Artificial intelligence models based on non-contrast chest CT for measuring bone mineral density

Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P>0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P<0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.

Self-supervised learning artificial intelligence noise reduction technology based on the nearest adjacent layer in ultra-low dose CT of urinary calculi

Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CXRs under different severity levels based on CXR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

选矿厂智能化建设目标与实施路径

选矿厂的智能化升级转型是建设现代矿山企业的必然选择,其建设目标及实施路径与广义智能工厂建设有着显著差异。本文结合选矿厂智能化建设背景、面临的问题,探讨了选矿厂智能化的建设目标,提出了智能选矿厂的建设路径:智能装备与智能感知、过程自动化稳定运行、工艺优化控制、信息化生产管理以及与之匹配的生产组织管理体系,最后给出了选矿厂智能化建设的实施步骤建议。

镍浮选过程智能控制系统开发与应用

针对国内某选矿厂镍浮选工艺来矿性质不稳定、精矿品位波动大、回收率不理想的特点,结合浮选生产现场检测设备不完备或者检测周期长、费用高的现状,设计了一套基于品位预测模型的浮选过程智能控制系统,系统投入运行后,泡沫流速稳定性显著提高,精矿品位波动性明显减小,证明了系统的实用性。

海上油田同心无缆分注工艺技术研究与应用

为节省平台作业空间,节约作业成本,提高同心边测边调工艺调配效率及精度,加快海上油田注水井智能化进程,提出了一种同心无缆分注工艺技术。该技术无需更换管柱,在同心边测边调工作筒中下入无缆智能测调工具,基于无线通讯技术实现对井下数据的实时监测及注水量实时调整,实现双向通讯。通过不动管柱作业实现了边测边调管柱转换为智能管柱,钢丝作业即可对无缆智能测调仪进行更换,同时保留原电缆测调方式。该技术在渤海B油田N5井成功现场应用,整井调配误差不超过5%,取得良好的应用效果,为海上油田注水井智能化转型提供新的技术选择。

利用煤矿瓦斯通过旋转式蓄热氧化装置在煤炭生产过程中应用

煤泥是煤矿开采生产过程中排出的煤粉,其形成是在选煤厂洗选加工过程中的副产品,其特点容易结块影响运输,且对空气污染有一定的危害。利用旋转式蓄热氧化装置通过对煤矿瓦斯抽采泵站中的低浓度瓦斯的利用,与乏风或空气掺混后产生高温烟气,通过热风换热装置进行煤泥烘干,处理后的煤泥水分含量可以从25%~28%降到12%左右,对煤矿瓦斯利用和煤矿生产具有客观的积极意义,同时提高了能源的综合利用。

选煤场浓缩池溢流浓度控制方法研究

浓缩池溢流浓度的监测与控制方法是选煤厂实现智能化选煤的关键之一,针对仅采用传感器的溢流浓度监测与控制方式会导致絮凝剂调节滞后的问题。搭建一套浓缩池自动加药系统,该系统采用BP神经网络与模糊自抗扰控制器(Active Disturbance Rejection Control,ADRC)相结合的监测与控制方法,实现浓缩池溢流浓度的稳定控制,避免因絮凝剂添加量问题直接影响浓缩反应,进而影响絮凝沉降效果。通过仿真对比,该方法比采用PID控制的自动加药系统稳定性更高,可有效应对絮凝剂调节的滞后问题,为选煤厂浓缩池溢流浓度控制提出了新的方案。

基于5G通信技术的变电站SF_(6)气瓶智能管理系统设计

为确保变电站六氟化硫(SF_(6))气瓶智能管理的时效性和可靠性,设计基于5G通信技术的变电站SF_(6)气瓶智能管理系统。通过SF_(6)气体采集器等装置,感知并采集变电站SF_(6)气瓶的状态数据。将该数据通过由5G通信技术构建的5G传输模块,传送至管理模块,并采用5G切片技术划分网络,以实现按需通信。管理模块接收经由5G传输模块传送的数据后,基于非分光红外差分检测方法,在线智能检测SF_(6)气瓶的泄漏浓度,并校核SF_(6)气体在线监测结果,以实现SF_(6)气瓶的智能管理。测试结果表明:网络传输平均速率在8~10 Gbit/s之间,极大程度接近传输速率峰值,通信的实时性良好;能够实时监测SF_(6)气瓶运行状态,在线监测精度较高,平均偏移程度低于0.035。该系统可全面呈现气体的泄漏情况,以及发生泄漏的时间和泄漏气体的浓度情况。

基于粉尘浓度实时监测的采煤机尘源跟踪喷雾参数智能调控技术研究

针对目前采煤机尘源跟踪喷雾降尘技术无法智能调节喷雾参数以匹配不断变化的采煤机截煤产尘强度的问题,提出针对采煤机截煤尘源处的粉尘浓度设置限值、根据采煤机截煤产尘强度变化相应调节喷雾降尘效率来保证采煤机截煤尘源处的粉尘浓度始终在限值以下的智能调控逻辑,并得出了工作面的粉尘浓度分布与即采煤机位置之间的关系规律,以及喷雾降尘效率与喷雾数量之间的函数关系,实现了基于实时监测的粉尘浓度智能调控采煤机尘源跟踪喷雾参数。在青龙寺煤矿5-20109综采工作面应用该技术后,采煤机司机和机尾10 m处的降尘效率均达到90.47%以上,降尘效果显著。

基于MC112传感器和RSSI测距的智能矿灯系统设计

为了保障矿井下工作人员安全,提出一种基于井下可燃气体感知和定位的新型智能矿灯系统。其中,以STC15W408AS单片机作为主控芯片,采用MC112型催化燃烧式传感器实现井下瓦斯浓度的采集与预警,使用RSSI测距定位算法实现井下人员的定位,利用无线发射和电力载波通信实现井下与后台系统的通信,通过RFCC1101芯片进行433 MHz信息的无线传输。系统测试表明,所设计的新型智能矿灯能耗为27 520 mAh,小于所采用的锂电池电量总和30 000 mAh,满足设计需求;系统各个功能完善,可实现井下瓦斯浓度实时监测与人员区域定位,同时具备瓦斯浓度报警和井下人员呼救功能,具有一定的实用性,为井下工作人员的生命安全提供了保障。

智能口罩的呼吸自动调节技术研究

针对传统呼吸阀式口罩容易引起呼吸不畅等问题,设计了一种由手环和口罩组成的新型智能口罩系统。手环集成了控制中心、血氧检测、信息显示等功能,口罩集成了气压及温度测量、微型电扇控制等功能。当手环接收到气压温度或血氧浓度异常信息时,能够通过无线信号启动微型电扇调节空气质量,同时进行蜂鸣报警。最后通过电路制作和软件编程,验证了新型智能口罩系统的自动呼吸调节功能。

碎矿、浓细度在线检测、智能加球、铜品位检测等智能选矿设备的研发

本文主要就选矿过程如何建立智能在线检测进行梳理,确定检测工序点,对各个检测点用什么设备来检测,检测设备的工作原理和结构,工作流程进行详细论述,通过对碎矿的筛分检测和矿浆的浓细度检测、智能加球机的应用、矿品位检测可以实现对球磨机工艺参数的调节,准确控制,保证了工艺参数控制的稳定。

张家峁煤矿智能化选煤厂系统设计

针对煤矿选煤系统“智能化”建设方面提出新的挑战与机遇,通过人工智能技术和选煤系统的交叉融合,实现选煤技术的智能化,从而提高选煤的生产效率。智能化在选煤系统主要包括视频安全监控系统、智能配煤定制化生产、设备运维系统、3D可视化信息采集与监控系统、信息管理与分析系统、配电安全与配电数据采集系统六大系统。通过张家峁选煤厂智能化系统设计,实现了设备状态智能监测、生产系统智能调整、工艺参数智能设定,大幅降低员工劳动强度,形成了生产现场管理的良性循环,为系统的高效安全运行提供了有效的技术支撑。

煤矿井下智能封闭辅助运输警戒系统的研究

针对煤矿进行集中轨道巷采用无极绳、卡轨车连续化运输的过程中,由于巷道岔口比较多,不能有效防止运输区间行人,易发生运输事故,提出了建设智能封闭运输警戒系统。本文以山西焦煤集团有限责任公司官地煤矿中六区轨道巷为研究对象,利用智能人员识别摄像仪、红外识别传感器、警戒栏、声光语音报警等装置组成警戒系统,实现了运输作业过程警戒栏自动关闭、人员闯入自动报警、智能抓拍、智能停车及远程人工警告等功能。现场实际应用表明,该方案能够有效地避免人员闯警戒的问题,实现运输区域智能封闭,保证了运输作业安全,减少了人员投入,提升辅助运输效率。

Ultrasensitive and stable X-ray detection using zero-dimensional lead-free perovskites

Sensitive and reliable X-ray detectors are essential for medical radiography,industrial inspection and security screening.Lowering the radiation dose allows reduced health risks and increased frequency and fidelity of diagnostic technologies for earlier detection of disease and its recurrence.Three-dimensional(3 D)organic-inorganic hybrid lead halide perovskites are promising for direct X-ray detection-they show improved sensitivity compared to conventional X-ray detectors.However,their high and unstable dark current,caused by ion migration and high dark carrier concentration in the 3 D hybrid perovskites,limits their performance and long-term operation stability.Here we report ultrasensitive,stable X-ray detectors made using zero-dimensional(0 D)methylammonium bismuth iodide perovskite(MA3Bi2I9)single crystals.The 0 D crystal structure leads to a high activation energy(Ea)for ion migration(0.46 e V)and is also accompanied by a low dark carrier concentration(~10^6 cm^-3).The X-ray detectors exhibit sensitivity of 10,620μC Gy-1 air cm-2,a limit of detection(Lo D)of 0.62 nG yairs-1,and stable operation even under high applied biases;no deterioration in detection performance was observed following sensing of an integrated X-ray irradiation dose of^23,800 m Gyair,equivalent to>200,000 times the dose required for a single commercial X-ray chest radiograph.Regulating the ion migration channels and decreasing the dark carrier concentration in perovskites provide routes for stable and ultrasensitive X-ray detectors.

Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks

The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.

VGG-CovidNet: Bi-Branched Dilated Convolutional Neural Network for Chest X-Ray-Based COVID-19 Predictions

The coronavirus disease 2019(COVID-19)pandemic has had a devastating impact on the health and welfare of the global population.A key measure to combat COVID-19 has been the effective screening of infected patients.A vital screening process is the chest radiograph.Initial studies have shown irregularities in the chest radiographs of COVID-19 patients.The use of the chest X-ray(CXR),a leading diagnostic technique,has been encouraged and driven by several ongoing projects to combat this disease because of its historical effectiveness in providing clinical insights on lung diseases.This study introduces a dilated bi-branched convoluted neural network(CNN)architecture,VGG-COVIDNet,to detect COVID-19 cases from CXR images.The front end of the VGG-COVIDNet consists of the first 10 layers of VGG-16,where the convolutional layers in these layers are reduced to two to minimize latency during the training phase.The last two branches of the proposed architecture consist of dilated convolutional layers to reduce the model’s computational complexity while retaining the feature maps’spatial information.The simulation results show that the proposed architecture is superior to all the state-of-the-art architecture in accuracy and sensitivity.The proposed architecture’s accuracy and sensitivity are 96.5%and 96%,respectively,for each infection type.

A Comparison Study of Soil Samples from Sinai Province in Egypt by Using X-Ray Diffraction and Gamma-Ray Analysis

Ten soil samples from Jabal Al Qur, Wadi Baba, and Wadi Sieh in Sinai, Egypt, were analyzed by XRD spectroscopy. The XRD spectroscopy results indicate that the major, minor and trace constituents varied from one sample to another. Samples were also analyzed by HPGe gamma spectrometer to determine the activity concentration of U-238, Th-232 series and K-40. The concentrations for 238U ranged from 57.03 to 4220.41 Bq/kg with an average 1110.75 Bq/kg, for 232Th, ranged from 13.55 to130.46 Bq/kg with an average 71.85 Bq/Kg. The concentrations for 40K were in the range from 12.18 to 948.93 Bq/kg with an average value 457.09 Bq/kg. The average activity concentration values of 226Ra, 232Th, and 40K, in all the collected samples were higher than the world average. The radium equivalent (Req), absorbed dose rate (DR), the effective dose rate (Deff), and hazard indices resulted due to the natural radionuclides in soil are also calculated. The Results show that the study area is not safe for human and environments.

Apply X-Ray Fluorescence and γ-Ray Spectroscopy to Analyze Igneous and Sedimentary Environmental Samples of Al-Atawilah (Al-Baha), Saudi Arabia

Igneous and sedimentary rocks contain an amount of natural radioactivity (NORM). U-238, Th-232 and their decay products, and K-40 are important sources of gamma-radiation. Knowledge of the radionuclide content of rocks is necessary to estimate the exposure of the population to the radiation. Many types of rocks are used in building and industries, thus the radiation detection is important, it provides a baseline map of levels of the radioactivity in the study region. The purpose of this study is to evaluate the activity concentrations of the natural radionuclides (U-238 (Ra-226), Th-232 and K-40) and the fallout nuclide (Cs-137) (if found) in thirty samples of igneous and sedimentary rocks of Al-Atawilah (Al-Baha). The samples were collected and prepared during 2018/2019, and analyzed with a good experimental instrument (High energy resolution γ-ray spectroscopy with HPGe detector), also these rock samples were analyzed with X-ray fluorescence to subdivided these rocks based on the major oxides proportions contained of each sample. The mean activity concentrations of naturally radionuclides were found in the igneous rock samples varied depending on the type of the igneous rock. For sedimentary rock samples, the activity concentrations were found high for quartz sandstone sample, which may be due to its high proportion of SiO2 and K2O. The estimated mean values of absorbed dose rate are within the permissible limit value. The findings indicate high dose level values in granite (rhyolite) and most of diorite (andesite) igneous rock samples while gabbro (basalt) igneous rock samples (except for one sample) record low levels of dose rate. All sedimentary rock samples have low dose rate (except for the quartz sand-stone sample).