Wireless Body Area Networks for Pervasive Healthcare and Smart Environments

Wireless body area networks （WBANs） use RF communication for interconnection of tiny sensor nodes located in, on, or in close prox- imity to the human body. A WBAN enables physiological signals, physical activity, and body position to be continuously monitored.

Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

ISHD:Intelligent Standing Human Detection of Video Surveillance for the Smart Examination Environment

In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligentstanding human detection (ISHD) method based on an improved single shot multibox detector to detect thetarget of standing human posture in the scene frame of exam room video surveillance at a specific examinationstage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posturefeature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the trainingstrategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the trainingdifficulty. The experiment proves that the model proposed in this paper has a better detection ability for the smalland medium-sized standing human body posture in video test scenes on the EMV-2 dataset.

Research on Postgraduate Classroom Construction Framework from the Perspective of Smart Education

Smart education is the future and development direction of higher education.Taking the graduate course Crime and Police Theory as the research subject,the shortcomings of smart education,which include the lack of understanding of the concept of smart education,the limited content structure of smart education,the poor cognition of smart education among teachers and students,and inadequate hardware and technical support for smart education,are systematically analyzed.In view of these shortcomings,several strategies are proposed,including improving the smart education curriculum development plan and management level,ensuring the construction quality of smart education projects,and raising funds for smart education construction from various sources.

Paving the Way for Economically Accepted and Technically Pronounced Smart Radio Environment

Recently,Reconfigurable Intelligent Surfaces(RISs)have been introduced to provide the necessary flexibility for the design of the Smart Radio Environment(SRE),which can be optimally shaped to facilitate efficient signal transmissions.In line with the concept of smart cities,SRE needs to be carefully designed with respect to the city infrastructure and utilization of resources.In this paper,we provide our vision on RIS integration into future Smart Cities by highlighting the potential technical,environmental,and economic motivations of RIS deployment in harmony with various ecosystems at a city level such as buildings facades.To this end,we are pointing out some scenarios for mitigating the conflict between RIS realization and the existing building facade’s ecosystems such as advertising display and solar cells on building walls.Also,in this fashion,the proposed vision supports a win-win relationship between all stakeholders of different ecosystems.This study presents guidelines for not only enabling seamless economically accepted RIS widespread utilization but,also more technically sounding SRE by supporting enhanced RIS features and more advanced applications that cannot be attained by traditional passive RIS.Moreover,based on the current research directions,we offer promising insights for cost-effective mass pro-duction through motivating two scenarios of“all on silicon”and“all as metasurface”fabrication technology.With this study,we aim to encourage the metasurface researchers,that for a broad deployment of the technical solution,economic,environmental,and other commercial requirements should be planned together,early on in the design phase.

Environmentally Smart Nitrogen (ESN)——Potential for Improving Modern Crop Production and N-Use Efficiency

Environmentally smart nitrogen(ESN)is polymer coated urea that is designed to release N in synchrony with crop requirements.Research on ESN was initiated in field crops in Ontario,Canada in 2006,initially on timothy,spring wheat and winter wheat and later(till date)on bromegrass,grass mixtures(timothy,bromegrass,orchardgrass),other forages(barley,silage corn,oat,MasterGraze corn and sorghum Sudangrass)and canola.In winter wheat,in three out of six years ESN gave^0.6 MT/ha higher grain yield than urea.In spring wheat,in a relatively warm year with well-distributed rainfall,ESN produced 1 MT/ha higher grain yield than urea;averaged over three years,two-thirds N from urea and one-third N from ESN could be recommended.Two-thirds N from urea and one-third N from ESN gave^0.75 MT/ha extra seed yield as compared to urea alone at 180 kg N/ha in canola during 2016 to 2018.The entire N in winter/spring wheat could be applied in seed rows at seeding as ESN without any detrimental effect.The highest barley forage yields were recorded by urea at 50 kg N/ha+ESN at 20 kg N/ha which produced 1.2 MT/ha more forage yield than urea at 70 kg N/ha.Partial substitution of N from urea with ESN improved forage dry matter yield of timothy and MasterGraze corn.In MasterGraze corn 100 kg N/ha from urea+ESN(3:1 on N basis)equaled that with urea at 150 kg N/ha in dry matter yield,%protein and relative feed value(RFV),but not in silage corn and sorghum Sudangrass.At equal rates of N,single/fall application of ESN in timothy and bromegrass gave equal yield to urea applied in two splits in spring/summer.Spring wheat grain yields were the same with fall/spring application of ESN.ESN/urea+ESN(3:1 on N basis)increased the grain/forage protein content in almost all crops by 1%-2%points at an extra cost of only$6.0-10.5/ha(with urea+ESN in 3:1 ratio on N basis).The results indicate that ESN could improve both crop yields and quality,make better use of N/or increase N-use efficiency.The paper summarizes results from over 10 years and the results could be applicable globally under situations of high N losses from readily available N sources such as urea.

面向环境司法智能审判场景的人工智能大模型应用探讨

环境司法审判是生态环境治理体系的重要组成部分,基于生成式人工智能(AI)技术的突破而形成的AI大模型为环境司法审判工作转向更高水平的智能审判体系提供了重大机遇。本文围绕推进AI大模型技术与环境司法审判工作融合、实现环境司法审判工作创新发展的主旨,探讨了AI大模型在环境司法智能审判中的赋能作用和应用实践,凝炼了当前环境司法AI大模型存在的数据质量“低劣”“算法黑箱”引发偏见、深度应用能力不足等突出问题;以生态环境保护类案件应用为例,构建了基于AI大模型的环境司法智能审判系统,阐述了相应系统的架构设计以及涉及的技术要素。进一步提出了注重顶层设计、建设环境司法高端智库,建设环境司法数据中台、健全司法数据标准及规范体系,构建算法治理机制、促进环境司法审判公平正义,完善环境司法多元问责机制、筑牢司法监督管理体系等发展建议,以期丰富环境司法审判工作的前沿技术认知,加快环境司法的智能化、智慧化、现代化转型进程。

融合GA-Attention-LSTM算法的温室樱桃环境参数预测与裂果预警

针对温室环境因素对樱桃的影响,设计一套大樱桃温室环境自动监测装置,用来采集温室内的环境参数值为樱桃裂果提供数字化预警支持及防治方案。基于采集的环境参数值,首先使用相关性分析得出与棚内裂果具有强相关性的环境参数特征;其次使用滑动窗口方法将输入的环境特征生成时间序列矩阵形式;随后提出一种融合GA-Attention-LSTM算法的预测模型,实现精准预测棚内的环境参数的功能;最后通过SPSS数据分析软件来分析不同大棚的环境参数和裂果率。所提的融合GA-Attention-LSTM算法的预测模型的平均绝对误差为0.112,均方误差为0.087,相比于LSTM网络模型高出12.80%和9.72%,对环境参数的预测精度更高,同时得出一套科学的樱桃环境参数值范围,为预测模型对樱桃裂果数字化预警提供有力支持。

基于农业互联网平台的灌溉监测信息系统研究

为进一步提高我国农作物智能灌溉系统的作业效率,基于农业互联网平台,针对其灌溉监测信息处理环节展开研究。以大数据及SOA平台为核心架构,建立土壤环境参数与灌溉系统数据监测之间的内在关系,完成监测信息的布局设计与精准灌溉作业实现。进行系统在互联网平台下的灌溉试验验证,结果表明:基于农业互联网平台的灌溉监测信息系统改进后,各灌溉模块的功能运转效率得到显著提升,信息监测准确率与系统控制精准度分别提升至95.56%与94.51%,灌溉系统效率相对平台应用前提升了7.79%。所设计的联网平台架构下的灌溉系统整体改善效果明显,是我国向智慧农业方向迈进的重要基础,对于类似智能灌溉装备广泛推广有重要的参考价值。

奶牛智慧养殖关键技术研究与应用

奶牛养殖业是我国农业产业的重要组成部分,也是支撑我国乳制品加工企业发展的关键产业。然而,传统的粗放式养殖模式下,需要大量的人力和物力投入,导致奶牛生产效率低下,牛奶质量也无法保证。随着物联网、人工智能等新一代信息技术的快速发展,中国的奶牛养殖业正逐步从过去的粗放型养殖模式向规模化、集约化、智能化的养殖模式发展。本文通过查阅文献,结合生产实际,综述了智能环境控制、个体识别、精准饲喂、生理和行为监测等智慧养殖关键技术的研究进展及其在奶牛生产中的应用,并为本文提到的关键技术绘制了一幅智慧养殖模式图,以期为我国奶牛养殖业提高生产效益和转型升级提供理论和技术支持。

Intrusion detection and prevention system for an IoT environment

Internet of Things(IoT)security is the act of securing IoT devices and networks.IoT devices,including industrial machines,smart energy grids,and building automation,are extremely vulnerable.With the goal of shielding network systems from illegal access in cloud servers and IoT systems,Intrusion Detection Systems(IDSs)and Network-based Intrusion Prevention Systems(NBIPSs)are proposed in this study.An intrusion prevention system is proposed to realize NBIPS to safeguard top to bottom engineering.The proposed NBIPS inspects network activity streams to identify and counteract misuse instances.The NBIPS is usually located specifically behind a firewall,and it provides a reciprocal layer of investigation that adversely chooses unsafe substances.Networkbased IPS sensors can be installed either in an inline or a passive model.An inline sensor is installed to monitor the traffic passing through it.The sensors are installed to stop attacks by blocking the traffic using an IoT signature-based protocol.

农业传感器:研究进展、挑战与展望

[目的/意义]农业传感器是数字农业、信息农业、智慧农业等现代农业发展模式的源头技术,也是推动农业科技迭代升级和农业生产方式变革的重要驱动力。农业传感器应用环境(水、气及土壤)和监测对象(动植物)多样复杂、规模大,因此,高环境适应性、高可靠性和低成本的农业传感器是实现智慧农业的基础与核心。[进展]本文对农业传感器进行分类,并对农业传感器前沿研究趋势进行分析,综述农业传感器在不同应用场景下的研究现状,从农业环境传感器(水、大气和土壤等)、动植物生命信息传感器、农产品质量安全传感器和农机传感器四大类进行深入分析,总结现有农业传感器在研发和使用过程中的通用性和局限性。[结论/展望]在农业传感器面临的挑战与展望中,具体分析了现阶段农业传感器大规模应用严重不足的核心瓶颈,包括低成本化、专用化、高稳定性及自适应,归纳出“农业泛在感知”的概念,为农业传感器技术研发提供思路和参考。

奶牛场污染物处理方法浅谈

随着奶牛规模化养殖的发展,养殖污染更趋集中和严重,本文通过对奶牛场污染源的调查分析,提出了减少挤奶厅污水、夏季喷淋污水及奶牛粪尿排放的源头减量;推广生物降解养殖及干粪回用的场内消纳;干粪生产有机肥,污水好氧结合厌氧处理降解排放等奶牛场污染物处理途径,并提出了从奶牛场设计的源头就综合考虑奶牛场动物防疫条件和污染物减排设施,降低奶牛场微生物和排泄物污染,减少奶牛养殖过程中的污染物排放,改善生态环境,推进奶牛养殖的可持续发展。

现代智慧生态环境治理评价体系的构建与应用研究

加强生态环境治理智慧化水平,构建符合中国国情的现代智慧生态环境治理评价体系,对于建设美丽中国具有重要意义。本文首先分析了现代智慧生态环境治理的理论基础与关键概念;其次梳理了现有生态环境治理评价指标的研究成果,基于理论框架构建了现代智慧生态环境治理评价体系;然后分析了评价指标选取依据,介绍了在现代生态环境智慧治理评价体系中引入CRITIC法实现加权评估的基本流程;最后,以中国东、中、西部具有代表意义的14个省级行政区为例,应用本文提出的评价体系,对各地区在现代智慧生态环境治理进程中的优势与劣势展开深入分析。

智慧农业环境监测平台设计

我国农业快速发展的同时存在水资源浪费、生产能耗高、无法为植物提供合适的生长环境等问题。因此,基于物联网相关技术,进行智慧农业发展模式的研究具有重要实际意义与应用价值。以STM32F103C8T6微控制器为控制中心,设计农业环境监测平台,实现农业信息数据采集、数据处理、终端控制等功能,并通过MQTT协议与手机APP进行通信,实现数据的远程监测与控制。

电除尘器安全在线检测预警研究及应用

随着大环境数字化和智慧电厂建设的不断发展,各大电力公司对除尘设备安全运行及预警的需求越来越大,通过应力和变向在线监控研究应用,分析电除尘器在超载负荷时的结构变化特征,找到可分阶段的预警指标,建立电除尘器应力三维在线模型和动态云图预警界面,开发电除尘器灰斗支架应力应变在线监控系统,实现与电除尘器控制软件的无缝衔接、监控室声报预警、远程预警等分级分层智能预警系统,并嵌入智慧除尘控制系统,在结合智慧除尘的基础上开发提高电除尘器安全性能的监控系统,对电除尘器安全运行增加多道防线,以进一步提升电除尘器的运行安全可靠近性,实现“环保安全运行”的创新模式。

基于长短时记忆的农作物生长环境数据预测

针对传统温室农作物生长监控系统控制灵活性差且精确度低等问题,设计了一个面向智慧农业的农作物生长闭环监控系统.引入单变量长短时记忆(long short-term memory,LSTM)网络模型,对土壤含水率、土壤温度和土壤电导率3个农作物生长环境数据进行预测研究.在优化时间步长参数的基础上,分析不同预测步长对单变量LSTM模型预测准确性的影响,采用不同时间段的测试集数据对模型的预测性能和稳定性进行验证.分别采用单变量LSTM模型、最小绝对值收敛和选择算法、随机森林回归、双向LSTM模型和编解码LSTM模型进行预测对比,结果表明,单变量LSTM模型预测的平均绝对误差值和均方根误差值均为最小,模型具有更好的准确性和稳定性.本研究设计的农作物生长闭环监控系统能有效预测农作物的生长环境数据,为农作物监控系统的智能控制提供有效数据支撑.

数字经济促进城市绿色发展的碳排放效应与影响因素研究

本文基于2010~2021年我国284个地级市面板数据,采用超效率SBM模型等方法,研究数字经济对城市绿色发展的碳排放效率及其影响机制。研究结果显示,数字经济发展有助于提升城市的碳排放效率。其中,财政支出绩效和环境规制强度起到中介作用;数字经济在经济发展速度和人力资本水平的调节下,更显著地促进城市的碳排放效率;同时,数字经济对城市碳排放效率的影响存在明显的异质性。因此,本文提出了加强数字经济发展与碳排放效率协同增长、优化政府财政支出、加快数字城市建设等建议。

基于智慧环保的珠海市生态环境大数据体系设计

随着智能化技术的飞速发展,越来越多的“环保+物联网”应用落地,环境监管必须与时俱进。基于智慧环保理念,利用大数据和信息管理等强大的功能,初步构建珠海市环境大数据信息平台,助力珠海市环境监管形成一盘棋、一本账,使得环境决策更高效、更精准和更智慧。

智慧城市视角下的老旧社区环境改造设计研究——以长春为例

当前,中国城市住宅建筑新增发展正在逐渐减缓,而未来中国城市规划建筑的发展也将逐渐从过去的新增需求转向现有建筑的更新和利用。国内正进行大规模老旧社区的提升与整治。文章在智慧化视角下探究老旧社区的环境改造设计,并以长春市为例分析老旧社区的问题,改善老旧社区的环境状态,塑造智慧化环境与智慧化生活。以期提高居民的幸福指数,为现代高质量生活的社区智慧化建设提供借鉴和启示。