Terahertz Three-Dimensional Imaging Based on Computed Tomography with Photonics-Based Noise Source

Computed tomography has been proven to be useful for non-destructive inspection of structures and materials. We build a three-dimensional imaging system with the photonically generated incoherent noise source and the Schottky barrier diode detector in the terahertz frequency band （90–140GHz）. Based on the computed tomography technique, the three-dimensional image of a ceramic sample is reconstructed successfully by stacking the slices at different heights. The imaging results not only indicate the ability of terahertz wave in the non-invasive sensing and non-destructive inspection applications, but also prove the effectiveness and superiority of the uni-traveling-carrier photodiode as a terahertz source in the imaging applications.

A Path-Based Approach for Data Aggregation in Grid-Based Wireless Sensor Networks

Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme （PBDAS） for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station （BS）, we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.

Mathematical methods for main tenance and operation cost prediction based on transfer learning in State Grid

The electric power enterprise is an important basic energy industry for national development,and it is also the first basic industry of the national economy.With the continuous expansion of State Grid,the progressively complex operating conditions,and the increasing scope and frequency of data collection,how to make reasonable use of electrical big data,improve utilization,and provide a theoretical basis for the reliability of State Grid operation,has become a new research hot spot.Since electrical data has the characteristics of large volume,multiple types,low-value density,and fast processing speed,it is a challenge to mine and analyze it deeply,extract valuable information efficiently,and serve for the actual problem.According to the features of these data,this paper uses artificial intelligence methods such as time series and support vector regression to establish a data mining network model for standard cost prediction through transfer learning.The experimental results show that the model in this paper obtains better prediction results on a small sample data set,which verifies the feasibility of the deep transfer model.Compared with activity-based costing and the traditional prediction method,the average absolute error of the proposed method is reduced by 10%,which is effective and superior.

Malicious Node Detection Using Confidence Level Evaluation in a Grid-Based Wireless Sensor Network

In this paper, we present a malicious node detection scheme using confidence-level evaluation in a grid-based wireless sensor network. The sensor field is divided into square grids, where sensor nodes in each grid form a cluster with a cluster head. Each cluster head maintains the confidence levels of its member nodes based on their readings and reflects them in decision-making. Two thresholds are used to distinguish between false alarms due to malicious nodes and events. In addition, the center of an event region is estimated, if necessary, to enhance the event and malicious node detection accuracy. Experimental results show that the scheme can achieve high malicious node detection accuracy without sacrificing normal sensor nodes.

Endoscopic Optical Doppler Tomography Based on Two-Axis Scanning MEMS Mirror

We study the feasibility of endoscopic optical Doppler tomography with a micro-electro-mechanical system(MEMS) mirror based probe. The additional phase shifts introduced by the probe are tracked and formulated.The suppression method of the probe phase shifts is proposed and validated by fluid flow detection experiments.In vivo blood flow detection is also implemented on a hairless mouse. The velocities of the blood flow in two directions are obtained to be-8.1 mm/s and 6.6 mm/s, respectively.

Identity-based authentication protocol for grid

Current grid authentication frameworks are achieved by applying the standard SSL authentication protocol （SAP）. The authentication process is very complicated, and therefore, the grid user is in a heavily loaded point both in computation and in communication. Based on identity-based architecture for grid （IBAG） and corresponding encryption and signature schemes, an identity-based authentication protocol for grid is proposed. Being certificate-free, the authentication protocol aligns well with the demands of grid computing. Through simulation testing, it is seen that the authentication protocol is more lightweight and efficient than SAP, especially the more lightweight user side. This contributes to the larger grid scalability.

Three Dimensional Microstructure and Image-Based Simulation of a Fly Ash/Al Syntactic Foam Using X-ray Micro-Computed Tomography

An aluminum matrix syntactic foam, incorporated with hollow-structured fly ash particles, was fabricated by pressure infiltration technique. X-ray micro-computed tomography was used to characterize its heterogeneous microstructure three dimensionally （3D）. The quantification of some microstructure features, such as content and size distribution of hollow fly ash particles, was acquired in 3D. The tomographic data were exploited as a rapid method to generate a microstructurally accurate and robust 3D meshed model. The thermal transport behavior has been modeled using a commercial finite-element code to conduct steady state analyses. Simulation of the thermal conductivity showed good correlation with experimental result.

PET image reconstruction with rotationally symmetric polygonal pixel grid based highly compressible system matrix

To achieve a maximum compression of system matrix in positron emission tomography (PET) image reconstruction, we proposed a polygonal image pixel division strategy in accordance with rotationally symmetric PET geometry. Geometrical definition and indexing rule for polygonal pixels were established. Image conversion from polygonal pixel structure to conventional rectangular pixel structure was implemented using a conversion matrix. A set of test images were analytically defined in polygonal pixel structure, converted to conventional rectangular pixel based images, and correctly displayed which verified the correctness of the image definition, conversion description and conversion of polygonal pixel structure. A compressed system matrix for PET image recon was generated by tap model and tested by forward-projecting three different distributions of radioactive sources to the sinogram domain and comparing them with theoretical predictions. On a practical small animal PET scanner, a compress ratio of 12.6:1 of the system matrix size was achieved with the polygonal pixel structure, comparing with the conventional rectangular pixel based tap-mode one. OS-EM iterative image reconstruction algorithms with the polygonal and conventional Cartesian pixel grid were developed. A hot rod phantom was detected and reconstructed based on these two grids with reasonable time cost. Image resolution of reconstructed images was both 1.35 mm. We conclude that it is feasible to reconstruct and display images in a polygonal image pixel structure based on a compressed system matrix in PET image reconstruction.

Using Cellular Automata for Grid-Based Fishery Management

This report introduced new concept and technics for a grid-based fishery management system. The fishing ground was first divided into small grid of equal area, each with predefined longitudes and latitudes (both 0.033 degrees or approximately 2 × 2 nautical miles in this study). All grids were laid and formatted into a Microsoft-Excel spreadsheet system, as defined by the coastline. Individual sheets were also constructed to represent different ecological characters, serving as supporting data of the main grid-map. Including individual fishing record, water depth, wind & current vector, benthic character, etc. Cellular automata (CA) mathematics was applied for simulation studies. They were programmed on the built-in Visual BASIC langrage in EXCEL. In a three-year research project, the author was able to accomplish the following major results: 1) An EXCEL-based spreadsheet system for storage of fishing effort in each grid. Provided that data of fishing yield is also available for each grid, research model for fishery management can be constructed, leading toward solutions for total allowable catch (TAC) as well as maximum economic yield (MEY). 2) A multi-layered, 2-dimentional spread-sheet system demonstrating the distribution of relative intensity for individual grids. The system can be decked up with different ecological data for more advanced studies. 3) Estimation of the nearest distance between two special grids as well as fishing harbors. This would help in more efficient navigation management and allocation of fishing rights for the fishing vessels.

Performance Analysis of Grid Based AODV Routing Algorithm for AD Hoc Wireless Networks

In many traditional On Demand routing algorithms in Ad hoc wireless networks, a simple flooding mechanism is used to broadcast route request (RREQ) packets when there is a need to establish a route from a source node to a destination node. The broadcast of RREQ may lead to high channel contention, high packet collisions, and thus high delay to establish the routes, especially with high density networks. Ad hoc on Demand Distance Vector Routing Protocol (AODV) is one among the most effective Reactive Routing Protocols in MANETs which use simple flooding mechanism to broadcast the RREQ. It is also used in Wireless Sensor Networks (WSN) and in Vehicular Ad hoc Networks (VANET). This paper proposes a new modified AODV routing protocol EGBB-AODV where the RREQ mechanism is using a grid based broadcast (EGBB) which reduces considerably the number of rebroadcast of RREQ packets, and hence improves the performance of the routing protocol. We developed a simulation model based on NS2 simulator to measure the performance of EGBB-AODV and compare the results to the original AODV and a position-aware improved counter-based algorithm (PCB-AODV). The simulation experiments that EGBB-AODV outperforms AODV and PCB-AODV in terms of end-to-end delay, delivery ratio and power consumption, under different traffic load, and network density conditions.

A Void Avoidance Scheme for Grid-Based Multipath Routing in Underwater Wireless Sensor Networks

This work proposes a geographic routing protocol for UWSNs based on the construction of a 3D virtual grid structure, called Void-Avoidance Grid-based Multipath Position-based Routing (VA-GMPR). It consists of two main components, the multipath routing scheme and the grid-based void avoidance (GVA) mechanism for handling routing holes. The multipath routing scheme adopts node-disjoint routes from the source to the sink in order to enhance network reliability and load balancing. While the GVA mechanism handles the problem of holes in 3D virtual grid structure based on three techniques: Hole bypass, path diversion, and path backtracking. The performance evaluation of the VA-GMPR protocol was compared to a recently proposed grid-based routing protocol for UWSNs, called Energy-efficient Multipath Geographic Grid-based Routing (EMGGR). The results showed that the VA-GMPR protocol outperformed the EMGGR protocol in terms of packet delivery ratio, and end-to end-delay. However, the results also showed that the VA-GMPR protocol exhibited higher energy consumption compared to EMGGR.

Multipath Grid-Based Enabled Geographic Routing for Wireless Sensor Networks

This work proposes an efficient disjoint multipath geographic routing algorithm for dense wireless sensor networks (WSN), called Multipath Grid-based Enabled Geographic Routing (MGEGR). The proposed algorithm relies on the construction of a 2-D logical grid in the geographical region of deployment. The objective of the proposed scheme is to determine optimal or near-optimal (within a defined constant) multiple disjoint paths (multipath) from a source node to the sink, in order to enhance the reliability of the network. The determined multiple disjoint paths would be used by the source node in a round-robin way to balance the traffic across the disjoint paths, and to avoid discovered paths with cell holes. The proposed scheme limits the use of broadcasting to the process of gateway election within each cell, and the process of maintaining the table of neighbors of each gateway. Our simulation results show the effectiveness and scalability of our routing scheme with increased network size compared to on-demand routing protocols.

MRI and PET images fusion based on human retina model

The diagnostic potential of brain positron emission tomography （PET） imaging is limited by low spatial resolution. For solving this problem we propose a technique for the fusion of PET and MRI images. This fusion is a trade-off between the spectral information extracted from PET images and the spatial information extracted from high spatial resolution MRI. The proposed method can control this trade-off. To achieve this goal, it is necessary to build a multiscale fusion model, based on the retinal cell photoreceptors model. This paper introduces general prospects of this model, and its application in multispectral medical image fusion. Results showed that the proposed method preserves more spectral features with less spatial distortion. Comparing with hue-intensity-saturation （HIS）, discrete wavelet transform （DWT）, wavelet-based sharpening and wavelet-a trous transform methods, the best spectral and spatial quality is only achieved simultaneously with the proposed feature-based data fusion method. This method does not require resampling images, which is an advantage over the other methods, and can perform in any aspect ratio between the pixels of MRI and PET images.

Application of control volume based finite element method for solving the black-oil fluid equations

This is the second paper of a series where we introduce a control volume based finite element method （CVFEM） to simulate multiphase flow in porous media. This is a fully conservative method able to deal with unstructured grids which can be used for representing any complexity of reservoir geometry and its geological objects in an accurate and efficient manner. In order to deal with the inherent heterogeneity of the reservoirs, all operations related to discretization are performed at the element level in a manner similar to classical finite element method （FEM）. Moreover, the proposed method can effectively reduce the so-called grid orientation effects. In the first paper of this series, we presented this method and its application for incompressible and immiscible two-phase flow simulation in homogeneous and heterogeneous porous media. In this paper, we evaluate the capability of the method in the solution of highly nonlinear and coupled partial differential equations by simulating hydrocarbon reservoirs using the black-oil model. Furthermore, the effect of grid orientation is investigated by simulating a benchmark waterflooding problem. The numerical results show that the formulation presented here is efficient and accurate for solving the bubble point and three-phase coning problems.

Multi-modality measurement and comprehensive analysis of hepatocellular carcinoma using synchrotron-based microscopy and spectroscopy

The visualization and data mining of tumor multidimensional information may play a major role in the analysis of the growth,metastasis,and microenvironmental changes of tumors while challenging traditional imaging and data processing techniques.In this study,a general trans-scale and multi-modality measurement method was developed for the quantitative diagnosis of hepatocellular carcinoma(HCC)using a combination of propagation-based phase-contrast computed tomography(PPCT),scanning transmission soft X-ray microscopy(STXM),and Fourier transform infrared micro-spectroscopy(FTIR).Our experimental results reveal the trans-scale micro-morpho-logical HCC pathology and facilitate quantitative data analysis and comprehensive assessment.These results include some visualization features of PPCT-based tissue microenvironments,STXM-based cellular fine structures,and FTIR-based bio-macromolecular spectral characteris-tics during HCC tumor differentiation and proliferation.The proposed method provides multidimensional feature data support for constructing a high-accuracy machine learning algorithm based on a gray-level histogram,gray-gradient co-occurrence matrix,gray-level co-occurrence matrix,and back-propagation neural network model.Multi-dimensional information analysis and diagnosis revealed the morphological pathways of HCC pathological evolution and we explored the relationships between HCC-related feature changes in inflammatory microenviron-ments,cellular metabolism,and the stretching vibration peaks of biomolecules of lipids,proteins,and nucleic acids.Therefore,the proposed methodology has strong potential for the visualization of complex tumors and assessing the risks of tumor differentiation and metastasis.

Solving Multi-Area Environmental/Economic Dispatch by Pareto-Based Chemical-Reaction Optimization Algorithm

In this study, we present a Pareto-based chemicalreaction optimization(PCRO) algorithm for solving the multiarea environmental/economic dispatch optimization problems.Two objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposed algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposed algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a selfadaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front. Furthermore,a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposed algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCRO algorithm is favorably compared with several algorithms, with regards to both solution quality and diversity.

能谱CT定量参数联合动脉期碘基图影像组学特征在肺鳞癌与肺腺癌鉴别诊断中的价值

目的基于能谱电子计算机断层扫描(computed tomography,CT)定量参数联合动脉期碘基图影像组学特征构建诺模图模型,探讨能谱CT在肺鳞癌(lung squamous cell carcinoma,LUSC)与肺腺癌(lung adenocarcinoma,LUAD)鉴别诊断中的临床应用价值。方法收集术前行能谱CT增强检查、经病理活检或手术证实的肺癌患者89例,其中LUAD组38例,LUSC组51例。分析LUAD组和LUSC组患者在60keV动脉期单能量图像中病灶水浓度、碘浓度(iodine concentration,IC)、标准化碘浓度(normalizediodineconcentration,NIC)、CT值、能谱曲线斜率(K)、有效原子序数(effective-Z,Eff-Z)及标准化有效原子序数(normalized effective-Z,NEff-Z)。采用Logistic回归分析构建LUAD和LUSC基于能谱CT特征的鉴别诊断模型。应用ITKSnap软件提取动脉期碘基图影像组学特征;采用组内相关系数(intraclass correlation coemcient,ICC)、递归特征消除(recursive featureelimination,RFE)和最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)对影像组学特征进行降维、筛选,采用Logistic回归构建基于影像组学特征的诊断模型,并计算该模型的影像组学评分(radiomics score,Rad-score),以多因素Logistic回归分析筛选出的能谱CT定量参数与Rad-score构建联合模型,并绘制诺模图。应用受试者工作特征(receiveroperatingcharacteristic,ROC)曲线、Delong检验、校正曲线、Hosmer-Lemeshow检验及临床决策曲线(decision curve analysis,DCA)对能谱CT模型、影像组学模型和诺莫图模型进行效能评价。结果LUAD组和LUSC组患者IC、NIC、CT值、K及NEff-Z比较差异均有统计学意义(P均<0.05)。多因素Logistic回归分析结果显示,IC、NIC及NEff-Z为独立影响因素,基于此构建能谱CT模型预测LUAD和LUSC的效能曲线下面积(areaunderthecurve,AUC)为0.768,准确率、灵敏度、特异度分别为70.73%、76.92%和67.86%;影像组学特征经降维后共筛选出的有意义特征有5个,一阶特征2个、二阶特征2个和形状特征1个,影像组学模型预测LUAD和LUSC的效能AUC为0.848,准确率、灵敏度、特异度分别为80.50%、83.33%和75.00%;诺莫图模型预测LUAD和LUSC的效能AUC为0.912,准确率、灵敏度、特异度分别为85.00%、92.31%和85.71%。经Delong检验显示,诺莫图模型AUC均明显高于影像组学模型和能谱CT模型(P均<0.05)。Hosmer-Lemeshow检验结果显示,能谱CT模型、影像组学模型及诺莫图模型的拟合度均良好(χ^(2)值分别为8.592、6.591、6.686,P值分别为0.378、0.581、0.570)。校准曲线分析显示,诺莫图模型预测LUAD和LUSC的预测概率曲线与理想曲线更接近,优于影像组学模型和能谱CT模型;DCA分析结果显示,诺莫图模型的AUC最大,均高于影像组学模型和能谱CT模型,临床净收益更高。结论基于能谱CT定量参数联合动脉期碘基图影像组学特征构建的诺模图模型在LUSC与LUAD鉴别诊断中具有潜在应用价值。

模块化高压大容量DC/DC变换器综述

随着新能源发电占比增加和电网规模的不断扩大,直流输电系统的容量不断提升,含有不同电压等级的直流电网是未来发展的必然趋势。在此背景下,DC/DC变换器作为直流电网关键设备,在多电压等级互联场景中发挥着重要作用。文中首先对已有的模块化高压大容量DC/DC变换器进行归纳、分类和比较,然后阐述了目前模块化高压大容量DC/DC变换器的拓扑结构、工作原理与特点,并针对其特点提出了不同变换器的适用场景,最后对未来的研究方向提出了建议。

组合高精度速度建模技术在渤海工区潜山成像中的应用

速度分析是地震数据处理的最重要环节之一,准确的速度场为偏移成像提供了最基础的保障,高精度的速度模型是提高潜山成像质量的关键。基于叠前深度偏移方法的速度建模过程中要同时兼顾好项目运行周期、建模效率、建模精度等不同要求。因此针对不同构造区,采用合适的速度建模方法尤为重要。文章以渤海某工区为例,首先探讨了不同速度建模方法的优缺点,然后根据工区目的层状况选择优势互补的速度建模方法。即针对浅层以平层构造为主的目的层采用网格层析方法,针对中深层潜山构造采用非线性反演方法,针对潜山内幕及基底构造区采用速度扫描的立体层析方法。实际资料处理结果表明:组合高精度速度建模方法可以比较快速的得到高精度速度模型,速度反演结果与测井数据及地震剖面高度吻合,在大大缩短项目运行周期的同时,提高了潜山构造区成像精度,验证了组合高精度速度建模技术的可行性、实用性和有效性。

无人机载智能塔基定位放样方法研究

电网工程架空线路塔杆放样多在复杂山区进行,传统基于经纬仪、全站仪和手持差分卫星定位等设备的放样方式劳动强度大、耗时长且数据易出错。提出了一种无人机载智能塔基定位的放样方法,该方法以无人机、激光投点器为硬件基础,研究开发智能放样软件。将电网工程架空线路上各塔杆中心桩坐标输入智能放样软件平台,可实时计算各塔杆20个放样点的平面坐标,并根据DEM模型、地表树木、交通线路及其他障碍物分布状况自动规划无人机放样航线、调整航高,在各放样点上方悬停后进行激光投点,高效引导地表人员快速确定放样点位。经过多轮测试,结果表明:采用无人机载智能塔基定位放样系统可快速、高精度完成输电线路塔杆放样,大幅降低了传统放样方式中放样点布设的次数与时长,为电网施工放样提供了有效支撑。