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.     

应用于矿井LED电子显示牌的点阵式绘图软件设计

针对矿井LED电子显示牌采用矢量图技术绘图存在的图形绘制不精确、可视性差等问题,提出一种可应用于不同型号电子显示牌的点阵式绘图软件设计方案,介绍了该软件的总体设计及BMP图像的灰度化步骤。点阵式绘图软件基于组件的设计理念,实现了所见即所得的绘图功能、文字点阵转换功能、BMP图片导入/转换功能、ICO图标导入/导出功能、模拟显示功能及对外通用接口功能。比较发现,采用点阵式绘图软件绘制的原始图形及实际显示效果均明显好于采用矢量图技术绘制的图形。     

基于双缓冲机制的多尺度导航电子地图显示设计

为了提高电子地图数据的精度和画面显示的速率,对地图数据格式加以改进,新增isvl参数准确标识出被描绘数据情报的尺度,对检索的数据缓存做了优化性能管理,依据地图数据的格式提出了坐标变换、数据解析和描画范围定位的准确算法,采用双缓冲机制绘制地图;经实验证明,使用该套方案实验的结果,具有以下特性:1)高精度,多尺度;2)画面合理的表达了空间关系;3)支持实时动态快速显示;可应用到电子地图的研发中。     

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。     

不用压边成形圆锥形零件过程中内皱发生时刻确定及其影响因素分析

利用内皱模型，结合圆锥形零件在成形过程中的几何条件及应力分析推导了圆锥形零件不用压边时也不发生内皱的l临界条件；进而利用VB语言编程绘制了圆锥形零件在成形过程中不同参数情况下拉深时刻与相对厚度的变化曲线，据此得到内皱发生的具体时刻，并分析了不同参数对内皱发生时刻的影响。此临界条件可作为圆锥形零件拉深过程中不用压边时也不发生内皱的判据。所得曲线组具有通用性，可供生产实践或理论研究参考。     

Analytical and experimental analysis of deep drawing process for bimetal elements

The paper presents the concepts of solving of deep drawing process for bimetal elements of sheet materials. The inertia forces, plastic hardening behaviour of the deformed material and dynamic stress are utilized in the problems of plasticity. The experimental results and general conclusion are presented at the end of the paper.     

电梯用含氮节镍奥氏体不锈钢QN1701的组织和性能

借助Thermo-Calc热力学相图计算软件,开发了用于电梯的含氮节镍奥氏体不锈钢QN1701(12Cr17Mn7Ni2Cu2N),以代替443(019Cr21CuTi)超纯铁素体不锈钢。通过OM、SEM和电化学工作站等方法研究了QN1701和443不锈钢的组织及性能。N原子起着间隙固溶和细晶强化的作用,使QN1701不锈钢的屈服强度提高至400 MPa以上,达到443不锈钢的1.32倍。QN1701不锈钢的点蚀电位为241 mV,低于443不锈钢的289 mV,但其点蚀速率为9.10 g/(m2·h),低于443不锈钢的14.58 g/(m2·h)。在电梯用研磨拉丝表面状态下,QN1701不锈钢在质量分数为10%NaCl中性盐雾和干湿循环盐雾等加速腐蚀试验中的耐蚀性能均优于443不锈钢。分析发现,443不锈钢添加一定量的Nb、Ti稳定化元素所生成的(Nb,Ti)(C,N)析出相经研磨拉丝处理后,暴露于表面或被拖拽后留下微坑,导致其耐蚀能力急剧下降。综上所述,相较443不锈钢,QN1701不锈钢具有强度更高、伸长率更大和在研磨拉丝表面状态下耐蚀性更好等特点,这对于电梯轻量化设计和长寿命具有重要价值。