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Smart hospitals are important components of smart cities. An intelligent medical system for brain tumor segmentation is required to construct smart hospitals. To achieve intelligent brain tumor segmentation, morphological variety and serious category imbalance must be managed effectively. Conventional deep neural networks have difficulty in predicting high-accuracy segmentation images due to these issues. To solve these problems, we propose using multimodal brain tumor images combined with the UNET and LSTM models to construct a new network structure with a mixed loss function to solve sample imbalance and describe an intelligent segmentation process to identify brain tumors. To verify the practicability of this algorithm, we used the open source Brain Tumor Segmentation Challenge dataset to train and verify the proposed network. We obtained DSCs of 0.91, 0.82, and 0.80; sensitivities of 0.93, 0.85, and 0.82; and specificities of 0.99, 0.99, and 0.98 in three tumor regions, including the whole tumor ( WT ), tumor core ( TC ), and enhanced tumor ( ET ). We also compared the results of the proposed network with those of other brain tumor segmentation methods, and the results showed that the proposed algorithm could segment different tumor lesions more accurately, highlighting its potential application value in the clinical diagnosis of brain tumors.

At present, medical images have played a more and more important role in clinical treatment. Lung images provide an important reference for doctors to make a diagnosis. Especially for surgical patients, a tumor can be accurately removed based on the full cognition about its size, position, and quantity. Therefore, computer-aided diagnosis for the analysis and treatment of a lot of lung tumor images is very important. Aiming at complexity and self-adaption of image segmentation in lung tumors, this article proposed a parallel deep learning algorithm with hybrid attention mechanism for image segmentation. First, lung parenchyma was extracted via preprocessing images. Then, images were input into hybrid attention mechanism and densely connected convolutional networks (DenseNet) module, respectively, where hybrid attention mechanism consisted of a spatial attention mechanism and a channel attention mechanism. Finally, four feasible solutions were proposed for the verification through changing the convolution quantity of dense block in DenseNet. The network structure with the better performance was achieved. The experimental results prove the parallel deep learning algorithm with hybrid attention mechanism performed well in image segmentation of lung tumors, and its accuracy can reach 94.61%.

We built a data-driven prediction model based on Time Series Neural Network regression model to explore the feasibility of utilizing functional connectivity (FC) to predict the continuous fatigue levels during underload task. When participants performed a 120-minute dull driving task, experimental data was recorded. The EEG and eye closure data used in this paper are from SJTU Emotion EEG Dataset, SEED. Unlike other researches, We proposed a functional brain networks method based on Phase Lag Index (PLI) to analyze EEG effectively and incorporate the temporal dependency of PERCLOS into model training. The functional connectivity corresponding to different fatigue degrees during whole experiment were analyzed. Meanwhile, taking the clustering coefficient (C) values, degree(D) and characteristic path length (L) values as input respectively while PERCLOS as output. The results suggest that brain network analysis approaches combined with Neural Network are effective to predicts individual mental fatigue during long time driving and can improve the performance of fatigue detection with a higher prediction correlation coefficient of 0.99 and a lower RMSE value of 0.01 on average.

Based on the requirement of industrial automation, this paper proposes a three-dimensional reconstruction algorithm of the dynamic environment for industrial operation environment perception, which can be applied to many different industrial fields. According to the actual situation of the inner wall of the rolling mill, a method of lidar raw data preprocessing is proposed in this paper. The original lidar data is tagged and the detailed local point cloud information of interest is isolated. Loop detection is introduced into the algorithm to establish a globally consistent point cloud graph. The overall environment modeling algorithm is constructed to realize the real-time dynamic modeling of indoor environment in the actual industrial environment operation. The method has been evaluated by real-time reconstruction of the three-dimensional model of the specified area, which provides prior information for the operation of the robotic manipulator. We compare the performance of three-dimensional with loam, using the actual industrial environment, and show that our algorithm achieves better accuracy with reduced computational expense.

As quadruped robot is well-balanced, flexible and adaptable to environment, it has wide applications and has attracted the attention of numerous institutes. This paper combines theories of bionics and kinematics to design a quadruped robot and establish a gait control model for it. Firstly, it studies the bone structure of horse to analyze its structure advantages which are then applied to the design of the quadruped robot with the segment ratio of 1:1. Then the Kinematics formula and the traditional CPG gait control strategy are combined to establish the gait control model for quadruped robot which could output the joint control signal curves under stable gait.

This paper presents a behavioural model to describe the realisation of a system objective from the services provides by the components it groups. The behavioural model takes into account both normal and faulty situations and allows to diagnose component faults. As behavioural model can be difficult to build and apprehend for large scale systems, the paper proposes an algorithm to diagnose system faults from the behavioural models of its subsystems without building the entire system behavioural model.

The quality of magnetic resonance imaging (MRI) features is key to the classification of Alzheimer’s disease. However, relevant research has as yet paid little attention to asymmetric MR features. In this paper, the asymmetric MR features of multiple anatomical structures are extracted. The MR feature types include volume feature and several kinds of texture features. Subsequently, the extracted features are selected based on the wrapper feature selection method with chain-like agent genetic algorithm (CAGA) and support vector machine (SVM). Finally, the selected asymmetric MR features are used for classification of Alzheimer’s disease. Experimental results show that the extracted features have apparent asymmetrical characteristics. The asymmetric volume feature of single anatomical structure can have better discrimination capability than the whole volume feature of same anatomical structure. Single selected asymmetric MR feature has displayed a superior discrimination capability in regards to three conditions of Alzheimer’s disease. The improvement is very apparent compared to before feature selection and the p-value-based feature selection method. In conclusion, this proposed method offer a new kind of feature type and can improve the classification rate for diagnosis of Alzheimer’s disease.

This paper presents a new formal modeling framework for reconfigurable control. The main idea is to use the STRIPS (STanford Research Institute Problem Solver), a formal language for automated planning which can integrate the cause-effect knowledge and the automated reasoning mechanism into one model. A qualitative model based on a graph of states is chosen. Transitions between states are determined from the possible actions (as defined as controls to the actuators). Each action is modeled in terms of preconditions and effects. The STRIPS can qualitatively define the fault models without requiring detail and precise knowledge of faulty components. Automated planning is an interesting tool for reconfigurable control. Since the process may end up in any one of a very large number of states after a break-down, it is not realistic to have recovery plans designed in advance for all such states. It is, thus, desirable to invoke a planner in the case of break-downs, which finds a plan that brings the process back into normal operation again, and that is the nature of reconfigurable control.

International audience; Systems are built by connecting different components (e.g., sensors, actuators, process components) that are, in turn, organized to achieve system objectives. But, when a system component fails, the system's objectives can no longer be achieved. For many years, numerous studies have proposed efficient fault detection and isolation (FDI) and fault-tolerant control (FTC) algorithms. This paper considers faults that lead to the complete failure of actuators. In this specific case, the system's physical structure changes, and the system model thus becomes incorrect. The potential that the system has to continue to achieve its objectives has to be re-evaluated from a qualitative point of view, before recalculating or modifying the control algorithms. To this end, this paper proposes a self-updating system model to reflect the current system potential, a formulation of system objectives using temporal logic, and a verification method based on model checking to verify if the objectives can still be achieved by the faulty system. The systems considered are discrete-continuous systems.

International audience; This paper is part I of a two part effort that is intended to present a framework of multi-faults diagnosis. Reiter [14] has proposed a consistency-based approach for multi-faults diagnosis. We extend his theory to deal with the dynamic and continuous systems and offer a necessary assumption and a formal demonstration. Multi-faults diagnosis is a partially observable problem because there is usually not enough information about faults. So the STRIPS, a classic technique of automated planning, is chosen to build the system model. It provides the reasoning ability for the multi-faults diagnosis when diagnosis is formalized as reasoning from effects to causes with causal knowledge.