Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation

Medical image segmentation is the most complex and important task in the field of medical image processing and analysis, as it is linked to disease diagnosis accuracy. However, due to the medical image's high complexity and noise, segmentation performance is limited. We propose a novel quadratic polynomial guided fuzzy C-means and dual attention mechanism composite network model architecture to address the aforementioned issues (QPFC-DA). It has mechanisms for channel and spatial edge attention, which guide the content and edge segmentation branches, respectively. The bi-directional long short-term memory network was added after the two content segmentation branches to better integrate multi-scale features and prevent the loss of important features. Furthermore, the fuzzy C-means algorithm guided by the quadratic polynomial can better distinguish the image's weak edge regions and has a degree of noise resistance, resulting in a membership matrix with less ambiguity and a more reliable segmentation result. We also conducted comparison and ablation experiments on three medical data sets. The experimental results show that this method is superior to several other well-known methods.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep learning framework called “Adversarial Segmentation and Registration Nets” (ASRNet) for the simultaneous estimation of the blood vessel segmentation and the registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills the blood vessel segmentation task, and (ii) A registration module R that estimates the spatial correspondence of an image pair. Based on the segmention-driven registration network, we train the segmentation network using a semi-supervised adversarial learning strategy. Our experimental results show that the proposed ASRNet can achieve state-of-the-art accuracy in segmentation and registration tasks performed with real MSI datasets.     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.     

A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings

The remaining useful life (RUL) prediction of a rolling element bearing is important for more reasonable maintenance of machinery and equipment. Generally, the information of a failure can hardly be acquired in advance while running and the degradation process varies in terms of different faults. Thus, fault identification is indispensable for a multi-condition RUL prediction, where, however, the fault identification and RUL prediction are separated in most studies. A new hybrid scheme is proposed in this paper for the multi-condition RUL prediction of rolling element bearings. The proposed scheme contains both classification and regression, where the 2D-DCNN based classifier and predictors are built concerning typical fault conditions of a bearing. For the online prediction, the raw signals are spanned in the time-frequency domain and then transferred into images as the input of the scheme. The classifier is used to monitor the vibration of rolling bearings for online fault recognition and excite the corresponding predictor for RUL prediction once a fault is detected. The output from the predictor is amended by the proposed adaptive delay correction method as the final prediction results. A demonstration is performed based on the XJTU-SY datasets and the results are compared with those from the state-of-the-art methods, which proves the superiority of the proposed scheme in improving the accuracy and linearity of RUL prediction. The time cost of the proposed online prediction scheme is also investigated and the results indicate high time effectiveness.     

Extremal optimization vs. learning automata: Strategies for spin selection in portfolio selection problems

Nowadays, various imitations of natural processes are used to solve challenging optimization problems faster and more accurately. Spin glass based optimization, specifically, has shown strong local search capability and parallel processing. But, spin glasses have a low rate of convergence since they use Monte Carlo simulation techniques such as simulated annealing (SA). Here, we propose two algorithms that combine the long range effect in spin glasses with extremal optimization (EO-SA) and learning automata (LA-SA). Instead of arbitrarily flipping spins at each step, these two strategies aim to choose the next spin and selectively exploiting the optimization landscape. As shown in this paper, this selection strategy can lead to faster rate of convergence and improved performance. The resulting two algorithms are then used to solve portfolio selection problem that is a non-polynomial (NP) complete problem. Comparison of test results indicates that the two algorithms, while being very different in strategy, provide similar performance and reach comparable probability distributions for spin selection. Furthermore, experiments show there is no difference in speed of LA-SA or EO-SA for glasses with fewer spins, but EO-SA responds much better than LA-SA for large glasses. This is confirmed by tests results of five of the world's major stock markets. In the last, the convergence speed is compared to other heuristic methods such as Neural Network (NN), Tabu Search (TS), and Genetic Algorithm (GA) to approve the truthfulness of proposed methods.     

渤海注聚井区油井深部解堵工艺研究及应用

针对渤海油田油井解堵效果变差、有效期降低等问题,提出了扩大解堵半径、优选解堵剂体系、强化解堵效果的解堵思路。通过工艺研究、工艺优选,形成了深穿透解堵技术。深穿透解堵技术主要包含基质解堵、降阻造缝和储层深部解堵三个过程。对深穿透解堵工艺机理进行了研究,并形成了一套现场可应用的工艺技术。针对锦州油田注聚井区堵塞问题,从聚合物溶液降黏、模拟含聚堵塞物、现场含聚堵塞物降解三个方面优选出一种复合解堵剂,结合工艺思路形成施工方案,并进行现场应用,取得了较好的增液、增油效果。     

FMS50柔性加工系统

本文对FMS50系统结构进行了介绍，着重阐述了该系统的多种物流模式．并分析了其通信控制系统的构成。     

Tube Diameter and Height Influence on the Ozonation Operating Conditions with a U-Tube

Ozone has been used for a number of years in water treatment for viricidal and bactericidal action, as well as for its oxidizing properties. Energy consumption during its use depends on the performance of the ozone generators, but also on the ozone transfer efficiency from the gas phase to the liquid phase. Therefore, it is important to use a contactor with a high transfer efficiency.A new ozonation reactor has been developed: the Deep U-Tube (DUT). This reactor works under pressure, which increases the saturation concentration of ozone at equilibrium (C ). and creates a great turbulence, which breaks the gas bubbles and increases the interfacial area of exchange. Further, this new contactor has a piston hydraulic behavior in the liquid phase, which leads to a very great efficiency and to a better reduction for all reaction kinetics.     

Adaptive receding horizon control for constrained MIMO systems

An adaptive control algorithm for open-loop stable, constrained, linear, multiple input multiple output systems is presented. The proposed approach can deal with both input and output constraints, as well as measurement noise and output disturbances. The adaptive controller consists of an iterative set membership identification algorithm, that provides a set of candidate plant models at each time step, and a model predictive controller, that enforces input and output constraints for all the plants inside the model set. The algorithm relies only on the solution of standard convex optimization problems that are guaranteed to be recursively feasible. The experimental results obtained by applying the proposed controller to a quad-tank testbed are presented.