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The effect of online reviews on addressing endogeneity in discrete choice models

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data Science and Management 2021年第2期2卷 1-11页

作者： Xinlong Tan Yuming Xiao bayes data intelligence technology service ltd. Xi'an710075China Shuyun Information technology ltd. Hangzhou310063China

This paper investigates the effectiveness of online reviews on addressing price endogeneity issue in an application to consumer demand for *** consider review variables as the substitutes of unobserved product quality in terms of a scalar variable as seen in previous *** aspect-based sentiment classification technique is designed to construct feature-related review variables from millions of review *** discuss the performance of review variables both in a hedonic pricing model and a conditional logit discrete choice *** results demonstrate that review variables show a good performance either as instruments for price or as explicit control variables in demand *** detail,the pricing prediction accuracy increases 3.4%,which is considered as a significant improvement in the practice of *** the discrete choice model,the estimated price coefficient is biased in the positive direction without endogeneity *** is adjusted in the expected way after including review *** findings indicate that online reviews provide alternative sources of information in dealing with endogeneity in discrete choice *** also analyze the differences in the preferences and needs of individual consumers to provide some practical implications of marketing.

关键词： Online reviews Endogeneity Discrete choice model Smartphone Sentiment classification

Vision, application scenarios, and key technology trends for 6G mobile communications

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Science China(Information Sciences) 2022年第5期65卷 5-31页

作者： Zhiqin WANG Ying DU Kejun WEI Kaifeng HAN Xiaoyan XU Guiming WEI Wen TONG Peiying ZHU Jianglei MA Jun WANG Guangjian WANG Xueqiang YAN Jiying XIANG He HUANG Ruyue LI Xinhui WANG Yingmin WANG Shaohui SUN Shiqiang SUO Qiubin GAO Xin SU China Academy of Information and Communications technology Huawei Technologies Canada Co. Ltd. Huawei Technologies Co. Ltd.(Hangzhou) Huawei Technologies Co. Ltd.(Chengdu) Huawei Technologies Co. Ltd.(Shanghai) ZTE Corporation datang Mobile Communications Equipment Co. Ltd.

With the global commercialization of the fifth-generation(5G) network, many countries, including China, USA, European countries, Japan, and Korea, have started exploring 6G mobile communication network, following the tradition of “planning the next while commercializing one generation”. Currently,studies on 6G networks are at the infancy stage. Research on the vision and requirements for 6G is still ongoing, and the industry is yet to clarify the key enabling technologies for 6G. However, 6G will certainly build on the success of 5G. Therefore, developing high-quality 5G networks and seamlessly integrating 5G with verticals are the priorities before 2030, when 6G is projected to be commercialized. Also, global 5G standards will keep evolving to better support vertical applications. As a milestone, the Third-Generation Partnership Project(3GPP) published Release 16 in July 2020, which continuously enhanced the capabilities of mobile broadband service based on Release 15 and realized the support for low-delay and high-reliability applications, such as Internet of Vehicles and industrial Internet. Currently, 3GPP is working on Releases 17 and 18, focusing on meeting the demands of medium-and high-data-rate machine communication with low-cost and high-precision positioning, which will be published in June 2022. Thus, 6G networks will further expand the application fields and scope of the Internet of Things to accommodate those services and applications that are beyond the capabilities of 5G networks. Herein, we present our vision, application scenarios,and key technological trends for 6G networks. Furthermore, we propose several future research opportunities in 6G networks with regard to industrialization and standardization.

关键词： sixth-generation (6G) mobile communication 6G vision application scenarios of 6G terahertz communication integrated sensing and communication integrated intelligence and communication ultra-massive MIMO reconfigurable intelligent surface co-frequency co-time full-duplex holographic radio technology

Influence of Solar Activity on Precise Orbit Prediction of LEO Satellites

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Research in Astronomy and Astrophysics 2023年第4期23卷 56-65页

作者： Jun-Jun Yuan Shan-Shi Zhou Cheng-Pan Tang Bin Wu Kai Li Xiao-Gong Hu Er-Tao Liang Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China Insight Position Digital intelligence technology service Co. Ltd.Shanghai 201109China Shanghai Key Laboratory of Space Navigation and Positioning Techniques Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China

The perturbations of low earth orbit(LEO)satellites operating in the orbit of 300~2000 km are *** particular,the atmospheric drag force and solar radiation pressure force change rapidly over a short period of time due to solar *** spaceborne global positioning system(GPS)data of the CHAMP,GRACE and SWARM satellites from 2002 to 2020,this paper studies in depth the influence of solar activity on LEO satellites’precise orbit prediction by performing a series of orbit prediction *** quality of GPS data is more susceptible to being influenced by solar activity during years when this activity is high and the changes in dynamic parameters are consistent with those of solar *** effects of solar activity on LEO orbit prediction accuracy are analyzed by comparing the predicted orbits with the precise *** years of high solar activity,the average root-mean-squares prediction errors at 10,20,and 30 minutes are 0.15,0.20,and 0.26 m,respectively,which are larger than the corresponding values in low-solar-activity years by 59%,63%,and 68%,*** results demonstrate that solar activity has a great influence on the orbit prediction accuracy,especially during high-solar-activity *** should strengthen the real-time monitoring of solar activity and geomagnetic activity,and formulate corresponding orbit prediction strategies for the active solar period.

关键词： Sun activity-atmospheric effects-space vehicles instruments-planets and satellites atmospheres-planets and satellites dynamical evolution and stability-planets and satellites fundamental parameters

Performance of the CMA-GD Model in Predicting Wind Speed at Wind Farms in Hubei, China

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Journal of Tropical Meteorology 2023年第4期29卷 473-481页

作者：许沛华成驰王文陈正洪钟水新张艳霞 Hubei Meteorological service Center Wuhan 430079 China Faculty of Artificial intelligence Education School of Educational Information TechnologyCentral China Normal UniversityWuhan 430079 China Hubei Energy Group New Energy Development Co. Ltd.Wuhan 430069 China Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction/Guangzhou Institute of Tropical and Marine Meteorology China Meteorological AdministrationGuangzhou 510641 China

This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,*** observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and *** the same time,the wind speed predicted by the EC model is also included for comparative *** results indicate that the CMA-GD model performs better than the EC model in Wind Farm *** CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD *** CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).

关键词： CMA-GD wind speed prediction wind farm root mean square error performance evaluation

Enhancing the Prediction of User Satisfaction with Metaverse service Through Machine Learning

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Computers, Materials & Continua 2022年第9期72卷 4983-4997页

作者： Seon Hong Lee Haein Lee Jang Hyun Kim Department of Applied Artificial intelligence/Department of Human-Artificial intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea Department of Interaction Science/Department of Human-Artificial intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea

Metaverse is one of the main technologies in the daily lives of several people,such as education,tour systems,and mobile application ***,the number of users of mobile metaverse applications is increasing owing to the merit of accessibility *** provide an improved service,it is important to analyze online reviews that contain user *** previous studies have utilized traditional methods,such as the structural equation model(SEM)and technology acceptance method(TAM)for exploring user satisfaction,using limited survey *** methods may not be appropriate for analyzing the users of mobile *** overcome this limitation,several researchers perform user experience analysis through online reviews and star ***,some online reviews occasionally have inconsistencies between the star rating and the sentiment of the *** variation disturbs the performance of machine *** alleviate the inconsistencies,Valence Aware Dictionary and sEntiment Reasoner(VADER),which is a sentiment classifier based on lexicon,is *** current study aims to build a more accurate sentiment classifier based on machine learning with *** this study,five sentiment classifiers are used,such as Naïve bayes,K-Nearest Neighbors(KNN),Logistic Regression,Light Gradient Boosting Machine(LightGBM),and Categorical boosting algorithm(Catboost)with three embedding methods(Bag-of-Words(BoW),Term Frequency-Inverse Document Frequency(TF-IDF),Word2Vec).The results show that classifiers that apply VADER outperform those that do not apply VADER,excluding one classifier(Logistic Regression with Word2Vec).Moreover,LightGBM with TF-IDF has the highest accuracy 88.68%among other models.

关键词： Metaverse ubiquitous computing user satisfaction online review big data VADER machine learning natural language processing

Application Study of Greenhouse Environment Monitoring System Based on ZigBee technology

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Asian Agricultural Research 2017年第2期9卷 39-43,71页

作者： Weiting ZHANG Hongye YANG Wentao FENG Rui WANG Inner Mongolia University of technology Inner Mongolia Heqing technology Co. Ltd.

Against the demand of intelligent greenhouse construction of facility agriculture,greenhouse environment monitoring system is *** system contains three-layer architecture: sensor network layer at the bottom,data transmission convergence layer in the middle and monitoring application layer on the top,which is different from design idea of the existing system *** bottom layer uses ZigBee wireless communication technology to construct wireless sensor network,and node type contains coordinator,router and acquisition *** terminal is distributed in each greenhouse to collect data and play the role of wireless transmission,and router plays the function of data forwarding as the bridge of acquisition terminal and *** layer is composed of monitoring software developed by Lab VIEW software of NI Company and coordinator,which is used to gather data from the bottom *** top layer is comprehensive monitoring platform developed by Java language,which is used to gather greenhouse data of all plantation bases in one region,thereby providing comprehensive information service for government,enterprise and *** environment monitoring system realizes data collection and sharing of greenhouse environment information(air temperature,air humidity,light intensity and carbon dioxide concentration).Via test verification,the system's operation is stable,with certain application value.

关键词： Facility agriculture intelligence System architecture Wireless sensor network

Smart OLT equipment of optical access network

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Optoelectronics Letters 2023年第3期19卷 159-163页

作者： TANG Zhifei GAO Junshi YANG Tianpu LIU Dongmei DAI Guangchong China Mobile Group Design Institute Co. Ltd.Beijing 100080China

A distributed architecture of optical line terminal(OLT)equipment is proposed for response to national bandwidth acceleration requirements and for future smooth evolution to 50G passive optical network(PON).This architecture moves the forwarding function of the control board to each service board to improve the switching capacity and performance of the *** traditional control boards of centralized architecture OLT equipment have exchange and traffic processing function,and every service board is only controlled by the control *** the distributed architecture of OLT equipment,control boards still maintain exchange function,whereas service boards have traffic processing *** method separates the exchange and traffic processing functions,which improves *** paper also presents optical access network equipment combined with telemetry technology that provides reliable guarantee for intelligent analysis and data *** with the traditional push mode of network management,the data collected by network management combined with telemetry technology and artificial intelligence(AI)analysis can be used for network planning,assurance of the bandwidth and accurate operation.

关键词： network OLT Smart

RS-SVM Machine Learning Approach Driven by Case data for Selecting Urban Drainage Network Restoration Scheme

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data intelligence 2023年第2期5卷 413-437页

作者： Li Jiang Zheng Geng Dongxiao Gu Shuai Guo Rongmin Huang Haoke Cheng Kaixuan Zhu School of Management Hefei University of TechnologyHefei 230002China College of Civil Engineering Hefei University of TechnologyHefei 230002China Yangtze Ecology and Environment Co. Ltd.Wuhan 430062China Luddy School of Intelligent System and Engineering Indianan universityBloomingtonIndiana 47404USA

Urban drainage pipe network is the backbone of urban drainage,flood control and water pollution prevention,and is also an essential symbol to measure the level of urban modernization.A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service *** repair of drainage pipe networks has attracted extensive attention from all walks of *** the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019,various provinces in the Yangtze River Basin,such as Anhui,Jiangxi and Hunan,have extensively carried out PPp projects for urban pipeline restoration,in order to improve the quality and efficiency of sewage *** on the management practice of urban pipe network restoration project in Wuhu City,Anhui Province,this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode,and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector *** validity and feasibility of the model are analyzed and verified by collecting the data in the project *** research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes,and its accuracy can reach 90%.The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects.

关键词： Drainage pipe network Machine learning Rough set Multilevel SVM Restoration scheme

Research on fault time prediction method for high speed rail BTM unit based on multi method interactive validation

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High-Speed Railway 2024年第3期2卷 164-171页

作者： Limin Fu Junqiang Gou Chao Sun Hanrui Li Wei Liu School of Information Science and technology Southwest Jiaotong UniversityChengdu 611756China China Railway Signal and Communication Research and Design Institute Group Co ltd. Beijing 100089China

The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial *** to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control *** conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive ***,a method for predicting equipment fault time targeting batch equipment is *** method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM ***,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on bayesian-optimized Gradient Boosting Regression Tree(bayes-GBRT)is *** method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of ***,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional *** results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction *** above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering va

关键词： High speed rail BTM unit Remaining faultless operating time Machine learning Multi method interactive verification