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Atom search optimization algorithm has good searching ability and has been successfully applied to calculate hydrogeological parameters and groundwater dispersion coefficient. Since the atom search optimization algorithm is only based on the atom force motion model in molecular dynamics, it has some shortcomings such as slow search speed and low precision during the later stage of iteration. A modified atom search optimization based on the immunologic mechanism and reinforcement learning is proposed to overcome the abovementioned shortcomings in this paper. The proposed algorithm introduces a vaccine operator to better utilize the dominant position in the current atom population so that the speed, accuracy, and domain search ability of the atom search optimization algorithm can be strengthened. The reinforcement learning operator is applied to dynamically adjust the vaccination probability to balance the global exploration ability and local exploitation ability. The test results of 21 benchmark functions confirm that the performance of the proposed algorithm is superior to seven contrast algorithms in search accuracy, convergence speed, and robustness. The proposed algorithm is used to optimize the permutation flow shop scheduling problem. The experimental results indicate that the proposed algorithm can achieve better optimization results than the seven comparative algorithms, so the proposed algorithm has good practical application value.

Many optimization algorithms have been proposed to solve hybrid flowshop scheduling problem (HFSP). However, with the development of industry and society, labor right and labor safety have become important problem to consider in production scheduling. So the green HFSP considering makespan, noise and dust pollution becomes an urgent problem to be solved. In this paper, the rider optimization algorithm (ROA) is modified into the multi-objective rider optimization algorithm (MOROA) using Pareto archive and neighborhood sorting techniques. The Pareto archive and neighborhood sorting technology make the Pareto optimal solution set of MOROA have higher coverage and more solutions. Then MOROA is discretized into discrete MOROA (DMOROA) to solve the HFSP considering makespan, noise and dust pollution. DMOROA is tested on 10, 30 and 50 jobs HFSP considering makespan, noise and dust pollution. The test results are compared with two multi-objective algorithms to verify the performance of DMOROA. And the test results verify that the DMOROA is superior to the comparison algorithms in search accuracy, number of non-dominated solutions, diversity of solution set and stability. Therefore, DMOROA is effective in solving multi-objective HFSP considering makespan, noise and dust pollution.

The optimal foraging algorithm (OFA) was proposed by summarizing the rules of the animal foraging behavior in a group. Therefore OFA also has the defects of the swarm intelligence algorithm, such as easy to trap into local optimum and low convergence accuracy. In order to overcome these defects, an optimal foraging algorithm based on differential evolution (DEOFA) is proposed. The differential evolution mechanism contains mutation and crossover operators. The mutation and crossover operators are used to accelerate the convergence speed and global search capability of the OFA. The mutation operator is adopted to perform mutation operations centered on the optimal individual of each iteration to raise the convergence accuracy of the OFA. The test results of 30 benchmark functions show that the performance of DEOFA is better than nine compared algorithms in search accuracy, convergence speed and robustness. In order to verify the effectiveness of the DEOFA in solving practical problems, DEOFA is applied to solve the 0-1 knapsack problem. The test results in the six examples of 0-1 knapsack problems indicate that the DEOFA achieves better performance in accuracy, stability and high dimension.

Taking a 1000 MW turbine generator as the research object, the short-circuit fault in electrical disturbance is analyzed. Since it is very difficult to carry out fault analysis experiments and research on actual systems, simulation analysis is one of the more effective means of electrical fault diagnosis, the simulation’s results approach the actual behavior of the system and are ideal tools for power system analysis, and can provide an empirical basis for practical applications. The short-circuit fault model of the SIMULINK power system is built to analyze the two types of faults of generator terminals short-circuit and power grid short-circuit. The impact load spectrum, fault current and speed fluctuation between low-voltage rotors were extracted and analyzed. The conclusion is that the impact value of electromagnetic torque at the generator terminal is greater than that on the power grid side. The impact value of a two-phase short-circuit at the generator terminal is the largest, and that of a three-phase short-circuit on the power grid side is the smallest. The transient impulse current of a three-phase short-circuit at any fault point is greater than that of a two-phase short-circuit, the impulse current of the grid side short-circuit is much greater than that of the generator terminal short-circuit, the speed fluctuation and fluctuation difference caused by the three-phase short-circuit in the grid side are the largest. The alternating frequency of the transient electromagnetic force of the four kinds of faults avoids the natural frequency of the torsional vibration of the shaft system, and the torsional resonance of the shaft system in the time domain of the short-circuit fault will not appear. However, after the fault is removed, the residual small fluctuation torque in the system has a potential impact on the rotor system. This research shows an analysis of the structural integrity and safe operation of turbine generator units after a short-circuit fault, which can not only be applied to engineering practice, but also provide a theoretical basis for subsequent research.

Characterized by less parameter settings, easy implementation, and strong optimization capacity, the crow search algorithm has been successfully applied to solve the optimization problem. As the basic crow search algorithm is a new kind of swarm intelligent algorithm only based on the crow's memory foraging mode, it also contains defects like slow search speed and low optimization precision in later iterations, which are especially obvious for the optimization of high-dimensional functions. In order to overcome these shortcomings, a new crow search algorithm based on neighborhood search of non-inferior solution set (NICSA) is proposed. The proposed algorithm makes the crow individual choose the memory search mode or neighborhood search mode automatically in the course of evolution by the determination factor of non-inferior solution. With this strategy, the local exploitation and the global exploration of the algorithm became more balanced. In the neighborhood search, the selectivity factor is used to guide non-inferior solutions to adaptively execute neighborhood search of Levy flight or Gaussian flight, to enhance the neighborhood searchability of the algorithm and improve the optimization precision. The result of simulation experiments with 23 benchmark test functions verifies that the proposed algorithm has good optimization effect in the aspects of search veracity, convergence rate and robustness.

Bearings play an irreplaceable role in various categories of high-precision mechanical equipment. To reduce the adverse impact caused by sudden bearing failures, this paper proposes a method based on improved particle swarm optimization (IPSO), empirical mode decomposition (EMD), and support vector regression (SVR) to predict the remaining useful life (RUL) of bearing. The EMD method decomposes the raw vibration signal, and the components are chosen out according to the kurtosis criterion to reconstruct the vibration signal. After filtering out the noise, the features in time-domain and frequency-domain are extracted from the reconstructed vibration signal. The IPSO method is used to optimize three important parameters of the SVR model. Then the SVR model is trained with the extracted features to predict the bearing RUL. Actual bearing failure dataset is used to verify the validity of this method. Three different methods are used to compare with this proposed method. The results indicate that this proposed method performs best among the four methods in different evaluation indexes. The proposed method makes sense for bearing failure prediction.

Most mechanism contains one or more joints. However, joint clearances which cannot be dislodged make accuracy analysis become complicated. In order to seek a method to investigate the impact of joint clearances on the accuracy of a parallel robot, this paper deals with the 3-SPS-1-S spatial parallel robot’s pose error predicting and sensitivity analyzing. In this paper, a nonlinear method for evaluating the sources of the pose errors is proposed on the condition that the pose errors only come from joint clearances. Firstly, based on the nonlinear contact modes inside the joints, the error equivalent method is outlined for spherical joint and sliding pair. Then the pose error distribution over its workspace is presented. Secondly, those areas where the pose errors present abnormally changing are analyzed. Thirdly, sensitivity models are defined to evaluate how much the external variable load affects the pose errors. Finally, the simulation results, which are consistent with the structural characteristics of the 3-SPS-1-S spatial parallel robot, proved the validity of the equivalent method proposed in this paper.

The risk of knee injuries in freestyle skiing athletes that perform aerials is high. The internal stresses in the knee joints of these athletes cannot easily be directly measured. In order to ascertain the mechanical response of knee joints during the landing phase, and to explore the mechanism of damage to the cartilage and ligaments, a finite element model of the knee joint was established. Three successful landing conditions (neutral, backward, or forward landing) from a triple kicker were analyzed. The results demonstrate that the risk of cruciate ligament damage during a neutral landing was lowest. A forward landing carried medium risk, while backward landing was of highest risk. Backward and forward landing carried risk of injury to the anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL), respectively. The magnitude of stress on the meniscus and cartilage varied for all three landing scenarios. Stress was largest during neutral landing and least in backward landing, while forward landing resulted in a medium level of stress. The results also provide the basis for training that is scientifically robust so as to reduce the risk of injury and assist in the development of a professional knee joint protector.

A flower pollination algorithm is proposed based on the hormone modulation mechanism (HMM-FPA) to solve the no-wait flow shop scheduling problem (NWFSP). This algorithm minimizes the maximum accomplished time. Random keys are encoded based on an ascending sequence of components to make the flower pollination algorithm (FPA) suitable for the no-wait flow shop scheduling problem. The hormone modulation factor is introduced to strengthen information sharing among the flowers and improve FPA cross-pollination to enhance the algorithm global search performance. A variable neighborhood search strategy based on dynamic self-adaptive variable work piece blocks is constructed to improve the local search quality. Three common benchmark instances are applied to test the proposed algorithm. The result verifies that this algorithm is effective.

Using consumer depth cameras at close range yields a higher surface resolution of the object, but this makes more serious noises. This form of noise tends to be located at or on the edge of the realistic surface over a large area, which is an obstacle for real-time applications that do not rely on point cloud post-processing. In order to fill this gap, by analyzing the noise region based on position and shape, we proposed a composite filtering system for using consumer depth cameras at close range. The system consists of three main modules that are used to eliminate different types of noise areas. Taking the human hand depth image as an example, the proposed filtering system can eliminate most of the noise areas. All algorithms in the system are not based on window smoothing and are accelerated by the GPU. By using Kinect v2 and SR300, a large number of contrast experiments show that the system can get good results and has extremely high real-time performance, which can be used as a pre-step for real-time human-computer interaction, real-time 3D reconstruction, and further filtering.

To the problem of the traditional parameters optimization algorithm may level into local optimal, a parameter optimization method crossover mutation artificial bee colony based on artificial bee colony algorithm is proposed to solve this problem and applied toi¾źintrusion detection. And introduced an improved artificial colony algorithm based on crossover mutation operator, the whole bee colony could be divided into two sub-populations according to the fitness value of colony and effectively avoid local optimum and enhance convergence speed,i¾źuse standard test functions to verify the effectiveness of the algorithm. And the proposed algorithm's performance is tested by the KDD-99 datasets, the experimental results show that this method can effective improve the classification performance of intrusion detection.

As traditional negative selection algorithms need to calculate the distances between initial detectors and all the self data, the efficiencies of them are too low. In this article, before the creation process of detectors, the self data is pretreated to be a SS-Tree, and then the negative selection process is transformed into the data query process in the self SS-Tree to improve the efficiency of detectors creation.

Attack and defense is the two important aspects of network security issue. Network security situational assessment has focused on attack roundly. Numbers of network security situational assessment models have been put forward based on attack and threat. Those methods have obtained good results. On the other hand, defense lacks careful consideration. Defense embodies system tolerance. System tolerance contains three important factors: system tolerance to attack, system asset tolerance, and system survivability. The article will introduce a network security situational assessment model based on attack and defense. Consult the existent models of network security situational assessment to summarize a hierarchical model. The hierarchical model based on attack mainly. Then we will use the three factors of system tolerance to mend the model. The new hierarchical model based on attack and defense will be closer to realistic security situation status. The corresponding experiment proves that the improved model considered attack and defense has better effect.

Forecast of network security situation is an important part of network security situational assessment. The concept of network security situational assessment will be introduced. Then a model of network security situational assessment is summarized based on the concept. On the other hand, The method of GM(1,1) modified by residual error is mentioned. And this method will be improved into optimal fuzzy grey model. The model of optimal fuzzy grey will be used to forecast value of network security situational assessment. The results show better effects in simulation test, compared to the original model.

The article by Wang, Lin and Chou illustrates the integration and control of a 500W self-humidified proton exchange membrane fuel cell (PEMC) system. By applying some classic modern control theory as tools, some robust controllers are designed to regulate the air and hydrogen flow rates. In this article, the authors give detailed design procedure from the mathematical modeling to the controller design. Somemodern control theories, such as systems identification, Hinf control and optimal control are synthetically used. In the Hinf control based design, a key problem is the selection of the weighting function. In this article, such a procedure is shown in details. Due to these reason, in our opinion thework of this article can be viewed as a successful practical application of advanced control theory. It may help to reduce the gap between control theory and control engineering. The overall design procedure of the considered 500W self-humidified PEMC system includes the following aspects.