. Image definition evaluation function based on frequency-domain is adopted in small step. Search strategy adopts threshold method and curve fitting method. Edge invariant moment is to recognize objects. The system can measure the distance of three dimensions between the cutting tool and the work-piece. Then the micro-fabrication operation is completed.

To improve the localization accuracy of the magnetically controlled capsule endoscope (MCCE), a new localization method, based on the magnetic dipole model, is proposed, where the anti-disturbance permanent magnet (APM) is used as the source of stable magnetic field, thus reducing the interference of the geomagnetic field or the electric conductor magnetic field in the system.

The coupling magnetic force model between the APM and the capsule endoscope is established to obtain the magnetic force relationship and magnetic induction intensity. Along the three axes, magnetic induction intensity data are collected by a 3 × 3 sensor array composed of nine magnetic field intensity sensors, while the data are uploaded to the main computer by the STM32F103C8T6 control board over a ESP8266 WIFI module connection. Next, the axial magnetic induction intensity data are decoupled to obtain the measurement trajectory, whereas the error function is established based on the calculated trajectory parameters. Finally, the Levenberg–Marquardt (L-M) algorithm is used to solve the position information of the MCCE.

Experiments show that the average localization error of an MCCE in a straight and circular bend tube is 4.76 mm, whereas in a U-bend tube, it is 6.82 mm.

The optimized simulation value in the linear and bending environment is in good agreement with the experimental value, which verifies the accuracy of the MCCE localization system based on magnetic field sensor array, exhibiting good performance in localization and position tracking while providing a theoretical basis for the subsequent research.

The stock indexes are an important issue for investors, and in this paper good trading strategies will be aimed to be adopted according to the accurate prediction of the stock indexes to chase high returns.

To avoid the problem of insufficient financial data for daily stock indexes prediction during modeling, a data augmentation method based on time scale transformation (DATT) was introduced. After that, a new deep learning model which combined DATT and NGRU (DATT-nested gated recurrent units (NGRU)) was proposed for stock indexes prediction. The proposed models and their competitive models were used to test the stock indexes prediction and simulated trading in five stock markets of China and the United States.

The experimental results demonstrated that both NGRU and DATT-NGRU outperformed the other recurrent neural network (RNN) models in the daily stock indexes prediction.

A novel RNN with NGRU and data augmentation is proposed. It uses the nested structure to increase the depth of the deep learning model.

At present, research on the preparation of corrosion inhibitors using modified pyrimidine derivatives is still blank. The purpose of this study is to synthesize a new cationic mercaptopyrimidine derivative quaternary ammonium salt, known as DTEBTAC, that can be used as a corrosion inhibitor to slow down the metal corrosion problems encountered in oil and gas extraction processes.

A new corrosion inhibitor was synthesized by the reaction of anti-Markovnikov addition and nucleophilic substitution. The weight loss method was used to study the corrosion inhibition characteristics of synthetic corrosion inhibitors. Electrochemical and surface topography analyses were used to determine the type of inhibitor and the adsorption state formed on the surface of N80 steel. Molecular dynamics simulations and quantum chemistry calculations were used to investigate the synthetic corrosion inhibitor’s molecular structure and corrosion inhibition mechanisms.

The results of the weight loss method show that when the dosage of DTEBTAC is 1%, the corrosion rate of N80 steel in hydrochloric acid solution at 90? is 3.3325 g m-2 h-1. Electrochemical and surface morphology analysis show that DTEBTAC can form a protective layer on the surface of N80 steel, and is a hybrid corrosion inhibitor that can inhibit the main anode. Quantum chemical parameter calculation shows that DTEBTAC has a better corrosion inhibition effect than DTP. The molecular dynamics simulation results show that DTEBTAC has stronger binding energy than DTP, and forms a network packing structure through hydrogen bonding, and the adsorption stability is enhanced.

A novel cationic mercaptopyrimidine derivative quaternium-ammonium salt corrosion inhibitor was designed and provided. Compared with the prior art, the preparation method of the synthesized mercaptopyrimidine derivative quaternary ammonium salt corrosion inhibitor is simple, and the presence of nitrogen-positive ions, sulfur atoms and nitrogen-rich atoms has an obvious corrosion inhibition effect, which can be used to inhibit metal corrosion during oil and gas field exploitation. It not only expands the application field of new materials but also provides a new idea for the research and development of new corrosion inhibitors.

The classical advection-dispersion equation (ADE) model cannot accurately depict the gas transport process in natural geological formations. This paper aims to study the behavior of CO₂ transport in fractal porous media by using an effective Hausdorff fractal derivative advection-dispersion equation (HFDADE) model.

Anomalous dispersion behaviors of CO₂ transport are effectively characterized by the investigation of time and space Hausdorff derivatives on non-Euclidean fractal metrics. The numerical simulation has been performed with different Hausdorff fractal dimensions to reveal characteristics of the developed fractal ADE in fractal porous media. Numerical experiments focus on the influence of the time and space fractal dimensions on flow velocity and dispersion coefficient.

The physical mechanisms of parameters in the Hausdorff fractal derivative model are analyzed clearly. Numerical results demonstrate that the proposed model can well fit the history of gas production data and it can be a powerful technique for depicting the early arrival and long-tailed phenomenon by incorporating a fractal dimension.

To the best of the authors’ knowledge, first time these results are presented.

An optimal solution method based on 2-norm is proposed in this study to solve the inverse kinematics multiple-solution problem caused by a high redundancy. The current research also presents a motion optimization based on the 2-Norm of high-redundant mobile humanoid robots, in which a kinematic model is designed through the entire modeling.

The current study designs a highly redundant humanoid mobile robot with a differential mobile platform. The high-redundancy mobile humanoid robot consists of three modular parts (differential driving platform with two degrees of freedom (DOF), namely, left and right arms with seven DOF, respectively) and has total of 14 DOFs. Given the high redundancy of humanoid mobile robot, a kinematic model is designed through the entire modeling and an optimal solution extraction method based on 2-norm is proposed to solve the inverse kinematics multiple solutions problem. That is, the 2-norm of the angle difference before and after rotation is used as the shortest stroke index to select the optimal solution. The optimal solution of the inverse kinematics equation in the step is obtained by solving the minimum value of the objective function of a step. Through the step-by-step cycle in the entire tracking process, the kinematic optimization of the highly redundant humanoid robot in the entire tracking process is realized.

Compared with the before and after motion optimizations based on the 2-norm algorithm of the robot, its motion after optimization shows minimal fluctuation, improved smoothness, limited energy consumption and short path during the entire mobile tracking and operating process.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

Motion optimization based on the 2-norm of a highly redundant humanoid mobile robot with the entire modeling is performed on the basis of the entire modeling. This motion optimization can make the highly redundant humanoid mobile robot’s motion path considerably short, minimize energy loss and shorten time. These researches provide a theoretical basis for the follow-up research of the service robot, including tracking and operating target, etc. Finally, the motion optimization algorithm is verified by the tracking and operating behaviors of the robot and an example.