In the last three decades much effort has been devoted in process integration as a way to improve economic and environmental performance of chemical processes. Although the established frameworks have undergone consta...In the last three decades much effort has been devoted in process integration as a way to improve economic and environmental performance of chemical processes. Although the established frameworks have undergone constant refinement toward formulating and solving complicated process integration problems, less attention has been drawn to the problem of sequential applications of mass integration. This work addresses this problem by proposing an algorithm for optimal ordering of the process sinks in direct recycling problems, which is compatible with the typical mass integration formulation. The order consists in selecting the optimal sink at a specific integration step given the selection of the previous steps and the remaining process sources. Such order is identified through a succession of preemptive goal programming problems, namely of optimization problems characterized by more objectives at different priority levels. Indeed, the target for each sink is obtained by maximizing the total flow recycled from the available process sources to this sink and then minimizing the use of pure sources, starting from the purest one;the hierarchy is respected through a succession of linear optimization problems with a single objective function. While the conditional optimality of the algorithm holds always, a thorough statistical analysis including structured to random scenarios of process sources and process sinks shows how frequently the sequential ordering algorithm is outperformed with respect to the total recycled amount by a different selection of process sinks with the same cardinality. Two more case studies proving the usefulness of ordering the process sinks are illustrated. Extensions of the algorithm are also identified to cover more aspects of the process integration framework.展开更多
ISP(Internet service providers)和企业部署网络监测系统以获取网络的性能数据,确保网络的安全性和连通性,最终加强和改善全局的网络性能.网络监测系统的设计和优化是目前的一个研究热点,其优化目标是最小化监测系统的部署代价和维护代...ISP(Internet service providers)和企业部署网络监测系统以获取网络的性能数据,确保网络的安全性和连通性,最终加强和改善全局的网络性能.网络监测系统的设计和优化是目前的一个研究热点,其优化目标是最小化监测系统的部署代价和维护代价,并使得对网络的影响尽可能地小.根据测量方式和收集框架的不同,可以设计出不同的网络测量部署模型.这些模型的最优化问题通常是NP难的,一般采用整数规划、设计近似算法和映射到经典优化问题等方法来求取模型的优化解.总结了网络测量部署模型及其优化算法的研究现状,指出了该领域中需要进一步研究的热点问题.展开更多
Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines,especially those involving big data.Successes in these areas also attract researchers from the comm...Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines,especially those involving big data.Successes in these areas also attract researchers from the community of fluid mechanics,especially in the field of active flow control(AFC).This article surveys recent successful applications of machine learning in AFC,highlights general ideas,and aims at offering a basic outline for those who are interested in this specific topic.In this short review,we focus on two methodologies,i.e.,genetic programming(GP)and deep reinforcement learning(DRL),both having been proven effective,efficient,and robust in certain AFC problems,and outline some future prospects that might shed some light for relevant studies.展开更多
Background Prenatal hyperglycaemia may increase metabolic syndrome susceptibility of the offspring. An underlying component of the development of these morbidities is hepatic gluconeogenic molecular dysfunction. We hy...Background Prenatal hyperglycaemia may increase metabolic syndrome susceptibility of the offspring. An underlying component of the development of these morbidities is hepatic gluconeogenic molecular dysfunction. We hypothesized that maternal hyperglycaemia will influence her offsprings hepatic peroxisome proliferator-activated receptor coactivator-la （PGC-la） expression, a key regulator of glucose production in hepatocytes. Method We established maternal hyperglycaemia by streptozotocin injection to induce the maternal hyperglycaemic Wistar rat model. Offspring from the severe hyperglycemia group （SDO） and control group （CO） were monitored until 180 days after birth. Blood pressure, lipid metabolism indicators and insulin resistance （IR） were measured. Hepatic PGC-la expression was analyzed by reverse transcription polymerase chain reaction and Western blotting, mRNA expression of two key enzymes in gluconeogenesis, glucose-6-phospha-tase （G-6-Pase） and phosphoenolpyruvate carboxykinase （PEPCK）, were analyzed and compared. Results In the SDO group, PGC-la expression at protein and mRNA levels were increased, so were expression of G-6-Pase and PEPCK （P〈0.05）. The above effects were seen prior to the onset of IR. Conclusion The hepatic gluconeogenic molecular dysfunction may contribute to the metabolic morbidities experienced by this population.展开更多
The distributed generation (DG) plays an important role in the context of the environmental problems and sustain- able development throughout the world. This paper proposes a DG siting and sizing model in an active di...The distributed generation (DG) plays an important role in the context of the environmental problems and sustain- able development throughout the world. This paper proposes a DG siting and sizing model in an active distribution network (ADN). The objective is to minimize the total cost, including investment, operation and maintenance costs. The proposed model is transferred to a Mixed Integer Second-Order Cone Programming (MISOCP) model based on a distribution network forward backward-sweep power flow and constraint relaxation. The CVX platform and GUROBI solver are used for the solution. The scenario analysis is used for the uncertainties of load and DG. Different numbers of operational scenarios are considered in order to analyze the effect of a non-network solution to the final planning result and total investment. The planning results with and without consideration of active managements, and the planning results with and without taking environmental profits into consideration, are compared and analyzed. The proposed methodology is verified with a modified IEEE 33 example.展开更多
Aiming at the shortcomings of a traditional centralized control in an active distribution network(AND),this paper proposes a leader-follower distributed group cooperative control strategy to realize multiple operation...Aiming at the shortcomings of a traditional centralized control in an active distribution network(AND),this paper proposes a leader-follower distributed group cooperative control strategy to realize multiple operation and control tasks for an ADN.The distributed information exchange protocols of the distributed generation(DG)group devoted to node voltage regulation or exchange power control are developed using a DG power utilization ratio as the consensus variable.On these bases,this study further investigates the leader optimal selection method for a DG group to improve the response speed of the distributed control system.Furthermore,a single or multiple leader selection model is established to minimize the constraints of the one-step convergence factor and the number of leaders to improve the response speed of the distributed control system.The simulation results of the IEEE 33 bus standard test system show the effectiveness of the proposed distributed control strategy.In addition,the response speed of a DG control group can be improved effectively when the single or multiple leaders are selected optimally.展开更多
Most existing distribution networks are difficult to withstand the impact of meteorological disasters. With the development of active distribution networks(ADNs), more and more upgrading and updating resources are app...Most existing distribution networks are difficult to withstand the impact of meteorological disasters. With the development of active distribution networks(ADNs), more and more upgrading and updating resources are applied to enhance the resilience of ADNs. A two-stage stochastic mixed-integer programming(SMIP) model is proposed in this paper to minimize the upgrading and operation cost of ADNs by considering random scenarios referring to different operation scenarios of ADNs caused by disastrous weather events. In the first stage, the planning decision is formulated according to the measures of hardening existing distribution lines, upgrading automatic switches, and deploying energy storage resources. The second stage is to evaluate the operation cost of ADNs by considering the cost of load shedding due to disastrous weather and optimal deployment of energy storage systems(ESSs) under normal weather condition. A novel modeling method is proposed to address the uncertainty of the operation state of distribution lines according to the canonical representation of logical constraints. The progressive hedging algorithm(PHA) is adopted to solve the SMIP model. The IEEE 33-node test system is employed to verify the feasibility and effectiveness of the proposed method. The results show that the proposed model can enhance the resilience of the ADN while ensuring economy.展开更多
The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle （LV） from multi-slice cine short axis cardiac magnetic reso...The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle （LV） from multi-slice cine short axis cardiac magnetic resonance （MR） images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting （LGDF） energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract （LVOT） and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions （MICCAI） 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.9781 and 0.9567, for LV mass these values are 0.9249 and 0.8554. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.展开更多
文摘In the last three decades much effort has been devoted in process integration as a way to improve economic and environmental performance of chemical processes. Although the established frameworks have undergone constant refinement toward formulating and solving complicated process integration problems, less attention has been drawn to the problem of sequential applications of mass integration. This work addresses this problem by proposing an algorithm for optimal ordering of the process sinks in direct recycling problems, which is compatible with the typical mass integration formulation. The order consists in selecting the optimal sink at a specific integration step given the selection of the previous steps and the remaining process sources. Such order is identified through a succession of preemptive goal programming problems, namely of optimization problems characterized by more objectives at different priority levels. Indeed, the target for each sink is obtained by maximizing the total flow recycled from the available process sources to this sink and then minimizing the use of pure sources, starting from the purest one;the hierarchy is respected through a succession of linear optimization problems with a single objective function. While the conditional optimality of the algorithm holds always, a thorough statistical analysis including structured to random scenarios of process sources and process sinks shows how frequently the sequential ordering algorithm is outperformed with respect to the total recycled amount by a different selection of process sinks with the same cardinality. Two more case studies proving the usefulness of ordering the process sinks are illustrated. Extensions of the algorithm are also identified to cover more aspects of the process integration framework.
基金Supported by the National Natural Science Foundation of China under Grant Nos.60603062 60373023 (国家自然科学基金)+1 种基金the National Basic Research Program of China under Grant No.2007CB310901 (国家重点基础研究发展计划(973))the Natural Science Foundation of Hu'nan Province of China under Grant No.06JJ3035 (湖南省自然科学基金)
文摘ISP(Internet service providers)和企业部署网络监测系统以获取网络的性能数据,确保网络的安全性和连通性,最终加强和改善全局的网络性能.网络监测系统的设计和优化是目前的一个研究热点,其优化目标是最小化监测系统的部署代价和维护代价,并使得对网络的影响尽可能地小.根据测量方式和收集框架的不同,可以设计出不同的网络测量部署模型.这些模型的最优化问题通常是NP难的,一般采用整数规划、设计近似算法和映射到经典优化问题等方法来求取模型的优化解.总结了网络测量部署模型及其优化算法的研究现状,指出了该领域中需要进一步研究的热点问题.
基金This work was support by the Research Grants Council of Hong Kong under General Research Fund(Grant Nos.15249316,15214418)the Departmental General Research Fund(Grant No.G-YBXQ).
文摘Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines,especially those involving big data.Successes in these areas also attract researchers from the community of fluid mechanics,especially in the field of active flow control(AFC).This article surveys recent successful applications of machine learning in AFC,highlights general ideas,and aims at offering a basic outline for those who are interested in this specific topic.In this short review,we focus on two methodologies,i.e.,genetic programming(GP)and deep reinforcement learning(DRL),both having been proven effective,efficient,and robust in certain AFC problems,and outline some future prospects that might shed some light for relevant studies.
文摘Background Prenatal hyperglycaemia may increase metabolic syndrome susceptibility of the offspring. An underlying component of the development of these morbidities is hepatic gluconeogenic molecular dysfunction. We hypothesized that maternal hyperglycaemia will influence her offsprings hepatic peroxisome proliferator-activated receptor coactivator-la （PGC-la） expression, a key regulator of glucose production in hepatocytes. Method We established maternal hyperglycaemia by streptozotocin injection to induce the maternal hyperglycaemic Wistar rat model. Offspring from the severe hyperglycemia group （SDO） and control group （CO） were monitored until 180 days after birth. Blood pressure, lipid metabolism indicators and insulin resistance （IR） were measured. Hepatic PGC-la expression was analyzed by reverse transcription polymerase chain reaction and Western blotting, mRNA expression of two key enzymes in gluconeogenesis, glucose-6-phospha-tase （G-6-Pase） and phosphoenolpyruvate carboxykinase （PEPCK）, were analyzed and compared. Results In the SDO group, PGC-la expression at protein and mRNA levels were increased, so were expression of G-6-Pase and PEPCK （P〈0.05）. The above effects were seen prior to the onset of IR. Conclusion The hepatic gluconeogenic molecular dysfunction may contribute to the metabolic morbidities experienced by this population.
基金This work was supported in part by the Shanghai Engineering Re-search Center of Green Energy Grid-Connected Technology under Grant 13DZ2251900the Key Laboratory of Control of Power Transmission and Conversion(SJTU),Ministry of Education(2016AA01,2016AA03).
文摘The distributed generation (DG) plays an important role in the context of the environmental problems and sustain- able development throughout the world. This paper proposes a DG siting and sizing model in an active distribution network (ADN). The objective is to minimize the total cost, including investment, operation and maintenance costs. The proposed model is transferred to a Mixed Integer Second-Order Cone Programming (MISOCP) model based on a distribution network forward backward-sweep power flow and constraint relaxation. The CVX platform and GUROBI solver are used for the solution. The scenario analysis is used for the uncertainties of load and DG. Different numbers of operational scenarios are considered in order to analyze the effect of a non-network solution to the final planning result and total investment. The planning results with and without consideration of active managements, and the planning results with and without taking environmental profits into consideration, are compared and analyzed. The proposed methodology is verified with a modified IEEE 33 example.
文摘Aiming at the shortcomings of a traditional centralized control in an active distribution network(AND),this paper proposes a leader-follower distributed group cooperative control strategy to realize multiple operation and control tasks for an ADN.The distributed information exchange protocols of the distributed generation(DG)group devoted to node voltage regulation or exchange power control are developed using a DG power utilization ratio as the consensus variable.On these bases,this study further investigates the leader optimal selection method for a DG group to improve the response speed of the distributed control system.Furthermore,a single or multiple leader selection model is established to minimize the constraints of the one-step convergence factor and the number of leaders to improve the response speed of the distributed control system.The simulation results of the IEEE 33 bus standard test system show the effectiveness of the proposed distributed control strategy.In addition,the response speed of a DG control group can be improved effectively when the single or multiple leaders are selected optimally.
基金supported by National Natural Science Foundation of China (No. U1866603)Innovation Support Program of Chongqing for Preferential Returned Chinese Scholars (No. cx2021036)Natural Science Foundation of Chongqing,China (No. CSTB2022NSCQ-BHX0729)。
文摘Most existing distribution networks are difficult to withstand the impact of meteorological disasters. With the development of active distribution networks(ADNs), more and more upgrading and updating resources are applied to enhance the resilience of ADNs. A two-stage stochastic mixed-integer programming(SMIP) model is proposed in this paper to minimize the upgrading and operation cost of ADNs by considering random scenarios referring to different operation scenarios of ADNs caused by disastrous weather events. In the first stage, the planning decision is formulated according to the measures of hardening existing distribution lines, upgrading automatic switches, and deploying energy storage resources. The second stage is to evaluate the operation cost of ADNs by considering the cost of load shedding due to disastrous weather and optimal deployment of energy storage systems(ESSs) under normal weather condition. A novel modeling method is proposed to address the uncertainty of the operation state of distribution lines according to the canonical representation of logical constraints. The progressive hedging algorithm(PHA) is adopted to solve the SMIP model. The IEEE 33-node test system is employed to verify the feasibility and effectiveness of the proposed method. The results show that the proposed model can enhance the resilience of the ADN while ensuring economy.
基金supported by Department of Science and Technology, Ministry of Science and Technology, India (No. DST/TSG/ICT/2010/08)
文摘The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle （LV） from multi-slice cine short axis cardiac magnetic resonance （MR） images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting （LGDF） energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract （LVOT） and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions （MICCAI） 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.9781 and 0.9567, for LV mass these values are 0.9249 and 0.8554. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.
