A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm

Neural networks play an increasingly important role in the field of machine learning and are included in many applications in society. Unfortunately, neural networks suffer from adversarial samples generated to attack them. However, most of the generation approaches either assume that the attacker has full knowledge of the neural network model or are limited by the type of attacked model. In this paper, we propose a new approach that generates a black-box attack to neural networks based on the swarm evolutionary algorithm. Benefiting from the improvements in the technology and theoretical characteristics of evolutionary algorithms, our approach has the advantages of effectiveness, black-box attack, generality, and randomness. Our experimental results show that both the MNIST images and the CIFAR-10 images can be perturbed to successful generate a black-box attack with 100\% probability on average. In addition, the proposed attack, which is successful on distilled neural networks with almost 100\% probability, is resistant to defensive distillation. The experimental results also indicate that the robustness of the artificial intelligence algorithm is related to the complexity of the model and the data set. In addition, we find that the adversarial samples to some extent reproduce the characteristics of the sample data learned by the neural network model

Weighted-Sampling Audio Adversarial Example Attack

Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks. Despite many research on this, the efficiency and the robustness of existing works are not yet satisfactory. In this paper, we propose~\textit{weighted-sampling audio adversarial examples}, focusing on the numbers and the weights of distortion to reinforce the attack. Further, we apply a denoising method in the loss function to make the adversarial attack more imperceptible. Experiments show that our method is the first in the field to generate audio adversarial examples with low noise and high audio robustness at the minute time-consuming level.Comment: https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuXL.9260.pd

A Typha Angustifolia-like MoS2/carbon nanofiber composite for high performance Li-S batteries

A Typha Angustifolia-like MoS2/carbon nanofiber composite as both a chemically trapping agent and redox conversion catalyst for lithium polysulfides has been successfully synthesized via a simple hydrothermal method. Cycling performance and coulombic efficiency have been improved significantly by applying the Typha Angustifolia-like MoS2/carbon nanofiber as the interlayer of a pure sulfur cathode, resulting in a capacity degradation of only 0.09% per cycle and a coulombic efficiency which can reach as high as 99%

Assessment of Wind Turbine Aero-Hydro-Servo-Elastic Modelling on the Effects of Mooring Line Tension via Deep Learning

As offshore wind turbines are moving to deeper water depths, mooring systems are becoming more and more significant for floating offshore wind turbines (FOWTs). Mooring line failures could affect power generations of FOWTs and ultimately incur risk to nearby structures. Among different failure mechanics, an excessive mooring line tension is one of the most essential factors contributing to mooring failure. Even advanced sensing offers an effective way of failure detections, but it is still difficult to comprehend why failures happened. Unlike traditional parametric studies that are computational and time-intensive, this paper applies deep learning to investigate the major driven force on the mooring line tension. A number of environmental conditions are considered, ranging from cut in to cut out wind speeds. Before formatting input data into the deep learning model, a FOWT model of dynamics was simulated under pre-defined environmental conditions. Both taut and slack mooring configurations were considered in the current study. Results showed that the most loaded mooring line tension was mainly determined by the surge motion, regardless of mooring line configurations, while the blade and the tower elasticity were less significant in predicting mooring line tension

Short-term Offshore Wind Speed Forecast by Seasonal ARIMA

For maintaining safe operations of wind farms and providing high-quality power supply to the end customers, it is significant to develop reliable short-term time series wind speed forecasting models. In this study, a Seasonal Auto-Regression Integrated Moving Average (SARIMA) model is proposed for predicting hourly-measured wind speeds in the coastal/offshore area of Scotland. The SARIMA model’s performance was further verified and compared with the newly developed deep- learning-based algorithms of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). Regardless of the recent development of computational power has triggered more advanced machine learning algorithms, the proposed SARIMA model has shown its outperformance in the accuracy of forecasting future lags of offshore wind speeds along with time series. The comparative study among three predictive models showed that the SARIMA model offered the highest accuracy and robust healthiness

Bounded Rationality and Irreversible Network Change

A network change is said to be irreversible if the initial network equilibrium cannot be restored by revoking the change. The phenomenon of irreversible network change has been observed in reality. To model this phenomenon, we develop a day-to-day dynamic model whose fixed point is a boundedly rational user equilibrium (BRUE) flow. Our BRUE based approach to modeling irreversible network change has two advantages over other methods based on Wardrop user equilibrium (UE) or stochastic user equilibrium (SUE). First, the existence of multiple network equilibria is necessary for modeling irreversible network change. Unlike UE or SUE, the BRUE multiple equilibria do not rely on non-separable link cost functions, which makes our model applicable to real-world large-scale networks, where well-calibrated non-separable link cost functions are generally not available. Second, travelers\u27 boundedly rational behavior in route choice is explicitly considered in our model. The proposed model is applied to the Twin Cities network to model the flow evolution during the collapse and reopening of the I-35W Bridge. The results show that our model can to a reasonable level reproduce the observed phenomenon of irreversible network change

Reconstructing solar wind inhomogeneous structures from stereoscopic observations in white-light: Small transients along the Sun-Earth line

The Heliospheric Imagers (HI) on board the two spacecraft of the Solar Terrestrial Relations Observatory (STEREO) provided white-light images of transients in the solar wind from dual perspectives from 2007 to 2014. In this paper, we develop a new method to identify and locate the transients automatically from simultaneous images from the two inner telescopes, known as HI-1, based on a correlation analysis. Correlation coefficient (cc) maps along the Sun-Earth line are constructed for the period from 1 Jan 2010 to 28 Feb 2011. From the maps, transients propagating along the Sun-Earth line are identified, and a 27-day periodic pattern is revealed, especially for small-scale transients. Such a periodicity in the transient pattern is consistent with the rotation of the Sun's global magnetic structure and the periodic crossing of the streamer structures and slow solar wind across the Sun-Earth line, and this substantiates the reliability of our method and the high degree of association between the small-scale transients of the slow solar wind and the coronal streamers. Besides, it is suggested by the cc map that small-scale transients along the Sun-Earth line are more frequent than large-scale transients by a factor of at least 2, and that they quickly diffused into background solar wind within about 40 Rs in terms of the signal-to-noise ratio of white-light emissions. The method provides a new tool to reconstruct inhomogeneous structures in the heliosphere from multiple perspectives.Comment: 24 pages, 9 figures, to be published on Journal of Geophysical Research - Space Physic

Pareto-improving and revenue-neutral congestion pricing schemes in two-mode traffic networks

This paper studies a Pareto-improving and revenue-neutral congestion pricing scheme on a simple two-mode (highway and transit) network: this scheme aims at simultaneously improving system performance, making every individual user better off, and having zero total revenue. Different Pareto-improving situations are explored when a two-mode transportation system serves for travel groups with different value-of-time (VOT) distributions. Since the congestion pricing scheme suggested here charges transit users negative tolls and automobile users positive tolls, it can be considered as a proper way to implement congestion pricing and transit subsidy in one step, while offsetting the inequity for the poor. For a general VOT distribution of commuters, the condition of Pareto-improving is established, and the impact of the VOT distribution on solving the inequity issue is explored. For a uniform VOT distribution, we show that a Pareto-improving and revenue-neutral pricing scheme always exists for any target modal split pattern that reduces the total system travel time

Forecasting of Two-Phase Flow Patterns in Upward Inclined Pipes via Deep Learning

Conventionally, the boundaries of gas-liquid flow regime transition are extremely sensitive to the inclination of flow channels. However, traditional two-dimensional flow regime maps have difficulties to reflect this fact as it can only accommodate two independent variables, which are often the gas and liquid superficial velocities. Few investigators have been able to propose a single model with accessible inputs under the considerations of the whole range of upward inclined angels. In this paper, we developed a novel approach by applying a typical machine learning (ML) method, artificial neural network (ANN), to predict flow pattern along upward inclined pipe (0 ~ 90°) using easily accessible parameters as inputs, namely, superficial velocities of individual phase and inclination angles. TensorFlow, a new generation and popular open-source foundation for ML programming, was used for building the ANN model, which was trained and tested by experimental data (1952 data points) that were reported in the literature. The predicting results show that ANN identifications have a satisfying agreement with experimental observations. The predicting accuracies of stratified smooth, stratified wavy, annular, intermittent, bubble flow are all above 90%, with the only exception of dispersed bubble flow (73%). In addition, the validation of the model was extended by comparing the ANN’s performance with well-established two-phase transition boundary models among different flow regimes. Comparing against conventional methods based on either correlation or flow regime map, the developed ANN model is expected to be a more efficient tool in flow pattern prediction. Furthermore, the impact of inclination angles on final ANN outputs was evaluated quantitatively. Results showed, given flow conditions fixed, variations of inclination angles have a significant influence on gas- liquid flow patterns in channels of conventional sizes