The study examined the nexus between operations improvement function (dimensioned by contingency planning, benchmarking and continuous improvement processes) and organisational adaptability of Petroleum tank farms in ...The study examined the nexus between operations improvement function (dimensioned by contingency planning, benchmarking and continuous improvement processes) and organisational adaptability of Petroleum tank farms in South-South, Nigeria. The contingency theory and the theory of routine dynamics underpinned the study, and positivism was the underlying philosophy. The study adopted the cross-sectional survey through the use of questionnaire. 820 middle and top-level managers constituted the elements of the population, and the Krejcie & Morgan’s formula was used to determine the sample size of 262 respondents. Structural Equation Modeling was deployed to test the hypotheses at a 0.05 significance level. The results showed that contingency planning;benchmarking and continuous improvement processes all have a significant positive relationship with organisational adaptability of Petroleum tank farms in South-South, Nigeria. The study concludes that Petroleum tank farms’ operations should focus on the adoption of contingency planning, benchmarking and continuous improvement processes to enhance organisational adaptability. Therefore, it is recommended that the management of Petroleum tank farms should put in place mechanisms to advance continuous improvement processes by allocating the necessary amount of resources, such as energy, time and money, in order to promote the continuous development of the continuous improvement systems. Furthermore, managers of Petroleum tank farms should make better the adoption of contingency planning, ensuring that there is as much necessary training and information for employees on how to act during a crises situation, in order to evaluate safety and prepare in advance for recovery from disasters.展开更多
Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select...Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select the web service composition with the highest comprehensive QoS is a NP hard problem. In this paper, an improved multi population genetic algorithm is proposed. Cosine adaptive operator is added to the algorithm to avoid premature algorithm caused by improper genetic operator and the disadvantage of destroying excellent individuals in later period. Experimental results show that compared with the common genetic algorithm and multi population genetic algorithm, this algorithm has the advantages of shorter time consumption and higher accuracy, and effectively avoids the loss of effective genes in the population.展开更多
High photosynthetic productivity and widephotosynthetic adaptability are two importantphysiological characteristics for the sustained highyield and wider growing range of rice.Preliminaryexperiments indicated that the...High photosynthetic productivity and widephotosynthetic adaptability are two importantphysiological characteristics for the sustained highyield and wider growing range of rice.Preliminaryexperiments indicated that the utilization ofbeterosis between two different ecotype rices,US展开更多
The greatest fear of global climate change is drought since in most areas where wheat is grown water is the most important factor influencing wheat yield. Average wheat yield throughout the world is only 30-60% of the...The greatest fear of global climate change is drought since in most areas where wheat is grown water is the most important factor influencing wheat yield. Average wheat yield throughout the world is only 30-60% of the attainable yield potential because water shortage is the major factor preventing the realization of maximum yield. Periods of drought alternating with short periods of available water are common conditions to influence wheat productivity. Such conditions include variable frequency of dry and wet periods, intensity of drought, rate of drought onset and patterns of soil water deficit and/or atmospheric water deficit. It is this deficit and variable water conditions in semiarid environments that influence wheat productivity variously. This paper reviewed the physiological adaptation and benefits associated with deficit and variable water conditions. In addition, it also highlights the compensative effect of limited irrigation and breeding of new varieties for high water use efficiency (WUE) that could improve wheat productivity under water-limited environments in the semiarid regions. Considerable potential for further improvement in wheat WUE and productivity in semiarid environments seems to depend on effective conservation of moisture and efficient use of this limited water such as soil fertility improvement, conservation tillage, residues and film mulch, rain water harvesting for limited irrigation, and breeding for water saving varieties. Different crop, soil and water management strategies should be adjusted according to the conditions that prevail in various semiarid areas. By combining soil and water conservation approaches and adjusting the cropping system by growing drought-tolerant and water-saving cultivars, increase in wheat WUE and productivity could be achieved.展开更多
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ...An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.展开更多
An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the bucklin...An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the buckling load of laminated composite plates(LCPs)under uniaxial and biaxial compressions.In this method,a novel iterative adaptive Kriging model,which is structured using two training sample sets as active and adaptive points,is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process.The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples.The cell-based smoothed discrete shear gap method(CS-DSG3)is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets.The buckling load of the LCPs is maximized by utilizing the IPSO algorithm.To demonstrate the efficiency and accuracy of the proposed methodology,the LCPs with different layers(2,3,4,and 10 layers),boundary conditions,aspect ratios and load patterns(biaxial and uniaxial loads)are investigated.The results obtained by proposed method are in good agreement with the literature results,but with less computational burden.By applying adaptive radial Kriging model,the accurate optimal resultsebased predictions of the buckling load are obtained for the studied LCPs.展开更多
In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied...In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.展开更多
The performance and efficiency of a baler deteriorate as a result of gearbox failure.One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes.Th...The performance and efficiency of a baler deteriorate as a result of gearbox failure.One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes.This paper proposes a fault feature selection method using an improved adaptive genetic algorithm for a baler gearbox.This method directly obtains the minimum fault feature parameter set that is most sensitive to fault features through attribute reduction.The main benefit of the improved adaptive genetic algorithm is its excellent performance in terms of the efficiency of attribute reduction without requiring prior information.Therefore,this method should be capable of timely diagnosis and monitoring.Experimental validation was performed and promising findings highlighting the relationship between diagnosis results and faults were obtained.The results indicate that when using the improved genetic algorithm to reduce 12 fault characteristic parameters to three without a priori information,100%fault diagnosis accuracy can be achieved based on these fault characteristics and the time required for fault feature parameter selection using the improved genetic algorithm is reduced by half compared to traditional methods.The proposed method provides important insights into the instant fault diagnosis and fault monitoring of mechanical devices.展开更多
This paper investiga tes a trajectory planning algorithm to reduce the manipulator’s working time.A t ime-optimal trajectory planning(TOTP)is conducted based on improved ad aptive genetic algorithm(IAGA)and combined ...This paper investiga tes a trajectory planning algorithm to reduce the manipulator’s working time.A t ime-optimal trajectory planning(TOTP)is conducted based on improved ad aptive genetic algorithm(IAGA)and combined with cubic triangular Bezier spline(CTBS).The CTBS based trajectory planning we did before can achieve continuous second and third derivation,hence it meets the stability requirements of the m anipulator.The working time can be greatly reduced by applying IAGA to the puma 560 trajectory planning when considering physical constraints such as angular ve locity,angular acceleration and jerk.Simulation experiments in both Matlab and ADAMS illustrate that TOTP based on IAGA can give a time optimal result with sm oothness and stability.展开更多
With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation method...With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.展开更多
The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved p...The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.展开更多
文摘The study examined the nexus between operations improvement function (dimensioned by contingency planning, benchmarking and continuous improvement processes) and organisational adaptability of Petroleum tank farms in South-South, Nigeria. The contingency theory and the theory of routine dynamics underpinned the study, and positivism was the underlying philosophy. The study adopted the cross-sectional survey through the use of questionnaire. 820 middle and top-level managers constituted the elements of the population, and the Krejcie & Morgan’s formula was used to determine the sample size of 262 respondents. Structural Equation Modeling was deployed to test the hypotheses at a 0.05 significance level. The results showed that contingency planning;benchmarking and continuous improvement processes all have a significant positive relationship with organisational adaptability of Petroleum tank farms in South-South, Nigeria. The study concludes that Petroleum tank farms’ operations should focus on the adoption of contingency planning, benchmarking and continuous improvement processes to enhance organisational adaptability. Therefore, it is recommended that the management of Petroleum tank farms should put in place mechanisms to advance continuous improvement processes by allocating the necessary amount of resources, such as energy, time and money, in order to promote the continuous development of the continuous improvement systems. Furthermore, managers of Petroleum tank farms should make better the adoption of contingency planning, ensuring that there is as much necessary training and information for employees on how to act during a crises situation, in order to evaluate safety and prepare in advance for recovery from disasters.
文摘Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select the web service composition with the highest comprehensive QoS is a NP hard problem. In this paper, an improved multi population genetic algorithm is proposed. Cosine adaptive operator is added to the algorithm to avoid premature algorithm caused by improper genetic operator and the disadvantage of destroying excellent individuals in later period. Experimental results show that compared with the common genetic algorithm and multi population genetic algorithm, this algorithm has the advantages of shorter time consumption and higher accuracy, and effectively avoids the loss of effective genes in the population.
文摘High photosynthetic productivity and widephotosynthetic adaptability are two importantphysiological characteristics for the sustained highyield and wider growing range of rice.Preliminaryexperiments indicated that the utilization ofbeterosis between two different ecotype rices,US
基金T his paper was supported by the Major State Basic Research Development Program of People's Republic of China(G1999011708)the Key Laboratory Visiting Scholar Foundation of Chinese Education Ministry.
文摘The greatest fear of global climate change is drought since in most areas where wheat is grown water is the most important factor influencing wheat yield. Average wheat yield throughout the world is only 30-60% of the attainable yield potential because water shortage is the major factor preventing the realization of maximum yield. Periods of drought alternating with short periods of available water are common conditions to influence wheat productivity. Such conditions include variable frequency of dry and wet periods, intensity of drought, rate of drought onset and patterns of soil water deficit and/or atmospheric water deficit. It is this deficit and variable water conditions in semiarid environments that influence wheat productivity variously. This paper reviewed the physiological adaptation and benefits associated with deficit and variable water conditions. In addition, it also highlights the compensative effect of limited irrigation and breeding of new varieties for high water use efficiency (WUE) that could improve wheat productivity under water-limited environments in the semiarid regions. Considerable potential for further improvement in wheat WUE and productivity in semiarid environments seems to depend on effective conservation of moisture and efficient use of this limited water such as soil fertility improvement, conservation tillage, residues and film mulch, rain water harvesting for limited irrigation, and breeding for water saving varieties. Different crop, soil and water management strategies should be adjusted according to the conditions that prevail in various semiarid areas. By combining soil and water conservation approaches and adjusting the cropping system by growing drought-tolerant and water-saving cultivars, increase in wheat WUE and productivity could be achieved.
基金Supported by the National Natural Science Foundation of China(51175262)the Research Fund for Doctoral Program of Higher Education of China(20093218110020)+2 种基金the Jiangsu Province Science Foundation for Excellent Youths(BK201210111)the Jiangsu Province Industry-Academy-Research Grant(BY201220116)the Innovative and Excellent Foundation for Doctoral Dissertation of Nanjing University of Aeronautics and Astronautics(BCXJ10-09)
文摘An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.
基金Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant number 107.02-2019.330.
文摘An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the buckling load of laminated composite plates(LCPs)under uniaxial and biaxial compressions.In this method,a novel iterative adaptive Kriging model,which is structured using two training sample sets as active and adaptive points,is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process.The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples.The cell-based smoothed discrete shear gap method(CS-DSG3)is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets.The buckling load of the LCPs is maximized by utilizing the IPSO algorithm.To demonstrate the efficiency and accuracy of the proposed methodology,the LCPs with different layers(2,3,4,and 10 layers),boundary conditions,aspect ratios and load patterns(biaxial and uniaxial loads)are investigated.The results obtained by proposed method are in good agreement with the literature results,but with less computational burden.By applying adaptive radial Kriging model,the accurate optimal resultsebased predictions of the buckling load are obtained for the studied LCPs.
基金Projects(61203020,61403190)supported by the National Natural Science Foundation of ChinaProject(BK20141461)supported by the Jiangsu Province Natural Science Foundation,China
文摘In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.
基金National Key R&D Program of China(2016YFd01304)Postgraduate Innovation Support Project of Shijiazhuang Tiedao University(YC20035).
文摘The performance and efficiency of a baler deteriorate as a result of gearbox failure.One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes.This paper proposes a fault feature selection method using an improved adaptive genetic algorithm for a baler gearbox.This method directly obtains the minimum fault feature parameter set that is most sensitive to fault features through attribute reduction.The main benefit of the improved adaptive genetic algorithm is its excellent performance in terms of the efficiency of attribute reduction without requiring prior information.Therefore,this method should be capable of timely diagnosis and monitoring.Experimental validation was performed and promising findings highlighting the relationship between diagnosis results and faults were obtained.The results indicate that when using the improved genetic algorithm to reduce 12 fault characteristic parameters to three without a priori information,100%fault diagnosis accuracy can be achieved based on these fault characteristics and the time required for fault feature parameter selection using the improved genetic algorithm is reduced by half compared to traditional methods.The proposed method provides important insights into the instant fault diagnosis and fault monitoring of mechanical devices.
基金Fund of Taishan Scholar in Shandong Province,Shandong University of Science and Technology Research Fund(No.2010KYTD101)
文摘This paper investiga tes a trajectory planning algorithm to reduce the manipulator’s working time.A t ime-optimal trajectory planning(TOTP)is conducted based on improved ad aptive genetic algorithm(IAGA)and combined with cubic triangular Bezier spline(CTBS).The CTBS based trajectory planning we did before can achieve continuous second and third derivation,hence it meets the stability requirements of the m anipulator.The working time can be greatly reduced by applying IAGA to the puma 560 trajectory planning when considering physical constraints such as angular ve locity,angular acceleration and jerk.Simulation experiments in both Matlab and ADAMS illustrate that TOTP based on IAGA can give a time optimal result with sm oothness and stability.
基金supported by the National Nature Science Foundation of China(NSFC 60622110,61471220,91538107,91638205)National Basic Research Project of China(973,2013CB329006),GY22016058
文摘With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.
基金This work was supported by Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.,China(J2022114,Risk Assessment and Coordinated Operation of Coastal Wind Power Multi-Point Pooling Access System under Extreme Weather).
文摘The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.