The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the produc...The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the production efficiency. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied for comparison with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.展开更多
Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in...Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.展开更多
Energy efficient communication is a plenary issue in Wireless Sensor Networks (WSNs). Contemporary energy efficient optimization schemes are focused on reducing power consumption in various aspects of hardware design,...Energy efficient communication is a plenary issue in Wireless Sensor Networks (WSNs). Contemporary energy efficient optimization schemes are focused on reducing power consumption in various aspects of hardware design, data processing, network protocols and operating system. In this paper, optimization of network is formulated by Cuckoo Based Particle Approach (CBPA). Nodes are deployed randomly and organized as static clusters by Cuckoo Search (CS). After the cluster heads are selected, the information is collected, aggregated and forwarded to the base station using generalized particle approach algorithm. The Generalized Particle Model Algorithm (GPMA) transforms the network energy consumption problem into dynamics and kinematics of numerous particles in a force-field. The proposed approach can significantly lengthen the network lifetime when compared to traditional methods.展开更多
With the construction of automated docks,health monitoring technology as a parallel safety assurance technology for unmanned hoisting machinery has become a hot spot in the development of the industry.Hoisting machine...With the construction of automated docks,health monitoring technology as a parallel safety assurance technology for unmanned hoisting machinery has become a hot spot in the development of the industry.Hoisting machinery has a huge structure and numerous welded joints.The complexity and nonlinearity of the welded structure itself makes the structural failure parts random and difficult to arrange for monitoring sensors.In order to solve the problem of effectiveness and stability of the sensor arrangement method for monitoring the structure of hoisting machinery.Using the global and local search capabilities enhanced by the complementary search mechanism,a structural vibration monitoring sensor placement algorithm based on the harmony genetic algorithm is proposed.Firstly,the model is established for modal analysis to obtain the displacement matrix of each mode.Secondly,the optimal parameter combination is established through parameter comparison,and the random search mechanism is used to quickly search in the modal matrix to obtain the preliminary solution,and then the preliminary solution is genetically summed The mutation operation obtains the optimized solution,and the optimal solution is retained through repeated iterations to realize the decision of the vibration sensor layout of the crane structure monitoring.Combining the comparison test of harmony genetic algorithm,harmony search algorithm and genet-ic algorithm,the fitness of harmony genetic algorithm in X,Y and Z directions were 0.0045,0.0084 and 0.0058,respectively,which were all optimal.And the average probability of deviating from the optimal path is 1.10%,19.34%,and 54.43%,which are also optimal.Harmony genetic algorithm has the advantages of simplicity,fastness and strong global search ability,and can obtain better fitness value and better search stability.展开更多
基金Supported by the National Natural Science Foundation of China (61074079)Shanghai Leading Academic Discipline Project(B504)+1 种基金Specialized Research Fund for the Doctoral Program of Higher Education of China (20100074120010)the Natural Science Foundation of Shanghai City (11ZR1409700)
文摘The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the production efficiency. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied for comparison with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Larg Groups project Under Grant Number(71/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R238)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR20.
文摘Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.
基金The Science and Technology Project Affiliated to the Education Department of Chongqing Municipality(KJ1500433)The Natural Science Foundation of Chongqing University of Posts and Telecommunications(A2012-97)~~
文摘Energy efficient communication is a plenary issue in Wireless Sensor Networks (WSNs). Contemporary energy efficient optimization schemes are focused on reducing power consumption in various aspects of hardware design, data processing, network protocols and operating system. In this paper, optimization of network is formulated by Cuckoo Based Particle Approach (CBPA). Nodes are deployed randomly and organized as static clusters by Cuckoo Search (CS). After the cluster heads are selected, the information is collected, aggregated and forwarded to the base station using generalized particle approach algorithm. The Generalized Particle Model Algorithm (GPMA) transforms the network energy consumption problem into dynamics and kinematics of numerous particles in a force-field. The proposed approach can significantly lengthen the network lifetime when compared to traditional methods.
基金The author gratefully acknowledges the support of this work by the national key research and development project under the Grant No.2017YFC0805100China Special Equipment Inspection and Research Institute under the Grant No.2021 Youth 17.
文摘With the construction of automated docks,health monitoring technology as a parallel safety assurance technology for unmanned hoisting machinery has become a hot spot in the development of the industry.Hoisting machinery has a huge structure and numerous welded joints.The complexity and nonlinearity of the welded structure itself makes the structural failure parts random and difficult to arrange for monitoring sensors.In order to solve the problem of effectiveness and stability of the sensor arrangement method for monitoring the structure of hoisting machinery.Using the global and local search capabilities enhanced by the complementary search mechanism,a structural vibration monitoring sensor placement algorithm based on the harmony genetic algorithm is proposed.Firstly,the model is established for modal analysis to obtain the displacement matrix of each mode.Secondly,the optimal parameter combination is established through parameter comparison,and the random search mechanism is used to quickly search in the modal matrix to obtain the preliminary solution,and then the preliminary solution is genetically summed The mutation operation obtains the optimized solution,and the optimal solution is retained through repeated iterations to realize the decision of the vibration sensor layout of the crane structure monitoring.Combining the comparison test of harmony genetic algorithm,harmony search algorithm and genet-ic algorithm,the fitness of harmony genetic algorithm in X,Y and Z directions were 0.0045,0.0084 and 0.0058,respectively,which were all optimal.And the average probability of deviating from the optimal path is 1.10%,19.34%,and 54.43%,which are also optimal.Harmony genetic algorithm has the advantages of simplicity,fastness and strong global search ability,and can obtain better fitness value and better search stability.
