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  • Hindawi Limited  (16)
  • 1
    Online Resource
    Online Resource
    Hindawi Limited ; 2019
    In:  Complexity Vol. 2019 ( 2019-02-24), p. 1-18
    In: Complexity, Hindawi Limited, Vol. 2019 ( 2019-02-24), p. 1-18
    Abstract: K nearest neighbor ( k NN) search is an important problem in  location-based services (LBS) and has been well studied on static road networks. However, in real world, road networks are often time-dependent; i.e., the time for traveling through a road always changes over time. Most existing methods for k NN query build various indexes maintaining the shortest distances for some pairs of vertices on static road networks. Unfortunately, these methods cannot be used for the time-dependent road networks because the shortest distances always change over time. To address the problem of k NN query on time-dependent road networks, we propose a novel voronoi-based index in this paper. Furthermore, we propose a novel balanced tree, named V - t r e e , which is a secondary level index on voronoi-based index to make our querying algorithm more efficient. Moreover, we propose an algorithm for preprocessing time-dependent road networks such that the waiting time is not necessary to be considered. We confirm the efficiency of our method through experiments on real-life datasets.
    Type of Medium: Online Resource
    ISSN: 1076-2787 , 1099-0526
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2004607-8
    SSG: 11
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  • 2
    Online Resource
    Online Resource
    Hindawi Limited ; 2017
    In:  Mathematical Problems in Engineering Vol. 2017 ( 2017), p. 1-12
    In: Mathematical Problems in Engineering, Hindawi Limited, Vol. 2017 ( 2017), p. 1-12
    Abstract: Freshness of products and timeliness of delivery are two critical factors which have impact on customer satisfaction in terminal delivery of perishable products. This paper investigates how to make a cost-saving vehicle scheduling for perishable products by maximizing customer satisfaction. Customer satisfaction is defined from the two aspects of freshness and time window. Then we develop a priority function based on customer satisfaction and use the hierarchical clustering method to identify customer service priority. Based on the priority, a multiobjective vehicle scheduling optimization model for perishable products is formulated to maximize customer satisfaction and minimize total delivery costs. To solve the proposed model, a priority-based genetic algorithm (PB-GA) is designed. Numerical experiments and sensitivity analysis are performed to show the validity and advantage of our approach. Results indicate that PB-GA can achieve better solutions than traditional genetic algorithm. The improvement of customer satisfaction is higher than the decrease rate of total costs within a certain shelf life range, which reveals that the proposed method is applicable to the terminal delivery of perishable products.
    Type of Medium: Online Resource
    ISSN: 1024-123X , 1563-5147
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2017
    detail.hit.zdb_id: 2014442-8
    SSG: 11
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  • 3
    Online Resource
    Online Resource
    Hindawi Limited ; 2017
    In:  Scientific Programming Vol. 2017 ( 2017), p. 1-2
    In: Scientific Programming, Hindawi Limited, Vol. 2017 ( 2017), p. 1-2
    Type of Medium: Online Resource
    ISSN: 1058-9244 , 1875-919X
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2017
    detail.hit.zdb_id: 2070004-0
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  • 4
    Online Resource
    Online Resource
    Hindawi Limited ; 2022
    In:  Security and Communication Networks Vol. 2022 ( 2022-4-11), p. 1-16
    In: Security and Communication Networks, Hindawi Limited, Vol. 2022 ( 2022-4-11), p. 1-16
    Abstract: DDoS attack effect evaluation is the basis of security strategy deployment. The traditional effect evaluation method relies on the original data, ignoring the relationship between features and the evaluation target and indicator data redundancy, which affects the accuracy and reliability of the evaluation result. To this end, we introduce distance entropy to measure the similarity between features and evaluation target and use LSTM and Triplet networks to measure multiple correlations simultaneously. Then, a 2D-CNN is used to mine deep feature information and filter irrelevant information. We also combine 1D-CNN and attention models to achieve hierarchical sampling of different local features. Finally, three fully connected layers’ training obtains a total evaluation value. We conducted experiments on five commonly used DDoS datasets. The results showed that the average ranking accuracy of the neural network-based DDoS attack evaluation method (NNDE) reached 87.2%, 91.3%, 88%, 85.6%, and 94.5%, respectively. Compared with other evaluation methods, an average increase of 19.73% indicates that this method can better evaluate the effect of DDoS attacks.
    Type of Medium: Online Resource
    ISSN: 1939-0122 , 1939-0114
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2415104-X
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  • 5
    Online Resource
    Online Resource
    Hindawi Limited ; 2021
    In:  Journal of Zoological Systematics and Evolutionary Research Vol. 59, No. 4 ( 2021-05), p. 902-917
    In: Journal of Zoological Systematics and Evolutionary Research, Hindawi Limited, Vol. 59, No. 4 ( 2021-05), p. 902-917
    Type of Medium: Online Resource
    ISSN: 0947-5745 , 1439-0469
    URL: Issue
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2021279-3
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Hindawi Limited ; 2019
    In:  Advances in Meteorology Vol. 2019 ( 2019-04-08), p. 1-10
    In: Advances in Meteorology, Hindawi Limited, Vol. 2019 ( 2019-04-08), p. 1-10
    Abstract: Autumn phenology plays a critical role in terrestrial ecosystem circulations. However, the changes in autumn phenology and their correlation with temperature remain uncertain because mean temperature alone was not able to determine the changes in autumn phenology at various sites. Here, the leaf coloring season (LCS) was defined as the period when the leaves of more than half of the species had recognized changes in color. We systematically studied the changes in peak, start, end, and duration of LCS and their correlations with five temperature parameters (mean temperature, accumulated cold temperature, day temperature, night temperature, and temperature difference between day and night) in four periods. Similarly to previous findings, the start date of LCS advanced and the end of LCS delayed over the past 50 years, which consequently led to a lengthened duration of LCS in Xi’an, Harbin, Minqin, and Shenyang. In general, the rise in mean temperature, day temperature, and night temperature would delay the peak, start, and end of LCS and lengthen the duration of LCS in most cases. We also proved that the changes in LCS metrics not only could completely be explained by mean temperature but also were influenced by day temperature, night temperature, temperature difference, and even other climatic factors such as precipitation, at different sites.
    Type of Medium: Online Resource
    ISSN: 1687-9309 , 1687-9317
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2486777-9
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  • 7
    Online Resource
    Online Resource
    Hindawi Limited ; 2022
    In:  Computational Intelligence and Neuroscience Vol. 2022 ( 2022-8-16), p. 1-20
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-8-16), p. 1-20
    Abstract: Distributed denial of service (DDoS) attacks are the most common means of cyberattacks against infrastructure, and detection is the first step in combating them. The current DDoS detection mainly uses the improvement or fusion of machine learning and deep learning methods to improve classification performance. However, most classifiers are trained with statistical flow features as input, ignoring topological connection changes. This one-sidedness affects the detection accuracy and cannot provide a basis for the distribution of attack sources for defense deployment. In this study, we propose a topological and flow feature-based deep learning method (GLD-Net), which simultaneously extracts flow and topological features from time-series flow data and exploits graph attention network (GAT) to mine correlations between non-Euclidean features to fuse flow and topological features. The long short-term memory (LSTM) network connected behind GAT obtains the node neighborhood relationship, and the fully connected layer is utilized to achieve feature dimension reduction and traffic type mapping. Experiments on the NSL-KDD2009 and CIC-IDS2017 datasets show that the detection accuracy of the GLD-Net method for two classifications (normal and DDoS flow) and three classifications (normal, fast DDoS flow, and slow DDoS flow) reaches 0.993 and 0.942, respectively. Compared with the existing DDoS attack detection methods, its average improvement is 0.11 and 0.081, respectively. In addition, the correlation coefficient between the detection accuracy of attack flow and the four source distribution indicators ranges from 0.7 to 0.83, which lays a foundation for the inference of attack source distribution. Notably, we are the first to fuse topology and flow features and achieve high-performance DDoS attack intrusion detection through graph-style neural networks. This study has important implications for related research and development of network security systems in other fields.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
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  • 8
    Online Resource
    Online Resource
    Hindawi Limited ; 2016
    In:  International Journal of Energy Research Vol. 40, No. 8 ( 2016-06-25), p. 1062-1072
    In: International Journal of Energy Research, Hindawi Limited, Vol. 40, No. 8 ( 2016-06-25), p. 1062-1072
    Type of Medium: Online Resource
    ISSN: 0363-907X
    URL: Issue
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2016
    detail.hit.zdb_id: 1480879-1
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  • 9
    Online Resource
    Online Resource
    Hindawi Limited ; 2019
    In:  Mathematical Problems in Engineering Vol. 2019 ( 2019-03-24), p. 1-10
    In: Mathematical Problems in Engineering, Hindawi Limited, Vol. 2019 ( 2019-03-24), p. 1-10
    Abstract: The application of pumps as turbines (PAT) has been developed in several applications for energy recovery schemes. Therefore, establishing a performance correlation between pump mode and turbine mode is essential for selecting the proper machine. However, slip phenomenon is the challenges of head prediction for PAT. In this paper, the slip phenomenon of pump and PAT was revealed, and the slip factor was studied using CFD. The effect of slip on head prediction for PAT was analyzed, and a theoretical prediction model was presented considering slip factors. In order to validate the head prediction model, six centrifugal pumps with specific speed ( n s ) from 9 to 54.8 were tested as turbines. Results showed that the predicted head by the proposed method was in good agreement with the experimental data, and it is more accurate than Stepanoff, Alatorre-Frenk, Sharma, and Derakhshan models. This method can be applied in head prediction for low specific speed PAT ( n s 〈 60).
    Type of Medium: Online Resource
    ISSN: 1024-123X , 1563-5147
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2014442-8
    SSG: 11
    Location Call Number Limitation Availability
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  • 10
    Online Resource
    Online Resource
    Hindawi Limited ; 2014
    In:  Advances in Meteorology Vol. 2014 ( 2014), p. 1-8
    In: Advances in Meteorology, Hindawi Limited, Vol. 2014 ( 2014), p. 1-8
    Abstract: In order to understand past plant phenological responses to climate change in China (1963–2009), we conducted trends analysis of spring phenophases based on observation data at 33 sites from the Chinese Phenological Observation Network (CPON). The phenological data on first leaf date (FLD) and first flowering date (FFD) for five broad-leaved woody plants from 1963 to 2009 were analyzed. Since most phenological time series are discontinuous because of observation interruptions at certain period, we first interpolated phenological time series by using the optimal model between the spring warming (SW) model and the UniChill model to form continuous time series. Subsequently, by using regression analysis, we found that the spring phenophases of woody plants in China advanced at a mean rate of 0.18 days/year over the past 50 years. Changes of spring phenophases exhibited strong regional difference. The linear trends in spring phenophases were −0.18, −0.28, −0.21, −0.04, and −0.14 days/year for the Northeast China Plain, the North China Plain, the Middle-Lower Yangtze Plain, the Yunnan-Guizhou Plateau, and South China, respectively. The spatial differences in phenological trends can be attributed to regional climate change patterns in China.
    Type of Medium: Online Resource
    ISSN: 1687-9309 , 1687-9317
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2014
    detail.hit.zdb_id: 2486777-9
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