Effectively monitoring maritime environments has become a vital problem in maritime applications. Traditional methods are not only expensive and time consuming but also restricted in both time and space. More recently, the concept of an industrial wireless sensor network (IWSN) has become a promising alternative for monitoring next-generation intelligent maritime grids, because IWSNs are cost-effective and easy to deploy. This paper focuses on solving the issue of 3-D IWSN deployment in a 3-D engine room space of a very large crude-oil carrier and also considers numerous power facilities. To address this 3-D IWSN deployment problem for maritime applications, a 3-D uncertain coverage model is proposed that uses a modified 3-D sensing model and an uncertain fusion operator. The deployment problem is converted into a multiobjective optimization problem that simultaneously addresses three objectives: coverage, lifetime, and reliability. Our goal is to achieve extensive coverage, long network lifetime, and high reliability. We also propose a distributed parallel cooperative coevolutionary multiobjective large-scale evolutionary algorithm for maritime applications. We verify the effectiveness of this algorithm through experiments by comparing it with five state-of-the-art algorithms. Numerical results demonstrate that the proposed method performs most effectively both in optimization performance and in minimizing the computation time.
Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We first introduce rough neurons and enhance the consequence nodes, and further integrate the interval type-2 fuzzy set into the existing fuzzy rough neural network model. Thus, several modified fuzzy rough neural network models are proposed. While simultaneously considering the objectives of prediction precision and network simplicity, each model is transformed into a multiobjective optimization problem by encoding the structure, membership functions, and the parameters of the network. To solve these optimization problems, distributed parallel multiobjective evolutionary algorithms are proposed. We enhance the optimization processes with several measures including optimizer replacement and parameter adaption. In the distributed parallel environment, the tedious and time-consuming neural network optimization can be alleviated by numerous computational resources, significantly reducing the computational time. Through experimental verification on complex stock time series prediction tasks, the proposed optimization algorithms and the modified fuzzy rough neural network models exhibit significant improvements the existing fuzzy rough neural network and the long short-term memory network.
Because the thin coal seam working face is narrow and complicated,a compact crawler robot with four rockers is designed,which has high capability of obstacle-crossing. According to the robot centroid position,the obstacle-crossing maximum heights of the robot with rockers and the robot without rockers are both optimized. The obstacle-crossing maximum height of the robot with rockers is 232.91 mm,which can meet the design requirements. The obstacle-crossing processes are virtually simulated in ADAMS,and the change rule of the drive motor output torque is analyzed for reasonable motor selection. Finally,the experimental walking mechanism prototype is tested for obstacle-crossing. The results have shown that this structure robot has high obstaclecrossing capability and high adaptability,which can meet the special requirements of the thin coal seam working face.
Traditional particle swarm optimization algorithms (PSO) targeted to solve large scale problems are mostly serial, such as CCPSO2, and the computing time is very long in general. Therefore, this paper presents a novel parallel PSO, which explores the usage of new probability distribution functions for the replacement of traditional Gaussian and Cauchy distributions, and the combination of GPSO and LPSO to make use of space exploration and speed up the convergence. As to the implementation of algorithm parallelization, we adopt the Spark platform, which is one of the currently most popular big data processing tools. We make modification to dynamic grouping and multiple calculations, in order to increase the degree of parallelism, reduce the computation time and improve algorithm efficiency as far as possible. Multiple computing refers to that in each single distribution of tasks, one computing node processes the particle position information of multiple algorithms. In the control of space exploration and convergence rate, we present a more efficient method to explore the solution space, which controls the convergence rate to enhance the exploration to a greater extent and also ensures fast convergence rate at the later stage, thus, it not only guarantees the calculation speed, but also improves the optimization effect as more as possible. We used twenty LSGO benchmark functions in CEC'2010 to make experiments, showing that the proposed algorithm could obtain satisfactory results, and for some functions, it outperforms DECC and MLCC.
Abstract In order to solve the key problem that most of the energy of wireless sensor network nodes is consumed in wireless data modulation, which is an extremely important and limited resource. The energy efficiency evaluation scheme of data compression algorithm based on the separation of hardware factor and algorithm factor is proposed; In order to improve the running efficiency of the compression algorithm and reduce the energy consumption of the algorithm itself, a program level energy-saving optimization method for the data compression algorithm is proposed; In order to keep the energy-saving benefits of the data compression algorithm when the wireless transmission power is adjusted, an adjustment mechanism of the compression algorithm which can adapt to the change of transmission power is proposed. The experiment shows that when the wireless transmission power is - 7dBm and below (k < 178.4), the data should be compressed by S-LZW algorithm, and when the wireless transmission power is - 5dBm and above (k > 178.4), the b ~ RLE algorithm should be used for compression. The validity of the method is verified.
Speech plays an important role in human-computer emotional interaction. FaceNet used in face recognition achieves great success due to its excellent feature extraction. In this study, we adopt the FaceNet model and improve it for speech emotion recognition. To apply this model for our work, speech signals are divided into segments at a given time interval, and the signal segments are transformed into a discrete waveform diagram and spectrogram. Subsequently, the waveform and spectrogram are separately fed into FaceNet for end-to-end training. Our empirical study shows that the pretraining is effective on the spectrogram for FaceNet. Hence, we pretrain the network on the CASIA dataset and then fine-tune it on the IEMOCAP dataset with waveforms. It will derive the maximum transfer learning knowledge from the CASIA dataset due to its high accuracy. This high accuracy may be due to its clean signals. Our preliminary experimental results show an accuracy of 68.96% and 90% on the emotion benchmark datasets IEMOCAP and CASIA, respectively. The cross-training is then conducted on the dataset, and comprehensive experiments are performed. Experimental results indicate that the proposed approach outperforms state-of-the-art methods on the IEMOCAP dataset among single modal approaches.
Security is crucial for industrial wireless sensor networks (IWSNs); therefore, in this article, we simultaneously consider the security, lifetime, and coverage issues by deploying sensor nodes and relay nodes in an industrial environment to analyze the multipath routing for enhancing security. For the security issue, the computation of disjoint routing paths is converted to a maximum flow problem. Then, the deployment problem is transformed into a multiobjective optimization problem, which we address by employing six state-of-the-art serial algorithms and two distributed parallel algorithms. Additionally, based on our prior work, by testing random grouping and prior knowledge-based grouping, as well as another optimizer, we propose enhanced distributed parallel algorithms. As verified by experiments, the proposed algorithms outperform their counterparts. Due to the characteristic of distributed parallelism, the time consumed by the proposed algorithms is significantly reduced compared to that of the serial algorithms. Therefore, the proposed algorithms can achieve better performance within a very limited time.
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