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Medical Waste Treatment Station Selection Based on Linguistic q-Rung Orthopair Fuzzy Numbers

Computer Modeling in Engineering & Sciences 2021年第10期129卷 117-148页

作者： Jie Ling Xinmei Li Mingwei Lin College of Mathematics and Informatics Fujian Normal UniversityFuzhou350117China digital fujian internet-of-things laboratory of environmental monitoring Fujian Normal UniversityFuzhou350117China

During the COVID-19 outbreak,the use of single-use medical supplies increased *** is essential to select suitable sites for establishing medical waste treatment *** is a big challenge to solve the medical waste treatment station selection problem due to some conflicting *** paper proposes a multi-attribute decision-making(MADM)method based on the partitioned Maclaurin symmetric mean(PMSM)*** the medical waste treatment station selection problem,the factors or attributes(these two terms can be interchanged.)in the same clusters are closely related,and the attributes in different clusters have no *** partitioned Maclaurin symmetric mean function(PMSMF)can handle these complex attribute ***,we extend the PMSM operator to process the linguistic q-rung orthopair fuzzy numbers(Lq-ROFNs)and propose the linguistic q-rung orthopair fuzzy partitioned Maclaurin symmetric mean(Lq-ROFPMSM)operator and its weighted form(Lq-ROFWPMSM).To reduce the negative impact of unreasonable data on the final output results,we propose the linguistic q-rung orthopair fuzzy partitioned dual Maclaurin symmetric mean(Lq-ROFPDMSM)operator and its weighted form(Lq-ROFWPDMSM).We also discuss the characteristics and typical examples of the above operators.A novel MADM method uses the Lq-ROFWPMSM operator and the Lq-ROFWPDMSM operator to solve the medical waste treatment station selection ***,the usability and superiority of the proposed method are verified by comparing it with previous methods.

关键词： Medical waste treatment station linguistic q-rung orthopair fuzzy sets aggregation operators partitioned dual maclaurin symmetric mean operators

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A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses

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Information Processing in Agriculture 2024年第2期11卷 143-162页

作者： Gedi Liu Keyang Zhong Huilin Li Tao Chen Yang Wang National Innovation Center for digital Fishery China Agricultural University100083 BeijingChina Key laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsChina Agricultural University100083 BeijingChina Beijing Engineering and Technology Research Center for internet of things in Agriculture China Agricultural University100083 BeijingChina College of Information and Electrical Engineering China Agricultural University100083 BeijingChina Department of Chemical and Process Engineering University of SurreyGU27XH GuildfordUnited Kingdom

Agricultural greenhouse production has to require a stable and acceptable environment,it is therefore essential for future greenhouse production to obtain full and precisely internal dynamic environment *** modeling based on machine learning methods,e.g.,intelligent time series prediction modeling,is a popular and suitable way to solve the above *** this article,a systematic literature review on applying advanced time series models has been systematically conducted via a detailed analysis and evaluation of 61 pieces selected from 221 *** historical process of time series model application from the use of data and information strategies was first ***,the accuracy and generalization of the model from the selection of model parameters and time steps,providing a new perspective for model development in this field,were compared and ***,the systematic review results demonstrate that,compared with traditional models,deep neural networks could increase data structure mining capabilities and overall information simulation capabilities through innovative and effective structures,thereby it could also broaden the selection range of environmental parameters for agricultural facilities and achieve environmental prediction end-to-end optimization via intelligent time series model based on deep neural networks.

关键词： Horticultural greenhouse environment Time series algorithms Prediction Deep neural networks

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A comparative survey of SSVEP recognition algorithms based on template matching of training trials

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International Journal of Intelligent Computing and Cybernetics 2023年第1期16卷 46-67页

作者： Tian-Jian Luo College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina digital fujian internet-of-Thing laboratory of environmental monitoring Fujian Normal UniversityFuzhouChina

Purpose-Steady-state visual evoked potential(SSVEP)has been widely used in the application of electroencephalogram(EEG)based non-invasive brain computer interface(BCI)due to its characteristics of high accuracy and information transfer rate(ITR).To recognize the SSVEP components in collected EEG trials,a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent *** this paper,a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been ***/methodology/approach-To survey and compare the recently proposed recognition algorithms for SSVEP,this paper regarded the conventional canonical correlated analysis(CCA)as the baseline,and selected individual template CCA(ITCCA),multi-set CCA(MsetCCA),task related component analysis(TRCA),latent common source extraction(LCSE)and a sum of squared correlation(SSCOR)for ***-For the horizontal comparative of the six surveyed recognition algorithms,this paper adopted the“Tsinghua JFPM-SSVEP”data set and compared the average recognition performance on such data *** comparative contents including:recognition accuracy,ITR,correlated coefficient and R-square values under different time duration of the SSVEP stimulus *** on the optimal time duration of stimulus presentation,the author has also compared the efficiency of the six compared *** measure the influence of different parameters,the number of training trials,the number of electrodes and the usage of filter bank preprocessing were compared in the ablation ***/value-Based on the comparative results,this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes,realtime and computational ***,the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.

关键词： Non-invasive brain-computer interface EEG signals Template matching of training trials Steadystate visual evoked potential Optimal projection vector

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Enhanced performance of triboelectric mechanical motion sensor via continuous charge supplement and adaptive signal processing

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Nano Research 2023年第7期16卷 10263-10271页

作者： Zitang Yuan Xiaosong Zhang Hengyu Li Ping Shen Jianming Wen Zhong Lin Wang Tinghai Cheng Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of SciencesBeijing 101400China The Institute of Precision Machinery and Smart Structure Zhejiang Normal UniversityJinhua 321004China State Key laboratory of internet of things for Smart City and Department of Civil and environmental Engineering University of MacaoMacao 999078China Georgia Institute of Technology AtlantaGA 30332-0245USA

The development of automation industry is inseparable from the progress of sensing *** a promising self-powered sensing technology,the durability and stability of triboelectric sensor(TES)have always been inevitable ***,a continuous charge supplement(CCS)strategy and an adaptive signal processing(ASP)method are proposed to improve the lifetime and robustness of *** CCS uses low friction brushes to increase the surface charge density of the dielectric,ensuring the reliability of sensing.A triboelectric mechanical motion sensor(TMMS)with CCS is designed,and its electrical signal is hardly attenuated after 1.5 million cycles after reasonable parameter optimization,which is unprecedented in linear *** that,the dynamic characteristics of the CCS-TMMS are analyzed with error rates of less than 1%and 2%for displacement and velocity,respectively,and a signal-to-noise ratio of more than 35 ***,the ASP used a signal conditioning circuit for impedance matching and analog-to-digital conversion to achieve a stable output of digital signals,while the integrated design and manufacture of each hardware module is ***,an intelligent logistics transmission system(ILTS)capable of wirelessly monitoring multiple motion parameters is *** work is expected to contribute to automation industries such as smart factories and unmanned warehousing.

关键词： triboelectric mechanical motion sensor continuous charge supplement adaptive signal processing durability and stability multiple motion parameters monitoring

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A high-precision system of fiber Bragg grating temperature sensing demodulation based on light power detection

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Optoelectronics Letters 2022年第8期18卷 461-467页

作者： HUANG Shuyan HU Xiaohua ZHANG Hao CHEN Weijuan HUANG Youping FAN Mengyi Department of Electronic Information Science Fujian Jiangxia UniversityFuzhou 350108China University of Chinese Academy of Sciences Beijing 100059China fujian Provincial Key laboratory for Advanced Micro-nano Photonics Technology and Devices Quanzhou Normal UniversityQuanzhou 362000China

A system of fiber Bragg grating(FBG)temperature sensing demodulation based on light power detection is proposed in this *** with the traditional demodulation method based on wavelength scanning,light power detection is more direct and avoids the use of ***,the light power in the system is converted into the electrical signal by the receiver optical subassembly(ROSA)and converted to the digital *** micro controller unit(MCU)processes the digital signal to realize the real-time temperature monitoring,which avoids the use of optical power meter(OPM).With the advantages of simple structure and low cost,the system is portable and *** experimental results show that the linearity coefficients R-square between light power and the sampling voltage are 0.99908 and 0.99893 in the temperature range from 10°C to 85°C,*** to the results,the proved sensor has a repeatability error of 1%,a linearity error of 1.35%,and a hysteresis error of 0.7%,which indicates that the system is of high stability and high *** experimental results are consistent with the theory,which verifies the system’s feasibility.

关键词： system power fiber

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internet of things to network smart devices for ecosystem monitoring

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Science Bulletin 2019年第17期64卷 1234-1245页

作者： Xin Li Ning Zhao Rui Jin Shaomin Liu Xiaomin Sun Xuefa Wen Dongxiu Wu Yan Zhou Jianwen Guo Shiping Chen Ziwei Xu Mingguo Ma Tianming Wang Yonghua Qu Xinwei Wang Fangming Wu Yuke Zhou Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing 100101 China State Key laboratory of Remote Sensing Science Faculty of Geographical Science Beijing Normal University Beijing 100875 China State Key laboratory of Remote Sensing Science Institute of Remote Sensing and Digital Earth Chinese Academy o f Sciences Beijing 100101 China Center for Excellence in Tibetan Plateau Earth Sciences Chinese Academy o f Sciences Beijing 100101 China Key laboratory of Remote Sensing of Gansu Province Heihe Remote Sensing Experimental Research Station Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences Lanzhou 730000 China State Key laboratory of Earth Surface Processes and Resource Ecology Faculty o f Geographical Science Beijing Normal University Beijing 100875 China Key laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China State Key laboratory of Vegetation and environmental Change Institute of Botany Chinese Academy of Sciences Beijing 100093 China Optoelectronics System laboratory Institute of Semiconductors Chinese Academy of Sciences Beijing 100083 China Research Base of Karst Eco-environments at Nanchuan in Chongqing Ministry of Nature Resources School of Geographical Sciences Southwest University Chongqing 400715 China Ministry of Education Key laboratory for Biodiversity Science and Engineering Monitoring and Research Center for Amur Tigers and Amur Leopards National Forestry and Grassland Administration & College of Life Sciences Beijing Normal University Beijing 100875 China

Smart, real-time, low-cost, and distributed ecosystem monitoring is essential for understanding and managing rapidly changing ecosystems. However, new techniques in the big data era have rarely been introduced into operational ecosystem monitoring, particularly for fragile ecosystems in remote *** introduce the internet of things(IoT) techniques to establish a prototype ecosystem monitoring system by developing innovative smart devices and using IoT technologies for ecosystem monitoring in isolated environments. The developed smart devices include four categories: large-scale and nonintrusive instruments to measure evapotranspiration and soil moisture, in situ observing systems for CO2 and d13 C associated with soil respiration, portable and distributed devices for monitoring vegetation variables, and Bi-CMOS cameras and pressure trigger sensors for terrestrial vertebrate monitoring. These new devices outperform conventional devices and are connected to each other via wireless communication networks. The breakthroughs in the ecosystem monitoring IoT include new data loggers and longdistance wireless sensor network technology that supports the rapid transmission of data from devices to wireless networks. The applicability of this ecosystem monitoring IoT is verified in three fragile ecosystems, including a karst rocky desertification area, the National Park for Amur Tigers, and the oasis-desert ecotone in China. By integrating these devices and technologies with an ecosystem monitoring information system, a seamless data acquisition, transmission, processing, and application IoT is created. The establishment of this ecosystem monitoring IoT will serve as a new paradigm for ecosystem monitoring and therefore provide a platform for ecosystem management and decision making in the era of big data.

关键词： Ecosystem monitoring Fragile ecosystem internet of things Wireless sensor network Smart device

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Fiber gas sensor-integrated smart face mask for roomtemperature distinguishing of target gases

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Nano Research 2018年第1期11卷 511-519页

作者： Zhiyi Gao Zhoe n g Lou Shuai Chen La Li Kai Jiang Zuoling Fu Wei Han Guozhen Shen Coherent Light and Atomic and Molecular Spectroscopy laboratory Key Laboratory of Physics and Technology for Advanced Batteries College of Physics Jilin University Changchun 130012 China State Key laboratory for Superlattices and Microstructures Institute of Semiconductors Chinese Academy of Sciences Beijing 100083 China Institute & Hospital of Hepatobiliary Surgery Key Laboratory of Digital Hepatobiliary Surgery of Chinese PLA Chinese PLA Medical School Chinese PLA General Hospital Beijing 100853 China Key laboratory of Physics and Technology for Advanced Batteries (Ministry of Education)-Intemational Center of Future Science Jilin University Changchun 130012 China College of Materials Science and Opto-electronic Technology University of Chinese Academy of Sciences Beijing 100029 China

Wearable gas sensors that are lightweight, portable, and inexpensive have great potential application in the real-time detection of human health and environmental monitoring. In this work, we fabricated flexible fiber gas sensors with single-walled carbon nanotube （SWCNT）, multi-walled carbon nanotube （MWCNT）, and ZnO quantum dot-decorated SWCNT （SWCNTs@ZnO） sensing elements. These flexible fiber gas sensors could be operated at room temperature to detect target gases with good sensitivity and recovery time. They also exhibited superior long-term stability, as well as good device mechanical bending ability and robustness. Integrating these flexible gas sensors into face masks, the fabricated wearable smart face masks could be used to selectively detect CaHsOH, HCHO, and NH3 by reading the corresponding LEDs with different colors. Such face masks have great potential application in the internet of things and wearable electronics.

关键词： wearable electronics gas sensors flexible fiber devices

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Investigation on environment monitoring system for a combination of hydroponics and aquaculture in greenhouse

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Information Processing in Agriculture 2022年第1期9卷 123-134页

作者： Shanhong Zhang Yu Guo Shuai Li Zhixin Ke Huajian Zhao Jinqi Yang Yang Wang Daoliang Li Liang Wang Wenhua Yang Zhili Zhang National Innovation Center for digital Fishery China Agricultural UniversityBeijing 100083China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Beijing Zhong Nong Tianlu Micro-nano Bubble Water Science and Technology Co. LtdChina

This work develops a distributed environmental monitoring system for the combination of hydroponics and aquaculture based on the internet of things technology,which mainly includes the information perception layer,the information transmission layer and the sys-tem *** system has employed multiple sensors terminal to real-time acqui-sition,including air and water temperatures,dissolved oxygen *** protocol is suitable for sending small data and the 4G was employed to collect data and send to the cloud *** is used to develop background applications,to access cloud platforms and local data *** on the collection and processing of environmental data and cloud service platform,the mobile application program client and remote login desktop have been *** has been implemented and tested in Tongzhou,Beijing for 3 months in *** results showed the proposed monitoring system stability for overall operation and accuracy data transmission,which can support the actual hydroponics and aquacul-ture production *** analysis of monitoring data collected from the devel-oped monitoring system,indoor air and water temperature have the obvious correlation with atmospheric pressure(0.7 and 0.9)and outdoor temperature(1.0 and 0.9),respectively.

关键词： monitoring system Hydroponics Aquaculture Greenhouse internet of things Greenhouse environment

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