Abstract-Particle Swarm Optimization algorithm (PSO) is a popular stochastic searching optimization algorithm to solve complicated optimization problems. The approach of retrieving duct parameters from the sea-surface reflected radar clutter is also known as Refractivity From Clutter (RFC) technique. RFC technique provides the near-real-time duct parameters to evaluate the radio system performance, without adding any hardware. Basic principles of PSO and its applications and RFC technique are introduced. Evaporation duct is retrieved based on RFC technique using PSO. The performance of PSO is validated using experiment data launched at East China Sea and compared with those of genetic algorithm (GA) and ant colony algorithm (ACA). The results indicate that PSO has the advantages of faster convergence and higher retrieval precision than the other two methods.

Intelligent manufacturing is one of the indispensable parts in Industry 4.0, where the smart machines can perceive and operate automatically in production process to provide higher and more convenient efficiency. Nevertheless, some potential failures influencing the manufacturing may exist. It is necessary to identify the potential failures and evaluate their risk. Meanwhile, resources and environment protections have been the consensus around the world. For enterprises, they must take measures to reduce the negative environmental impacts during the industrial production to maintain their sustainable development. Failure mode and effects analysis (FMEA) can effectively recognize failures and evaluate their risk in a production process or a system. However, environmental aspects of the identified failure modes are often ignored in the FMEA. Besides, conventional FMEA usually uses the risk priority number to obtain the failures' risk, which has a lot of shortcomings in the industrial application, such as the equal weights for different risk factors, crisp numbers used in the evaluation without considering vagueness, and so on. Based on the above two main problems, this paper develops an extended FMEA which introduces environmental impacts as one of the risk factors and utilizes Technique for order preference by similarity to an ideal solution (TOPSIS) method based on rough sets to cope with vague information. Finally, the developed FMEA is applied to evaluate the potential failure risk of an optical cable automatic arranging robot to verify its feasibility and effectiveness. INDEX TERMS Failure mode and effects analysis, intelligent manufacturing, environmental risk factor, subjective judgments.

Abstract. Aerosol retrieval using ozone lidars in the ultraviolet spectral region is challenging but necessary for correcting aerosol interference in ozone retrieval and for studying the ozone–aerosol correlations. This study describes the aerosol retrieval algorithm for a tropospheric ozone lidar, quantifies the retrieval error budget, and intercompares the aerosol retrieval products at 299 nm with those at 532 nm from a high spectral resolution lidar (HSRL) and with those at 340 nm from an AErosol RObotic NETwork radiometer. After the cloud-contaminated data are filtered out, the aerosol backscatter or extinction coefficients at 30 m and 10 min resolutions retrieved by the ozone lidar are highly correlated with the HSRL products, with a coefficient of 0.95 suggesting that the ozone lidar can reliably measure aerosol structures with high spatiotemporal resolution when the signal-to-noise ratio is sufficient. The actual uncertainties of the aerosol retrieval from the ozone lidar generally agree with our theoretical analysis. The backscatter color ratio (backscatter-related exponent of wavelength dependence) linking the coincident data measured by the two instruments at 299 and 532 nm is 1.34±0.11, while the Ångström (extinction-related) exponent is 1.49±0.16 for a mixture of urban and fire smoke aerosols within the troposphere above Huntsville, AL, USA.

The maritime tropospheric duct is a low-altitude anomalous refractivity structure over the ocean surface, and it can significantly affect the performance of many shore-based/shipboard radar and communication systems. We propose the idea that maritime tropospheric ducts can be retrieved from ocean forward-scattered low-elevation global positioning system (GPS) signals. Retrieval is accomplished by matching the measured power patterns of the signals to those predicted by the forward propagation model as a function of the modified refractivity profile. On the basis of a parabolic equation method and bistatic radar equation, we develop such a forward model for computing the trapped propagation characteristics of an ocean forward-scattered GPS signal within a tropospheric duct. A new GPS scattering initial field is defined for this model to start the propagation modeling. A preliminary test on the performance of this model is conducted using measured data obtained from a 2009-experiment in the South China Sea. Results demonstrate that this model can predict GPS propagation characteristics within maritime tropospheric ducts and serve as a forward model for duct inversion.

As an essential part of the network-based intrusion detection systems (IDS), malicious traffic detection using deep learning methods has become a research focus in network intrusion detection. However, even the most advanced IDS available are challenging to satisfy real-time detection because they usually need to accumulate the packets into particular flows and then extract the features, causing processing delays. In this paper, using the deep learning approach, we propose a deep hierarchical network for malicious traffic detection at the packet-level, capable of learning the features of traffic from raw packet data. It used the one-dimensional convolutional layer to extract the spatial features of raw packets and Gated Recurrent Units (GRU) structure to extract the temporal features. To evaluate the performance of our approach, experiments were conducted to examine the efficiency of the proposed deep hierarchical network based on the ISCX2012 dataset, USTC-TFC2016 dataset and CICIDS2017 dataset, respectively. Accuracy (ACC), detection rate (DR) and false alarm rate (FAR) are the metrics for evaluation. In the ISCX2012 dataset, our approach achieved 99.42%, 99.74%, 1.77% on ACC, DR and FAR, respectively. In USTC-TFC2016, there were 99.94%, 99.99%, 0.99%. In CICIDS2017, there were 100%, 100%, 0%. Furthermore, we discussed the impact of data balanced on classification performance and the time efficiency between the Long Short-Term Memory (LSTM) model and the GRU model. Experiments show that our approach can effectively detect malicious traffic and outperforms many other state-of-the-art methods in terms of ACC and DR.