Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

Traffic identification method based on multiple probabilistic neural network model

Neural Computing and Applications - Traffic identification is currently an important challenge for network management and security. In this paper, we propose a novel application identification...     

Torsional statics of two-dimensionally functionally graded microtubes

A size-dependent governing equation is derived to investigate the torsional static behaviors of two-dimensionally functionally graded microtubes based on the modified couple stress theory. The shear modulus is assumed to vary along the tube’s length direction according to an exponential distribute function, and varies along the tube’s radius direction according to a power-law function. A generalized differential quadrature method is developed to determine the rotational angle and shear stresses. Some illustrative examples are given to investigate the effects of applied torques, the length scale parameter and various material compositions on the torsional angle and shear stresses.     

Fabrication of BSCF-based mixed ionic-electronic conducting membrane by electrophoretic deposition for oxygen separation application

Ba_0.5Sr_0.5Co_0.8Fe_0.2O_3-δ (BSCF), which exhibits a high mixed oxide ionic-electronic conduction, was used for the fabrication of an oxygen separation membrane. An asymmetric structure, which was a thin and dense BSCF membrane layer supported on a porous BSCF substrate, was fabricated by the electrophoretic deposition method (EPD). Porous BSCF supports were prepared by the uniaxial pressing method using a powder mixture with BSCF and starch as the pore-forming agent (0–50 wt.%). The sintering behaviors of the porous support and the thin layer were separately characterized by dilatometry to determine the co-fired temperature at which cracking did not occur. A crack-free and thin dense membrane layer, which had about a 15 μm thickness and >95% relative density, was obtained after optimizing the processes of EPD and sintering. The dense/porous interface was well-bonded and the oxygen permeation flux was 2.5 ml (STP) min⁻¹ cm^-2 at 850 °C.     

高碘酸盐的活化及降解水体有机污染物研究进展

根据高碘酸盐活化方式、活化机理和相关自由基种类等方面,叙述了国内外高碘酸盐高级氧化体系降解水体有机污染物方面的工作。基于氧化剂为高碘酸盐的高级氧化技术氧化能力强,能在较宽的pH范围内高效降解包括抗氧化能力极强的全氟辛酸(PFOA)在内的多种有机污染物,在水处理领域具有较好的应用潜力。依据目前的研究现状,结合实际需求,认为进行废水处理的小试或者中试、优化活化理论研究,以及明确各活性物质对有机物选择性降解的机理,是将来需要关注的问题以及研究方向。     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.     

Maneuverability modeling and trajectory tracking for fish robot

This study develops a 6-DOF mathematical model for a robotic fish that considers surge, sway, heave, roll, pitch, and yaw. The model considers the conditions of a fish swimming in ocean current perturbations similar to the ocean current perturbations of the slender-body autonomous underwater vehicles. For swimming and turning behaviors, a nonlinear, dynamic, carangiform locomotion model is derived by using a planar four-link model. A 2-DOF barycenter mechanism is proposed to provide body stabilization and to serve as an actuating device for active control design. A barycenter control scheme is developed to change the center of gravity of the robot fish body by moving balancing masses along two axes. The projected torque on x and y axes propel pitch and roll angles to the desired settings. A Stabilizing controller, fish-tail mechanism, rigid body dynamics, and kinematics are incorporated to enable the fish robot to move in three dimensional space. Simulation results have demonstrated maneuverability and control system performance of the developed controller which is proposed to conduct path tracking of the robot fish as it swims under current perturbations.     

Improved synergetic excitation control for transient stability enhancement and voltage regulation of power systems

This paper proposes decentralized improved synergetic excitation controllers (ISEC) for synchronous generators to enhance transient stability and obtain satisfactory voltage regulation performance of power systems. Each generator is considered as a subsystem, for which an ISEC is designed. According to the control objectives, a manifold, which is a linear combination of the deviation of generator terminal voltage, rotor speed and active power, is chosen for the design of ISEC. Compared with the conventional synergetic excitation controller (CSEC), a parameter adaptation scheme is proposed for updating the controller parameter online in order to improve the transient stability and voltage regulation performance simultaneously under various operating conditions. Case studies are undertaken on a single-machine infinite-bus power system and a two-area four-machine power system, respectively. Simulation results show the ISEC can provide better damping and voltage regulation performance, compared with the CSEC without parameter adaptation scheme and the conventional power system stabilizer.