A transformer-based generative adversarial network for brain tumor segmentation.

Brain tumor segmentation remains a challenge in medical image segmentation tasks. With the application of transformer in various computer vision tasks, transformer blocks show the capability of learning long-distance dependency in global space, which is complementary to CNNs. In this paper, we proposed a novel transformer-based generative adversarial network to automatically segment brain tumors with multi-modalities MRI. Our architecture consists of a generator and a discriminator, which is trained in min-max game progress. The generator is based on a typical "U-shaped" encoder-decoder architecture, whose bottom layer is composed of transformer blocks with Resnet. Besides, the generator is trained with deep supervision technology. The discriminator we designed is a CNN-based network with multi-scale L 1 loss, which is proved to be effective for medical semantic image segmentation. To validate the effectiveness of our method, we conducted exclusive experiments on BRATS2015 dataset, achieving comparable or better performance than previous state-of-the-art methods. On additional datasets, including BRATS2018 and BRATS2020, experimental results prove that our technique is capable of generalizing successfully.

Distributed Model-Free Sliding-Mode Predictive Control of Discrete-Time Second-Order Nonlinear Multiagent Systems With Delays.

In this article, the tracking problem of networked discrete-time second-order nonlinear multiagent systems (MASs) is studied. First, for the MASs without communication delay, a novel method, called distributed model-free sliding-mode control algorithm is proposed, which can make the system converge quickly without the accurate model. Furthermore, for the MASs with delay, in order to eliminate the influence of time delay on the system, a distributed model-free sliding-mode predictive control strategy based on time-delay compensation technology is proposed, which can actively compensate for time delay while ensuring system stability and consensus tracking performance requirements. Both the simulation and experiment results reveal the superiority of the proposed methods.

Adaptive filtering for MEMS gyroscope with dynamic noise model.

MEMS (Micro-Electro-Mechanical Systems) gyroscope is the core component in the posture recognition and assistant positioning, of which the complex noise limits its performance. It is essential to filter the noise and obtain the true value of the measurements. Then an adaptive filtering method was proposed. Firstly, noises of MEMS gyroscope were analyzed to build the basic framework of the dynamic noise model. Secondly, the dynamic Allan variance was improved with a novel truncation window based on the entropy features, which referred to the parameters in the noise model. Thirdly, the adaptive Kalman filter was derived from the dynamic noise model. Finally, the simulation and experiment were carried out to verify the method. The results prove that the improved dynamic Allan variance can extract noise feature distinctly, and the filtering precision in the new method is relatively high.

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model.

The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.

Research about DoS Attack against ICPS.

This paper studies denial-of-services (DoS) attacks against industrial cyber-physical systems (ICPSs) for which we built a proper ICPS model and attack model. According to the impact of different attack rates on systems, instead of directly studying the time delay caused by the attacks some security zones are identified, which display how a DoS attack destroys the stable status of the ICPS. Research on security zone division is consistent with the fact that ICPSs' communication devices actually have some capacity for large network traffic. The research on DoS attacks' impacts on ICPSs by studying their operation conditions in different security zones is simplified further. Then, a detection method and a mimicry security switch strategy are proposed to defend against malicious DoS attacks and bring the ICPS under attack back to normal. Lastly, practical implementation experiments have been carried out to illustrate the effectiveness and efficiency of the method we propose.

An adaptive image enhancement method for a recirculating aquaculture system.

Due to the low and uneven illumination that is typical of a recirculating aquaculture system (RAS), visible and near infrared (NIR) images collected from RASs always have low brightness and contrast. To resolve this issue, this paper proposes an image enhancement method based on the Multi-Scale Retinex (MSR) algorithm and a greyscale nonlinear transformation. First, the images are processed using the MSR algorithm to eliminate the influence of low and uneven illumination. Then, the normalized incomplete Beta function is used to perform a greyscale nonlinear transformation. The function's optimal parameters (α and ß) are automatically selected by the particle swarm optimization (PSO) algorithm based on an image contrast measurement function. This adaptive image enhancement method is compared with other classic enhancement methods. The results show that the proposed method greatly improves the image contrast and highlights dark areas, which is helpful during further analysis of these images.

A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information.

Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches' ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method's rationality and feasibility when using different data from different sources.