Compared Insights on Machine-Learning Anomaly Detection for Process Control Feature

Anomaly detection is becoming increasingly significant in industrial cyber security, and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks. However, different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples. As a sequence, after developing one feature generation approach, the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm. Based on process control features generated by directed function transition diagrams, this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching abilities. Furthermore, this paper not only describes some qualitative properties to compare their advantages and disadvantages, but also gives an in-depth and meticulous research on their detection accuracies and consuming time. In the verified experiments, two attack models and four different attack intensities are defined to facilitate all quantitative comparisons, and the impacts of detection accuracy caused by the feature parameter are also comparatively analyzed. All experimental results can clearly explain that SVM (Support Vector Machine) and WNN (Wavelet Neural Network) are suggested as two applicable detection engines under differing cases

Efficient Routing Protection Algorithm Based on Optimized Network Topology

Network failures are unavoidable and occur frequently. When the network fails, intra-domain routing protocols deploying on the Internet need to undergo a long convergence process. During this period, a large number of messages are discarded, which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers (ISP). Therefore, improving the availability of intra-domain routing is a trending research question to be solved. Industry usually employs routing protection algorithms to improve intra-domain routing availability. However, existing routing protection schemes compute as many backup paths as possible to reduce message loss due to network failures, which increases the cost of the network and impedes the methods deployed in practice. To address the issues, this study proposes an efficient routing protection algorithm based on optimized network topology (ERPBONT). ERPBONT adopts the optimized network topology to calculate a backup path with the minimum path coincidence degree with the shortest path for all source purposes. Firstly, the backup path with the minimum path coincidence with the shortest path is described as an integer programming problem. Then the simulated annealing algorithm ERPBONT is used to find the optimal solution. Finally, the algorithm is tested on the simulated topology and the real topology. The experimental results show that ERPBONT effectively reduces the path coincidence between the shortest path and the backup path, and significantly improves the routing availability

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Solvent effects on the measured bubble-point pressures and pseudo bubble-point pressures of different heavy crude oil−solvent systems

In this paper, the bubble-point pressures and pseudo bubble-point pressures of various heavy crude oil−solvent systems were measured and studied by conducting the constant-composition-expansion (CCE) tests, during which the test pressure was depleted in a stepwise manner. A total of fourteen CCE tests were performed for five heavy crude oil−CO2 systems, four heavy crude oil−CH4 systems, and five heavy crude oil−C3H8 systems, respectively. All the CCE tests were conducted by using a pressure−volume−temperature (PVT) system. It was found that for most heavy crude oil−solvent systems with relatively low solvent concentrations, the measured PVT cell pressure vs. molar volume (Pcell−νmix) data in the CCE tests had three distinct regions, which were one-liquid phase region (Regions I), foamy-oil region (Region II) and two-phase region (Region III). Accordingly, the PVT cell pressure at the intersection point of Regions I and II was referred to as the measured bubble-point pressure, whereas the PVT cell pressure at the intersection point of Regions II and III was termed as the measured pseudo bubble-point pressure. For some heavy crude oil−solvent systems with high solvent concentrations, however, the measured Pcell−νmix data may have two regions only. In this special case, the PVT cell pressure at the intersection point of the two regions was considered as the measured bubble-point pressure and no pseudo bubble-point pressure could be obtained. It was also found that the heavy crude oil−CH4 system had not only the highest bubble-point pressure and pseudo bubble-point pressure but also the largest difference between the bubble-point pressure and pseudo bubble-point pressure, in comparison with the heavy crude oil−CO2 system and the heavy crude oil−C3H8 system at the same solvent concentration. These facts indicate that CH4 not only is the most volatile solvent but also can remain as dispersed gas bubbles to induce the most stable foamy oil in the largest pressure range after it is nucleated from a live heavy oil

Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields

Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed for the GF-3 satellite based on a backscattering coefficient simulation database. We adopted eight optical vegetation indices to determine the relationships between these indices and vegetation water content (VWC) by combining Landsat-8 data and field measurements. A backscattering coefficient database was built using an advanced integral equation model (AIEM). The effects of vegetation on backscattering coefficients were corrected using the water cloud model (WCM) to obtain the bare soil backscattering coefficient ( σ s o i l ° ). Then, soil moisture retrievals were obtained at HH, VV and HH+VV combination respectively by minimizing the observed bare soil backscattering coefficient ( σ s o i l ° ) and the AIEM-simulated backscattering coefficient ( σ soil-simu ° ). Finally, the proposed algorithm was validated in agriculture region of wheat and corn in China using ground soil moisture measurements. The results showed that the normalized difference infrared index (NDII) had the best fit with measured VWC values (R = 0.885) among the eight vegetation water indices; thus, it was adopted to correct the effects of vegetation. The proposed algorithm using GF-3 satellite data performed well in soil moisture retrieval, and the scheme combining HH and VV polarization exhibited the highest accuracy, with a root mean square error (RMSE) of 0.044 m3m−3, followed by HH polarization (RMSE = 0.049 m3m−3) and VV polarization (RMSE = 0.053 m3m−3). Therefore, the proposed algorithm has good potential to operationally estimate soil moisture from the new GF-3 satellite data

Engineering Design Analysis of Large-Scale Wind Turbine in a Port

[Introduction] Ports are facing significant electricity demand and carbon reduction pressure. The abundant wind energy resources in port areas make wind power highly promising for port applications. The site selection and design of wind turbines directly impact the economic benefits of power plants and production safety in the port area, thus, it has important research significance in the engineering design of wind energy systems. [Method] Taking Chuanshan port area of Ningbo-Zhoushan Port as an example, this study investigated the engineering design principles and limiting factors for large-scale wind turbines in port areas and analyzed the wind resources and the potential for wind energy utilization in the port and the special weather affecting the port area based on statistic history data; based on the actual conditions of the port, the site selection and design of wind turbines were analyzed considering the limiting factors of the port area. The study also examined the selection and design methods of wind turbines and analyzed the selection and design from multiple dimensions, taking four mainstream models on the market as examples.[Result] Ultimately, the WTG2 wind turbine that meets at least IEC Class I standards and has been specially designed to withstand typhoons is selected. The annual electricity generation can reach 24.53 GWh, resulting in cost savings of 0.233 hundred million and a reduction in CO2 emissions of approximately 1.425 1 ten thousand tons.[Conclusion] The proposed engineering design method for large-scale wind turbines in port areas, based on actual conditions, has been verified as feasible and can achieve significant economic and ecological benefits. It can contribute to carbon peaking and energy self-sufficiency in ports, providing valuable references for the engineering design of wind farms in port areas