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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Li, Duo Milonas, Elizabeth Zhang, Qiping Shenyang Institute of Technology Department of Big Data Management and Application Fushun China Brooklyn United States Palmer School of Library and Information Science Long Island University Brookville United States

ISBN: (纸本)9798350351507

This research-to-practice full paper compares data science education strategies in China and the United States, exploring whether different approaches can achieve similar educational outcomes. In the U.S., data science programs are typically developed by individual schools to meet labor market demands, integrating data science as an interdisciplinary subject. In contrast, Chinese universities follow a uniform plan set by the education department, resulting in specialized fields like 'big data Management and application' and 'data Science and big data technology.' This research collected data on course offerings and university rankings in China and the U.S., analyzing curriculum content and program characteristics. Major findings indicate that in the U.S., data Science and Business Analytics programs focus on technical skills and are primarily found in top-ranking universities, while in China, data Science and big data Management programs emphasize management and are more widely distributed across different ranking groups. Despite these differences, both countries show similar geographic concentration patterns in data science programs. The study concludes that China and the U.S. adopt different educational strategies but achieve comparable effectiveness in data science education. © 2024 IEEE.

关键词： data Science

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Analysis of Line Slope Between Urban Rial Stations Based on Energy-Saving Operation 11th

Analysis of Line Slope Between Urban Rial Stations Based on ...

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11th International Conference on Traffic and Transportation Studies, ICTTS 2024

作者： Zhang, Zengyong Han, Jianlong Xu, Qi Economic Research Department CRRC Academy Co. Ltd. Beijing China Key Laboratory of Big Data Application Technology for Integrated Transportation in Transportation Industry Beijing Jiaotong University Beijing China

ISBN: (纸本)9789819796397

Green transportation is the object of transportation in the future, and reducing energy consumed in transportation is of great significance to sustainable development of the society. This paper mainly studies the impact of line slope between stations on the energy consumed by the train. The paper starts with description of typical line conditions between stations. Three control modes: traction, coasting and braking are considered in the process of train running. Based on these, the basic dynamic model of the train running energy consumption, and the corresponding energy-saving operation algorithm are set. Then, in the presumption that the distance between stations of urban rail transit is 3 km, energy consumption curve of the train and the corresponding velocity curve distribution of different slope and slope length are analyzed, and the optimal combination between station section line slope and train energy-saving operation was obtained. This provides a reference for the design of station section lines based on train energy-saving operation in urban rail transit. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Energy saving

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A Machine Learning Process-based Training Task Execution Method in the Field of Power Grid Regulation

A Machine Learning Process-based Training Task Execution Met...

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2022 Asian Conference on Frontiers of Power and Energy, ACFPE 2022

作者： Lao, Yingying Wang, Yudong Chen, Ziyun Kong, Yanru Shen, Jialing Li, Hao Big Data Technology Research and Development Department Nari Group Corporation Nanjing China

ISBN: (数字)9781665470841

ISBN: (纸本)9781665470841

With the rapid development of artificial intelligence technology, the power industry has entered the era of big data, business data is rapidly accumulated, and the traditional Spring-Boot-based microservice architecture raises more and more requirements for hardware resources, which can no longer meet our requirements for service invocation performance, data consistency, elastic scaling and flexible deployment requirements. In response to the above problems, the distributed container technology which is based on Kubernetes and Docker is introduced, and a unified JSON-based machine learning process description language structure is proposed, some useful configuration templates are provided for machine learning training processes, including algorithm selection, hyper-parameter setting, loss function, optimization function and execution plan. In response to the needs of enterprise business development, a machine learning model training task scheduling system adapted to business scenarios in the field of power grid regulation is designed and constructed, which solves the problems of inability to reuse sample data and waste of resources and realizes resource isolation and elastic scaling. By building a visualized machine learning task process, implementing model training and evaluation, supporting real-time display of the execution status of each algorithm node, the platform implements a multi-tenant resource isolation and elastic scaling containerized machine learning model training environment. Keywords © 2022 IEEE.

关键词： Containers

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A digital economy development index based on an improved hierarchical data envelopment analysis approach

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European Journal of Operational Research 2024年第3期316卷 1146-1157页

作者： Guo, Chuanyin Song, Qiwei Yu, Ming-Miin Zhang, Jian School of Economics and Management Beijing Information Science & Technology University Beijing 100192 China Department of Transportation Science National Taiwan Ocean University Keelung Taiwan Beijing Key Lab of Green Development Decision Based on Big Data Beijing 100192 China

The digital economy is playing an increasingly important role in the global economy. National and international organizations commonly utilize a composite index composed of multi-dimensional indicators to monitor performance, analyse policies, and communicate in the digital economy. This study introduces a hierarchical framework for constructing a Digital Economy development Index (DEDI). One of the key challenges is determining the attribute weights to be assigned by aggregating the sub-indicators with hierarchical dimensions for DEDI. The current study shows that an H-DEA model can be transformed into a parametric linear programming problem and develops an improved golden section algorithm to search for approximate optimal solutions. The newly developed method is highly robust and provides an alternative procedure to determine the optimal weights for building a DEDI. We established a hierarchical evaluation framework and utilized a new approach to measure the DEDI of 30 provinces in China from 2015 to 2020. The results show that inter-provincial digital economy development in China shows a sequential weakening from the east, centre, and northeast to the west. The East leads the country's digital economy overall, while there is a clear gradient gap between provinces. The central region needs to create a cluster for the development of digital industries. The Northeast should enhance the competitiveness of the industry across the board. The West must strengthen the construction of innovative talent and new infrastructure for the digital economy. These findings can serve as guidelines for designing China's ongoing digital economy development. © 2024 Elsevier B.V.

关键词： data envelopment analysis

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Pressure-Sensor-Based Sleep Status and Quality Evaluation System

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IEEE Sensors Journal 2023年第9期23卷 9739-9754页

作者： Kau, Lih-Jen Wang, Mao-Yin Zhou, Houcheng National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan Silicon Application Corporation Taipei11568 Taiwan Shenzhen Health Development Research and Data Management Center Guangdong Shenzhen518028 China

Sleep quality evaluation is a major approach to clinical diagnosis of different types of sleep and mental disturbances. How to render it possible for test subjects to complete sleep status detection and quality assessment in a natural environment is what this article mainly highlights. Clinically, sleep disturbances are analyzed by the so-called polysomnography (PSG). However, due to limitations in space and high cost, as well as the fact that test subjects must wear various types of physiological detection equipment during the test, the collected signal is susceptible to interference due to uncomfortable sensations. As such, it usually does not reflect the realistic situation of the subject. For this reason, we propose in this article a pressure-sensor-based smart mattress to realize sleep status detection and quality evaluation. Regarding sleep posture recognition, a proposed convolutional neural network (CNN) model in conjunction with a pressure distribution image formed by the pressure-sensing matrix is applied. With respect to assessing the subjects' time in bed, a support vector machine (SVM) is used to determine sitting or lying postures and further recognize the actual time in bed. As for sleep quality assessment, Fuzzy inference is adopted in this article based on a set of four predefined sleep parameters. Compared with some of prior arts, the proposed system does not require the test subjects to wear any equipment, and as such, the subjects can complete the test in a natural environment. Experimental results show that the accuracy of the proposed SVM classifier for differentiating sitting and lying posture and that of the CNN model for the recognition of four different sleep postures can be up to 99.986% and 96.987%, respectively. With the proposed model, precise sleep parameters, including time in bed, the number of times of bed leaving, the number of times of body movements all night, standard deviation of time interval between body movements, and sleep

关键词： Pressure sensors

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Maximizing Parking Operation Revenue via Joint Control of Channels and Overselling 8

Maximizing Parking Operation Revenue via Joint Control of Ch...

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8th IEEE International Conference on Intelligent Transportation Engineering, ICITE 2023

作者： Hua, Liru Meng, Fei Liang, Cuilan Yang, Shu Sun, Liang Wu, Jingyuan China Communications Information Technology Group Co. Ltd. Research and Development Center of Transport Industry of Big Data Processing Technologies and Application for Comprehensive Transport Ministry of Transport Beijing China

ISBN: (纸本)9798350314212

With the acceleration of urbanization, the problem of 'parking difficulty' has become a key issue that needs to be addressed in cities of all sizes. As a new parking method, reservation-based parking can effectively reduce the waiting time for parking and solve the problem of urban traffic congestion. It is widely used to alleviate parking difficulties. This article proposes a method for obtaining maximum parking revenue. Based on reservation-based parking, big data technology is used for analysis and prediction. By jointly controlling the channel ratio and overselling ratio in the reservation process, the optimal parking utilization rate and economic benefits are achieved from low to high reservation penetration rates. In summary, reservation-based parking, as an important means of solving parking difficulties, not only improves parking efficiency and alleviates urban traffic pressure but can also explore the practice of maximizing revenue using big data technology, contributing to the intelligent and green development of urban transportation. © 2023 IEEE.

关键词： Traffic congestion

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Optimizing Intersection Signal Via Reinforcement Learning for Cooperative Adaptive Cruise Control 8

Optimizing Intersection Signal Via Reinforcement Learning fo...

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8th IEEE International Conference on Intelligent Transportation Engineering, ICITE 2023

作者： Yang, Shu Yuan, Rui Wang, Miao Sun, Liang Wu, Jingyuan China Communications Information Technology Group Co. Ltd. Research and Development Center of Transport Industry of Big Data Processing Technologies and Application for Comprehensive Transport Ministry of Transport Beijing China

ISBN: (纸本)9798350314212

With the continuous advancement of urbanization, traffic congestion becomes a major challenge for social development. Intelligent driving technology can ease the traffic pressure at intersections, which is the trend of future traffic development. Cooperative Adaptive Cruise Control (CACC) is a specific application of intelligent driving technology and a necessary step toward autonomous driving. This paper proposes a signal control method for CACC homogeneous traffic flow at single intersections based on deep reinforcement learning, which considers the operation characteristics of CACC vehicles at intersections and uses a grid-based state space input to improve the model prediction accuracy. On this basis, this paper proposes a variable phase duration strategy to enhance the phase control flexibility and a platoon clearance time to reduce the vehicle travel delay. Simulation experiments show that the signal control method based on deep reinforcement learning has a significant advantage over traditional signal control methods in the CACC homogeneous traffic flow environment and is more suitable for intersections with high traffic volume. The research results of this paper not only contribute to enriching the related research field but also offer a valuable reference for improving traffic safety and efficiency at intersections. © 2023 IEEE.

关键词： Adaptive cruise control

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Toward Intelligent Financial Advisors for Identifying Potential Clients:A Multitask Perspective

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big data Mining and Analytics 2022年第1期5卷 64-78页

作者： Qixiang Shao Runlong Yu Hongke Zhao Chunli Liu Mengyi Zhang Hongmei Song Qi Liu Department of Anhui Province Key Laboratory of Big Data Analysis and Application(BDAA) School of Computer Science and TechnologyUniversity of Science and Technology of China Department of College of Management and Economics Tianjin University School of Management Hefei University of Technology Department of Information Technology China Merchants Bank

Intelligent Financial Advisors(IFAs)in online financial applications(apps)have brought new life to personal investment by providing appropriate and high-quality portfolios for *** real-world scenarios,identifying potential clients is a crucial issue for IFAs,i.e.,identifying users who are willing to purchase the ***,extracting useful information from various characteristics of users and further predicting their purchase inclination are ***,two critical problems encountered in real practice make this prediction task challenging,i.e.,sample selection bias and data *** this study,we formalize a potential conversion relationship,i.e.,user→activated user→client and decompose this relationship into three related ***,we propose a Multitask Feature Extraction Model(MFEM),which can leverage useful information contained in these related tasks and learn them jointly,thereby solving the two problems *** addition,we design a two-stage feature selection algorithm to select highly relevant user features efficiently and accurately from an incredibly huge number of user feature ***,we conduct extensive experiments on a real-world dataset provided by a famous fintech *** results clearly demonstrate the effectiveness of MFEM.

关键词： Intelligent Financial Advisor(IFA) potential client identification MultiTask Learning(MTL) feature selection

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development and validation of a nomogram model for predicting overall survival in patients with gastric carcinoma

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World Journal of Gastrointestinal Oncology 2025年第2期17卷 132-143页

作者： Guan-Zhong Liang Xiao-Sheng Li Zu-Hai Hu Qian-Jie Xu Fang Wu Xiang-Lin Wu Hai-Ke Lei Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer HospitalChongqing 400030China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center Chongqing University Cancer HospitalChongqing 400030China Department of Health Statistics School of Public HealthChongqing Medical UniversityChongqing 400016China Research Center for Medicine and Social Development School of Public HealthChongqing Medical UniversityChongqing 400016China The Research Center of Big Data Chongqing University Cancer HospitalChongqing 400030China

BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health *** forecasting of overall survival(OS)is of paramount importance for the clinical management of individuals afflicted with this *** To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric *** data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and *** absolute shrinkage and selection operator,univariate,and multivariate Cox regression analyses were employed to identify independent prognostic factors.A nomogram model was developed to predict gastric cancer patient *** model's predictability and discriminative ability were evaluated via receiver operating characteristic *** evaluate the clinical utility of the model,Kaplan-Meier and decision curve analyses were *** A total of ten independent prognostic factors were identified,including body mass index,tumor-node-metastasis(TNM)stage,radiation,chemotherapy,surgery,albumin,globulin,neutrophil count,lactate dehydrogenase,and platelet-to-lymphocyte *** area under the curve(AUC)values for the 1-,3-,and 5-year survival prediction in the training set were 0.843,0.850,and 0.821,*** AUC values were 0.864,0.820,and 0.786 for the 1-,3-,and 5-year survival prediction in the validation set,*** model exhibited strong discriminative ability,with both the time AUC and time C-index exceeding *** with TNM staging,the model demonstrated superior clinical ***,a nomogram was developed via a web-based *** This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients,which demonstrat

关键词： Gastric carcinoma Prediction Overall survival Nomogram Prospective

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Efficient energy-preserving methods for the Schrödinger-Boussinesq equation

Efficient energy-preserving methods for the Schrödinger-Bou...

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作者： Yan, Jinliang Zheng, Lianghong Lu, Fuqiang Zhang, Qiongyue Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry College of Mathematics and Computer Fujian354300 China Department of Information and Computer Technology No.1 Middle School of Nanping Fujian353000 China Changzhou Institute of Technology Changzhou213032 China

In this paper, we present two novel energy-preserving schemes for the Schrödinger-Boussinesq system based on the second-order Adams prediction-correction method. We first introduce supplementary variables to the initial model simultaneous its invariants to obtain a consistent and structural stable system. Then, we discretize the extended system by directly using the Fourier pseudo-spectral method in space and second-order Adams prediction-correction method in time to construct conservative schemes. Numerical results show that the proposed schemes can reach second-order accuracy and can conserve the discrete invariants very well. © 2022 John Wiley & Sons, Ltd.

关键词： Forecasting

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