高速铁路日常客运量的EMD-Informer组合预测方法

铁路客流需求的科学预测是进行运输组织方案决策的重要依据。以高速铁路历史客票数据为基础,结合经验模态分解(empirical mode decomposition,EMD)与机器深度学习中的注意力机制,提出高速铁路日客流量的EMD-Informer组合预测方法。首先采用EMD方法分解高速铁路客流量序列,获得具有周期特征和线路客流内在特征的模态分量,再利用Informer模型分别训练和预测各模态分解分量,并通过多头注意力机制高效挖掘客流数据内在规律和捕捉数据序列中的关键特征,在此基础上重组各分量预测值,从而得到高速铁路日常客流的整体高精度预测值。同时,根据结合问题特征的大量实验,明确可供实际运用参考的超参数设置规则。基于京沪高速铁路全线的实例计算分析表明,相对对比预测方法,EMD-Informer组合预测方法在高速铁路客流的单步预测及超前预测上均具有明显更小的预测误差。

Hazards Assessment of Regional Debris Flows Based on Geographic Information Science

Supported by the spatial analysis feature of geographic information science and assessment model of regional debris flows, hazards degrees of the debris flows in the Upper Yangtze River Watershed （UYRW） are divided into five grades based on grid cell. The area of no danger, light danger, medium danger, severe danger and extreme severe danger regions respectively are 278 000, 288 000, 217 000, 127 000, 15 000 km^2. Furthermore, the counties in the UYRW are classified into four classes based on the hazards degrees in each county. The number of severe danger, medium danger, light danger and no danger counties respectively are 49, 82, 77 and 105. The assessment results will be provided for the hazards forecasting and mitigation in the UYRW and ongoing regionalization of Main Function Regions in China as data and technique framework.

Distributed Information Flow Verification for Secure Service Composition in Smart Sensor Network

Accelerate processor, efficient software and pervasive connections provide sensor nodes with more powerful computation and storage ability, which can offer various services to user. Based on these atomic services, different sensor nodes can cooperate and compose with each other to complete more complicated tasks for user. However, because of the regional characteristic of sensor nodes, merging data with different sensitivities become a primary requirement to the composite services, and information flow security should be intensively considered during service composition. In order to mitigate the great cost caused by the complexity of modeling and the heavy load of single-node verification to the energy-limited sensor node, in this paper, we propose a new distributed verification framework to enforce information flow security on composite services of smart sensor network. We analyze the information flows in composite services and specify security constraints for each service participant. Then we propose an algorithm over the distributed verification framework involving each sensor node to participate in the composite service verification based on the security constraints. The experimental results indicate that our approach can reduce the cost of verification and provide a better load balance.

Information Flow Security Models for Cloud Computing

Cloud computing provides services to users through Internet.This open mode not only facilitates the access by users,but also brings potential security risks.In cloud computing,the risk of data leakage exists between users and virtual machines.Whether direct or indirect data leakage,it can be regarded as illegal information flow.Methods,such as access control models can control the information flow,but not the covert information flow.Therefore,it needs to use the noninterference models to detect the existence of illegal information flow in cloud computing architecture.Typical noninterference models are not suitable to certificate information flow in cloud computing architecture.In this paper,we propose several information flow models for cloud architecture.One model is for transitive cloud computing architecture.The others are for intransitive cloud computing architecture.When concurrent access actions execute in the cloud architecture,we want that security domain and security domain do not affect each other,that there is no information flow between security domains.But in fact,there will be more or less indirect information flow between security domains.Our models are concerned with how much information is allowed to flow.For example,in the CIP model,the other domain can learn the sequence of actions.But in the CTA model,the other domain can’t learn the information.Which security model will be used in an architecture depends on the security requirements for that architecture.

Information Flow Scheduling in Concurrent Multi-Product Development Based on DSM

Multi-product collaborative development is adopted widely in manufacturing enterprise, while the present multi-project planning models don＇t take techni- cal/data interactions of multiple products into account. To decrease the influence of technical/data interactions on project progresses, the information flow scheduling models based on the extended DSM is presented. Firstly, infor- mation dependencies are divided into four types： series, parallel, coupling and similar. Secondly, different types of dependencies are expressed as DSM units, and the exten- ded DSM model is brought forward, described as a block matrix. Furthermore, the information flow scheduling methods is proposed, which involves four types of opera- tions, where partitioning and clustering algorithm are modified from DSM for ensuring progress of high-priority project, merging and converting is the specific computation of the extended DSM. Finally, the information flow scheduling of two machine tools development is analyzed with example, and different project priorities correspond to different task sequences and total coordination cost. The proposed methodology provides a detailed instruction for information flow scheduling in multi-product development, with specially concerning technical/data interactions.

Evolving a Bayesian network model with information flow for time series interpolation of multiple ocean variables

Based on Bayesian network (BN) and information flow (IF),a new machine learning-based model named IFBN is put forward to interpolate missing time series of multiple ocean variables. An improved BN structural learning algorithm with IF is designed to mine causal relationships among ocean variables to build network structure. Nondirectional inference mechanism of BN is applied to achieve the synchronous interpolation of multiple missing time series. With the IFBN,all ocean variables are placed in a causal network visually,making full use of information about related variables to fill missing data. More importantly,the synchronous interpolation of multiple variables can avoid model retraining when interpolative objects change. Interpolation experiments show that IFBN has even better interpolation accuracy,effectiveness and stability than existing methods.

Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM

In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.

Glacial debris flow susceptibility mapping based on combined models in the Parlung Tsangpo Basin,China

Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited application because of the uncertainties in identifying negative samples.The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows(GDFs)and is characterized by a prominent landform featuring deep gullies.Considering the limitations of the ML model,we developed and compared two combined statistical models(FA-WE and FA-IC)based on factor analysis(FA),weight of evidence(WE),and the information content(IC)method.The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation.The results show that the FA-IC model has the best performance.The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream,on both sides of the valley in the middle and downstream of the Parlung Tsangpo River,and in the narrow valley section of each tributary.These areas encompass 86 gullies and are characterized as"narrow and steep".

The Information Flow Analyzing Based on CPC

The information flow chart within product life cycle is given out based on collaborative production commerce （CPC） thoughts. In this chart, the separated information systems are integrated by means of enterprise knowledge assets that are promoted by CPC from production knowledge. The information flow in R＆D process is analyzed in the environment of virtual R＆D group and distributed PDM. In addition, the information flow throughout the manufacturing and marketing process is analyzed in CPC environment.

Functional connectivity and information flow in cerebral motor regions of healthy people following finger movement training

BACKGROUND：Neuro-rehabilitative training has been shown to promote motor function recovery in stroke patients,although the underlying mechanisms have not been fully clarified.OBJECTIVE：To investigate the effects of finger movement training on functional connectivity and information flow direction in cerebral motor areas of healthy people using electroencephalogram （EEG）.DESIGN,TIME AND SETTING：A self-controlled,observational study was performed at the College of Life Science and Bioengineering,Beijing University of Technology between December 2008 and April 2009.PARTICIPANTS：Nineteen healthy adults,who seldom played musical instruments or keyboards,were included in the present study.METHODS：Specific finger movement training was performed,and all subjects were asked to separately press keys with their left or right hand fingers,according to instructions.The task comprised five sessions of test train test train-test.Thirty-six channel EEG signals were recorded in different test sessions prior to and after training.Data were statistically analyzed using one-way analysis of variance.MAIN OUTCOME MEASURES：The number of effective performances,correct ratio,average response time,average movement time,correlation coefficient between pairs of EEG channels,and information flow direction in motor regions were analyzed and compared between different training sessions.RESULTS：Motor function of all subjects was significantly improved in the third test comparedwith the first test （P〈 0.01）.More than 80% of connections were strengthened in the motor-related areas following two training sessions,in particular the primary motor regions under the C4 electrode.Compared to the first test,a greater amount of information flowed from the Cz and Fcz electrodes （corresponding to supplementary motor area） to the C4 electrode in the third test.CONCLUSION：Finger task training increased motor ability in subjects by strengthening connections and changing information flow in the motor areas.These results provided a greater understanding of the mechanisms involved in motor rehabilitation.

Information Detection of Seismic Debris Flow by UAV High-resolution Image Based on Transfer Learning

A large number of debris flow disasters(called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning(TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network(CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.

Enhance Information Flow Tracking with Function Recognition

With the spread use of the computers, a new crime space and method are presented for criminals. Thus computer evidence plays a key part in criminal cases. Traditional computer evidence searches require that the computer specialists know what is stored in the given computer. Binary-based information flow tracking which concerns the changes of control flow is an effective way to analyze the behavior of a program. The existing systems ignore the modifications of the data flow, which may be also a malicious behavior. Thus the function recognition is introduced to improve the information flow tracking. Function recognition is a helpful technique recognizing the function body from the software binary to analyze the binary code. And that no false positive and no false negative in our experiments strongly proves that our approach is effective.

Highway Transport Development Strategy Based on Information Flow Impact

The characteristics of highway transport and the application of information technology in highway transport service are combined; the information flows＇ promotion and substitution on highway transport are analyzed; and the highway transport development strategy based on information flow impact is proposed.

Performance Improvement in Manufacturing Shop Floor Operations of Developing Countries Based on Three Characteristics of Information Flow

The management of information flow for production improvement has always been a target in the research. In this paper, the focus is on the analysis model of the characteristics of information flow in shop floor operations based on the influence that dimension (support or medium), direction and the quality information flow have on the value of information flow using machine learning classification algorithms. The obtained results of classification algorithms used to analyze the value of information flow are Decision Trees (DT) and Random Forest (RF) with a score of 0.99% and the mean absolute error of 0.005. The results also show that the management of information flow using DT or RF shows that, the dimension of information such as digital information has the greatest value of information flow in shop floor operations when the shop floor is totally digitalized. Direction of information flow does not have any great influence on shop floor operations processes when the operations processes are digitalized or done by operators as machines.

Analysis of information flows of passive mobility

The health mobility is a right of the citizens that they can turn to any structure, without territorial restrictions, to look for an answer to their needs. Often produces inappropriateness and increases costs without affecting the quality of care. The purpose of this work is to study in depth the phenomenon of passive hospital mobility of Calabrian citizens through detailed and comparative analysis relating to the years 2014 and 2015, and the analysis of inappropriateness.

Research on the Processing Methods of the Computer Format Information Flow in the Network English Translation System

The development o f the network technology, and especially the web search engine, has brought great changes to the field of the English translation. Translators can acquire the background information of the translated texts by using the web search engine correctly, inquire about the correct translation methods of the rare professional terms, apply the fixed sentence patterns, and check the correctness of the translation, so as to improve the translation speed and quality.

A time sequence analysis on the informal information flow mechanism of microblogging

Microblog is a new Internet featured product, which has seen a rapid development in recent years. Researchers from different countries are making various technical analyses on microblogging applications. In this study, through using the natural language processing(NLP) and data mining, we analyzed the information content transmitted via a microblog, users' social networks and their interactions, and carried out an empirical analysis on the dissemination process of one particular piece of information via Sina Weibo.Based on the result of these analyses, we attempt to develop a better understanding about the rule and mechanism of the informal information flow in microblogging.

基于因果卷积和Informer模型的城市公交客流预测

为了有效缓解日常交通拥堵,倡导健康绿色出行,通过城市客流特征分析和预测精准掌握城市交通中的客流量分布特点,综合考虑天气、节假日等多种影响因素对客流量的影响,基于卷积神经网络提出了一种因果卷积自注意力的城市公交客流量CCN-Informer预测模型,改善输入序列局部信息提取能力,获取长期依赖信息,从而提高预测准确性。实验使用因果卷积自注意力进行依赖性特征提取,通过控制编码器的样本计算成本,采用生成式解码器提高模型预测效率。结果表明,CCN-Informer模型考虑周围重要特征因素影响,样本数据拟合程度明显提高,与基线模型对比,标准误差下降范围为4%~20%,有更高的预测精度和运行效率,验证了模型的有效性,可以为城市交通客流量预测提供依据。

May Protein Be as the Genetic Information Carrier?

The 'central dogma 'of molecular biology indicated that the direction of the genetic information flow is from DNA - RNA - protein. However, up to now, the central dogma has not obtained a sufficient theoretical support from cybernetics and information theory. In addition, some special cases in biology, such as, although the scrapie prion is irreversibly inactivated by alkali, five procedures with more specificity for modifying nucleic acids failed to cause inactivation and when a resting cell is activated by some factors and division occurs, protein synthesis has begun before DNA synthesis etc., are also very difficult to explain clearly by the central dogma. A broad outline of a mechanism for reverse translation can easily be 'designed', based on the normal translation process, and this serves both to prove that there is no fundamental theoretical reason for the central dogma, and to illustrate why the redundancy of genetic code is not a problem.This paper, based on some previous research work of authors, from the view of cybernetics, information theory and theoretical biology, explored the possibility of protein as a genetic information carrier, the probable pairing ways between ammo acids-codons, and the direction of genetic information flows etc., at theory, by comparing and analyzing theoretically the characteristics of information carriers existing in DNA and protein. The authors inferred that perhaps protein may join the informational transferring as a genetic information carrier; the direction of genetic information flows, besides the way described by the central dogma, seem also to have another type, that is, genetic information flowing from protein - DNA (RNA) - protein, which also includes the genetic information flow in the central dogma. Undoubtedly, the research on problems about the position and roles of protein during the genetic information transferring will have an important effect on the investigation and development of molecular biology, molecular genetics and gene engineering.

Chemical Reactivity Description in Density-Functional and Information Theories

In Quantum Information Theory(QIT) the classical measures of information content in probability distributions are replaced by the corresponding resultant entropic descriptors containing the nonclassical terms generated by the state phase or its gradient(electronic current). The classical Shannon(S[p]) and Fisher(I[p]) information terms probe the entropic content of incoherent local events of the particle localization, embodied in the probability distribution p, while their nonclassical phase-companions, S[ Φ ] and I[ Φ ], provide relevant coherence information supplements.Thermodynamic-like couplings between the entropic and energetic descriptors of molecular states are shown to be precluded by the principles of quantum mechanics. The maximum of resultant entropy determines the phase-equilibrium state, defined by "thermodynamic" phase related to electronic density,which can be used to describe reactants in hypothetical stages of a bimolecular chemical reaction.Information channels of molecular systems and their entropic bond indices are summarized, the complete-bridge propagations are examined, and sequential cascades involving the complete sets of the atomic-orbital intermediates are interpreted as Markov chains. The QIT description is applied to reactive systems R = A―B, composed of the Acidic(A) and Basic(B) reactants. The electronegativity equalization processes are investigated and implications of the concerted patterns of electronic flows in equilibrium states of the complementarily arranged substrates are investigated. Quantum communications between reactants are explored and the QIT descriptors of the A―B bond multiplicity/composition are extracted.