This paper presents an improved SIFT (Simplified Inverse Filtering Technique) method for accurate pith estimation. In order to save computing time as well as ensuring the precision of autocorrelation, different re-sam...
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Mel-scale frequency cepstrum coefficienls (MFCCs) are commonly used katues in speaker recognition systems, but MFCC values are not very robust in the presence of noise. thus, the modified MFCCs (named as SMN-CMN-MFCC)...
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Fuzzy time series (FTS) is an effective method in forecasting problems due to its salient capabilities of tracking uncertainty and vagueness in observation data. However, in FTS forecasting, it is required about 5-7 i...
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Financial forecasting has become an important and challenging task for both researchers and investors. In order to improve the forecasting accuracy rate, in this paper, a modified heuristic model of fuzzy time series ...
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Fuzzy time series (FTS) has become an effective method for forecasting some typical time series-enrollments, stock price and daily price of foreign exchange-due to its salient capacities for dealing with uncertainty, ...
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Entity extraction involves multi-factors, and the different factor has an impact on the answer in varying degrees, this paper presents a machine learning approach to parameter learning for entity answer. Firstly, in v...
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The network simulation (NS) is an important issue in the study of network technology. However, The error between the network performance simulation on split-object model and the true network environment is considerabl...
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By using BP artificial nerve network's error reversion transmission. Summing up various data of comprehensive logging can solve the problem of low accurate rate for identifying oil, gas, water zones. The software ...
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In this work, we present a monitor and rescue system utilizing hybrid networks which is a integration of stationary sensor networks and mobile sensor networks: stationary sensor networks comprised of large numbers of ...
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ISBN:
(纸本)9781424459889
In this work, we present a monitor and rescue system utilizing hybrid networks which is a integration of stationary sensor networks and mobile sensor networks: stationary sensor networks comprised of large numbers of small, simple, and inexpensive wireless sensors, and the mobile sensor network contains a set of mobile sensors (robots). The static sensors in our network have "monitoring" ability, i.e., any activated static sensor can detect the event as long as its sensing range intersects the event region. And the mobile sensors have "moving" and "rescuing" ability, e.g., they can move toward the event region with limited speed and further perform certain rescuing/processing operations on the event. We can consider the event as a hazard, e.g., wild fire, and the mobile sensors as fireman robots. As soon as the fire is detected by the static sensors, the fireman robots are expected to move from its initial location to the hazard region within minimum latency. We define the reaction delay of the system as the delay from the occurrence of event till at least one mobile sensor reaches the event. In order to satisfy certain reaction delay requirement while minimizing the total cost, we propose a number of deployment strategies for the stationary sensor network and mobile sensor network respectively. We further design a random wake-up scheduling for the static sensors for the sake of energy efficiency. Finally, we propose a pure distributed motion strategy for mobile sensors without reliance on localization services such as GPS, focusing on simple algorithms for distributed decision making and information propagation. We demonstrate the efficacy of our system in simulation, providing empirical results. keywords-Sensor networks, mobility, detection, delay.
This paper presents an entity answer extraction method based on list web table. Firstly, extract table from page using the features of web page table and label, segment the table that includes the potential entity ans...
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