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E-commerce businessmodel mining and prediction

Zhou-zhou HE,Zhong-fei ZHANG,Chun-ming CHEN,Zheng-gang WANG

《信息与电子工程前沿（英文）》 2015年第16卷第9期页码 707-719 doi: 10.1631/FITEE.1500148

摘要： We study the problem of business model mining and prediction in the e-commerce context. Unlike most existing approaches where this is typically formulated as a regression problem or a time-series prediction problem, we take a different formulation to this problem by noting that these existing approaches fail to consider the potential relationships both among the consumers (consumer influence) and among the shops (competitions or collaborations). Taking this observation into consideration, we propose a new method for e-commerce business model mining and prediction, called EBMM, which combines regression with community analysis. The challenge is that the links in the network are typically not directly observed, which is addressed by applying information diffusion theory through the consumer-shop network. Extensive evaluations using Alibaba Group e-commerce data demonstrate the promise and superiority of EBMM to the state-of-the-art methods in terms of business model mining and prediction.

关键词： E-commerce Business model prediction Consumer influence Social network Sales prediction

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Energy storage resources management: Planning, operation, and business model

《工程管理前沿（英文）》页码 373-391 doi: 10.1007/s42524-022-0194-4

摘要： With the acceleration of supply-side renewable energy penetration rate and the increasingly diversified and complex demand-side loads, how to maintain the stable, reliable, and efficient operation of the power system has become a challenging issue requiring investigation. One of the feasible solutions is deploying the energy storage system (ESS) to integrate with the energy system to stabilize it. However, considering the costs and the input/output characteristics of ESS, both the initial configuration process and the actual operation process require efficient management. This study presents a comprehensive review of managing ESS from the perspectives of planning, operation, and business model. First of all, in terms of planning and configuration, it is investigated from capacity planning, location planning, as well as capacity and location combined planning. This process is generally the first step in deploying ESS. Then, it explores operation management of ESS from the perspectives of state assessment and operation optimization. The so-called state assessment refers to the assessment of three aspects: The state of charge (SOC), the state of health (SOH), and the remaining useful life (RUL). The operation optimization includes ESS operation strategy optimization and joint operation optimization. Finally, it discusses the business models of ESS. Traditional business models involve ancillary services and load transfer, while emerging business models include electric vehicle (EV) as energy storage and shared energy storage.

关键词： energy storage system energy storage resources management planning configuration operational management business model

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新一代人工智能引领下的制造业新模式与新业态研究

“新一代人工智能引领下的制造业新模式新业态研究”课题组

《中国工程科学》 2018年第20卷第4期页码 66-72 doi: 10.15302/J-SSCAE-2018.04.011

摘要：

在新一代人工智能技术引领下，制造业的生产技术、组织方式、竞争策略等，都将面临重大调整，为制造业新模式与新业态的形成提供了可能。受新一代人工智能技术驱动，制造业实践中不断涌现由服务而产生的新模式新业态是本课题的研究核心。课题重点围绕由开展智能服务而产生的新模式和新业态进行研究，分析了在人工智能技术引领下，制造业的模式与业态的演进趋势，新模式与新业态的典型类型，支撑性、关键性技术；提出了新模式与新业态的发展方针、目标与途径。根据我国工业领域的发展基础与现状，选择远程运维服务和规模定制服务作为突破，提出两大业务模式在相关领域的发展方向以及发展目标，并提出相关政策建议。

关键词：人工智能制造业新模式新业态

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年第16卷第3期页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost deep neural network healthcare risk prediction

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基于产业链视角的我国风电设备产业商业模式创新研究

胡绪华,时方艳,徐骏杰

《中国工程科学》 2015年第17卷第3期页码 88-95

摘要：

本文从产业链的视角分析了我国风电设备产业零部件制造、整机组装、运营服务等环节的发展状况，并借助于宏观环境分析方法（即PEST分析方法）讨论了我国风电设备产业商业模式的战略格局，进一步结合我国风电设备产业的市场细分差别化设计商业模式创新的细分型、整合型、收缩型和扩张型四大模式。最后，提出了保障风电设备产业商业模式创新的政策建议。

关键词：风电设备产业；商业模式创新；产业链；PEST分析

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

《结构与土木工程前沿（英文）》 2013年第7卷第1期页码 72-82 doi: 10.1007/s11709-013-0185-y

摘要： A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor ( ) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

关键词： earthquake cone penetration test liquefaction support vector machine (SVM) prediction

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A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

《环境科学与工程前沿（英文）》 2023年第17卷第7期 doi: 10.1007/s11783-023-1688-y

摘要：

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality.

关键词： Water quality prediction Grasshopper optimization algorithm Variational mode decomposition Long short-term memory neural network

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Improved analytical model for residual stress prediction in orthogonal cutting

null

《机械工程前沿（英文）》 2014年第9卷第3期页码 249-256 doi: 10.1007/s11465-014-0310-1

摘要：

The analytical model of residual stress in orthogonal cutting proposed by Jiann is an important tool for residual stress prediction in orthogonal cutting. In application of the model, a problem of low precision of the surface residual stress prediction is found. By theoretical analysis, several shortages of Jiann’s model are picked out, including: inappropriate boundary conditions, unreasonable calculation method of thermal stress, ignorance of stress constraint and cyclic loading algorithm. These shortages may directly lead to the low precision of the surface residual stress prediction. To eliminate these shortages and make the prediction more accurate, an improved model is proposed. In this model, a new contact boundary condition between tool and workpiece is used to make it in accord with the real cutting process; an improved calculation method of thermal stress is adopted; a stress constraint is added according to the volume-constancy of plastic deformation; and the accumulative effect of the stresses during cyclic loading is considered. At last, an experiment for measuring residual stress in cutting AISI 1045 steel is conducted. Also, Jiann’s model and the improved model are simulated under the same conditions with cutting experiment. The comparisons show that the surface residual stresses predicted by the improved model is closer to the experimental results than the results predicted by Jiann’s model.

关键词： residual stress analytical model orthogonal cutting cutting force cutting temperature

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Fracture model for the prediction of the electrical percolation threshold in CNTs/Polymer composites

Yang SHEN, Pengfei HE, Xiaoying ZHUANG

《结构与土木工程前沿（英文）》 2018年第12卷第1期页码 125-136 doi: 10.1007/s11709-017-0396-8

摘要： In this paper, we propose a 3D stochastic model to predict the percolation threshold and the effective electric conductivity of CNTs/Polymer composites. We consider the tunneling effect in our model so that the unrealistic interpenetration can be avoided in the identification of the conductive paths between the CNTs inside the polymer. The results are shown to be in good agreement with reported experimental data.

关键词： electrical percolation CNTs/Polymer composites fracture model electric conductivity tunnelling effects

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Performance prediction of switched reluctance generator with time average and small signal models

Jyoti KOUJALAGI, B. UMAMAHESWARI, R. ARUMUGAM

《能源前沿（英文）》 2013年第7卷第1期页码 56-68 doi: 10.1007/s11708-012-0216-8

摘要： This paper presents the complete mathematical model and predicts the performance of switched reluctance generator with time average and small signal models. The complete mathematical model is developed in three stages. First, a switching model is developed based on quasi-linear inductance profile. Next, based on the switching behaviour, a time average model is obtained to measure the difference between the excitation and generation time in each switching cycle. Finally, to track control voltage and current wave shapes, a small signal model is designed. The effectiveness of the complete multilevel model combining electrical machine, power converter, load and control with programming language is demonstrated through simulations. A PI controller is used for controlling the voltage of the generator. The results presented show that the controller exhibits accurate tracking control of load voltage under different operating conditions. This demonstrates that the proposed model is able to perform an accurate control of the generated output voltage even in transient situations. The simulation is performed to choose the control parameters and study the performance of switched reluctance generator prior to its actual implementation. Initial experimental results are presented using NI-Data acquisition card to control the output power according to load requirements.

关键词： generator reluctance switching model small signal model time average model

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Pathway to energy technical innovation and commercialization based on Internet plus DES

Huiping LIU

《能源前沿（英文）》 2016年第10卷第1期页码 65-78 doi: 10.1007/s11708-015-0391-5

摘要： The distributed energy system (DES) is a type of energy cascade utilization on the client side or close to the client, and it has become an important option of global energy transformation. In China, based on the experience of demonstration projects, the DES is now being commercialized. Under the new opportunity of energy production and consumption promoted by the national “Internet Plus” action plan, the development of the DES was reviewed in this paper; four categories of market demand and five key issues for DES deployment were analyzed; five types of potential DES users and five key points of technical path implementation proposed based on many years of engineering practices and hundreds of project case studies were proposed. 4E elements should be used to evaluate and choose the project and lead the innovation model of DES by energy production and consumption revolution with the sustainable development of the Internet plus DES. The future innovation models include intelligent energy modularity and menu-type services with the demands of the client side, and the kind of new thinking for DES services that “you are in charge of your own energy production and consumption, while we are also at service when needed for installation and maintenance.” The aim of innovation mode is to give the energy sovereign back to the people, and form a perfect Internet plus DES ecosystem.

关键词： “Internet Plus” distributed energy system (DES) business model technical innovation commercialization DES industry ecosystem energy revolution

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Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

《结构与土木工程前沿（英文）》 2022年第16卷第8期页码 976-989 doi: 10.1007/s11709-022-0840-2

摘要： Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.

关键词： damage prediction ANN BOA FEM experimental modal analysis

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基于修正Anderson 模型的冲击载荷下地基振动响应预测方法

房波

《中国工程科学》 2014年第16卷第11期页码 96-102

摘要：

提出了一个预测潜在冲击载荷下振动效应的理论模型与现场实测相结合的综合预测方法。通过一系列具有针对性的室外重锤冲击振动试验，以及现场实测数据对Anderson 模型进行了验证并修正，然后利用修正的Anderson 模型预测冲击荷载的振动效应。将预测结果和现场试验结果进行对比分析，结果表明：预测结果与实测结果吻合较好。

关键词：预测方法冲击载荷振动效应 Anderson 模型

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业务架构集成中的企业级业务组件识别方法 Article

Jiong FU, Xue-shan LUO, Ai-min LUO, Jun-xian LIU

《信息与电子工程前沿（英文）》 2017年第18卷第9期页码 1320-1335 doi: 10.1631/FITEE.1601836

摘要：基于组件的军事信息系统业务架构集成是军事领域中一个重要研究内容，而识别企业级业务组件是业务架构集成中一个关键问题。当前业务组件识别的方法多是关注于软件层面业务组件，忽略了诸如组织、资源等企业级因素；而目前企业级业务组件识别方法被证明非常低效。因此本文提出一种企业级业务组件识别的新方法，该方法全面考虑了业务组件的内聚度、耦合度、粒度、可维护性、可复用性五个设计原则。首先基于业务组件模型和DoDAF（Department of Defense Architecture Framework）模型对业务组件进行了定义和形式化描述，为了对业务组件进行定量化分析，将业务模型转为一个CRUD（create, read, update, and delete）矩阵并提出了6类指标；然后将业务组件识别问题转化为一个多目标优化问题，并采用了模拟退火遗传算法（simulated annealing hybrid genetic algorithm, SHGA）进行求解。最后通过案例分析验证了本文方法较先前的方法对企业级业务组件识别具有更好的适用性和高效性。

关键词：业务架构集成；业务组件；组件识别；CRUD矩阵；启发式

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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

《机械工程前沿（英文）》 2022年第17卷第3期 doi: 10.1007/s11465-022-0688-0

摘要： The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of many key engineering components. In this study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structural components. The aim is to classify three typical features of a structural component—squares, slots, and holes—into various categories based on their dimensional errors (i.e., “high precision,” “pass,” and “unqualified”). Two different types of classification schemes have been considered in this study: those that perform feature extraction by using the convolutional neural networks and those based on an explicit feature extraction procedure. The classification accuracy of the popular machine learning methods has been evaluated in comparison with the proposed deep learning model. Based on the experimental data collected during the milling experiments, the proposed model proved to be capable of predicting dimensional accuracy using cutting parameters (i.e., “static features”) and cutting-force data (i.e., “dynamic features”). The average classification accuracy obtained using the proposed deep learning model was 9.55% higher than the best machine learning algorithm considered in this paper. Moreover, the robustness of the hybrid model has been studied by considering the white Gaussian and coherent noises. Hence, the proposed hybrid model provides an efficient way of fusing different sources of process data and can be adopted for prediction of the machining quality in noisy environments.

关键词： precision milling dimensional accuracy cutting force convolutional neural networks coherent noise