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SESQ: A model-driven method for Building Object Level Vertical Search Engines

ER 2008 Workshops held in Conjunction with the 27th International Conference on Conceptual modeling

作者： Lin, Ling He, Yukai Guo, Hang Fan, Ju Zhou, Lizhu Guo, Qi Li, Gang Tsinghua Univ Beijing 100084 Peoples R China Tsinghua Univ Sohu Res Dev Div Beijing Peoples R China

ISBN: (纸本)9783540878766

In vertical search engine research, many works have been reported. But most of them focus on its key issues such as crawling, extraction, and query and few of them give a total solution for building a complete vertical search engine from scratch in a systematic method. To address this issue, we propose a model-driven method and its supporting tool SESQ. Based on a user defined ER schema for a target domain, the tool can help to build a complete search engine by integrating tasks of crawling, extraction. data management and query within One Unified framework.

关键词： model-driven method schema object-level vertical search engine

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Optimal ordering policy for platelets:Data-driven method vs model-driven method

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Fundamental Research 2021年第5期1卷 508-516页

作者： Mingfang Yang Xu Chen Zheng Luo School of Management and Economics University of Electronic Science and Technology of ChinaChengdu 611731P.K China

Platelets,one of the most significant materials in treating leukemia,have a limited shelf life of approximately five *** platelets cannot be manufactured and can only be centrifuged from whole or donated blood directly,an accurate ordering policy is necessary for the efficient use of this limited blood *** this motivation,the present study examines an ordering policy for platelets to minimize the expected shortage and *** than using the two-step model-driven method that first fits a demand distribution and then optimizes the order quantity,we solve the issue using an integrated datadriven ***,the data-driven method works directly with demand data and does not rely on the assumption of demand ***,we derive theoretical insights into the optimal *** a comparative analysis,we find that the data-driven method has a mean anchoring effect,and the amounts of shortage and overage reduced by this method are greater than those reduced by the model-driven ***,we present an extended model with the service level requirement and conclude that the order decided by the data-driven method can precisely satisfy the service level requirement;however,the order decided by the model-driven method may be either higher or lower than the service level requirement and can lead to a higher cost.

关键词： Platelets Ordering policy model-driven method Data-driven method Mean anchoring effect Service requirement

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Cerebrovascular segmentation from TOF-MRA using model- and data-driven method via sparse labels

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NEUROCOMPUTING 2020年第0期380卷 162-179页

作者： Zhang, Baochang Liu, Shuting Zhou, Shoujun Yang, Jian Wang, Cheng Li, Na Wu, Zonghan Xia, Jun Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Sch Grad Sch Shenzhen Shenzhen Peoples R China Beijing Inst Technol Sch Opt & Elect Beijing Peoples R China Shenzhen Univ Shenzhen Second Peoples Hosp Hlth Sci Ctr Dept RadiolAffiliated Hosp 1 Shenzhen Peoples R China

Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) data is of great importance in blood supply structure analysis, diagnosis, and treatment of cerebrovascular pathologies. However, complete and accurate segmentation is still a challenge due to the complex image context and vascular morphology. The existing model-driven methods are often difficult to obtain prominent accuracy and robustness. Deep-learning based technology has achieved unimaginable success, but always faces the problem of insufficient labeled data. In this paper, a novel strategy is proposed to automate cerebrovascular segmentation, which integrates model- and data-driven methods. Firstly, the TOF-MRA data are sparsely labeled by three radiologists. Secondly, a semi-supervised mixture probability model is proposed to fit the cerebrovascular intensity distribution precisely, which starts from the sparse annotations and generates massive labeled points. Thirdly, mislabeled points are corrected by a Clean-Mechanism model, to acquire a well-labeled point-set of good quality. Finally, we construct and train a dilated dense convolution network (DD-CNN) by the resultant labeled point-set. The proposed method is validated on 109 clinical TOR-MRA data from a public dataset. Compared with the other state-of-the-art segmentation methods, our method segments cerebrovascular structure with better completeness and sensibility, especially for slender vascularity. The experimental results show that our method reaches an average dice score of 93.20%, which also indicates that the DD-CNN is very competent for cerebrovascular segmentation from TOF-MRA volume. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Cerebrovascular segmentation Deep-learning Dilated convolution Sparse label model-driven method Data-driven method

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Remaining useful life prediction of PEMFCs based on mode decomposition and hybrid method under real-world traffic conditions

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ENERGY 2025年 314卷

作者： Chen, Li Yang, Jibin Wu, Xiaohua Deng, Pengyi Xu, Xiaohui Peng, Yiqiang Xihua Univ Sch Automobile & Transportat Vehicle Measurement Control & Safety Key Lab Prov Engn Res Ctr New Energy Vehicle Intelligent C Chengdu 610039 Sichuan Peoples R China

Accurately predicting the remaining useful life (RUL) of proton-exchange membrane fuel cells (PEMFCs) is essential for the health management of vehicle-oriented PEMFCs. Utilizing operational data of a demonstration PEMFC city bus in Chengdu, China, an RUL prediction method for PEMFCs that combines mode decomposition and a hybrid prediction model is developed. The improved complete ensemble empirical mode decomposition with adaptive noise is used to decompose and reconstruct the data into high- and low-frequency components to cope with the voltage recovery phenomenon. The hybrid prediction model comprises two phases. In the datadriven phase, a bidirectional long short-term memory (BiLSTM) neural network is used to predict the lowfrequency components, while a mixed model comprising a convolutional neural network, BiLSTM, and an attention mechanism is used to predict the high-frequency components. The dung beetle optimization algorithm is used to optimize the parameters of the mixed model. The prediction results of the data-driven phase are then used as observed values in the model-driven phase, which involves applying the adaptive unscented Kalman filter to a voltage degradation model and obtaining the final prediction. The results show that the mean absolute percentage error of the proposed method is less than 1 % under real-world traffic conditions.

关键词： Proton-exchange membrane fuel cell Remaining useful life Mode decomposition Hybrid method Data-driven method model-driven method

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A comprehensive review on uncertainty modeling methods in modern power systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2025年 166卷

作者： Wang, Zhaoyuan Bu, Siqi Wen, Jiaxin Huang, Can Hong Kong Polytech Univ Dept Elect & Elect Engn Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen Guangdong Peoples R China Hong Kong Polytech Univ Res Ctr Grid Modernisat Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Int Ctr Urban Energy Nexus Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy Kowloon Hong Kong Peoples R China China Southern Power Grid Energy Dev Res Inst Guangzhou Guangdong Peoples R China Pacific Gas & Elect Co Oakland CA USA

In recent years, modern power systems integrated with renewable energy sources and communication technologies have been rapidly developed. However, the randomness and intermittency of renewable energy sources, together with the unexpected disturbances in the vulnerable cyber layer, introduce uncertainties into power systems, which pose huge threats to the stable operation of modern power systems and should be carefully considered. Moreover, there is a lack of a comprehensive review of uncertainty modeling in modern power systems, which is one of the significant tasks in quantifying uncertainties. In this review, the methods and respective characteristics of uncertainty modeling in modern power systems are analyzed. According to the outputs of methods, they are categorized as model-driven and data-driven methods. The existing uncertainties in modern power systems and the respective modeling methods are investigated from the aspects of the physical layer, cyber layer, and economic and social layer. The applications of uncertainty modeling methods in modern power system operation are summarized from the aspects of steady state, dynamic, and risk analysis of modern power systems. Also, the prospective future research topics are recommended.

关键词： Uncertainty modeling model-driven method Data-driven method Renewable energy Modern power system

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A New Semi-Automated method for Service Identification

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JOURNAL OF WEB ENGINEERING 2022年第3期21卷 569-607页

作者： Hekmat, Shahrzad Parsa, Saeed Vaziri, Babak Islamic Azad Univ Dept Comp Engn Cent Tehran Branch Tehran Iran Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

Service identification plays a key role in the design of service-oriented systems. There are non-model-based and model-based methods for extracting services from business processes. These methods suggest a set of mostly descriptive solutions that do not pay sufficient attention to service design guidelines and the conceptual relations between tasks. The challenge is to develop an algorithm to automatically identify services from business processes to simplify the analysis and reduce the gap between information technology and business needs. In this paper, we develop a semi-automated service identification method that addresses this gap. This method incorporates the Goal, Data, and Business Process models (BPM) to identify services based on related tasks, shared data, and business requirements. It advances previous methods by simultaneously considering both semantic and structural relations between tasks which permits better and more accurate identification of services. Moreover, the proposed method considers the principles of service design such as internal cohesion of service methods, loose coupling of services, and reusability of the identified services.

关键词： Business process model model-driven method service identification software engineering

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Production Capacity Prediction method of Shale Oil Based on Machine Learning Combination model

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Energy Engineering 2024年第8期121卷 2167-2190页

作者： Qin Qian Mingjing Lu Anhai Zhong Feng Yang Wenjun He Min Li Petroleum Engineering Technology Research Institute of Shengli Oilfield SINOPECDongying257000China Postdoctoral Scientific Research Working Station of Shengli Oilfield SINOPECDongying257000China

The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well *** relationships,nonlinear in nature,pose challenges for accurate description through physical *** field data provides insights into real-world effects,its limited volume and quality restrict its *** this,numerical simulation models offer effective *** harness the strengths of both data-driven and model-driven approaches,this study established a shale oil production capacity prediction model based on a machine learning combination *** fracturing development data from 236 wells in the field,a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil *** the combination model integrating support vector machine(SVM)algorithm and back propagation neural network(BPNN),a model-driven shale oil production capacity prediction model is developed,capable of swiftly responding to shale oil development performance under varying geological,fluid,and well *** results of numerical experiments show that the proposed method demonstrates a notable enhancement in R2 by 22.5%and 5.8%compared to singular machine learning models like SVM and BPNN,showcasing its superior precision in predicting shale oil production capacity across diverse datasets.

关键词： Shale oil production capacity data-driven model model-driven method machine learning

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An empirical comparative evaluation of gestUI to include gesture-based interaction in user interfaces

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SCIENCE OF COMPUTER PROGRAMMING 2019年 172卷 232-263页

作者： Parra, Otto Espana, Sergio Ignacio Panach, Jose Pastor, Oscar Univ Cuenca Dept Comp Sci Cuenca Ecuador Univ Politecn Valencia PROS Res Ctr Valencia Spain Univ Utrecht Dept Informat & Comp Sci Utrecht Netherlands Univ Valencia Dept Informat Escola Tecn Super Engn Valencia Spain

Currently there are tools that support the customisation of users' gestures. In general, the inclusion of new gestures implies writing new lines of code that strongly depend on the target platform where the system is run. In order to avoid this platform dependency, gestUI was proposed as a model-driven method that permits (i) the definition of custom touch-based gestures, and (ii) the inclusion of the gesture-based interaction in existing user interfaces on desktop computing platforms. The objective of this work is to compare gestUI (a MDD method to deal with gestures) versus a code-centric method to include gesture-based interaction in user interfaces. In order to perform the comparison, we analyse usability through effectiveness, efficiency and satisfaction. Satisfaction can be measured using the subjects' perceived ease of use, perceived usefulness and intention to use. The experiment was carried out by 21 subjects, who are computer science ***. and Ph.D. students. We use a crossover design, where each subject applied both methods to perform the experiment. Subjects performed tasks related to custom gesture definition and modification of the source code of the user interface to include gesture-based interaction. The data was collected using questionnaires and analysed using non-parametric statistical tests. The results show that gestUI is more efficient and effective. Moreover, results conclude that gestUI is perceived as easier to use than the code-centric method. According to these results, gestUI is a promising method to define custom gestures and to include gesture-based interaction in existing user interfaces of desktop-computing software systems. (C) 2018 Elsevier B.V. All rights reserved.

关键词： model-driven method Human-computer interaction Code-centric method Gesture-based interaction Comparative empirical evaluation

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Proton Exchange Membrane Fuel Cell Prognosis Based on Frequency-Domain Kalman Filter

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2021年第4期7卷 2332-2343页

作者： Ao, Yunjin Laghrouche, Salah Depernet, Daniel Chen, Kui Univ Bourgogne Franche Comte French Natl Ctr Sci Res CNRS Federat Fuel Cell Res FCLAB Energy DeptFR CNRS 3539FEMTO ST Inst UMR 6174 F-90400 Belfort France

The degradation seriously affects the durability and cost of the proton exchange membrane fuel cell (PEMFC). This article presents a novel model-driven method based on the frequency-domain Kalman filter (FDKF) and voltage degradation model to predict the degradation of PEMFC in the frequency domain. The advantage of the proposed FDKF method is that it can process the data in groups;thus, the computation time can be greatly reduced with high accuracy. Two degradation experiments under constant and quasi-dynamic currents have been used to demonstrate its prognosis performances under different conditions and different training times. Compared with the traditional time-domain extended Kalman filter method and literature, it has been demonstrated that the proposed one has higher accuracy and requires much less calculation time.

关键词： Degradation Prognostics and health management Data models Fuel cells Computational modeling Predictive models Load modeling Degradation prognosis frequency-domain Kalman filter (FDKF) model-driven method proton exchange membrane fuel cell (PEMFC) voltage degradation model

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An adaptive adjustment strategy for bolt posture errors based on an improved reinforcement learning algorithm

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APPLIED INTELLIGENCE 2021年第6期51卷 3405-3420页

作者： Luo, Wentao Zhang, Jianfu Feng, Pingfa Liu, Haochen Yu, Dingwen Wu, Zhijun Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Tribol Beijing 100084 Peoples R China Tsinghua Univ Beijing Key Lab Precis Ultraprecis Mfg Equipment Beijing 100084 Peoples R China Tsinghua Univ Grad Sch Shenzhen Div Adv Mfg Shenzhen 518055 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

Designing an intelligent and autonomous system remains a great challenge in the assembly field. Most reinforcement learning (RL) methods are applied to experiments with relatively small state spaces. However, the complicated situation and high-dimensional spaces of the assembly environment cause traditional RL methods to behave poorly in terms of their efficiency and accuracy. In this paper, a model-driven adaptive proximal proximity optimization (MAPPO) method was presented to make the assembly system autonomously rectify the bolt posture error. In the MAPPO method, a probabilistic tree and adaptive reward mechanism were used to improve the calculation efficiency and accuracy of the traditional PPO method. The size of the action space was reduced by establishing a hierarchical logical relationship for each parameter with a probabilistic tree. Based on an adaptive reward mechanism, the phenomenon that the algorithm easily falls into local minima could be improved. Finally, the proposed method was verified based on the Unity simulation engine. The advancement and robustness of the proposed model were also validated by comparing different cases in simulations and experiments. The results revealed that MAPPO has better algorithm efficiency and accuracy compared with other state-of-the-art algorithms.

关键词： model-driven method Intelligent assembly Probabilistic tree Adaptive reward mechanism Reinforcement learning Physical simulation engine

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