Gradient Index in Wear Debris Image Collection

In order to solve a problem of oil on-line monitoring,this instrument adopts a principium of self-focus lens of Gradient index fiber(GRIN Len)to design optics system and magnetic circuit.For the magnetic circuit,the monitor can catch particle wear debris in oil.And for the optics circuit.GRIN Len can transfer image of debris to apparatus of gather image,e.g,CCD and camera.And the image of debris is transferred to computer for analyzing seize and physiognomy of debris.The character of the monitor is of micro weight,micro volume and curve imaging.And it is directly pluged into oil to catch image of wear particles.

Automatic Classification for Various Images Collections Using Two Stages Clustering Method

In this paper, we propose an automatic classification for various images collections using two stage clustering method. Here, we have used global and local image features. First, we review about various types of feature vector that is suita-ble to represent local and global properties of images, and similarity measures that can be represented an affinity be-tween these images. Second, we consider a clustering method for image collection. Here, we first build a coarser clus-tering by partitioning various images into several clusters using the flexible Mean shift algorithm and K-mean cluster-ing algorithm. Second, we construct dense clustering of images collection by optimizing a Gaussian Dirichlet process mixture model taking initial clusters as given coarser clustering. Finally, we have conducted the comparative experi-ments between our method and existing methods on various images datasets. Our approach has significant advantage over existing techniques. Besides integrating temporal and image content information, our approach can cluster auto-matically photographs without some assumption about number of clusters or requiring a priori information about initial clusters and it can also generalize better to different image collections.

Design and implementation of railway green performance basic data collection system

This study addressed the issues related to the collection and management of basic data for railway green performance. A railway green performance basic database has been constructed based on metadata and data exchange schemas. A data classification system has been established from the perspectives of businesses, processes,and entities. A BIM(Building Information Modelling) model data extraction scheme is proposed based on field similarity matching and a document content extraction scheme is proposed based on image recognition. A railway green performance basic data collection system has been developed, achieving efficient collection and integrated management of railway green performance basic data. This system can provide data support for applications such as railway carbon emissions accounting, green cost-benefit analysis, and evaluation of green design solutions.

Pancreatic fluid collections: What is the ideal imaging technique?

Pancreatic fluid collections(PFCs) are seen in up to 50% of cases of acute pancreatitis. The Revised Atlanta classification categorized these collections on the basis of duration of disease and contents, whether liquid alone or a mixture of fluid and necrotic debris. Management of these different types of collections differs because of the variable quantity of debris; while patients with pseudocysts can be drained by straight-forward stent placement, walledoff necrosis requires multi-disciplinary approach. Differentiating these collections on the basis of clinical severity alone is not reliable, so imaging is primarily performed. Contrast-enhanced computed tomography is the commonly used modality for the diagnosis and assessment of proportion of solid contents in PFCs; however with certain limitations such as use of iodinated contrast material especially in renal failure patients and radiation exposure. Magnetic resonance imaging(MRI) performs better than computed tomography(CT) in characterization of pancreatic/peripancreatic fluid collections especially for quantification of solid debris and fat necrosis(seen as fat density globules), and is an alternative in those situations where CT is contraindicated. Also magnetic resonance cholangiopancreatography is highly sensitive for detecting pancreatic duct disruption and choledocholithiasis. Endoscopic ultrasound is an evolving technique with higher reproducibility for fluid-to-debris component estimation with the added advantage of being a single stage procedure for both diagnosis(solid debris delineation) and management(drainage of collection) in the same sitting. Recently role of diffusion weighted MRI and positron emission tomography/CT with ^(18)F-FDG labeled autologous leukocytes is also emerging for detection of infection noninvasively. Comparative studies between these imaging modalities are still limited. However we look forward to a time when this gap in literature will be fulfilled.

Artificially Generated Facial Images for Gender Classification Using Deep Learning

Given the current expansion of the computer visionfield,several appli-cations that rely on extracting biometric information like facial gender for access control,security or marketing purposes are becoming more common.A typical gender classifier requires many training samples to learn as many distinguishable features as possible.However,collecting facial images from individuals is usually a sensitive task,and it might violate either an individual's privacy or a specific data privacy law.In order to bridge the gap between privacy and the need for many facial images for deep learning training,an artificially generated dataset of facial images is proposed.We acquire a pre-trained Style-Generative Adversar-ial Networks(StyleGAN)generator and use it to create a dataset of facial images.We label the images according to the observed gender using a set of criteria that differentiate the facial features of males and females apart.We use this manually-labelled dataset to train three facial gender classifiers,a custom-designed network,and two pre-trained networks based on the Visual Geometry Group designs(VGG16)and(VGG19).We cross-validate these three classifiers on two separate datasets containing labelled images of actual subjects.For testing,we use the UTKFace and the Kaggle gender dataset.Our experimental results suggest that using a set of artificial images for training produces a comparable performance with accuracies similar to existing state-of-the-art methods,which uses actual images of individuals.The average classification accuracy of each classifier is between 94%and 95%,which is similar to existing proposed methods.

Global Memory Net and the development of digital image information management system: Experience and practice

Global Memory Net (GMNet) is intended to be an effective gateway to the world cultural, historical, and heritage image collections from selected academic educational and research partners in the world. Much of these unique collections of great value to education and research are not currently accessible due to distance, form, and technical barriers. This project is to find new ways to enable users to access and exploit these significant research collections via global network. As GMNet is ending its first 5-year phase in October 2005, it has contributed substantially to the community building in digital library development by ac- commodating numerous collaborators and technical staff from various parts of the world to spend 3 to 5 months as a full-member of the GMNet team in Boston. They have come from different parts of China—such as Sichuan, Hainan, Shanghai and Xi’an; Croatia; and Hanoi, Vietnam. In addition to contribute to the overall system development and enhancement of system function- alities, they have brought valuable sample image collections of their own institutions/countries, and actually developed prototype collections as a part of GMNet. This paper describes the exciting and productive experience of the first of this visiting research group in developing the GMNet’s Version 2.0 PHP-based system under Prof. Chen’s overall supervision. It also describes both the system’s technical level structure—user/Web-based application/data, and complex functionalities with multi-collection, multi-lingual, multi-modal searching capabilities; system management capabilities; as well as provisions for user uploads and retrieval for our own projects. This Version 2.0 system is built on the Linux/Apache/PHP/MySQL platform. What is described in this paper is an actual case which has formed a base for further new development by others in the research group. It demonstrates fully the value of the synergistic collaboration among global partners for universal digital library development. More information can be found in http://www.memorynet.org/.

Radiometric Characteristics of the Landsat Collection 1 Dataset

This study evaluates the long-term radiometric performance of the USGS new released Landsat Collection 1 archive, including the absolute calibration of each Landsat sensor as well as the relative cross-calibration among the four most popular Landsat sensors. A total of 920 Landsat Collection 1 scenes were evaluated against the corresponding Pre-Collection images over a Pseudo-Invariant Site, Railroad Valley Playa Nevada, United States (RVPN). The radiometric performance of the six Landsat solar reflective bands, in terms of both Digital Numbers (DNs) and at-sensor Top of Atmosphere (TOA) reflectance, on the sensor cross-calibration was examined. Results show that absolute radiometric calibration at DNs level was applied to the Landsat-4 and -5 TM (L4 TM and L5 TM) by –1.119% to 0.126%. For L4 TM and L5 TM, the cross-calibration decreased the radiometric measurement level by rescaling at-sensor radiance to DN values. The radiometric changes, –0.77% for L4 TM, 0.95% for L5 TM, –0.26% for L7 ETM+, and –0.01% for L8 OLI, were detected during the cross-calibration stage of converting DNs into TOA reflectance. This study has also indicated that the long-term radiometric performance for the Landsat Collection 1 archive is promising. Supports of these conclusions were demonstrated through the time-series analysis based on the Landsat Collection 1 image stack. Nevertheless, the radiometric changes across the four Landsat sensors raised concerns of the previous Landsat Pre-Collection based results. We suggest that Landsat users should pay attention to differences in results from Pre-Collection and Collection 1 time-series data sets.

Digitalization of Underwater Video Image Using High Speed DSP Chip

This paper introduces a system based on Tls fifth generation DSP(Digital Signal Processor) device-TMS320C50 to construct the simplest system of digitalizing underwater video signal. The system realizes collecting 3 different density image data by means of software designation. The system may expand its outer data memory to 4 Giga byte by using a technology of memory page extension. Two different interface circuits for different speed peripheral devices and C50 are also designed: one is high speed A/D, and the other is static memory whose access time is 70ns. The system can digitalize analog video signal and process the gathered data in limited time.

Acute pancreatitis:A pictorial review of early pancreatic fluid collections

Acute pancreatitis is a common acute inflammatory disease involving the pancreas and peripancreatic tissues or remote organs.The revised Atlanta classification 2012 of acute pancreatitis divides patients into mild,moderately severe and severe groups.Major changes of the classification include acute fluid collection terminology.However,some inappropriate terms of the radiological diagnosis reports in the daily clinical work or available literature may still be found.The aim of this review article is:to present an image-rich overview of different morphologic characteristics of the early-stage(within 4 wk after symptom onset)local complications associated with acute pancreatitis by computed tomography or magnetic resonance imaging;to clarify confusing imaging concepts for pancreatic fluid collections and underline standardised reporting nomenclature;to assist communication among treating physicians;and to facilitate the implications for clinical management decision-making.

Application of High-Density Seismic Image in Nondestructive Exploration of Cut-off Cement-Soil Wall in Earth Dam

In order to quickly explore the quality of cut-off wall in dams, a new method of high-density seismic image was adopted and estimated by model and in-situ wall tests.The vibration exciter was employed and several parameters such as hypocentral distance, length of signal record and sampling space in signal collection were determined, which are 8 m, 0.25 ms and 128 ms respectively. Through time and frequency field signal analyses, it is concluded that, the smaller arrival times of reflected longitudinal and surface waves, and the higher their main frequencies, the higher the strength of the wall, vice versa. Accordingly the construction quality of the wall can be evaluated quickly by high-density seismic image.

基于激光视觉传感器的机械零件形貌检测方法

形貌检测一直机械零件加工的关键技术,当前机械零件形貌检测方法存在许多不足,形貌检测误差,耗时比较长,为了获得更加理想的机械零件形貌检测结果,设计了基于激光视觉传感器的机械零件形貌检测方法。首先研究机械零件形貌检测的研究现状,找到引起机械零件形貌检测效果差的原因,然后引入激光视觉传感器对机械零件图进行采集,并对原始图像进行去噪、均衡化处理,提取机械零件形貌检测特征,最后根据特征进行机械零件形貌检测,并进行了机械零件形貌检测仿真测试,结果表明,本方法对零件1的检测误差为3μm,对零件2的检测误差为5μm;对零件1的检测时间为7 ms,对零件2的检测时间为6.5 ms;有效减少了机械零件形貌检测误差,提高了机械零件形貌检测精度,具有更高的实际应用价值。

蚕蛹性腺图像在线采集与雌雄自动识别分选装置设计与优化

针对当前蚕蛹雌雄鉴别环节人工劳动强度大、蚕蛹雌雄识别分选装置准确率和效率不高、适用品种范围小等问题,根据雌雄蚕蛹性腺特征差异是区别不同品种蚕蛹雌雄的可靠依据,该研究设计了一种蚕蛹性腺图像在线采集与雌雄自动识别分选装置。首先,在对蚕蛹基本物理特性参数大量统计的基础上,对振动排序上料机、蚕蛹输送滑轨和蚕蛹翻转拍照装置进行了结构设计与理论分析。在识别方法和计算机硬件条件不变基础上,对装置中蚕蛹排序输送螺旋倾角、滑轨倾角和转动棒带动蚕蛹旋转转速进行了优化设计,以提高装置的识别分选速度。并设计了以SIEMENS-S7-200SMART-ST30为控制器,基于激光、光电传感器检测的信息融合控制系统。分选装置工作性能优化结果为:螺旋倾角为15.62°,滑轨倾角为31.77°,转速为21.09 r/min,单粒蚕蛹平均分选时间为5.13 s,准确率为95.97%,破损率为0.07%。样机试验结果表明,在最优参数下,利用卷积神经网络的全新轻量级雌雄识别分类模型,单粒蚕蛹平均分选时间为5.03 s,分选准确率为96.47%,破损率为0.09%,满足蚕蛹雌雄识别分选实际应用要求。该蚕蛹性腺图像在线采集与雌雄自动识别分选装置能够实现蚕蛹自动排序上料、蚕蛹带性腺图像在线采集、自动识别与雌雄自动分选的一体化,提高了蚕蛹雌雄自动识别分选的效率和准确率。研究结果可为雌雄蚕蛹自动识别分选装置的研发提供理论依据和技术支持。

明遗民集序书写的纪传与想象

别集序在文体初创时期,就已经确立了传记写作的传统。明末清初的遗民通过诗文来回应国家和自我身份认同等问题。这种回应体现在别集序的书写中,使其中的纪传部分更加注重真实性的表达。然而,由于别集序的主观创作空间较大,真实与想象的成分在遗民的别集序中交织在一起,这无疑影响了通过“知人论世”的传统批评方法所得批评主张的有效性。此外,集序作者往往通过个人经历的想象和性情的投射来塑造遗民的形象。这种塑造可能源于作者真实情感的流露,也可能出于对经典文本的模仿。因此,遗民集序中的人物形象可能与他们的历史行为存在差异,这进一步影响了集序所传达的文学批评要素的接受和传播。

义必明雅 辞必巧丽——台湾骚体赋论

台湾骚体赋自清代以降一直是重要的文学课题。台湾赋的整理校注工作始自2005年由许俊雅、吴福助主持编纂的《全台赋》。该书承续传统赋作形式,多记台湾形胜地景、自然生态与历史文化。53篇骚体赋应用“兮”字,间有赋中系诗。赋末乱辞亦沿《楚辞》之“歌曰”“辞曰”“和曰”,有简约全篇、收尾之用。《全台赋》作者或为仕台官员或居台文人,其中少数作者具有编纂方志的经验。《全台赋》展现了他们深厚的文化底蕴以及惊采绝艳的视角,涵濡之深可见骚学之递嬗与创新。

基于被试间相关性探究针刺治疗丘脑卒中后麻木患者的群体脑效应

目的探究针刺对脑卒中后肢体麻木患者的疗效及群体脑效应。方法招募24例脑卒中后肢体麻木患者(患者组)及年龄、性别匹配的健康受试者(对照组)23名,对2组受试者进行每周3次、持续4周的形神共调针刺治疗,观察针刺前后麻木VAS量表评分变化。对2组受试者进行功能核磁共振扫描,并在扫描时进行针刺干预,分别采集静息态、针刺任务态和留针态3种状态下的血氧依赖水平数据,运用fMRIPrep、BrainIAK等软件进行数据处理,计算被试间相关性(ISC),分别比较不同条件下2组受试者各脑区的ISC值。结果针刺治疗显著降低麻木VAS量表评分。静息状态下,2组受试者ISC值均未出现显著变化。针刺条件下,患者组左侧中央前回、左侧中央旁小叶、左侧中央后回、左侧岛叶、左侧前扣带回、右侧额中回、右侧前扣带回脑区ISC值显著升高。留针条件下,对照组两侧中央前回部分脑区出现轻微负激活。患者组针刺态较静息态左侧额上回、左侧中央前回、左侧中央旁小叶、左侧楔前叶、左侧中央后回、左侧岛叶、左侧前扣带回、左侧丘脑、右侧额上回、右侧额中回、右侧中央前回、右侧前扣带回ISC值显著升高。结论针刺能改善卒中后肢体麻木症状,并能在患者中引起一种特异性的群体激活模式,该模式涉及中央前回、中央后回、丘脑、岛叶、前额叶、扣带回等区域。针刺可能通过激活感觉运动网络和皮质-丘脑-皮质环路增强对感觉信息的处理能力,同时调节中央执行网络和下行疼痛控制通路促进疼痛症状的缓解。

蜂群信息采集技术研究进展和应用展望

综述了国内外蜂群信息采集技术相关研究进展,对所采用的技术进行总结,并展望未来发展方向,以期为我国养蜂生产及相关产业的信息化发展提供借鉴。

智能巡检机器人在自然环境试验中的应用探讨

目的 探索建立自然环境试验智能巡检系统,使用机器人替代部分人工巡检工作。方法 通过分析巡检作业环境和巡检任务,确定智能巡检机器人的功能要求和技术参数,进而开展机器人本体结构设计,融合控制后台、远程数据库等设备,实现智能巡检系统构建。结果 开展了智能巡检数据采集的使用验证,巡检机器人采集的样品图像对焦清晰、分辨率高,样品在图像中的位置大小合理,腐蚀特征展示清楚。巡检系统可对样品图像进行自动分割和智能识别,对蚀点、流痕等损伤特征识别准确,精度可达0.1%。结论 智能巡检系统可以满足自然环境试验的巡检要求,替代人工完成部分巡检工作,智能巡检数据便于入库和使用挖掘,对于自然环境试验的数字化转型有积极的推动作用。

基于协同卷积的轻量级行为检测方法

时空行为检测是计算机视觉领域重要的研究方向,为了减小模型体量,提高检测速度,提出一种基于协同卷积(collective convolution, CoConv)的轻量级行为检测方法。将视频的时序信息转换为时空图像(spatio-temporal image, STI),利用协同卷积获取相同位置不同时间的时空特征信息。在YOLOv5的基础上将骨干网络和检测头部替换为协同卷积模块构建时空行为检测网络结构,通过后处理对时空图像的检测结果进行连接,快速形成视频结果,提高网络的行为检测性能。实验结果表明,提出的方法可以在保证准确率和不增加参数量的情况下,减少网络计算量,提高网络检测速度,且优于现有的行为检测方法。

体育赛事直播画面法律保护的适用困境与纾解路径——基于31个案例的实证分析

体育赛事直播已成为我国体育产业的重要组成部分,但其现有的法律保护路径仍不完善。综合运用文献资料、案例分析等方法,梳理2012-2023年31起体育赛事直播画面侵权案例后发现:由于相关权益性质不明,现有法律框架下著作权法、反不正当竞争法以及体育法等相关法律法规对体育赛事直播画面保护出现距离性,受害人通常难以实现权利救济。研究总结了当前体育赛事直播画面纠纷处理过程中在体育赛事直播画面定性、权利主体认定、权益保护法律适用三方面的现存困境。据此提出纾解路径,即立法将体育赛事直播画面定义为“实时数据集合”,并将其纳入“新型知识产权”客体范畴;扩大广播组织的定义,使其涵盖网络广播组织;在《体育法》中创设“体育赛事视听信息权”。

缩短PET/CT采集时间对帕金森病诊断的影响

目的探究缩短氘代-氟[^(18)F]丙那嗪(^(18)F-D6-AV133)PET/CT采集时间对图像质量及帕金森病诊断效能的影响,减少长时间显像造成的运动伪影。资料与方法回顾性选取2021年10月—2022年6月广州医科大学附属第一医院帕金森病患者27例与健康对照者24例,受试者注射示踪剂^(18)F-D6-AV133(371.04±16.30)MBq,60 min后进行图像采集,时长10 min。应用CT衰减校正重建为采集时间3、5、7、10 min的4组图像,对所得PET图像进行半定量分析,计算纹状体相对枕叶的标准化摄取值比值(SUVR),2位医师分别对不同组图像进行定性分析。比较不同采集时间SUVR、视觉评分的差异,使用受试者工作特征曲线分析获得SUVR最佳临界值。结果2位医师图像质量视觉分析一致性较强(ICC=0.853,P<0.001),采集时间为5 min的图像能达到临床工作中普遍的质量水平,占78.4%(40/51)。图像质量半定量结果显示,采集时间5 min和10 min所得SUVR与诊断效能差异无统计学意义(Z=1.821,P=0.069),且两组最佳临界值一致,均为3,即适当缩短采集时间对帕金森病诊断结果无影响。结论随着技术设备的发展进步,PET图像质量逐步提高,扫描时间逐渐缩短,可以将^(18)F-D6-AV133采集时间从常规的10 min缩短为5 min。