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Stainless steel (SS) is widely used in many fields including aeronautics, automobiles, marine and mechanical industries due to its outstanding feature such as good corrosion resistance and hardness. However, changes in material properties under stress, particularly changes in Young’s modulus, result in the formation of cracks, a reduction in load-bearing capacity, and fatigue damage. So, the structural integrity needs to be evaluated based on a precise measurement of mechanical properties. In this study, Stainless Steel 304 (SS-304) is used as the base material and various tensile stresses are applied ranging from 0MPa to 100MPa with increment of 10MPa in each step. Nondestructive Laser Ultrasound Technique (LUT) has been used to characterize the elastic modulus under various tensile stresses. An inverse program was developed based on the Particle Swarm Optimization (PSO) algorithms to determine material properties. Nonlinear Gauss fitting method was proposed and established the fitting equation and nonlinear curve for Young’s modulus and residual stress. The outcome of this research shows that when tensile stress is applied, the mechanical properties decrease by shifting the dispersion curve and also it is evident that the dispersion curves move toward the high-frequency-thickness while increasing the tensile stress. When the tensile stress was increased from 0MPa to 100MPa, the value of Young’s modulus decreased from 201.7GPa to 193.5GPa. Especially, the predominant changes were observed during 30–100MPa. This observation displays the bonding strength and binding energy between the atomics. Further, the proposed nonlinear Gauss fitting substantiated the experimental values and confirmed that the thickness accuracy is close to the inversion values, with an average difference of 4.32%. This research suggests a potential nondestructive method to determine the residual stress of a material by calculating the changes in the elastic modulus.

Modeling for an unknown plant is a very important and primary task in the control system engineering. By using this established model, an adequate controller can be further designed to meet certain control specifications. This paper will propose a new modeling method which is based on the infinite impulse response (IIR) digital filter structure. A modified particle swarm optimization (PSO) with multiple subpopulations is utilized to solve for the IIR filter coefficients to model an unknown digital system. The developed PSO algorithm is able to overcome the multimodal error surface problem that often occurs in the system modeling of IIR digital filter. Finally, three different kinds of digital systems are examined to demonstrate the feasibility of the proposed method, including the same system order and reduced order structures. It is concluded from several simulation results that the proposed method has an excellent performance on solving the IIR digital modeling problem.

A CMOS ON–OFF-keying 3–10.6-GHz transmitter with low power consumption and low complexity used to Impulse Radio Ultra-Wide Band (IR-UWB) communication system is presented in this work. This architecture is designed and optimized via particle swarm optimization (PSO) algorithm. The IR-UWB transmitter is adapted to generate a high bandwidth frequency and it has a band switching capability. It consists of a switching inductance–capacitance voltage-controlled oscillator (LC_VCO), a pulse generator circuit, an injection-locked frequency divider (ILFD) circuit, a buffer and an antenna. The VCO is switched ON/OFF by the pulse signal produced by a generator circuit which is realized through synchronizing the received data by a clock signal. The used technique for transmitting a discontinuous signal is based on a complementary switch-mode ON–OFF LC_VCO, whose main advantage is to reduce power consumption. In this work, a best agreement between the results of the optimization technique and those of the simulation is obtained. The simulated results illustrate a signal of pulse width of 2.5 ns and a pulse repetition rate (PRR) of 200 MHz. The output spectra are centered at 4-GHz and 8-GHz frequencies with 1,332-MHz and 1,350-MHz bandwidths, respectively. The peak-to-peak amplitude of a UWB signal output is 154mV. The IR-UWB transmitter power consumption is 11.4mW which corresponds to the consumption energy of 28.5 pJ/pulse @ 200MHz. The power spectral densities (PSDs) of the output signals of both circuits viz. ON–OFF LC_VCO and ILFD are less than $-41.3$dBm/MHz, which agreed well with the Federal Communication Commission (FCC) regulation. The transmitter design is well implemented using a TSMC 0.18-$\mu$m CMOS process technology in an Advanced Design System (ADS).

In order to realize the resource optimization allocation in the green innovation system of China’s shipbuilding industry under the internet environment, to improve the level of green innovation and to reduce the resource consumption, a resource optimization allocation model and the corresponding allocation strategy are proposed. The model integrates and shares the innovation resource data through Internet of Things (IOT) technology, and optimizes the allocation decision by using the cooperative differential game and Particle Swarm Optimization (PSO) algorithm. At the same time, it ensures the robustness of green innovation system and realizes the optimization allocation of resources. A case study is given to illustrate the feasibility of the model. The results show that the green innovation subject can carry out strategic interaction by adjusting the allocation proportion of innovation resources through the proposed model, so as to optimize the overall green innovation benefits of the system.

The running optimization of pipelines is an important measure to decrease the energy consumption for transporting oil. The running optimization of pipelines is a complex integral programming question, it is usually solved in dynamic programming algorithm, genetic algorithm and so on, these algorithms are difficult to operate and sometimes get the answers deviating far from the true optimal answer. This article aimed at solving the optimal model of pipelines running with particle swarm optimization(PSO) algorithm, calculation on a real example proved that the PSO algorithm had profits of high precision and fast speed of iteration, it can be used in the study of pipelines running optimization well.

A novel scheduling method based on disruption management is presented to solve the problem of increased energy consumption with the increase of make span caused by the interference in co-smelting furnace, steelmaking and continuous casting process. The scheduling model is built considering both the targets to minimize total weighted completion time (the original objective) and the target to minimize total weighted delay time (the disruption repairing objective). By combining multi-objective approaching policy with the bi-level particle swarm optimization and stochastic probability multi-neighborhood search mechanism, a heuristic hybrid algorithm is proposed to improve scheduling efficiency while reducing energy consumption indicator so as to achieve green manufacturing goal. The numerical experiments show that the hybrid PSO algorithm is effective.