The present research deals with the problem of development of an integrated expert-analytical system for optimum selection of calculated oil-field-geophysical parameters of oil and gas deposits with the purpose of inc...The present research deals with the problem of development of an integrated expert-analytical system for optimum selection of calculated oil-field-geophysical parameters of oil and gas deposits with the purpose of increasing the accuracy of assessment of the reserves of oil and gas deposits. The purpose of the system is to make current adequate decisions on determining of oil-and-gas saturation of strata and future identification of the most significant methods for that, with these methods forming the foundation of knowledge bases for oil-and-gas deposits of the Apsheron peninsula of Azerbaijan. The system architecture allows for expanding the system with its subsequent transformation into a cluster of expert-analytical systems. A logical model of the proposed system is presented. The paper contains a detailed description of the mechanism of operation of the system as a whole and of its individual blocks. Mathematical and formal-logical bases of the intelligent system are explained. The system is equipped with a tool for dynamic statistical analysis of decisions made by it, with representation of the results in real-time mode. The results of the system testing on specific oil-and-gas deposit of the Apsheron peninsula of Azerbaijan in 2013 are given.展开更多
The purpose of reoptimization using approximation methods—application of knowledge about the solution of the initial instance I, provided to achieve a better quality of approximation (approximation ratio) of an algor...The purpose of reoptimization using approximation methods—application of knowledge about the solution of the initial instance I, provided to achieve a better quality of approximation (approximation ratio) of an algorithm for determining optimal or close to it solutions of some “minor” changes of instance I. To solve the problem Ins-Max-EkCSP-P (reoptimization of Max-EkCSP-P with the addition of one constraint) with approximation resistant predicate P exists a polynomial threshold (optimal) -approximation algorithm, where the threshold “random” approximation ratio of P). When the unique games conjecture (UGC) is hold there exists a polynomial threshold (optimal) -approximation algorithm (where and the integrality gap of semidefinite relaxation of Max-EkCSP-P problem Z) to solve the problem Ins-Max-EkCSP-P.展开更多
The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of manufacturing.These technologies enable collecting,storin...The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of manufacturing.These technologies enable collecting,storing,and retrieving essential information from the manufacturing stage.Data collected at sites are shared with others where execution automatedly occurs.The obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing process.However,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major concern.The current research validates the information optimally to offer a minimum set of activities to complete the disassembly process.An optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in manufacturing.However,finding an optimal DSP is complex because of its combinatorial nature.The genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP problem.The converging nature at local optima is a limitation in the traditional GA.This study improvised the GA workability by integrating with the proposed priori crossover operator.An optimality function is defined to reduce disassembly effort by considering directional changes as parameters.The enhanced GA method is tested on a real-time product to evaluate the performance.The obtained results reveal that diversity control depends on the operators employed in the disassembly attributes.The proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the product.The effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.展开更多
文摘The present research deals with the problem of development of an integrated expert-analytical system for optimum selection of calculated oil-field-geophysical parameters of oil and gas deposits with the purpose of increasing the accuracy of assessment of the reserves of oil and gas deposits. The purpose of the system is to make current adequate decisions on determining of oil-and-gas saturation of strata and future identification of the most significant methods for that, with these methods forming the foundation of knowledge bases for oil-and-gas deposits of the Apsheron peninsula of Azerbaijan. The system architecture allows for expanding the system with its subsequent transformation into a cluster of expert-analytical systems. A logical model of the proposed system is presented. The paper contains a detailed description of the mechanism of operation of the system as a whole and of its individual blocks. Mathematical and formal-logical bases of the intelligent system are explained. The system is equipped with a tool for dynamic statistical analysis of decisions made by it, with representation of the results in real-time mode. The results of the system testing on specific oil-and-gas deposit of the Apsheron peninsula of Azerbaijan in 2013 are given.
文摘The purpose of reoptimization using approximation methods—application of knowledge about the solution of the initial instance I, provided to achieve a better quality of approximation (approximation ratio) of an algorithm for determining optimal or close to it solutions of some “minor” changes of instance I. To solve the problem Ins-Max-EkCSP-P (reoptimization of Max-EkCSP-P with the addition of one constraint) with approximation resistant predicate P exists a polynomial threshold (optimal) -approximation algorithm, where the threshold “random” approximation ratio of P). When the unique games conjecture (UGC) is hold there exists a polynomial threshold (optimal) -approximation algorithm (where and the integrality gap of semidefinite relaxation of Max-EkCSP-P problem Z) to solve the problem Ins-Max-EkCSP-P.
基金The authors are grateful to the Raytheon Chair for Systems Engineering for funding.
文摘The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of manufacturing.These technologies enable collecting,storing,and retrieving essential information from the manufacturing stage.Data collected at sites are shared with others where execution automatedly occurs.The obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing process.However,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major concern.The current research validates the information optimally to offer a minimum set of activities to complete the disassembly process.An optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in manufacturing.However,finding an optimal DSP is complex because of its combinatorial nature.The genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP problem.The converging nature at local optima is a limitation in the traditional GA.This study improvised the GA workability by integrating with the proposed priori crossover operator.An optimality function is defined to reduce disassembly effort by considering directional changes as parameters.The enhanced GA method is tested on a real-time product to evaluate the performance.The obtained results reveal that diversity control depends on the operators employed in the disassembly attributes.The proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the product.The effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.
