Functional Optical Fiber Sensors Detecting Imperceptible Physical/Chemical Changes for Smart Batteries

The battery technology progress has been a contradictory process in which performance improvement and hidden risks coexist.Now the battery is still a“black box”,thus requiring a deep understanding of its internal state.The battery should“sense its internal physical/chemical conditions”,which puts strict requirements on embedded sensing parts.This paper summarizes the application of advanced optical fiber sensors in lithium-ion batteries and energy storage technologies that may be mass deployed,focuses on the insights of advanced optical fiber sensors into the processes of one-dimensional nano-micro-level battery material structural phase transition,electrolyte degradation,electrode-electrolyte interface dynamics to three-dimensional macro-safety evolution.The paper contributes to understanding how to use optical fiber sensors to achieve“real”and“embedded”monitoring.Through the inherent advantages of the advanced optical fiber sensor,it helps clarify the battery internal state and reaction mechanism,aiding in the establishment of more detailed models.These advancements can promote the development of smart batteries,with significant importance lying in essentially promoting the improvement of system consistency.Furthermore,with the help of smart batteries in the future,the importance of consistency can be weakened or even eliminated.The application of advanced optical fiber sensors helps comprehensively improve the battery quality,reliability,and life.

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

复杂系统视角下数字领域“smart”概念的国际标准化共识构建及应用

进入数字时代,伴随大数据和人工智能技术快速发展和普遍应用,带有“smart”的指称不断涌现,然而关于“smart”概念的认知尚缺少跨领域和跨国际标准组织的标准化共识构建研究。采用ISO 704:2022的概念构建原则和方法,基于复杂系统论视角,对数字领域国际标准定义中涉及“smart”的概念特征进行了识别。基于跨领域国际标准组织专家的研讨、问卷调查和国际共识构建,提出了适应于复杂系统数字领域“smart”的通用概念,并将其用于指导《智慧城市城市智能服务体系构建指南》国家标准的制定过程。该研究对推进国家标准和国际标准兼容具有重要战略意义。

中国加入CPTPP的农产品贸易效应研究--基于WITS-SMART模型

粮食安全是国家安全的基石,面对国际市场不稳定性,加入CPTPP有望为中国夯实农产品贸易韧性提供新契机。在考察中国与CPTPP国家农产品贸易竞合关系的基础上,运用WITS-SMART模型建立局部均衡分析框架,研究中国加入CPTPP的农产品贸易效应,结果发现:第一,中国农产品竞争力整体不强,但与CPTPP国家依存关系较高,双边贸易潜力在关税完全削减时充分释放。第二,零关税情境下,贸易创造效应普遍大于转移效应,中国对CPTPP多数国家农产品贸易规模大幅扩张,进口增长主要源自种植业、畜牧业部门以及加拿大、澳大利亚、英国、日本等国家,出口增长主要集中于种植业部门以及日本、英国、马来西亚、墨西哥等国家。第三,加入CPTPP显著改善了双边经济福利,不过关税损失较为严重。因此,中国应破除部门利益障碍,加强与墨西哥、日本、英国等国经贸联系以充分挖掘CPTPP国家市场潜力,释放贸易自由化福利,持续提高农产品出口质量和国际竞争力以应对加入CPTPP带来的挑战。

基于SMART原则的三阶段教学法的中医院神经外科规培护士培训评价

目的探讨基于SMART原则的三阶段教学法对神经外科规范化培训(以下简称“规培”)护士的教学效果。方法选择2020年1月—2022年7月在广东省中医院神经外科规培的护士80人作为研究对象,采用随机数字表法分为对照组(40人)和观察组(40人)。观察组采用基于SMART原则的三阶段教学法,对照组使用常规规培方法;培训结束后比较两组理论考试、临床实践考试成绩及护士核心能力测评结果。结果观察组规培护士理论知识考核及临床实践得分均高于对照组(P<0.05);观察组与对照组在评判性思维/科研、临床护理能力、人际关系、专业发展能力、教育咨询能力及总分方面差异有统计学意义(P<0.05),而在法律/伦理实践、领导能力方面差异无统计学意义(P>0.05)。结论与传统教学方法相比,基于SMART原则的三阶段教学法更有助于提高神经外科规培护士的综合护理能力,尤其是在神经外科急危重症护理方面;另外,规培护士的领导能力培养易被忽略,今后应加强相关研究。

二维同步插补算法及其在S7-200 Smart PLC上的应用

针对插补运动系统中存在机械振动较大的问题,提出一类基于恒定加加速度的二维直线插补算法。在确定加加速度的前提下,将直线插补的运动过程分为7个不同的阶段。利用运动学定律分析每个阶段的加速度、速度和位移的表达式,获取各运动阶段的初始条件。在基于二维系统的位移要求确定二维同步关系的基础上,实现了各阶段算法的离散化,最终完成了基于PLC(可编程逻辑控制器)的算法设计。实测效果表明,该算法同步精度小于0.5%,运行时间误差小于1 s,运行效果良好,满足应用场景的需求。

工业机器人Smart组件夹具的应用研究

运用ABB RobotStudio,探究了ABB工业机器人的末端执行器在物料搬运工作站中的仿真应用。结合实际生产流程和需求,科学规划搬运工作站空间布局,并精确设置Smart组件信号与属性及传感器动作。通过精准调控搬运工作站逻辑设定,实现了物料搬运工作的精确仿真。该工作站不仅可以适应广泛的应用场景,还可以通过优化仿真环境,提升工作站效率与稳定性,为工业生产智能化、自动化升级提供有力支撑。

3D smart mA调控技术对不同BMI患者图像采集时间质量及辐射剂量的影响

目的:分析3D智能管电流(3D smart mA)调控技术对不同体质量指数(BMI)患者图像采集时间、质量及辐射剂量的影响。方法:择取的180例行胸部CT扫描患者选自西安交通大学第一附属医院2021年6月至2022年12月期间所收治,按照BMI将患者分为三组,A组(18.5 kg/m^(2)≤BMI≤23.9kg/m^(2),n=75)、B组(23.9kg/m^(2)0.05);两位医师对肺部不同层面图像质量(IQS)评分进行评价,Kappa一致性非常好(Kappa值=0.768、0.812、0.861);三组肺部不同层面IQS评分对比,差异无统计学意义(P>0.05);三组肺部不同层面CT对比,差异有统计学意义,且随着BMI增加而下降(P<0.05),三组肺部不同层面图像标准差(SD)值对比,差异无统计学意义(P>0.05);三组容积CT剂量指数(CTDIvol)对比,差异无统计学意义(P>0.05);A组DLP、ED均低于B、C组,B组DLP、ED低于C组(P<0.05)。结论:不同BMI患者应用3D smart mA调控技术,在保证图像质量的前提下,可有效降低辐射剂量。

Self-Healing Liquid Metal Magnetic Hydrogels for Smart Feedback Sensors and High-Performance Electromagnetic Shielding

Hydrogels exhibit potential applications in smart wearable devices because of their exceptional sensitivity to various external stimuli.However,their applications are limited by challenges in terms of issues in biocompatibility,custom shape,and self-healing.Herein,a conductive,stretchable,adaptable,self-healing,and biocompatible liquid metal GaInSn/Ni-based composite hydrogel is developed by incorporating a magnetic liquid metal into the hydrogel framework through crosslinking polyvinyl alcohol(PVA)with sodium tetraborate.The excellent stretchability and fast self-healing capability of the PVA/liquid metal hydrogel are derived from its abundant hydrogen binding sites and liquid metal fusion.Significantly,owing to the magnetic constituent,the PVA/liquid metal hydrogel can be guided remotely using an external magnetic field to a specific position to repair the broken wires with no need for manual operation.The composite hydrogel also exhibits sensitive deformation responses and can be used as a strain sensor to monitor various body motions.Additionally,the multifunctional hydrogel displays absorption-dominated electromagnetic interference(EMI)shielding properties.The total shielding performance of the composite hydrogel increases to~62.5 dB from~31.8 dB of the pure PVA hydrogel at the thickness of 3.0 mm.The proposed bioinspired multifunctional magnetic hydrogel demonstrates substantial application potential in the field of intelligent wearable devices.

Visualization of real-time displacement time history superimposed with dynamic experiments using wireless smart sensors and augmented reality

Wireless smart sensors(WSS)process field data and inform inspectors about the infrastructure health and safety.In bridge engineering,inspectors need reliable data about changes in displacements under loads to make correct decisions about repairs and replacements.Access to displacement information in the field and in real-time remains a challenge as inspectors do not see the data in real time.Displacement data from WSS in the field undergoes additional processing and is seen at a different location.If inspectors were able to see structural displacements in real-time at the locations of interest,they could conduct additional observations,creating a new,information-based,decision-making reality in the field.This paper develops a new,human-centered interface that provides inspectors with real-time access to actionable structural data during inspection and monitoring enhanced by augmented reality(AR).It summarizes and evaluates the development and validation of the new human-infrastructure interface in laboratory experiments.The experiments demonstrate that the interface that processes all calculations in the AR device accurately estimates dynamic displacements in comparison with the laser.Using this new AR interface tool,inspectors can observe and compare displacement data,share it across space and time,visualize displacements in time history,and understand structural deflection more accurately through a displacement time history visualization.

深度学习重建联合Smart去金属伪影算法在口腔金属植入物患者头颈CT血管成像中的应用

目的:探讨深度学习重建(DLR)联合Smart去金属伪影(MAR)算法在口腔金属植入物患者头颈CT血管成像(CTA)中的应用价值。方法:选择郑州大学第一附属医院2023年2月至6月口腔有不可拆卸金属植入物行头颈CTA的患者70例,采用以下3种方法重建图像:基于混合模型的自适应迭代重建(ASIR-V)50%算法(IR),ASIR-V50%联合Smart MAR算法(IR-S),高水平DLR联合Smart MAR算法(DLR-S)。测量不受伪影影响的颈内动脉C1段和头夹肌感兴趣区CT值的标准差(SD)2和SD4,作为图像噪声指标;计算颈内动脉C1段和舌部的金属伪影指数(AI)1和AI2;对颈内动脉C1段和口腔整体图像质量进行主观评分。结果:与IR组和IR-S组比较,DLR-S组SD2和SD4降低(P<0.05)。与IR组比较,IR-S组和DLR-S组AI1、AI2降低;与IR-S组比较,DLR-S组AI1、AT2降低(P<0.05)。与IR组比较,IR-S组和DLR-S组口腔整体和颈内动脉C1段图像质量主观评分均增高;与IR-S组比较,DLR-S组图像质量主观评分增高(P<0.05),9例患者舌部可见新的伪影。结论:Smart MAR联合DLR可减少口腔植入物造成的金属伪影,提高头颈CTA图像质量。但Smart MAR可能引入新的伪影,需联合未加入Smart MAR的图像进行分析。

基于SMART数据模式的HDD硬盘状态预测方法

硬盘广泛应用于各类信息系统中,其工作状态预测对信息系统的正常运行管理有着重要意义。现有基于SMART(Self Monitoring Analysis and Reporting Technology)属性的机器学习预测算法为保证其通用性,普遍选取部分典型属性作为特征,带来一定的信息丢失。在分析SMART数据特点的基础上,提出数据模式分类后再进行机器学习预测的SMART数据处理方法。实际测试结果表明,经分类处理后,采用简单的机器学习算法即可获得与强分类器接近的性能,同时,该方法可有效简化SMART数据机器学习时的特征选择过程,有效降低算法的资源消耗。

Wearable Smart Sensor System for Medical Monitoring with an Assessment of the Level of Blood Loss and Pain Shock Because of Trauma or Injury

Blood loss in peacetime is mainly due to the normal menstrual cycle in women or diseases with surgical intervention. In wartime, blood loss in military personnel is a characteristic sign of a closed or open injury of the body during internal or external bleeding. Access to clinical care for wounded military personnel injured on the battlefield is limited and has long delays compared to patients in peacetime. Most of the deaths of wounded military personnel on the battlefield occur within the first hour after being wounded. The most common causes are delay in providing medical care, loss of time for diagnosis, delay in stabilization of pain shock and large blood loss. Some help in overcoming these problems is provided by the data in the individual capsule, which each soldier of the modern army possesses;however, data in an individual capsule is not sufficient to provide emergency medical care in field and hospital conditions. This paper considers a project for development of a smart real-time monitoring wearable system for blood loss and level of shock stress in wounded persons on the battlefield, which provides medical staff in field and hospital conditions with the necessary information to give timely medical care. Although the hospital will require additional information, the basic information about the victims will already be known before he enters the hospital. It is important to emphasize that the key term in this approach is monitoring. It is tracking, and not a one-time measurement of indicators, that is crucial in a valid definition of bleeding.

Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors

Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road accidents.As a result,reckless driving behaviour can cause congestion and delays.Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem.Previous research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s face.On the other hand,driving a car is a complicated action that requires a wide range of body move-ments.In this work,we proposed a ResNet-SE model,an efficient deep learning classifier for driving activity clas-sification based on signal data obtained in real-world traffic conditions using smart glasses.End-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning model.Sensor data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this study.We performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its performance.According to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%.

Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors

Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substantial personality traits under scrutiny.This research focuses on recognizing key personality traits,including neuroticism,extraversion,openness to experience,agreeableness,and conscientiousness,in line with the bigfive model of personality.We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors.For experimentation,we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks for a critical evaluation.After data pre-processing,we extracted selected features from each data segment and then applied four multiclass machine learning algorithms for training and classifying the dataset corresponding to the users’Big-Five Personality Traits Profiles(BFPT).Experimental results and performance evaluation of the classifiers revealed the efficacy of the proposed scheme for all big-five traits.

Security Analysis in Smart Agriculture: Insights from a Cyber-Physical System Application

Smart agriculture modifies traditional farming practices,and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies.In today’s world where technology is everything,these technologies are utilized to streamline regular tasks and procedures in agriculture,one of the largest and most significant industries in every nation.This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems.Divided into three main sections-security analysis,system architecture and design and risk assessment of Cyber-Physical Systems(CPS)applications-the study delves into various elements crucial for smart farming,such as data sources,infrastructure components,communication protocols,and the roles of different stakeholders such as farmers,agricultural scientists and researchers,technology providers,government agencies,consumers and many others.In contrast to earlier research,this work analyzes the resilience of smart agriculture systems using approaches such as threat modeling,penetration testing,and vulnerability assessments.Important discoveries highlight the concerns connected to unsecured communication protocols,possible threats from malevolent actors,and vulnerabilities in IoT devices.Furthermore,the study suggests enhancements for CPS applications,such as strong access controls,intrusion detection systems,and encryption protocols.In addition,risk assessment techniques are applied to prioritize mitigation tactics and detect potential hazards,addressing issues like data breaches,system outages,and automated farming process sabotage.The research sets itself apart even more by presenting a prototype CPS application that makes use of a digital temperature sensor.This application was first created using a Tinkercad simulator and then using actual hardware with Arduino boards.The CPS application’s defenses against potential threats and vulnerabilities are strengthened by this integrated approach,which distinguishes this research for its depth and usefulness in the field of smart agriculture security.

An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.

基于SMART原则的两癌筛查项目标准化管理研究

目的 分析基于SMART原则的两癌筛查项目标准化管理的作用和价值。方法 选取2022年1—12月在本院完成女性“两癌”筛查的患者3555例,并采取常规管理方法,将其作为对照组;选取2023年1月-7月在本院完成女性“两癌”筛查的患者2775例,并采取基于SMART原则的两癌筛查项目标准化管理,将其作为观察组,从两组筛查资料中各随机抽取500例作为研究对象,分析两组“两癌”筛查项目管理指标。结果 观察组两癌筛查记录本填写漏项率、错项率明显小于对照组,而完整率高于对照组(P<0.05);观察组宫颈癌(含癌前病变)、乳腺癌(含癌前病变)随访率高于对照组(P<0.05);观察组“两癌”筛查管理考核评分(方案、计划、组织管理、技术服务、信息管理)高于对照组(P<0.05);观察组“两癌”筛查人员服务满意率高于对照组(P<0.05)。结论 在两癌筛查过程中采取基于SMART原则的项目标准化管理,能够提高填写完整率,保证重点人员的随访率,提升两癌筛查服务质量与管理水平,有重要的应用价值。

Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.

Progress of Proximity Sensors for Potential Applications in Electronic Skins

Recently,electronic skins and fl exible wearable devices have been developed for widespread applications in medical monitoring,artifi cial intelligence,human–machine interaction,and artifi cial prosthetics.Flexible proximity sensors can accurately perceive external objects without contact,introducing a new way to achieve an ultrasensitive perception of objects.This article reviews the progress of fl exible capacitive proximity sensors,fl exible triboelectric proximity sensors,and fl exible gate-enhanced proximity sensors,focusing on their applications in the electronic skin fi eld.Herein,their working mechanism,materials,preparation methods,and research progress are discussed in detail.Finally,we summarize the future challenges in developing fl exible proximity sensors.