基于最短路径树的优化生存时间路由算法

为提高无线传感网的生存时间,提出基于最短路径树的优化生存时间路由算法(LORA_SPT).该算法引入节点分类概念,构造基于链路能耗因子、自身节点剩余能量因子、邻居节点剩余能量因子和类型权重因子等多个因子的权值函数.针对不同类型的节点采用不同的权重因子,最后利用dijkstra算法完成最短路径树,所有节点沿着最短路径树将...     

车辆TPMS轮速传感器误差消除方法研究

轮胎压力监测系统能够实时地自动监测行驶中汽车的轮胎气压,对于提高行车安全具有重要的意义。基于轮胎压力监测系统硬件平台,阐述了轮速信号的获取,研究了异点剔除方法,提出了基于弹性BP神经网络分析轮速传感器误差的方法,实现了对轮速信号的修正。仿真和实验结果表明,该轮速传感器误差消除方法的精度可达2×10-4rad,能够显著降低传感器误差,提高轮速信号的获取精度。     

无线传感器网络中一种基于移动Sink的数据收集算法

针对移动无线传感器网络设计一种不依赖于节点地理位置的基于移动汇聚节点(Sink)的数据收集算法(Mobile Sink-based Data Gathering,MSDG)。该算法解决了无线传感器网络中多跳路由通信时出现能量空洞的"热点"问题。Sink沿途以最近的固定节点作为根节点动态构建路由树。簇内移动节点感知的数据经簇头进行数据融合计算,然后将融合后的数据沿路由树反向逐跳转发给Sink。仿真结果表明,MSDG在节点的平均能耗和网络生存时间等方面的性能远超过LEACH、ACE-L等数据收集协议。     

无线传感器网络簇内实时数据融合研究

现有的无线传感器网络数据融合技术存在数据延迟和数据正确性两方面的问题。针对该问题设计一种基于簇内实时数据融合的方法．该方法首先通过量化计算将数据划分为紧急数据和非紧急数据并作标记,将紧急数据迅速融合传给用户以提高实时性。在融合过程中引入传感器测量修正并结合关系矩阵法对数据进行相应的融合处理．然后对融合结果进行分析并进行反馈调整。仿真实验结果表明．该方法能够提高簇内实时数据融合正确性并且降低数据延迟．     

单位计算机网络的建设与维护

计算机网络有效地提高了工的作效率和信息的使用效率,计算机网络的建设既要抓住时机,又要结合本单位的实际情况,注重网络的实际应用价值。从电子政务、办公自动化、数据库以及交通信息化等角度分析了单位计算机网络的建设工作,并对网络的维护工作进行了探讨。     

Modeling air flow in sanitary sewer systems: A review

Current designs of sanitary collection systems normally only consider the transport of wastewater without attention on the air movement in the sewer airspaces. Under anaerobic conditions, hydrogen sulfide (H₂S) can be generated in the liquid phase in sewer systems. H₂S is corrosive to concrete and steel structures and odorous or even toxic to human, which can cause corrosion and sewer odor issues. To develop a feasible sewer corrosion and odor control strategy, it is necessary to understand the mechanisms of air flow in sewer systems for developing practical tools to predict and control the air flow. This paper comprehensively reviewed previous efforts on modeling the air flow in sewer systems and provided recommendations on predicting the air flow for engineering applications. The air flow in a single pipe was firstly reviewed followed by the air flow in sewer structures as well as air flow models in sewer networks. Some other considerations such as temperature driven flow, transient water flow, and wind effect were also reviewed. The knowledge gaps were then identified, and recommendations on the further studies were provided.     

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%.     

Speech Enhancement via Residual Dense Generative Adversarial Network

Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which can extract rich local features of LPS through densely connected convolution layers. Meanwhile, sequential RDB connections are incorporated on various scales of LPS. It significantly increases the feature learning flexibility and robustness in the time-frequency domain. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes. It indicates that our method is more generalized in untrained conditions.     

Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning

Process monitoring in additive manufacturing may allow components to be certified cheaply and rapidly and opens the possibility of healing defects, if detected. Here, neural networks (NNs) and convolutional neural networks (CNNs) are trained to detect flaws in layerwise images of a build, using labeled XCT data as a ground truth. Multiple images were recorded after each layer before and after recoating with various lighting conditions. Classifying networks were given a single image or multiple images of various lighting conditions for training and testing. CNNs demonstrated significantly better performance than NNs across all tasks. Furthermore, CNNs demonstrated improved generalizability, i.e., the ability to generalize to more diverse data than either the training or validation data sets. Specifically, CNNs trained on high-resolution layerwise images from one build showed minimal loss in performance when applied to data from an independent build, whereas the performance of the NNs degraded significantly. CNN accuracy was also demonstrated to be a function of flaw size, suggesting that smaller flaws may be produced by mechanisms that do not alter the surface morphology of the build plate. CNNs demonstrated accuracies of 93.5 % on large (>200 μm) flaws when testing and training on components from the same build and accuracies of 87.3 % when testing on a previously unseen build. Finally, evidence linking the formation of large lack-of-fusion defects to the presence of process ejecta is presented.