Quality traceability plays an essential role in assembling and welding offshore platform blocks.The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platf...Quality traceability plays an essential role in assembling and welding offshore platform blocks.The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry.Currently,qualitymanagement remains in the era of primary information,and there is a lack of effective tracking and recording of welding quality data.When welding defects are encountered,it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data.In this paper,a composite welding quality traceability model for offshore platform block construction process is proposed,it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm.By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm,the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems.Furthermore,the model and the quality traceability algorithm are checked by cases in actual working conditions.Verification analyses suggest that the proposed early-warningmodel for welding quality and the algorithmfor optimizing backtracking requests are effective and can be applied to the actual construction process.展开更多
Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, sev- eral applications can benefit from a system capable of...Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, sev- eral applications can benefit from a system capable of recom- mending packages of items, in the form of sets. Sample appli- cations include travel planning with a limited budget （price or time） and twitter users wanting to select worthwhile tweeters to follow, given that they can deal with only a bounded num- ber of tweets. In these contexts, there is a need for a system that can recommend the top-k packages for the user to choose from. Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value （rating） and a cost associated with it, and the user specifies a maximum total cost （budget） for any recommended set of items. Our composite recommender system has access to one or more component recommender systems focusing on different do- mains, as well as to information sources which can provide the cost associated with each item. Because the problem of deciding whether there is a recommendation （package） whose cost is under a given budget and whose value exceeds some threshold is NP-complete, we devise several approximation algorithms for generating the top-k packages as recommen- dations. We analyze the efficiency as well as approximation quality of these algorithms. Finally, using two real and two synthetic datasets, we subject our algorithms to thorough ex- perimentation and empirical analysis. Our findings attest tothe efficiency and quality of our approximation algorithms for the top-k packages compared to exact algorithms.展开更多
基金funded by Ministry of Industry and Information Technology of the People’s Republic of China[Grant No.2018473].
文摘Quality traceability plays an essential role in assembling and welding offshore platform blocks.The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry.Currently,qualitymanagement remains in the era of primary information,and there is a lack of effective tracking and recording of welding quality data.When welding defects are encountered,it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data.In this paper,a composite welding quality traceability model for offshore platform block construction process is proposed,it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm.By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm,the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems.Furthermore,the model and the quality traceability algorithm are checked by cases in actual working conditions.Verification analyses suggest that the proposed early-warningmodel for welding quality and the algorithmfor optimizing backtracking requests are effective and can be applied to the actual construction process.
文摘Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, sev- eral applications can benefit from a system capable of recom- mending packages of items, in the form of sets. Sample appli- cations include travel planning with a limited budget （price or time） and twitter users wanting to select worthwhile tweeters to follow, given that they can deal with only a bounded num- ber of tweets. In these contexts, there is a need for a system that can recommend the top-k packages for the user to choose from. Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value （rating） and a cost associated with it, and the user specifies a maximum total cost （budget） for any recommended set of items. Our composite recommender system has access to one or more component recommender systems focusing on different do- mains, as well as to information sources which can provide the cost associated with each item. Because the problem of deciding whether there is a recommendation （package） whose cost is under a given budget and whose value exceeds some threshold is NP-complete, we devise several approximation algorithms for generating the top-k packages as recommen- dations. We analyze the efficiency as well as approximation quality of these algorithms. Finally, using two real and two synthetic datasets, we subject our algorithms to thorough ex- perimentation and empirical analysis. Our findings attest tothe efficiency and quality of our approximation algorithms for the top-k packages compared to exact algorithms.
