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Estimating Aircraft Fuel Consumption using Radar Tracks Data
Fangzi Liu, Chao Wang, and Lei Wang
Int J Performability Eng    2018, 14 (10): 2249-2260.   doi: 10.23940/ijpe.18.10.p1.22492260
Abstract43)      PDF (1404KB)(4)       Save

For accurately measuring the energy-saving contribution of air traffic management technology on air transportation, this paper proposed a calculation method of fuel consumption in the air traffic control area based on radar tracks. This paper firstly analyzed nine influencing factors, including aircraft type, flight state, true airspeed, and altitude, that could affect aircraft fuel consumption. Taking air traffic trajectory data as input, a fuel flow time series prediction model based on echo state network was built. The predicted approximate error of the model can reach 0.032%, 1.79%, and -1.11% in level flight, climbing state, and descending state, respectively. Due to aircraft weight and missed calibrated airspeed data in radar tracks, a key influencing factors extraction method for fuel consumption based on sensitivity analysis has been further explored. Input parameters of the ESN fuel flow time series approximate model have been simplified reasonably. The Xiamen ATC area was taken as an example, and the total fuel consumption of 1021 flights on a specific day within the Xiamen control area was calculated to be 1044.84 tons. Research results in this paper will construct a technical foundation for measuring air traffic control system performance through implementation of the ASBU plan.


Submitted on May 26, 2018; Revised on July 8, 2018; Accepted on August 16, 2018
References: 19
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A Personalized Recommendation Algorithm based on Text Mining
Ningbin Zhang
Int J Performability Eng    2018, 14 (7): 1401-1410.   doi: 10.23940/ijpe.18.07.p3.14011410
Abstract15)      PDF (656KB)(14)       Save

The recommendation system is a new technology used to recommend products for customers from huge amounts of products by inferring objective users’ preferences based on their personal information or online behavior. This paper studied the main personalized recommendation technology for current e-commerce. It proposed a hybrid recommendation algorithm based on opinion mining. This system combines web data mining technology, i.e., takes advantage of user-generated content by mining customers’ online reviews. It is well known that online reviews can directly reflect a customer’s real emotions and expectations, so it is appropriate to extract a customer’s latent interest and preference from his/her reviews, thus refining recommendations and improving accuracy. Meanwhile, an experiment was conducted and the result demonstrated that our system could generate a reliable and realistic recommendation.


Submitted on March 29, 2018; Revised on May 3, 2018; Accepted on June 19, 2018
References: 15
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Decision Tree Incremental Learning Algorithm Oriented Intelligence Data
Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun
Int J Performability Eng    2018, 14 (5): 849-856.   doi: 10.23940/ijpe.18.05.p3.849856
Abstract10)      PDF (500KB)(18)       Save

Decision tree is one of the most popular classification methods because of its advantages of easy comprehension. However, the decision tree constructed by existed methods is usually too large and complicated. So, in some applications, the practicability is limited. In this paper, combining NOLCDT with IID5R algorithm, an improved hybrid classifier algorithm, HCS, is proposed. HCS algorithm consists of two phases: building initial decision tree and incremental learning. The initial decision tree is constructed according to the NOLCDT algorithm, and then the incremental learning is performed with IID5R. The NOLCDT algorithm selects the candidate attribute with the largest information gain and divides the node into two branches, which avoids generating too many branches. Thus, this prevents the decision tree is too complex. The NOLCDT algorithm also improves on the selection of the next node to be split, which computes the corresponding nodal splitting measure for all candidate splits, and always selects the node which has largest information gain from all candidate split nodes as the next split node, so that each split has the greatest information gain. In addition, based on ID5R, an improved algorithm IID5R is proposed to evaluate the quality of classification attributes and estimates a minimum number of steps for which these attributes are guaranteed such a selection. HCS takes advantage of the decision tree and the incremental learning method, which is easy to understand and suitable for incremental learning. The contrast experiment between the traditional decision tree algorithm and HCS algorithm with UCI data set is proposed; the experimental results show that HCS can solve the increment problem very well. The decision tree is simpler so that it is easy to understand, and so the incremental phase consumes less time.


Submitted on January 29, 2018; Revised on March 12, 2018; Accepted on April 23, 2018
References: 11
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