Human intrusion detection for high-speed railway perimeter under all-weather condition

Purpose – The paper aims to solve the problem of personnel intrusion identification within the limits of highspeed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy ofobject recognition in dark and harsh weather conditions.Design/methodology/approach – This paper adopts the fusion strategy of radar and camera linkage toachieve focus amplification of long-distance targets and solves the problem of low illumination by laser lightfilling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm formulti-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposesa linkage and tracking fusion strategy to output the correct alarm results.Findings – Simulated intrusion tests show that the proposed method can effectively detect human intrusionwithin 0–200 m during the day and night in sunny weather and can achieve more than 80% recognitionaccuracy for extreme severe weather conditions.Originality/value – (1) The authors propose a personnel intrusion monitoring scheme based on the fusion ofmillimeter wave radar and camera, achieving all-weather intrusion monitoring;(2) The authors propose a newmulti-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring underadverse weather conditions;(3) The authors have conducted a large number of innovative simulationexperiments to verify the effectiveness of the method proposed in this article.

Progress in non-invasive detection of liver fibrosis

Liver fibrosis is an important pathological precondition for hepatocellular carcinoma.The degree of hepatic fibrosis is positively correlated with liver cancer.Liver fibrosis is a series of pathological and physiological process related to liver cell necrosis and degeneration after chronic liver injury,which finally leads to extracellular matrix and collagen deposition.The early detection and precise staging of fibrosis and cirrhosis are very important for early diagnosis and timely initiation of appropriate therapeutic regimens.The risk of severe liver fibrosis finally progressing to liver carcinoma is>50%.It is known that biopsy is the gold standard for the diagnosis and staging of liver fibrosis.However,this method has some limitations,such as the potential for pain,sampling variability,and low patient acceptance.Furthermore,the necessity of obtaining a tissue diagnosis of liver fibrosis still remains controversial.An increasing number of reliable non-invasive approaches are now available that are widely applied in clinical practice,mostly in cases of viral hepatitis,resulting in a significantly decreased need for liver biopsy.In fact,the noninvasive detection and evaluation of liver cirrhosis now has good accuracy due to current serum markers,ultrasound imaging,and magnetic resonance imaging quantification techniques.A prominent advantage of the non-invasive detection and assessment of liver fibrosis is that liver fibrosis can be monitored repeatedly and easily in the same patient.Serum biomarkers have the advantages of high applicability(>95%)and good reproducibility.However,their results can be influenced by different patient conditions because none of these markers are liver-specific.The most promising techniques appear to be transient elastography and magnetic resonance elastography because they provide reliable results for the detection of fibrosis in the advanced stages,and future developments promise to increase the reliability and accuracy of the staging of hepatic fibrosis.This article aims to describe the recent progress in the development of non-invasive assessment methods for the staging of liver fibrosis,with a special emphasize on computer-aided quantitative and deep learning methods.

Vascular endothelial growth factor and microvessel density for detection and prognostic evaluation of invasive breast cancer

Objective The purpose of this study was to evaluate the distribution of vascular endothelial growth factor(VEGF) and CD105-microvessel density(MVD) in invasive breast carcinomas. We also aimed to analyze the relationship between VEGF and MVD expression with other standard prognostic parameters associated with invasive breast cancer, such as size, grade, stage of the cancer, metastases, and tumor recurrence. Methods Immunohistochemistry via the Ultra Sensitive TM S-P method was used to detect VEGF and MVD expression in 128 cases of invasive breast carcinoma. Specimens were evaluated for CD105 expression. Positively stained microvessels were counted in dense vascular foci under 400× magnification. MVD in the peripheral area adjacent to the lesion and in the central area within the lesion in invasive breast carcinomas and benign leisions groups were also assessed. Fifty cases of benign breast disease tissue were selected as the control group.Results Results showed that 64.1% of invasive breast cancer samples were VEGF-positive, higher than in benign breast disease tissue(22.0%, P < 0.05). There was a positive correlation between VEGF overexpression and histological grade, lymph node metastasis, and distant metastasis of invasive breast cancer. VEGF expression was not related to age or size of the tumor(P > 0.05). MVD of the peripheral area adjacent to the lesion was significantly higher than those central area within the lesion in both invasive breast cancer and benign breast disease groups(P < 0.01 for each group). There were significant differences in the mean CD105-MVD, between invasive breast tumors with a histological grade of I or II and grade III; between tumors with lymph node or distant metastasis; and between patients with or without recurrence(P < 0.05). However, there was no difference in the mean MVD between the two age groups(≤ 50 years vs. > 50 years) or the two tumor diameter groups(≤ 2 cm vs. > 2 cm), P > 0.05.Conclusion Overexpression of VEGF and MVD may be important biological markers for invasion and lymph node and distant metastases of invasive breast cancer. Combined detection of the two tumor markers could provide better prognostic monitoring for disease recurrence and metastasis, as well as aid with clinical staging of breast tumors. Prediction of the risk for metastasis and recurrence, as well as recurrence patterns based on VEGF and MVD post-surgery, could aid design of better follow-up regimens and appropriate treatment strategies for breast cancer patients.

Diagnostic aids for detection of oral precancerous conditions

Oral cancer has a tendency to be detected at late stage which is detrimental to the patients because of its high mortality and morbidity rates. Early detection of oral cancer is therefore important to reduce the burden of this devastating disease. In this review article, the most common oral precancerous lesions are discussed and the importance of early diagnosis is emphasized. In addition, the most common non-invasive oral cancer devices that can aid the general practitioners in early diagnosis are also discussed.

Resampling Simulator for the Probability of Detecting Invasive Species in Large Populations

This paper proposes a resampling simulator that will calculate probabilities of detecting invasive species infesting hosts that occur in large numbers. Different methods were examined to determine the bias of observed cumulative distribution functions (c.d.f.s), generated from prototype resampling simulators. One involved seeing if they matched theoretical c.d.f.s, which were generated using formulae for calculating the probability of the union of many events (union formulae), which are known to be correct. Others involved assessing the bias of observed c.d.f.s, generated from using prototype resampling simulators operating on much larger simulated populations, when computation of theoretical c.d.f.s from the union formulae was not practical. Examples are given for using the proposed resampling simulator for detecting an invasive insect pest within the context of an invasive species management system.

Mural Defects Detection with Enhanced Terahertz Images

In mausoleum murals, existing bubbles are one kind of the most harmful defects for the repair and protection of relics. For this reason, it is necessary to detect bubbles, especially the ones with small size. A method to detect the small bubbles with enhanced terahertz （THz） images is proposed. To simulate the bubbles in the mausoleum murals, circular grooves have been hidden in the plaster and then measured by the THz reflected time domain spectroscopy imaging system. To observe the small bubbles in murals, a comprehensive enhancement algorithm is adopted to process the obtained THz images. With the enhanced method, the circular grooves in the murals can be observed clearly, even for the circular groove with a diameter of 1.5 mm. The results indicate that the proposed comprehensive method can be used to detect the tiny defects of murals.

基于时频域综合分析的无创血糖检测技术研究

无创血糖检测技术是一种间接测量血液中葡萄糖含量的方法,其不损伤人体组织具有安全、快捷、无创的特点,打破了传统血糖检测的局限性,具有重要的研究价值。光电容积脉搏波信号因携带多种生理病理信息,被广泛应用于各种临床研究,也是目前实现无创血糖检测技术的重点关注对象。目前基于光电容积脉搏波信号的无创血糖检测研究,仅考虑了时间域或频率域单独作用时对系统建模的贡献。信号的时域分析虽能描述PPG信号幅值随时间的变化,却无法直观反映PPG信号频率的能量分布,因此单一域的信号分析不能全面表达PPG信号,从而导致信息丢失。采用频域分析方法提取信号频谱时,需要利用信号的全部时域信息,是一种全局的变换,可能会造成特定时间或特定频率段内的信号特性丢失。提出了一种基于光电容积脉搏波(PPG)时频域综合分析的无创血糖检测新方法,采用时域-频域并行法综合考量光电容积脉搏波信号与血糖间的联系。以时域分析为基准,利用聚类分析法在PPG信号时域中提取代表波形,分析波形特征点与血糖相关性,确定波形时域特征参数。在此基础上,利用快速傅里叶变换将脉搏波时域信号转换至频率域,采取主成分分析手段研究频谱信息,确立频域特征量。通过口服葡萄糖耐糖实验(OGTT)对获取的波形信号提取时频域特征参数,以实时检测的有创血糖浓度值作为参考,构建基于BP神经网络的无创血糖检测模型,同时为提升模型精度实现模型最优化,应用遗传算法对模型进行二次修正,最终实现模型测试集平均绝对误差(MAE)为1.13 mmol·L^(-1),均方根误差(RMSE)为1.42 mmol·L^(-1)。Parkers共识网络栅格(Parkers CEG)评估结果显示:在A区与B区的预测结果分别占80.3%、19.7%,实验结果表明该方法具有良好的预测精度,为实现日常血糖无创监测可行性提供了理论基础及可靠性依据。有助于完善糖尿病的检测与监测体系,更好地全面判断病情,及时预防、指导、治疗糖尿病。

基于SerialLite Ⅱ协议的磁共振采集数据传输系统设计

磁共振成像(MRI)是一种无创检测人体内部结构的技术,随着接收通道数量增多,产生的采集数据量越来越大,给快速成像带来了巨大的挑战。文中设计一种基于SerialLite Ⅱ协议的磁共振采集数据传输系统。系统功能主要由FPGA实现,分为数据缓存模块和数据传输模块两类,数据缓存模块基于双缓存区和状态机控制的乒乓操作,有效地解决了因数据量大而带来的读写冲突问题;数据传输模块基于FPGA建立SerialLite Ⅱ数据传输链路,实现了采集数据的光纤传输。通过仿真和实验验证了系统功能的正确性,表明其能够实现磁共振采集数据的高速稳定传输。

变电站多尺度异常入侵目标轻量化检测方法

智能电网的建设思路决定了远郊变电站异常入侵监测的无人化趋势,促进了变电站异常入侵智能检测方法的快速发展。现阶段尚未拥有该场景下异常入侵目标数据集,且现有的目标检测方法也未针对变电站边缘计算端进行轻量化优化设计,不适用于需要全天候实时监测的变电站边缘设备。针对上述问题,从实际应用需求出发,构建变电站异常入侵目标数据集(Dataset for Anomaly Invasion Targets in Substations,SAITD),基于YOLOv5s模型提出适用于变电站边缘检测设备的轻量化异常入侵目标检测网络YOLOv5-Substation。添加微小尺度目标特征提取层与上采样轻量化算子CARAFE,在扩大感受野的同时,充分保留特征图中多尺度目标的语义信息,从架构端提高原有模型的检测精度。基于知识蒸馏模型,使用网络剪枝(Network-slimming)策略对原有模型进行轻量化改进,在保证原模型检测精度的同时,加速模型推理。仿真实验表明,轻量化后的边缘端计算模型精度相较于YOLOv5s提高了3.3%,推理速度提升了41.9%,可为智能电网的全速运行提供强有力的数据基础、技术支撑与安全保障。

基于支持向量回归的血红蛋白浓度无创检测模型

为实现血红蛋白浓度的无创检测,设计基于支持向量回归的血红蛋白浓度检测方法。首先,基于Beer-Lambert定律建立血红蛋白无创检测数学模型;然后,对采集的光电容积脉搏波描记法(PPG)信号进行降噪和滤除基线漂移处理后提取出血红蛋白浓度特征信息,并使用递归特征消除算法对提取的特征信息进行选择,以去除冗余特征;最后,将筛选出的29个特征信息作为回归模型的输入特征,并采用支持向量回归算法构建血红蛋白预测回归模型。通过对249例临床数据进行试验验证(其中199例作为训练数据集,50例作为测试数据集),得出预测值与参考值的均方根误差为1.83 g/dL,相关系数为0.75(P<0.01),试验结果表明本文方法与传统有创检测方法具有较强的一致性。

皮肤肿瘤早期筛查的快速无创电阻抗检测方法

目的随着环境问题和臭氧层空洞化的加剧,皮肤肿瘤的患病率也大幅增加,但是皮肤肿瘤前期隐蔽性高、症状不明显,导致大部分病例都是在中晚期发现的。因此,本文基于生物阻抗谱(bioimpedance spectroscopy,BIS)技术,提出一种皮肤肿瘤早期筛查的快速无创电阻抗检测方法。方法首先,建立四层皮肤模型,采用数值分析方法研究角质层对BIS测量的阻碍作用。其次,使用去除角质层的皮肤模型研究混有不同半径和浸润深度的皮肤肿瘤组织电学特性。最后,使用凝胶处理后的猪皮组织实验验证肿瘤浸润深度(h)的影响。结果角质层仿真结果表明,去除角质层的皮肤对激励信号的响应更明显。皮肤肿瘤模型仿真表明,当肿瘤半径(R_(tumor))及h>1.5 mm时能够区分肿瘤组织与正常组织。同时根据实验结果中正常组织与肿瘤组织虚部弛豫阻抗(Z_(imag-relax))定义了组织病变度(ε_(worse),为肿瘤组织虚部阻抗相对于正常组织虚部阻抗变化的百分比),并绘制了肿瘤组织浸润深度(Depth)与Z_(imag-relax)的拟合曲线。当ε_(worse)=0.4920时,BIS即可识别微浸润肿瘤组织,且拟合曲线修正决定系数为0.9468,拟合效果良好。结论基于BIS技术的方法具有实时、便捷、无创的优点,有望应用于皮肤肿瘤的检测。

近红外漫射光在人体组织中的传播及探测器排布研究

为了研究漫反射光谱法在检测内部组织有效信息方面的径向最远探测距离和合适的探测器排布方式,构建了人体皮下组织模型,利用蒙特卡罗(MC)方法对760 nm近红外光在组织中的传输过程进行了模拟。根据不同组织的光学特性,建立了脂肪-肌肉双层MC模型。在模型中,通过调整脂肪和肌肉双层组织的垂直厚度模拟了双层组织的多种情况,分析了不同情况下、不同距离处逃逸光子的分布、数量和剩余权重。结果表明,在该模型下,将探测器排布在距离光源大约3 cm或更近的位置,采集到的光子信号可以有效体现组织内部的信息。基于MC模拟的结果,分析设计了三种可应用于无创血流或血氧检测的探测器排布方式。通过比较发现,多光源-多探测器混合排布设计不仅可以更有效地收集组织的漫射光,提供更均匀的光子分布,而且相较于单光源-多探测器,该设计可以最大限度地利用探测器的数量实现对组织同一深度更大面积的检测。这为设计近红外血流或血氧无创检测光谱仪器提供了思路。

基于多波长近红外光谱的血红蛋白浓度无创检测技术研究

针对传统的血红蛋白浓度检测需要抽血采样,检测流程复杂且无法连续监测血红蛋白浓度的变化趋势等问题,本研究设计了一种基于多波长近红外光谱的无创血红蛋白浓度检测方法。首先,基于Beer-Lambert定律建立了血红蛋白无创检测数学模型,并依据该模型设计了八波长近红外光电容积脉搏波(photoplethysmography,PPG)信号采集系统;然后,对采集的PPG信号进行降噪和滤除基线漂移等预处理,并根据建立的无创检测模型对特征信息进行提取与选择;最后,基于Stacking算法构建血红蛋白预测回归模型。通过对249例临床数据进行实验验证,得到无创检测模型的预测值与参考值的均方根误差为1.17 g/dL,相关系数为0.75。实验结果表明,本研究方法可有效实现血红蛋白浓度的无创检测。

深部真菌感染诊断中G实验与真菌培养联合检测研究

目的:探讨在深部真菌感染(IFI)诊断中G实验与真菌培养联合检测的价值。方法:选取2021年1月—2022年5月北京航天总医院同时做过G实验检测和真菌培养的164例患者的检测资料,取不同患者的痰液、引流液、胸腹水、尿液、肺泡灌洗液、血液和脑脊液等进行G实验和真菌培养,分别测定两种检测方法的阳性率、灵敏度、特异性等进行分析。结果:所有检测患者中,G实验阳性91例、真菌培养阳性72例、二者均阳性66例、均阴型76例。G实验的灵敏度为93.18%、特异性为83.95%、阳性预测值为85.71%、阴性预测值为93.15%;真菌培养的灵敏度为83.13%、特异度为96.3%、阳性预测值为95.83%、阴性预测值为84.78%;两种方法联合诊断的灵敏度为97.59%、特异度为97.53%、阳性预测值为97.56%、阴性预测值为96.34%。结论:在IFI的诊断中G实验在感染早期诊断有一定意义,真菌培养可以基本确定真菌感染以及感染的菌属;G实验和真菌培养联合检测对于诊断IFI具有明显的临床诊断价值。

非侵入式电解质溶液电导率测量的研究

针对电解质溶液这种低电导率物质测量,传统的侵入式电极类传感器具有容易污染被测溶液,电极污损以及电极表面极化等缺点。为了改进以上不足,该文利用电涡流效应的原理设计了一款基于FPGA芯片的非侵入式电解质溶液电导率测量系统。改进了传统的信号处理电路,通过信号频率的变化来显示电导率的大小。实验结果证明该测量系统具有较高的灵敏度,约为44.5 Hz/(S/m)。

Nanogenerator-based bidirectional pressure sensor array and its demonstration in underwater invasive species detection

Lakes areas,which cause catastrophic damages in both commercial fishery and ecological systems.However,current assessment strategies may pose challenges for lake-wide abundance estimation and non-target anadromous species preservation.Here,we demonstrate an efficacious species-specific non-destructive sensing system based on porous ferroelectret nanogenerator for in-situ monitoring of lamprey spawning migration using their unique suction behavior.Simulations show that the porous structure enables a redistribution of surface charges under bidirectional deformations,which allows the detection of both positive and negative pressures.The quasi-piezoelectric effect is further validated by quantitative analysis in a wide pressure range of−50 to 60 kPa,providing detailed insights into transduction working principles.For reliable lamprey detection,a 4×4-pixel sensor array is developed and integrated with a complementary metal-oxide-semiconductor(CMOS)based signal processing array thus constituting a sensing panel capable of recording oral suction patterns in an underwater environment.

基于人工智能的网络安全技术与应用

现如今随着网络技术的不断发展,网络安全问题也日益凸显,传统的网络安全防护手段已经难以应对复杂多变的网络攻击。人工智能作为一种新兴的网络安全技术,正在被越来越多的企业和机构应用于网络安全防护。文章在人工智能背景下,分析网络安全技术与应用,为网络安全领域的研究工作提供借鉴和参考。文章主要概述人工智能技术,介绍一些主流的基于人工智能的网络安全技术算法,并探讨基于人工智能的网络安全技术的应用,以期为网络安全领域的研究和实践提供参考。

一种无创预测血压的改进LightGBM学习方法

为提高无创血压预测模型的准确率,减小个人身体差异对准确率的影响,提出一种基于鲸鱼优化算法(WOA)与轻量级梯度提升机(LightGBM)的无创血压检测模型WOA-LightGBM。该模型首先提取预处理后的光电容积脉搏波、心电信号特征,并结合人体特征组成输入特征矩阵;然后通过核主成分分析法对输入特征矩阵进行降维,减少冗余;最后运用WOA优化LightGBM模型参数。实验结果表明,WOA-LightGBM模型预测的收缩压和舒张压的平均绝对误差均满足美国医疗仪器促进协会制定的标准(±5mmHg),与传统模型相比具有一定优势,且与传统水银血压计测量结果有高度一致性。

用电行为分析系统的设计

随着居民用电需求的急剧增加,电能消耗日益庞大,这使得有效管理和控制电能的消耗显得尤为重要。此外,居民用电通常表现出一定的集中性,因此对电网的高负荷运行和电力供应稳定性构成了挑战。文章设计了一种用电行为分析系统,基于非侵入式负荷监测技术和区块链技术,该系统可实现居民用电行为分解、用电安全预警等功能,为居民安全用电和电力系统的稳定运行提供保障。