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The automatic segmentation of brain glioma in MRI images is of great significance for clinical diagnosis and treatment planning. However, achieving precise segmentation requires effective post-processing of the segmentation results. Current post-processing methods fail to differentiate processing based on the glioma category, limiting the improvement of MRI segmentation accuracy. This paper proposes a novel knowledge fusion decision method for post-processing brain glioma MRI segmentation. The method takes grading information and the area ratio from the initial segmentation as input, performs fuzzy reasoning based on formulated rules, and generates decision coefficients for different segmentation regions. To address class imbalance in the segmentation network, a Boundary Region Voxel Dynamic Weighted Loss Function is introduced. On the BraTS2019 validation set, our method achieves DSC values of 0.756, 0.990, and 0.805 for ET, WT, and TC regions, respectively, along with HD values of 4.02mm, 10.73mm, and 9.52mm. Compared to state-of-the-art methods, our proposed approach demonstrates superior segmentation performance. Validation on the BraTS2020 dataset further confirms the stability and reliability of our method. [ABSTRACT FROM AUTHOR]

Abstract: One of the most concerned performance indices of leak detection and location systems is the smallest detectable leakage flow rate (SDLFR). Based on the physical model of pipeline, mathematical description for the amplitude change of negative pressure wave (NPW) and its attenuation traveling along the pipeline have been deduced. On the basis of mathematical description, the methods for evaluating SDLFR and determining the sensitive point of the pipeline have been proposed. Further study shows that factors such as leakage position, pressures at inlet and outlet, flow rate, density, wave speed in the pipeline, precision of the instruments and noise will have influences on SDLFR. And the results are of great significance to evaluate the performance of the systems based on NPW method. [Copyright &y& Elsevier]

In recent years, various data-driven algorithms have been applied to the classification and staging of brain glioma MRI detection. However, the restricted availability of brain glioma MRI data in purely data-driven deep learning algorithms has presented challenges in extracting high-quality features and capturing their complex patterns. Moreover, the analysis methods designed for 2D data necessitate the selection of ideal tumor image slices, which does not align with practical clinical scenarios. Our research proposes an novel brain glioma staging model, Medical Cognition Embedded (MCE) model for 3D data. This model embeds knowledge characteristics into data-driven approaches to enhance the quality of feature extraction. Approach includes the following key components: (1) Deep feature extraction, drawing upon the imaging technical characteristics of different MRI sequences, has led to the design of two methods at both the algorithmic and strategic levels to mimic the learning process of real image interpretation by medical professionals during film reading; (2) We conduct an extensive Radiomics feature extraction, capturing relevant features such as texture, morphology, and grayscale distribution; (3) By referencing key points in radiological diagnosis, Radiomics feature experimental results, and the imaging characteristics of various MRI sequences, we manually create diagnostic features (Diag-Features). The efficacy of proposed methodology is rigorously evaluated on the publicly available BraTS2018 and BraTS2020 datasets. Comparing it to most well-known purely data-driven models, our method achieved higher accuracy, recall, and precision, reaching 96.14%, 93.4%, 97.06%, and 97.57%, 92.80%, 95.96%, respectively. [ABSTRACT FROM AUTHOR]

Abstract: This paper improves kernel principal component analysis (KPCA) for fault detection from two aspects. Firstly, a feature vector selection (FVS) scheme based on a geometrical consideration is given to reduce the computational complexity of KPCA when the number of samples becomes large. Secondly, a KPCA plus Fisher discriminant analysis (FDA) scheme is adopted to improve the fault detection performance of KPCA. Simulation results are given to show the effectiveness of these improvements for fault detection performance in terms of low computational cost and high fault detection rate. [Copyright &y& Elsevier]

A batch-to-batch optimal control approach for batch processes based on batch-wise updated nonlinear partial least squares (NLPLS) models is presented in this article. To overcome the difficulty in developing mechanistic models for batch/semi-batch processes, a NLPLS model is developed to predict the final product quality from the batch control profile. Mismatch between the NLPLS model and the actual plant often exists due to low-quality training data or variations in process operating conditions. Thus, the optimal control profile calculated from a fixed NLPLS model may not be optimal when applied to the actual plant. To address this problem, a recursive nonlinear PLS (RNPLS) algorithm is proposed to update the NLPLS model using the information newly obtained after each batch run. The proposed algorithm is computationally efficient in that it updates the model using the current model parameters and data from the current batch. Then the new optimal control profile is recalculated from the updated model and implemented on the next batch. The procedure is repeated from batch to batch and, usually after several batches, the control profile will converge to the optimal one. The effectiveness of this method is demonstrated on a simulated batch polymerization process. Simulation results show that the proposed method achieves good performance, and the optimization with the proposed NLPLS model is more effective and stable than that with a batch-wise updated linear PLS model. [ABSTRACT FROM AUTHOR]

ABSTRACT Leak detection of gas pipelines based on acoustic signals has drawn intensive attention in recent years because of its advantages over the widely adopted method based on negative pressure wave. However, detection of a small leakage in a long gas pipeline with a low pressure and complex disturbances is still a challenging problem, because in this case, the leak acoustic signal that arrives at a transducer may be weak and thus thoroughly covered by disturbances. This paper reports on the application of the Wigner-Ville distribution (WVD) method in leak detection of gas pipelines based on acoustic signals. The WVD of the acoustic transducer signal is used to find the sensitive frequency band corresponding to a leak and to detect the leak acoustic signal from strong disturbances. To suppress the influence of cross terms of the WVD, we adopted a time-averaging WVD. Offline tests based on historical leak experimental data of a real pipeline show the effectiveness of the method in detecting leak faults of a long gas pipeline with a low pressure and strong disturbances. © 2012 Curtin University of Technology and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we address fault detection for networked control systems subject to random packet dropout. The packet dropout is assumed to be existing in the sensor-to-controller link and the controller-to-actuator link. Both parity space and observer based residual generation and evaluation approaches are proposed. In parity space based fault detection scheme, a new optimization index is proposed to deal with stochastic system parameters caused by random packet dropout, while in observer based scheme, this is accomplished by introducing a reference model. In order to evaluate performance of the designated threshold, the corresponding false alarm rate is given. The two fault detection schemes can ensure both robustness to packet dropout as well as disturbance and sensitivity to fault. An experimental study is employed to verify that the proposed method performs better than the existing approaches. [Copyright &y& Elsevier]

Fault detection (FD) of networked control systems (NCS) with limited communication is addressed in this article. In periodic communication sequence-based NCS, at each time instant only parts of the actuators and sensors are allowed to communicate with the central FD system, which leads to incomplete plant information at the central station. In this article, a parity space based multi-solution FD scheme is proposed based on different information structures of the incomplete plant information. In this multi-solution scheme, each solution is an independent residual generator and has different properties. It is proved that there is a tradeoff between fast FD and detection performance, i.e. the solution with a smaller FD delay has a poorer fault sensitivity and thus a larger miss detection rate, whereas the one with a higher fault sensitivity has a larger FD delay. The FD scheme can be realised by diagnostic observers as well, which have more design freedom. Simulation results are also given to illustrate the results obtained. [ABSTRACT FROM AUTHOR]