A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection.

Remaining Useful Life Prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder

In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.

ENCODE计划和功能基因组研究

人类基因组计划完成以来,科学家们一直在努力阐释基因组信息所代表的生物学意义。自2003年开始,美国国家人类基因组研究所(National Human Genome Research Institute,NHGRI)投资近3亿美元启动"DNA元件百科全书(Encyclopedia of DNA Elements,ENCODE)"计划,集结了来自美国、中国、英国、日本、西班牙和新加坡等国家的32个实验室的440余名科学家,共同鉴定并分析人类基因组中所有的功能调控元件。高通量测序技术等实验手段的发展和生物信息学技术的不断完善使得ENCODE计划取得了丰硕的成果:确定了甲基化和组蛋白修饰等表观修饰区域及其对染色质结构的作用,进而确定染色质结构的改变影响基因表达;确定了转录因子及其结合位点的信息,并构建了转录因子调控网络;进一步修订更新了假基因和非编码RNA数据库;并确定了调控序列的单核苷酸多态性(Single nucleotide polymorphism,SNP)并与疾病相关联。这些发现一方面有助于系统解析基因和基因组信息、调控元件的调控作用以及非编码区转录调控等分子机制;同时也将为转化医学等生命科学研究领域提供丰富的数据来源。文章综述了高通量测序技术等实验手段的发展和生物信息学技术的不断完善对ENCODE计划的贡献、表观遗传学研究与ENCODE计划的关联性、ENCODE计划的主要科学成果等,同时展望了ENCODE计划对基础医学、临床医学和转化医学等生命科学研究领域的巨大推动作用。

ASP源代码加密程序Script Encoder算法研究

微软为了保护脚本代码的安全性 ,以 COM组件的形式提供了一种对脚本代码进行编码加密的技术 .但其安全性到底如何呢 ?本文将通过“反编译”的方式对其加密。

基于ENCODER_ATT机制的远程监督关系抽取

在信息抽取中,关系抽取是一项准确识别自然语言中实体间关系的关键技术。针对关系抽取模型中容易丢失关键语义特征问题及远程监督的基本假设容易引入噪声数据的问题,本文提出一种基于远程监督的ENCODER_ATT关系抽取模型。基于循环神经网络构造的ENCODER模型在以词级别进行特征记忆提取,并在句子层面进行语义特征信息整合,保证不遗失关键语义特征的同时去除冗余特征。然后在句子层面引入了注意力机制来降低噪声数据对实验结果的影响。在真实的数据集上进行实验,并绘制准确率-召回率曲线,实验结果表明ENCODER_ATT模型对比同类型的关系抽取方法有明显的提升。

Encode~愈合基台技术在前牙美学修复的应用

目的:对3I种植系统Encode愈合基台水平取模技术进行美学区域种植修复的临床评价。方法:拔除患牙,即刻植入3IOsseotiteTMCertain直形种植体,软组织愈合后临时基台修复。8周后换Encode愈合基台,行愈合基台水平取模,模型送往技工中心利用CAD/CAM设计、制作个性化Encode基台和全瓷冠,临床完成基台和冠的戴入。结果:临时基台修复促成了软组织成形和良好的牙龈外形,之后由Encode愈合基台取模,操作简便;在此基础上的印模转移了进行Encode个性化基台修复的所有信息,制作完成了个别Encode基台和全瓷冠,达到了个性化的美学修复。结论:3I种植系统Encode愈合基台水平取模技术是理想的个性化美学修复方法。

在Flash中基于Adobe Media Encoder组件的视频导入与应用方法

在flash cs6的默认情况下,Flash cs6只支持flv和f4v格式的视频.如果不是这种格式的视频,我们可以使用Flash cs6自带的视频转换组件Adobe Media Encoder将其他视频格式转换成FLV和F4V格式.本文主要讲解如何使用flash自带的Adobe Media Encoder组件进行视频文件的转换,导入和使用.

Blind recognition of k/n rate convolutional encoders from noisy observation

Blind recognition of convolutional codes is not only essential for cognitive radio, but also for non-cooperative context. This paper is dedicated to the blind identification of rate k/n convolutional encoders in a noisy context based on Walsh-Hadamard transformation and block matrix (WHT-BM). The proposed algorithm constructs a system of noisy linear equations and utilizes all its coefficients to recover parity check matrix. It is able to make use of fault-tolerant feature of WHT, thus providing more accurate results and achieving better error performance in high raw bit error rate (BER) regions. Moreover, it is more computationally efficient with the use of the block matrix (BM) method. © 2017 Beijing Institute of Aerospace Information.

基于改进Encoder-Decoder模型的新闻摘要生成方法

针对通过Extractive方式实现自动文摘而存在文本连贯性欠缺和出现未登录词问题,提出一种基于改进Encoder-Decoder模型的新闻摘要生成方法。首先,在数据预处理的过程中融入额外的语言特征,如词语的词性和TF-IDF,使词语具有多维度的含义;其次,采用Decoder/Pointer机制在摘要中指向原文本中的位置对低频词进行处理;最后,采用注意力机制来协助模型记忆输入数据并确定其注意程度。在News2016zh数据集上进行实验,结果表明基于改进Encoder-Decoder模型与基线Encoder-Decoder相比,ROUGE-1、ROUGE-2和ROUGE-L值分别提高了32.1%、30.5%和32.5%,在摘要连贯性方面也得到了较好提升。

Upconversion Nanoparticles-Encoded Hydrogel Microbeads-Based Multiplexed Protein Detection

Fluorescently encoded microbeads are in demand for multiplexed applications in different fields.Compared to organic dye-based commercially available Luminex's x MAP technology, upconversion nanoparticles(UCNPs) are better alternatives due to their large antiStokes shift, photostability, nil background, and single wavelength excitation. Here, we developed a new multiplexed detection system using UCNPs for encoding poly(ethylene glycol) diacrylate(PEGDA) microbeads as well as for labeling reporter antibody. However, to prepare UCNPs-encoded microbeads, currently used swellingbased encapsulation leads to non-uniformity, which is undesirable for fluorescence-based multiplexing. Hence,we utilized droplet microfluidics to obtain encoded microbeads of uniform size, shape, and UCNPs distribution inside. Additionally, PEGDA microbeads lack functionality for probe antibodies conjugation on their surface.Methods to functionalize the surface of PEGDA microbeads(acrylic acid incorporation, polydopamine coating)reported thus far quench the fluorescence of UCNPs. Here,PEGDA microbeads surface was coated with silica followed by carboxyl modification without compromising the fluorescence intensity of UCNPs. In this study, droplet microfluidics-assisted UCNPs-encoded microbeads of uniform shape, size, and fluorescence were prepared.Multiple color codes were generated by mixing UCNPs emitting red and green colors at different ratios prior to encapsulation. UCNPs emitting blue color were used to label the reporter antibody. Probe antibodies were covalently immobilized on red UCNPs-encoded microbeads for specific capture of human serum albumin(HSA) as a model protein. The system was also demonstrated for multiplexed detection of both human C-reactive protein(hCRP) and HSA protein by immobilizing anti-h CRP antibodies on green UCNPs.

A correction method of encoder bias in satellite laser ranging system

In a satellite laser ranging telescope system, well-aligned encoders of the elevation and azimuth axes are essential for tracking objects. However, it is very difficult and time-consuming to correct the bias between the absolute-position indices of the encoders and the astronomical coordinates, especially in the absence of a finder scope for our system. To solve this problem, a method is presented based on the phenomenon that all stars move anti-clockwise around Polaris in the northern hemisphere. Tests of the proposed adjustment procedure in a satellite laser ranging （SLR）system demonstrated the effectiveness and the time saved by using the approach, which greatly facilitates the optimization of a trackin~ svstem.

A compound objective reconfiguration of distribution networks using hierarchical encoded particle swarm optimization

With the development of automation in smart grids,network reconfiguration is becoming a feasible approach for improving the operation of distribution systems.A novel reconfiguration strategy was presented to get the optimal configuration of improving economy of the system,and then identifying the important nodes.In this strategy,the objectives increase the node importance degree and decrease the active power loss subjected to operational constraints.A compound objective function with weight coefficients is formulated to balance the conflict of the objectives.Then a novel quantum particle swarm optimization based on loop switches hierarchical encoded was employed to address the compound objective reconfiguration problem.Its main contribution is the presentation of the hierarchical encoded scheme which is used to generate the population swarm particles of representing only radial connected solutions.Because the candidate solutions are feasible,the search efficiency would improve dramatically during the optimization process without tedious topology verification.To validate the proposed strategy,simulations are carried out on the test systems.The results are compared with other techniques in order to evaluate the performance of the proposed method.

Determination of optimal period of absolute encoders with single track cyclic gray code

Low cost and miniaturized rotary encoders are important in automatic and precise production. Presented here is a code called Single Track Cyclic Gray Code (STCGC) that is an image etched on a single circular track of a rotary encoder disk read by a group of even spread reading heads to provide a unique codeword for every angular position and features such that every two adjacent words differ in exactly one component, thus avoiding coarse error. The existing construction or combination methods are helpful but not sufficient in determining the period of the STCGC of large word length and the theoretical approach needs further development to extend the word length. Three principles, such as the seed combination, short code removal and ergodicity examination were put forward that suffice determination of the optimal period for such absolute rotary encoders using STCGC with even spread heads. The optimal periods of STCGC in 3 through 29 bit length were determined and listed.

Multi-scale attention encoder for street-to-aerial image geo-localization

The goal of street-to-aerial cross-view image geo-localization is to determine the location of the query street-view image by retrieving the aerial-view image from the same place.The drastic viewpoint and appearance gap between the aerial-view and the street-view images brings a huge challenge against this task.In this paper,we propose a novel multiscale attention encoder to capture the multiscale contextual information of the aerial/street-view images.To bridge the domain gap between these two view images,we first use an inverse polar transform to make the street-view images approximately aligned with the aerial-view images.Then,the explored multiscale attention encoder is applied to convert the image into feature representation with the guidance of the learnt multiscale information.Finally,we propose a novel global mining strategy to enable the network to pay more attention to hard negative exemplars.Experiments on standard benchmark datasets show that our approach obtains 81.39%top-1 recall rate on the CVUSA dataset and 71.52%on the CVACT dataset,achieving the state-of-the-art performance and outperforming most of the existing methods significantly.

Application of Instantaneous Rotational Speed to Detect Gearbox Faults Based on Double Encoders

Considerable studies have been carried out on fault diagnosis of gears, with most of them concentrated on conventional vibration analysis. However, besides the complexity of gear dynamics, the diagnosis results in terms of vibration signal are easily misjudged owing to the interference of sensor position or other components. In this paper, an alternative gearbox fault detection method based on the instantaneous rotational speed is proposed because of its advantages over vibration analysis. Depending on the timer/counter-based method for the pulse signal of the optical encoder, the varying rotational speed can be obtained e ectively. Owing to the coupling and meshing of gears in transmission, the excitations are the same for the instantaneous rotational speed of the input and output shafts. Thus, the di erential signal of instantaneous rotational speeds can be adopted to eliminate the e ect of the interference excitations and extract the associated feature of the localized fault e ectively. With the experiments on multistage gearbox test system, the di erential signal of instantaneous speeds is compared with other signals. It is proved that localized faults in the gearbox generate small angular speed fluctuations, which are measurable with an optical encoder. Using the di erential signal of instantaneous speeds, the fault characteristics are extracted in the spectrum where the deterministic frequency component and its harmonics corresponding to crack fault characteristics are displayed clearly.

Absolute multi-pole encoder with a simple structure based on an improved gray code to enhance the resolution

We developed a novel absolute multi-pole encoder structure to improve the resolution of the multi-pole encoder, realize absolute output and reduce the manufacturing cost of the encoder. The structure includes two ring alnicos defined as index track and sub-division track, respectively. The index track is magnetized based on the improved gray code, with linear halls placed around the track evenly. The outputs of linear halls show the region the rotor belongs to. The sub-division track is magnetized to N-S-N-S (north-south-north-south), and the number of N-S pole pairs is determined by the index track. Three linear hall sensors with an air-gap of 2 mm are used to translate the magnetic filed to voltage signals. The relative offset in a single N-S is obtained through look-up. The magnetic encoder is calibrated using a higher-resolution incremental optical encoder. The pulse output from the optical encoder and hall signals from the magnetic encoder are sampled at the same time and transmitted to a computer, and the relation between them is calculated, and stored in the FLASH of MCU (micro controller unit) for look-up. In the working state, the absolute angle is derived by looking-up with hall signals. The structure is simple and the manufacturing cost is very low and suitable for mass production.

Flexible reduced field of view magnetic resonance imaging based on single-shot spatiotemporally encoded technique

In many ultrafast imaging applications, the reduced field-of-view（r FOV） technique is often used to enhance the spatial resolution and field inhomogeneity immunity of the images. The stationary-phase characteristic of the spatiotemporallyencoded（SPEN） method offers an inherent applicability to r FOV imaging. In this study, a flexible r FOV imaging method is presented and the superiority of the SPEN approach in r FOV imaging is demonstrated. The proposed method is validated with phantom and in vivo rat experiments, including cardiac imaging and contrast-enhanced perfusion imaging. For comparison, the echo planar imaging（EPI） experiments with orthogonal RF excitation are also performed. The results show that the signal-to-noise ratios of the images acquired by the proposed method can be higher than those obtained with the r FOV EPI. Moreover, the proposed method shows better performance in the cardiac imaging and perfusion imaging of rat kidney, and it can scan one or more regions of interest（ROIs） with high spatial resolution in a single shot. It might be a favorable solution to ultrafast imaging applications in cases with severe susceptibility heterogeneities, such as cardiac imaging and perfusion imaging. Furthermore, it might be promising in applications with separate ROIs, such as mammary and limb imaging.

Design and implementation of LDPC encoder based on FPGA

A low density parity check(LDPC)encoder with the codes of(8176,7154)and encoding rate of 7/8 under CCSDS standard for near space communication is designed.Based on LDPC encoding theory,the FPGA-based coding algorithm is designed.Based on the characteristics of LDPC generating matrix,the cyclic shift register is introduced as the core of the encoding circuit,and the shift-register-Adder-Accumulator(SRAA)structure is adopted to realize the fast calculation of matrix multiplication,so as to construct the encoding module with partial parallel encoding circuit as the core.In addition,the serial port input and output module,RAM storage module and control module are also designed,which together constitute the encoder system.The design scheme is implemented by FPGA hardware and verified by simulation and experiment.The results show that the test results of the designed LDPC encoder are consistent with the theoretical results.Therefore,the coding system is practical,and the design method is simple and efficient.

Bus Encoded LUT Multiplier for Portable Biomedical Therapeutic Devices

DSP operation in a Biomedical related therapeutic hardware need to beperformed with high accuracy and with high speed. Portable DSP hardware’s likepulse/heart beat detectors must perform with reduced operational power due to lack ofconventional power sources. This work proposes a hybrid biomedical hardware chip inwhich the speed and power utilization factors are greatly improved. Multipliers are thecore operational unit of any DSP SoC. This work proposes a LUT based unsignedmultiplication which is proven to be efficient in terms of high operating speed. For n bitinput multiplication n*n memory array of 2 n bit size is required to memorize all thepossible input and output combination. Various literature works claims to be achieve highspeed multiplication with reduced LUT size by integrating a barrel shifter mechanism.This paper work address this problem, by reworking the multiplier architecture with aparallel operating pre-processing unit which used to change the multiplier and multiplicandorder with respect to the number of computational addition and subtraction stages required.Along with LUT multiplier a low power bus encoding scheme is integrated to limit the powerconstraint of the on chip DSP unit. This paper address both the speed and power optimizationtechniques and tested with various FPGA device families.

Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis

The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders.However,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia diagnosis.In this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven models.Specifically,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation robust.Also,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions.The typical topological properties and discriminative features can be preserved entirely.Furthermore,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive diseases.Attempts on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional networks.The classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related augmentedmodels.Overall,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks.