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The Influencing Factors of sCER Price Dynamics Under the Clean Development Mechanism: Theory and Econometric Analysis

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Journal of Systems Science & Complexity 2018年第5期31卷 1244-1272页

作者： ZHANG Chen WU Yaqi YANG Yu School of Management Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization & Intelligent Decision Making of Ministry of Education Hefei 230009 China

In order to explore the factors and their complex mechanism affecting the price dynamics under the clean development mechanism （CDM）, this article employs the secondary Certified Emission Reduction （sCER） carbon price as the study object, and analyzes its influencing factors from aspects of the international carbon-reduction policies, macroeconomic fluctuations, energy and similar carbon products prices. The innovation of this paper lies in： Introducing necessary factor （the developing countries pricing power） and the application of several international representative indicators to un- derline the ＂world＂ nature of CDM; utilizing different econometric models to obtain noteworthy and more robust results. The authors test the theoretical findings with multiple stationary time series from the launch of CDM to present （2008-2016）. The results reveal that sCER price fluctuation shows the characteristic of asymmetry and substantial persistence. There is a strong statistically significant relationship between macroeconomic conditions, coal and oil prices, with the price of sCER. The authors discover that the pricing power of developing countries indeed has a clear but small impact on the sCER price changes, whereas the price elasticity of supply under CDM is so weak. The interaction between EU emission allowances （EUAs） and sCER presents a shift from dependency to substitution.

关键词： Clean development mechanism econometric analysis influencing factors sCER carbonprice.

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Science as Exploration in a Knowledge Landscape: Tracing Hotspots or Seeking Opportunity?

arXiv

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arXiv 2023年

作者： Liu, Feifan Zhang, Shuang Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No.2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in deep learning techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists' topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Solving the Observing and Downloading Integrated Scheduling Problem of Earth Observation Satellite with a Quantum Genetic Algorithm

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Journal of Systems Science and Information 2018年第5期9卷 399-420页

作者： Ye ZHANG Xiaoxuan HU Waiming ZHU Peng JIN School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education

This paper addresses the integrated Earth observation satellite scheduling problem. It is a complicated problem because observing and downloading operations are both involved. We use an acyclic directed graph model to describe the observing and downloading integrated scheduling *** on the model which considering energy constraints and storage capacity constraints, we develop an efficient solving method using a novel quantum genetic algorithm. We design a new encoding and decoding scheme that can generate feasible solution and increase the diversity of the *** results of the simulation experiments show that the proposed method solves the integrated Earth observation satellite scheduling problem with good performance and outperforms the genetic algorithm and greedy algorithm on all instances.

关键词： Earth observation satellite integrated scheduling quantum genetic algorithm

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A combined computational-fluid-dynamics model and control strategies for perfusion bioreactor systems in tissue engineering

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IFAC-PapersOnLine 2021年第3期54卷 324-329页

作者： Ioana Nascu Daniel Sebastia-Saez Tao Chen Wenli Du Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 200092 China Department of Chemical and Process Engineering University of Surrey GU2 7XH Guildford United Kingdom

This work sets the foundations for the design of control algorithms to facilitate manufacturing of a cell growth process using a continuous perfusion bioreactor. The algorithms are designed to work with different types of cell cultures and deal with major disturbances that might appear in the process. Different types of control strategies are designed, implemented and tested. First, a comprehensive mathematical model of convection and diffusion in a perfusion bioreactor, combined with cell growth kinetics, is developed and implemented using Computational Fluid Dynamics. The model describes the spatio-temporal evolution of glucose concentration and cell density within a 3D polymeric scaffold. Since such a model is too complex to be used directly for control studies, a simplified version is used for the design of the controllers. Finally, the performances of the control strategies are validated against the original high-fidelity CFD model, thus closing the loop. The simulations show good performances and satisfactory behavior.

关键词： tissue engineering perfusion bioreactor CFD modeling PID

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Global Sensitivity Analysis for a perfusion bioreactor system in tissue engineering

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IFAC-PapersOnLine 2021年第15期54卷 550-555页

作者： Ioana Nascu Tao Chen Wenli Du Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 200092 China Department of Chemical and Process Engineering University of Surrey GU2 7XH Guildford United Kingdom

This work presents a global sensitivity analysis and simulations of a perfusion bioreactor process using the method of high-dimensional model representation (HDMR). This method was developed to express the input–output relationships of a complex model with a high dimensional input space. The comprehensive mathematical model of convection and diffusion in a perfusion bioreactor, combined with cell growth kinetics, is developed and implemented using Computational Fluid Dynamics (with the commercial software COMSOL Multiphysics v5.5). The model describes the spatio-temporal evolution of glucose concentration, oxygen concentration, lactate concentration and cell density within a 3D polymeric scaffold. A quantitative analysis of the complex kinetic mechanisms using recent development of advanced mathematical approaches to global sensitivity and uncertainty analysis through HDMR can be exploited to investigate the important features of the perfusion bioreactor process as well as possible factors underlying qualitative discrepancies.

关键词： tissue engineering perfusion bioreactor CFD modeling high-dimensional model representation global sensitivity analysis

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Multi-stage hybrid multiobjective evolutionary algorithm for multi-type vehicle routing problem with simultaneous pickup and delivery and time windows

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Computers & Industrial Engineering 2025年 207卷

作者： Wenqiang Zhang Shun Li Miaolei Deng Yashuang Mu Peng Li Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education China Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology China School of Artificial Intelligence and Big Data Henan University of Technology China College of Information Science and Engineering Henan University of Technology China Institute for Complexity Science Henan University of Technology China

The vehicle routing problem (VRP) is a fundamental and extensively studied problem in logistics and transportation. Despite its significance, real-world applications often require more complex variants to address specific operational constraints and objectives. To address these limitations, this paper introduces the multi-type vehicle routing problem with simultaneous pickup and delivery and time windows (MTVRPSPDTW). Although hybrid multiobjective algorithms have been successful in combination optimization problems in recent years, it is still challenging to improve the performance of the algorithms by combining them with different timing. We propose a multi-stage hybrid evolutionary multi-objective optimization with a multi-region sampling strategy (MS-HEMO-MRSS) to optimize both vehicle number and wait time of MTVRPSPDTW. The algorithm integrates a three-stage hybrid approach, combining a global search using the multi-region sampling strategy (MRSS) with a local search based on routing sequence differential evolution (RSDE). The initial stage employs MRSS to quickly position the population near the Pareto front from various directions. The second stage utilizes RSDE to accelerate convergence towards central and edge areas of the Pareto front. In the final stage, individuals on Pareto front are selected and RSDE is used again to guide them towards the edge regions to enhance distribution performance. Specialized encoding, decoding techniques, and genetic operators tailored for two vectors are introduced to optimize MTVRPSPDTW. Comparative experiments with traditional multiobjective evolutionary algorithms demonstrate significant convergence and notable distribution performances, highlighting the benefits of employing different optimization strategies at different evolutionary stages.

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Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms

arXiv

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arXiv 2020年

作者： Mo, Haimiao Ding, Shuai Yang, Shanlin Vasilakos, Athanasios V. Zheng, Xi The School of Management Hefei University of Technology Anhui Hefei23009 China The Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education China The National Engineering Laboratory of Big Data Distribution and Exchange Technologies China Grimstad Norway The College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China The School of Computing Macquarie University Sydney Australia

Monitoring the respiratory rate is crucial for helping us identify respiratory disorders. Devices for conventional respiratory monitoring are inconvenient and scarcely available. Recent research has demonstrated the ability of non-contact technologies, such as photoplethysmography and infrared thermography, to gather respiratory signals from the face and monitor breathing. However, the current non-contact respiratory monitoring techniques have poor accuracy because they are sensitive to environmental influences like lighting and motion artifacts. Furthermore, frequent contact between users and the cloud in real-world medical application settings might cause service request delays and potentially the loss of personal data. We proposed a non-contact respiratory rate monitoring system with a cooperative three-layer design to increase the precision of respiratory monitoring and decrease data transmission latency. To reduce data transmission and network latency, our three-tier architecture layer-by-layer decomposes the computing tasks of respiration monitoring. Moreover, we improved the accuracy of respiratory monitoring by designing a target tracking algorithm and an algorithm for eliminating false peaks to extract high-quality respiratory signals. By gathering the data and choosing several regions of interest on the face, we were able to extract the respiration signal and investigate how different regions affected the monitoring of respiration. The results of the experiment indicate that when the nasal region is used to extract the respiratory signal, it performs experimentally best. Our approach performs better than rival approaches while transferring fewer data. © 2020, CC BY.

关键词： Edge computing

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Can constraint energy-saving policy facilitate industrial green production performance in China? 3

Can constraint energy-saving policy facilitate industrial gr...

引用

3rd International Conference on Advances in Energy and Environment Research, ICAEER 2018

作者： Yao, Yue Jiao, Jianling Han, Xiaofei School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei Anhui230009 China

The role of constraint energy-saving policy (ESP) playing in the coordinated development of energy conservation, emission reduction, and economic growth is of great significance to the country's sustainable progress. Applying a non-radial and non-oriented DEA model incorporating innovation output and multiple undesirable outputs, this paper measures the green production performance (GPP) of China's industrial sectors from 2002 to 2015. And then the effect of implementing energy-saving policy on GPP is investigated through Quasi-difference-in-differences (Quasi-DID) method. The results show that China's industrial GPP rises during 2002-2004 and then declines with fluctuations. Technology change (TC) is the dominant driver. Energy-saving policy positively affects industrial GPP in general yet further dynamic analysis reveals that such a positive effect remains unstable and finally manifests a reverse impact at the end of every five-year-plan period. Therefore, China should properly introduce market mechanisms, formulate comprehensive policy mix strategies to balance sustainable improvement of industrial economy and ecological environment. © The Authors, published by EDP Sciences, 2018.

关键词： Energy conservation

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Finite-time Sliding Mode Control Under Dynamic Event-triggered Scheme ⁎

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IFAC-PapersOnLine 2020年第2期53卷 5069-5074页

作者： Jiarui Li Yugang Niu Bei Chen The Key Laboratory of Advanced Control and Optimization for Chemical Process (East China University of Science and Technology) Ministry of Education and The Shanghai Institute of Intelligent Science and Technology (Tongji University) Shanghai China The School of Electric and Electronic Engineering Shanghai University of Engineering Science Shanghai 201620 China

In this work, we investigate the finite-time boundedness (FTB) problem for a class of continuous-time uncertain systems via sliding mode control (SMC) method. A dynamic event-triggered scheme is introduced to determine whether the measurement output will be transmitted. And then, a state observer is designed by means of the transmitted output information, based on which a sliding surface in the estimation space is construct. It is shown that the corresponding SMC law can drive the system trajectories onto the specified sliding surface in a finite (possibly short) time. Meanwhile, a partitioning strategy is introduced to analyze the FTB over both reaching phase and the sliding motion, respectively. Finally, a numerical simulation is given.

关键词： Sliding mode control finite-time boundedness event-triggered control external disturbance partitioning strategy

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Uncertainty Forecasting for Streamflow based on Support Vector Regression Method with Fuzzy Information Granulation

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Energy Procedia 2019年 158卷 6189-6194页

作者： Yaoyao He Yudong Yan Xu Wang Chao Wang School of Management Hefei University of Technology Hefei 230009 China Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei 230009 China China Institute of Water Resources and Hydropower Research Beijing 100048 China

Accurate and comprehensive forecasting of streamflow plays an important role in the uncertainly analysis of the hydrologic system. It is widely accepted that prediction interval (PI) can provide more precise and detailed information than deterministic forecasting when the uncertainty level of streamflow increases. Support vector regression (SVR) is a supervised learning model for classification and regression analysis based on associated learning algorithms. In this paper, fuzzy information granulation (FIG) is combined with SVR model (FIG-SVR) for uncertainty forecasting of streamflow. On behalf of evaluating the performance of the forecasting results, the evaluation metrics of point forecasting and interval prediction results are introduced. The real streamflow data from the Three Gorges in the Yangtze River are used to validate the proposed method based on the proposed method. The results show that the proposed method provides the high-quality point predictand and PIs, and the uncertainly of streamflow can be well handled.

关键词： Streamflow forecasting Support vector regression Fuzzy information Granulation Triangular fuzzy particles

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