Modeling for Action of Recovering from Erroneous Driving Condition Based on Revised Decision Tree

Driving behavior modeling is very important in the research area of road traffic systems safety analysis. The characteristics of action of recovering from erroneous driving condition underlying road traffic accident or incident scenarios is quantitatively analyzed, the model of action of recovering from erroneous driving condition is set up according to the identification of erroneous driving condition and the measurement of correction from erroneous driving condition. And then, the probability of action of recovering from erroneous driving condition has been measured based on a revised decision tree. The measure process uses a combination of test data and subjective judgments of driving behavior. It can provide a very helpful theoretical basis for the further analysis of driving behavior in road traffic system.

Contribution to the taxonomy of the genus Lycodon H.Boie in Fitzinger,1827(Reptilia:Squamata:Colubridae)in China,with description of two new species and resurrection and elevation of Dinodon septentrionale chapaense Angel,Bourret,1933

While considerable progress has been made in the taxonomic studies of the genus Lycodon in Asia,questions remain to be clarified regarding the taxonomy of certain groups,particularly those containing species in China.Not only do many regions in China remain overlooked by herpetologists,resulting in the possibility of undiscovered new species,but the surveyed areas also have suspicious records of recognized congeners that require taxonomic confirmations.Combining both morphological and genetic data,we tackle these outstanding issues in the taxonomy of Lycodon in China.In particular,we discover two new species of Lycodon:one from the previously neglected hot-dry valley in the northern Hengduan Mountain Region close to Tibet,and another recluse and cryptic species from the L.fasciatus complex in the downtown park of a major city in southern Sichuan Province.Additionally,we clarify the distribution of L.septentrionalis in China and resurrect and elevate its junior synonym subspecies,Dinodon septentrionale chapaense,as a full,valid species,and we synonymize the recently described L.namdongensis to the resurrected L.chapaensis comb.nov..Lycodon chapaensis comb.nov.thus represents a new national record of reptilian fauna of China.Lastly,based on literature review,we also correct some of the erroneous records of L.fasciatus and L.ruhstrati in China,point out remaining taxonomic issues of the genus for future research,and update the dichotomous key and distribution of the 20 species of Lycodon currently recorded from China.

Throughput Analysis of the IEEE 802.11 Distributed Coordination Function Considering Erroneous Channel and Capture Effects

This paper presents a performance study of the distributed coordination function （DCF） of 802.11 networks considering erroneous channel and capture effects under non-saturated traffic conditions employing a basic access method.The aggregate throughput of a practical wireless local area network （WLAN） strongly depends on the channel conditions.In a real radio environment,the received signal power at the access point from a station is subjected to deterministic path loss,shadowing,and fast multipath fading.The binary exponential backoff （BEB） mechanism of IEEE 802.11 DCF severely suffers from more channel idle time under high bit error rate （BER）.To alleviate the low performance of IEEE 802.11 DCF,a new mechanism is introduced,which greatly outperforms the existing methods under a high BER.A multidimensional Markov chain model is used to characterize the behavior of DCF in order to account both non-ideal channel conditions and capture effects.

A Robust Wi-Fi Fingerprinting Indoor Localization Coping with Access Point Movement

A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.

Buffer management optimization strategy for satellite ATM

ECFD （erroneous cell tail drop）, a buffer management optimization strategy is suggested which can improve the utilization of buffer resources in satellite ATM （asynchronous transfer mode） networks. The strategy, in which erroneous cells caused by satellite channel and the following cells that belong to the same PDU （protocol data Unit） are discarded, concerns non-real-time data services that use higher layer protocol for retransmission. Based on EPD （early packet drop） policy, mathematical models are established with and without ECTD. The numerical results show that ECTD would optimize buffer management and improve effective throughput （goodput）, and the increment of goodput is relative to the CER （cell error ratio） and the PDU length. The higher their values are, the greater the increment. For example, when the average PDU length values are 30 and 90, the improvement of goodput are respectively about 4% and 10%.

An improvement on the method of the pattern recognition ICHAM and its application

In this paper, an improvement is made on the method of the pattern recognition ICHAM. It is proposed to use the iterative centre as the Hamming kernel. And new weight coefficient calculating the Euclidean distance is given. Through varying the control parameter, the minimum ratio of the erroneous recognition is sought. Through classsificatory recognition on two calculative examples and strong earthquakes in the north section of the North-South Seismic Belt, the results indicate that the classificatory effect of the method improved in this paper is better than the ICHAM method. It is more suitable to do the classificatory recognition for general distributive samples.

AN INTERPOLATION FORMULA OF THE DERIVATIVES OF HIGHER ORDER

In this article we shall obtain an interpolation formula passing given a serial points and satisfying initial values of the derivatives of higher order in preceding points Finally we shall give the erroneous estimate of the preceding interpolation formula.

On the issue of erroneous translation in the Chinese version of The Da Vinci code

With eight typical examples, this paper discusses the erroneous translation in the Chinese version of The Da Vinci code, points out the possible reasons and suggests the importance of faithfulness and that of sense of responsibility.

Range estimation based on symmetry polynomial aided Chinese remainder theorem for multiple targets in a pulse Doppler radar

To avoid Doppler ambiguity,pulse Doppler radar may operate on a high pulse repetition frequency(PRF).The use of a high PRF can,however,lead to range ambiguity in many cases.At present,the major efficient solution to solve range ambiguity is based on a waveform design scheme.It adds complexity to a radar system.However,the traditional multiple-PRF-based scheme is difficult to be applied in multiple targets because of unknown correspondence between the target range and measured range,especially using the Chinese remainder theorem(CRT)algorithm.We make a study of the CRT algorithm for multiple targets when the residue set contains noise error.In this paper,we present a symmetry polynomial aided CRT algorithm to effectively achieve range estimation of multiple targets when the measured ranges are overlapped with noise error.A closed-form and robust CRT algorithm for single target and the Aitken acceleration algorithm for finding roots of a polynomial equation are used to decrease the computational complexity of the proposed algorithm.

Novel passive localization algorithm based on double side matrix-restricted total least squares

In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares （DSMRTLS） is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms.

Analyzing Interlanguage of Non-English Majors by Proposed Semantic Criteria

本研究旨在从语义视角对中介语进行分析探讨。首先,笔者对吉林省一所独立高校中随机抽取的118名非英语专业学生英语作文中的错误表达进行分析。从语言的核心成分——语义出发,提出了五个语义分析标准, 即: 语义是否可被猜出,是否可被接受,是否有歧义,是否适切,是否表达形式正确。研究表明,这五个语义标准的提出对于分析中介语现象较具说服力,因为它弥补了传统分析方法"重形式,轻意义"的不足,为中介语研究提供了新的分类标准。

Erroneous pixel prediction for semantic image segmentation

We consider semantic image segmentation.Our method is inspired by Bayesian deep learning which improves image segmentation accuracy by modeling the uncertainty of the network output.In contrast to uncertainty,our method directly learns to predict the erroneous pixels of a segmentation network,which is modeled as a binary classification problem.It can speed up training comparing to the Monte Carlo integration often used in Bayesian deep learning.It also allows us to train a branch to correct the labels of erroneous pixels.Our method consists of three stages:(i)predict pixel-wise error probability of the initial result,(ii)redetermine new labels for pixels with high error probability,and(iii)fuse the initial result and the redetermined result with respect to the error probability.We formulate the error-pixel prediction problem as a classification task and employ an error-prediction branch in the network to predict pixel-wise error probabilities.We also introduce a detail branch to focus the training process on the erroneous pixels.We have experimentally validated our method on the Cityscapes and ADE20K datasets.Our model can be easily added to various advanced segmentation networks to improve their performance.Taking DeepLabv3+as an example,our network can achieve 82.88%of mloU on Cityscapes testing dataset and 45.73%on ADE20K validation dataset,improving corresponding DeepLabv3+results by 0.74%and 0.13%respectively.