To obtain good trade-offs between complexity and performance onpeak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM)using partial transmitting sequence (PTS) schemes, a trel...To obtain good trade-offs between complexity and performance onpeak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM)using partial transmitting sequence (PTS) schemes, a trellis structure based PTS factor searchmethod is proposed. The trellis search is with a variant constraint length L_C, 1 ≤ L_C ≤ V-1,where V is the number of PTS subblocks. The method is to decide a PTS factor by searching all thepossible paths obtained by varying L_C consecutive factors. The trellis search can be viewed as ageneral PTS factor search model. If L_C = V-1, it is a full search, and if L_C = 1, it is aniterative search. Using different constraint lengths, trellis factor search PTS exhibits differentPAPR reduction performances. A larger L_C results in a better performance and L_C = V-1 results inthe optimum. However, a larger L_C requires more computation. This helps to choose a good trade-offbetween complexity and performance.展开更多
Focusing on the problem that it is hard to utilize the web multi-fields information with various forms in large scale web search,a novel approach,which can automatically acquire features from web pages based on a set ...Focusing on the problem that it is hard to utilize the web multi-fields information with various forms in large scale web search,a novel approach,which can automatically acquire features from web pages based on a set of well defined rules,is proposed.The features describe the contents of web pages from different aspects and they can be used to improve the ranking performance for web search.The acquired feature has the advantages of unified form and less noise,and can easily be used in web page relevance ranking.A special specs for judging the relevance between user queries and acquired features is also proposed.Experimental results show that the features acquired by the proposed approach and the feature relevance specs can significantly improve the relevance ranking performance for web search.展开更多
The car sequencing problem(CSP)concerns a production sequence of different types of cars in the mixed-model assembly line.A hybrid algorithm is proposed to find an assembly sequence of CSP with minimum violations.Firs...The car sequencing problem(CSP)concerns a production sequence of different types of cars in the mixed-model assembly line.A hybrid algorithm is proposed to find an assembly sequence of CSP with minimum violations.Firstly,the hybrid algorithm is based on the tabu search and large neighborhood search(TLNS),servicing as the framework.Moreover,two components are incorporated into the hybrid algorithm.One is the parallel constructive heuristic(PCH)that is used to construct a set of initial solutions and find some high quality solutions,and the other is the small neighborhood search(SNS)which is designed to improve the new constructed solutions.The computational results show that the proposed hybrid algorithm(PCH+TLNS+SNS)obtains100best known values out of109public instances,among these89instances get their best known values with100%success rate.By comparing with the well-known related algorithms,computational results demonstrate the effectiveness,efficiency and robustness of the proposed algorithm.展开更多
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
One of the important steps in mining event sequences is to find frequent episodes. Once the frequent episodes are discovered, rules about temporal relationships can he derived. In this paper, an cfficient algorithm fo...One of the important steps in mining event sequences is to find frequent episodes. Once the frequent episodes are discovered, rules about temporal relationships can he derived. In this paper, an cfficient algorithm for discovering frequent episodes is presented based on the level-wise search algorithm WINEPI. The proposed algorithm gains hetter candidate generation quality by introducing a new Lemma to help to target the combinations of episodes that are interesting in the next level and thins reduces the execution time. Experimental results on artificial and real data show the enhanced efficiency of the algorithm.展开更多
文摘To obtain good trade-offs between complexity and performance onpeak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM)using partial transmitting sequence (PTS) schemes, a trellis structure based PTS factor searchmethod is proposed. The trellis search is with a variant constraint length L_C, 1 ≤ L_C ≤ V-1,where V is the number of PTS subblocks. The method is to decide a PTS factor by searching all thepossible paths obtained by varying L_C consecutive factors. The trellis search can be viewed as ageneral PTS factor search model. If L_C = V-1, it is a full search, and if L_C = 1, it is aniterative search. Using different constraint lengths, trellis factor search PTS exhibits differentPAPR reduction performances. A larger L_C results in a better performance and L_C = V-1 results inthe optimum. However, a larger L_C requires more computation. This helps to choose a good trade-offbetween complexity and performance.
基金The National Natural Science Foundation of China(No.60673087)
文摘Focusing on the problem that it is hard to utilize the web multi-fields information with various forms in large scale web search,a novel approach,which can automatically acquire features from web pages based on a set of well defined rules,is proposed.The features describe the contents of web pages from different aspects and they can be used to improve the ranking performance for web search.The acquired feature has the advantages of unified form and less noise,and can easily be used in web page relevance ranking.A special specs for judging the relevance between user queries and acquired features is also proposed.Experimental results show that the features acquired by the proposed approach and the feature relevance specs can significantly improve the relevance ranking performance for web search.
基金Project(51435009) supported by the National Natural Science Foundation of ChinaProject(LQ14E080002) supported by the Zhejiang Provincial Natural Science Foundation of ChinaProject supported by the K.C.Wong Magna Fund in Ningbo University,China
文摘The car sequencing problem(CSP)concerns a production sequence of different types of cars in the mixed-model assembly line.A hybrid algorithm is proposed to find an assembly sequence of CSP with minimum violations.Firstly,the hybrid algorithm is based on the tabu search and large neighborhood search(TLNS),servicing as the framework.Moreover,two components are incorporated into the hybrid algorithm.One is the parallel constructive heuristic(PCH)that is used to construct a set of initial solutions and find some high quality solutions,and the other is the small neighborhood search(SNS)which is designed to improve the new constructed solutions.The computational results show that the proposed hybrid algorithm(PCH+TLNS+SNS)obtains100best known values out of109public instances,among these89instances get their best known values with100%success rate.By comparing with the well-known related algorithms,computational results demonstrate the effectiveness,efficiency and robustness of the proposed algorithm.
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.
文摘One of the important steps in mining event sequences is to find frequent episodes. Once the frequent episodes are discovered, rules about temporal relationships can he derived. In this paper, an cfficient algorithm for discovering frequent episodes is presented based on the level-wise search algorithm WINEPI. The proposed algorithm gains hetter candidate generation quality by introducing a new Lemma to help to target the combinations of episodes that are interesting in the next level and thins reduces the execution time. Experimental results on artificial and real data show the enhanced efficiency of the algorithm.
