时间神和创造神之祖的双重角色——东方古神帝俊谱系的破译解析

帝俊是中国古代东方大神 ,又名帝喾 ,而不是虞舜。帝俊在家族谱系中有时作为时间神出现 ,相关神话围绕光明与黑暗的纠结展开 ,注重神灵形象的亮度色彩。帝俊有时还作为创造之神的始祖出现 ,许多发明家都是他的后裔。发明创造之神的谱系既反映帝俊系统的先民在历史发展中作出的贡献 ,又融入先民对宇宙、人生多类事象的认识和解说。帝俊谱系往往以幻象出现 ,需要采用科学的方法进行历史还原。

时间神话的终结

时间神话的终结唐晓渡对＂文学发展趋势＂之类的话题，我历来发怵。因为这话题太大，还有某种抵押自己的判断力的味道。正如我由衷佩服有人敢于预测例如２０２７年的文学将如何如何一样，我也担心别人背后骂我＂蠢货＂。就我有限的经验而言，我所亲见或亲闻的有关＂发展趋...

祖宗谱系神话的遗失和疏离——从先楚祖宗谱系看屈原的创作

先楚祖宗谱系记载许多楚族神话 ,屈原的文学作品 ,对先楚祖宗谱系神话这份文化遗产有许多遗失和疏离。屈原作品的太阳树神话 ,涉及到东方和西北 ,而没有提到若水的太阳树 ;作品中的时间神取自东夷族 ,而不是来自先楚谱系 ;共工早期是平治水土的英雄 ,但这段历史没有进入屈原的视域。本文注重文字的训诂考证 。

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

TIME SERIES NEURAL NETWORK MODEL FOR HYDROLOGIC FORECASTING

Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible.

APPLICATION OF INTELLIGENCE FORECASTING METHOD IN TRAFFIC ANALYSIS OF EGCS

Traffic flow forecasting is an important part of elevator group control system (EGCS).This paper applies time series prediction theories based on neural networks(NN) to EGCSs traffic analysis,and establishes a time series NN traffic flow forecasting model.Simulation results show its validity.

Prediction of Gas Emission Based on Infor-mation Fusion and Chaotic Time Series

In order to make more exact predictions of gas emissions, information fusion and chaos time series are com- bined to predict the amount of gas emission in pits. First, a multi-sensor information fusion frame is established. The frame includes a data level, a character level and a decision level. Functions at every level are interpreted in detail in this paper. Then, the process of information fusion for gas emission is introduced. On the basis of those data processed at the data and character levels, the chaos time series and neural network are combined to predict the amount of gas emission at the decision level. The weights of the neural network are gained by training not by manual setting, in order to avoid subjectivity introduced by human intervention. Finally, the experimental results were analyzed in Matlab 6.0 and prove that the method is more accurate in the prediction of the amount of gas emission than the traditional method.

Stability analysis of discrete-time BAM neural networks based on standard neural network models

To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.

Time-series analysis with a hybrid Box-Jenkins ARIMA

Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.

Clinicopathological characterization of gastroenteropancreatic neu-roendocrine neoplasms： a retrospective study of 48 cases

Objective Gastroenteropancreatic neuroendocrine neoplasms （GEP-NENs） constitute a rare and heterogeneous group of tumors with varied biology and still constitute a diagnostic and therapeutic challenge for physicians of all specialties. In the present study, we aimed to review and study the clinicopathological characteristics of GEP-NENs applying the World Health Organization （WHO） 2010 grading criterion. Methods A total of 48 patients were enrolled in the study. The study included patients diagnosed with GEP-NENs who were treated and followed up at our Hospital from January 2013 to December 2017. Data regarding clinicopathological features of the patients were retrospectively evaluated. The expression of neuroendocrine markers was measured using the immunohistochemical Ultra SensitiveTM S-P method of staining in 48 cases of primary GEP-NENs; and serum levels of neuron-specific enolase, carbohydrate an-tigen 19-9, and carcinoembryonic antigen in 36 GEP-NEN patients were measured using the electrochemiluminescence method. Results The median age at presentation was 59.3 （range 48-82） years, and 39 cases （81.3%） were seen between the 5th and 6th decades. There was a male predilection （male： female=3：1）. In 79.2% cases （38/48）, tumors were hormonally nonfunctional. The most common presentation was abdominal pain, and the most frequent primary site of the tumor was the rectum, followed by the stomach （n = 15, 31.3%）, colon （n = 5, 10.4%）, and so on. Of the 48 tumors, 16 （33.3%） were G1,6 （12.5%） cases were G2, 16 （33.3%） were neuroendocrine carcinoma （NEC）, and 10 （20.8%） were mixed adenoneuroendocrine carcinoma （MANEC）. According to the AJCC/UICC classification, 45.8% （n = 22） were diagnosed at low stage （stage Ⅰ or Ⅱ） while 54.2% （n = 26） were diagnosed at high stage （stage Ⅲ or Ⅳ） （the majority of NEC, G3, and MANEC）. A male preponderance was noted for all tumors except for G2 neoplasms, which showed no gender predilection. Thirty-nine patients underwent endoscopic biopsy. The lesions in 18.8% （n = 9） of the patients were indentified only radiologically. After the surgical procedures, 36 had at least one follow-up visit with a median follow-up duration of 5 months; the mean follow-up period was 28 ± 16 months. The one- year and three-year survival rates were 72.2% （26/36） and 61.1% （22/36）, respectively. This study did not find an effect of grade 3 （G3） of tumor on the short-term clinical outcome of these patients. In the survival analysis, NEN G3, higher stage （stage Ⅲ or Ⅳ） according to the AJCC/UICC classification （P 〈 0.05）, and metastases at diagnosis （P 〈 0.05） were associated with poorer prognosis. Conclusion Most GEP-NENs are nonfunctional and nonspecific in presentation. The most frequent primary site of the tumor was the rectum and the commonest ages at diagnosis were the 5th and 6th decades. Endoscopic biopsy is the main diagnostic and histological grading method for GEP-NEN. In the survival analysis, NEN G3, a higher stage according to the AJCC/UICC classification, and metastases at diagnosis are associated with poorer prognosis.

Nonlinear Time Series Prediction Using Chaotic Neural Networks

A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm.

A Strategy of Density-Based Partitioning for Scalability Problem in Network Self-management

A density-based partitioning strategy is proposed for large domain networks in order to deal with the scalability issue found in autonomic networks considering, as a scenario, the autonomic Quality of Service （QoS） management context. The approach adopted focus as on obtaining dense network partitions having more paths for a given vertices set in the domain. It is demonstrated that dense partitions improve autonomic processing scalability, for instance, reducing routing process complexity. The solution looks for a significant trade-off between partition autonomic algorithm execution time and path selection quality in large domains. Simulation scenarios for path selection execution time are presented and discussed. Authors argue that autonomic networks may benefit from the dense partition approach proposed by achieving scalable, efficient and near real-time support for autonomic management systems.

Phenomenological Simulation Study of Neuronal Activity Synchronization in 110 Elements Network

The phenomenon of activity synchronization in biological neural network is considered. Simulation of neurons dynamics in the 6-layer neural network with 110 elements in different regimes： regular spikes, chaotic spikes, regular and chaotic bursting, etc was performed. Izhykevich＇s phenomenological model that displays different types of activity inherent for real biological neurons was used for simulation. Space-time diagram for the entire network and raster plots for the whole structure and for each layer separately were built for visual inspection of neural network activity synchronization. Synchronization coefficients based on cross-correlation times of action potentials for all neurons pairs were calculated for the whole neural system and for each layer separately.

Remarks on the Efficiency of Bionic Optimisation Strategies

Bionic optimisation is one of the most popular and efficient applications of bionic engineering. As there are many different approaches and terms being used, we try to come up with a structuring of the strategies and compare the efficiency of the different methods. The methods mostly proposed in literature may be classified into evolutionary, particle swarm and artificial neural net optimisation. Some related classes have to be mentioned as the non-sexual fern optimisation and the response surfaces, which are close to the neuron nets. To come up with a measure of the efficiency that allows to take into account some of the published results the technical optimisation problems were derived from the ones given in literature. They deal with elastic studies of frame structures, as the computing time for each individual is very short. General proposals, which approach to use may not be given. It seems to be a good idea to learn about the applicability of the different methods at different problem classes and then do the optimisation according to these experiences. Furthermore in many cases there is some evidence that switching from one method to another improves the performance. Finally the identification of the exact position of the optimum by gradient methods is often more efficient than long random walks around local maxima.

Prediction of Departure Aircraft Taxi Time Based on Deep Learning

With the continuous increase in the number of flights,the use of airport collaborative decision-making(ACDM)systems has been more and more widely spread.The accuracy of the taxi time prediction has an important effect on the A-CDM calculation of the departure aircraft’s take-off queue and the accurate time for the aircraft blockout.The spatial-temporal-environment deep learning(STEDL)model is presented to improve the prediction accuracy of departure aircraft taxi-out time.The model is composed of time-flow sub-model(airport capacity,number of taxiing aircraft,and different time periods),spatial sub-model(taxiing distance)and environmental sub-model(weather,air traffic control,runway configuration,and aircraft category).The STEDL model is used to predict the taxi time of departure aircraft at Hong Kong Airport and the results show that the STEDL method has a prediction accuracy of 95.4%.The proposed model also greatly reduces the prediction error rate compared with the other machine learning methods.

An Incremental Time-delay Neural Network for Dynamical Recurrent Associative Memory

An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.

Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.

Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks

An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.

A Radio Communication System for Neuronal Signals

To collect neuronal activity data from awake, freely behaving animals, we developed miniature telemetry recording system. The integrated system consists of four major components： l） Microelectrodes and micro-driver assembly, 2） analog front end （AFE）, 3） programmable system on chip （PSoC）, and 4） ra- dio transceiver and the LabVIEW were used as a platform for the graphic user interface. The result showed the system was able to record and analyze neuronal recordings in freely moving animals and lasted continuously for a time period of a week or more. This is very useful for the study of the interdisciplinary research of neu- roscience and information engineering techniques. The circuits and architecture of the devices can be adapted for neurobiology and research with other small animals.

Modeling the complex and long term swelling behavior of argillaceous rocks

The swelling behavior of argillaceous rocks is a complex phenomenon and has been determined using a lot of indexes in the literature. Determining the required modeling indexes that need to be performed requires expensive tests and extensive time in different laboratories. In some of the cases, it is too diffi- cult to find a relation between the effective variables and swelling potential. This paper suggests a method for modeling the time dependent swelling pressure of argillaceous rocks. The trend of short term swelling potential during the first 3 days of the swelling pressure testing is used for modeling the long term swelling pressure of mudstone that is recorded during months. The artificial neural network (ANN) as a power tool is used for modeling this nonlinear and complex behavior. This method enables predicting the swelling potential of argillaceous rocks when the required indexes and also correlation between them is unattainable. This method facilitates the model of all studied samples under a unique formulation.