Estimation of Biodiversity Conservation Value of Forest Ecosystem in Nyingchi Prefecture of Tibet

The estimation was made for the conservation value of forest ecosystem biodiversity in Nyingchi Prefecture of Tibet.The results showed that the annual economic benefits of biodiversity in Nyingchi Prefecture were about 21.4 billion yuan,and the annual economic benefits of global biodiversity were about 3 trillion USD.It indicated that the ecological value of forest ecosystem in Nyingchi Prefecture is high,forest ecosystem has extremely important ecological value.Therefore,in the decision-making process,it is necessary to strengthen the protection of forest ecosystem,with particular emphasis on the restoration of damaged ecosystem.

Online 3D Packing Problem Based on Bi-Value Guidance

The online 3D packing problem has received increasing attention in recent years due to its practical value. However, the problem itself possesses some peculiar properties, such as sequential decision-making and the large size of the state space, which have made the use of reinforcement learning with Markov decision processes a popular approach for solving this problem. In this paper, we focus on the problem of high variance in value estimation caused by reward uncertainty in the presence of highly uncertain dynamics. To address this, proposed a solution based on auxiliary tasks and intrinsic rewards for the online 3D bin packing problem, guided by a binary-valued network, to assist the agent in learning the policy within the framework of actor-critic deep reinforcement learning. Specifically, the maintenance of two-valued networks and the utilization of multi-valued network estimates are employed to replace the original value estimates, aiming to provide better guidance for the learning of policy networks. Experimentally, it has been demonstrated that our model can achieve more robust learning and outperform previous works in terms of performance.

Estimation of dynamic stress spectrum distribution in structural fatigue test

The determination of structural dynamic stress spectrum distribution is of great signifi- cance in the structural fatigue strength evaluation as well as reliability design. In previous empirical data processing methods, the data grouping and distribution fitting were excessively coarse and contained distinctive defects. This paper proposed an effective approach to statistically group actual measured dynamic stress data and validly extrapolate the combined distribution to fit the dynamic stress spectrum distribution. This approach has been verified its effectiveness through chi-square test, stress spectrum extrapolation and damage calculation in dynamic stress study.

Determining the time and space domains forestimating b value in seismic zoning

To take the seismic zone that includes the great shock with M S8.5 as the statistical unit of estimating b value can often lead to more large variance, because the seismogenic zone of the great shock with M S8.5 are larger than that delineated in general seismic zone. Two-level statistical units are considered in this paper. The seismic province is the first level unit that is suitable for group of earthquakes including the great shock of M S8.5. A seismic province can be divided into several seismic zones. They can be taken as the second level unit for group of quakes in which the super magnitude of the greatest shock do not exceed 8. Because of the nonstationarity in time of seismic activity, the unbalancedness of data and differential of seismic temporal series feature in different areas need to be considered when we select the time period for estimating b value. According to local conditions, the time period is selected at one′s discretion in order to reflect seismicity level of this statistical unit in future 100 years.

Physical background on estimating bvalue

The G R relation lg N=a-bM( 1954)is an empirical formula used widely in the seismicity research. But the linearity of b curves has great difference in different time and space domains. An interested question in this paper is that in how large a space time strength domain the b value has certain physical connotation. This study told us that we can get optimal statistical results of b value in those space time domains which can develop correspondent strong shocks with magnitude interval( M S≥8.5, 8.0≤ M S<8.5, 7.0≤ M S<8.0). Thus, the possible seismogenic areas in which strong shocks with different magnitude intervals develop can be inferred in different regions of the mainland of China. Finally, some new problems are proposed, such as the delimitation of seismic province, the seismicity parameter determination in seismic hazard analysis and in earthquake predictions by using b value.

Multilinear Calderón-Zygmund Operators and Their Commutators with BMO Functions in Herz-Morrey Spaces with Variable Smoothness and Integrability

In this paper, we obtain that multilinear Calderón-Zygmund operators and their commutators with BMO functions are bounded on products of Herz-Morrey spaces with variable smoothness and integrability. The vector-valued setting of multilinear Calderón-Zygmund operators is also considered.

Estimation of maximum inclusion by statistics of extreme values method in bearing steel

A statistic method, statistics of extreme values （SEV）, was described in detail, which can esti mate the size of maximum inclusion in steel. The characteristic size of the maximum inclusion in a high clean bearing steel （GCrl5） was evaluated by this method, and the morphology and corn position of large inclusions found were analyzed by scanning electron microscopy （SEM）. When standard inspection area （S0） is 280 mm2, the characteristic size of the biggest inclusion found in 30 standard inspection area is 23.93 μm, and it has a 99.9% probability of the characteristic size of maximum inclusion predicted being no larger than 36.85μm in the experimental steel. SEM result shows that large inclusions found are mainly composed of CaS, calcium-aluminate and MgO. Compositing widely exists in large inclusions in high clean bearing steel. Compared with traditional evaluation method, SEV method mainly focuses on inclusion size, and the esti- mation result is not affected by inclusion types. SEV method is suitable for the inclusion eval uation of high clean bearing steel.

An algorithm to solve autocorrelation matrix singular value based on SNR estimation

SNR estimation of communication signals is important to improve demodulation performance and channel quality of communication system,thus it is an important research issue of communication field.According to the core problem of autocorrelation matrix singular value in SNR estimation process,through making use of householder transforming autocorrelation matrix into tridiagonal matrix,and by using the relation of corresponding characteristic equation coefficients and singular value,a numerical algorithm is given to obtain autocorrelation matrix singular value,and the algorithm is used for SNR solving process.Theoretical analysis shows that the algorithm can satisfy the requirements in the aspect of constringency speed and stability.

Relation between the Actual and Estimated Maximum Ground Level Concentration of Air Pollutant and Its Downwind Locations

In this work we used the Gaussian plume model to calculate the actual maximum ground level concentration (MGLC) of air pollutant and its downwind location by using different systems of dispersion parameters and for different stack heights. An approximate formula for the prediction of downwind position that produces the MGLC of a pollutant based on the Gaussian formula was derived for different diffusion parameters. The derived formula was used to calculate the approximate MGLC. The actual and estimated values are presented in tables. The comparison between the actual and estimated values was investigated through the calculation of the relative errors. The values of the relative errors between the actual and estimated MGLC lie in the range from: 0 to 70.2 and 0 to 1.6 for Pasquill Gifford system and Klug system respectively. The errors between the actual and estimated location of the MGLC lies in the range from: 0.2 to 227 and 0.7 to 9.4 for Pasquill Gifford system and Klug system respectively.

Research on Leak Location of Liquid-filled Pipe Based on Frequency Dispersion Characteristics

Due to the material problems and force majeure factors,the leakage will be occurred on the liquid-filled pipe resulting in waste of resources,environmental pollution and even endangering safety.Acoustic wave detection technology is widely used in buried pipeline leak detection,this technology mainly uses the wave(n=0,s=1)in the pipeline acoustic wave to locate the leak.When the leakage acoustic signal propagates along the liquid-filled pipe,the frequency dispersion characteristics can be obtained by wavelet decomposition.And there is a time delay(time difference)value between the leaky acoustic signals collected by the sensors at both ends of the leak.The outputs show that the results obtained by wavelet decomposition are in good agreement with the theoretical calculation results.Based on the obtained dispersion relation,the time delay values at different characteristic frequencies are analyzed by the cross-correlation method,and the leak location accuracy is discussed.This research content provides theoretical support and engineering application guidance for pipe leakage location technology.

Minimum Time Extrema Estimation for Large-Scale Radio-Frequency Identification Systems

We consider the extrema estimation problem in large-scale radio-frequency identification(RFID)systems,where there are thousands of tags and each tag contains a finite value.The objective is to design an extrema estimation protocol with the minimum execution time.Because the standard binary search protocol wastes much time due to inter-frame overhead,we propose a parameterized protocol and treat the number of slots in a frame as an unknown parameter.We formulate the problem and show how to find the best parameter to minimize the worst-case execution time.Finally,we propose two rules to further reduce the execution time.The first is to find and remove redundant frames.The second is to concatenate a frame from minimum value estimation with a frame from maximum value estimation to reduce the total number of frames.Simulations show that,in a typical scenario,the proposed protocol reduces execution time by 79%compared with the standard binary search protocol.

Some Simple Estimates for the Singular Values of Matrices

We first provide a simple estimate for || A-1||∞and||A-1||1 of a strictly diagonally dominant matrix A. On the Basis of the result, we obtain an estimate for the smallest singular value of A. Secondly, by scaling with a positive diagonal matrix D, we obtain some simple estimates for the smallest singular value of an H-matrix, which is not necessarily positive definite. Finally, we give some examples to show the effectiveness of the new bounds.

Optimal L_∞ Estimates for Galerkin Methods for Nonlinear Singular Two-point Boundary Value Problems

In this paper, we apply the symmetric Galerkin methods to the numerical solutions of a kind of singular linear two-point boundary value problems. We estimate the error in the maximum norm. For the sake of obtaining full superconvergence uniformly at all nodal points, we introduce local mesh refinements. Then we extend these results to a class of nonlinear problems. Finally, we present some numerical results which confirm our theoretical conclusions.

Multivariate time series imputation for energy data using neural networks

Multivariate time series with missing values are common in a wide range of applications,including energy data.Existing imputation methods often fail to focus on the temporal dynamics and the cross-dimensional correlation simultaneously.In this paper we propose a two-step method based on an attention model to impute missing values in multivariate energy time series.First,the underlying distribution of the missing values in the data is learned.This information is then further used to train an attention based imputation model.By learning the distribution prior to the imputation process,the model can respond flexibly to the specific characteristics of the underlying data.The developed model is applied to European energy data,obtained from the European Network of Transmission System Operators for Electricity.Using different evaluation metrics and benchmarks,the conducted experiments show that the proposed model is preferable to the benchmarks and is able to accurately impute missing values.

Controller area network node reliability assessment based on observable node information

Controller area network(CAN) based fieldbus technologies have been widely used in networked manufacturing systems. As the information channel of the system, the reliability of the network is crucial to the system throughput, product quality, and work crew safety. However, due to the inaccessibility of the nodes' internal states, direct assessment of the reliability of CAN nodes using the nodes' internal error counters is infeasible. In this paper, a novel CAN node reliability assessment method, which uses node's time to bus-off as the reliability measure, is proposed. The method estimates the transmit error counter(TEC) of any node in the network based on the network error log and the information provided by the observable nodes whose error counters are accessible.First, a node TEC estimation model is established based on segmented Markov chains. It considers the sparseness of the distribution of the CAN network errors. Second, by learning the differences between the model estimates and the actual values from the observable node, a Bayesian network is developed for the estimation updating mechanism of the observable nodes. Then, this estimation updating mechanism is transferred to general CAN nodes with no TEC value accessibility to update the TEC estimation. Finally, a node reliability assessment method is developed to predict the time to reach bus-off state of the nodes. Case studies are carried out to demonstrate the effectiveness of the proposed methodology. Experimental results show that the estimates using the proposed model agree well with actual observations.

A Fast Ambient Occlusion Method for Real-Time Plant Rendering

Global illumination effects are crucial for virtual plant rendering. Whereas real-time global illumination rendering of plants is impractical, ambient occlusion is an efficient alternative approximation. A tree model with millions of triangles is common, and the triangles can be considered as randomly distributed. The existing ambient occlusion methods fail to apply on such a type of object. In this paper, we present a new ambient occlusion method dedicated to real time plant rendering with limited user interaction. This method is a three-step ambient occlusion calculation framework which is suitable for a huge number of geometry objects distributed randomly in space. The complexity of the proposed algorithm is O（n）, compared to the conventional methods with complexities of O（n^2）. Furthermore, parameters in this method can be easily adjusted to achieve flexible ambient occlusion effects. With this ambient occlusion calculation method, we can manipulate plant models with millions of organs, as well as geometry objects with large number of randomly distributed components with affordable time, and with perceptual quality comparable to the previous ambient occlusion methods.

On the Third and Fourth Power Moments of Fourier Coefficients of Cusp Forms

The asymptotic formulae for the third and fourth power moments of Fourier coefficients of cusp forms are proved in this paper.

Statistical considerations for genomic selection

Genomic selection is becoming increasingly important in animal and plant breeding, and is attracting greater attention for human disease risk prediction. This review covers the most commonly used statistical methods and some extensions of them, i.e., ridge regression and genomic best linear unbiased prediction, Bayesian alphabet, and least absolute shrinkage and selection operator.Then it discusses the measurement of the performance of genomic selection and factors affecting the prediction of performance. Among the measurements of prediction performance, the most important and commonly used measurement is prediction accuracy. In simulation studies where true breeding values are available, accuracy of genomic estimated breeding value can be calculated directly. In real or industrial data studies, either trainingtesting approach or k-fold cross-validation is commonly employed to validate methods. Factors influencing the accuracy of genomic selection include linkage disequilibrium between markers and quantitative trait loci, genetic architecture of the trait, and size and composition of the training population. Genomic selection has been implemented in the breeding programs of dairy cattle, beef cattle, pigs and poultry. Genomic selection in other species has also been intensively researched, and is likely to be implemented in the near future.