A Study on Evaluation Model of IT Industry’s Innovation Capability Based Variable Weight Theory

In this paper, IT Industry＇s innovation capability is considered to be the innovation output capability after complex operation of industry input in industry system. In this complex process, R＆D personnel input and R＆D expense input are un-substitutable, and for evaluation of innovation capability, innovation input and innovation output also are un-substitutable. Based on this theory, an evaluation model of sustaining strength index is put forward. Considering both input scale and output contribution of IT industry＇s innovation system, this model reflects the un-substitutability of every evaluation aspects. The measurement result not only shows the industry innovation capability, but also reflects the support degree to economy. At last the data of IT industry in China are provided between 1994 and 2004 for empirical study.

Investigation on the Long Term Operational Stability of Underground Energy Storage in Salt Rock

Underground energy storage is an important function of all energy supply systems,and especially concerning the seemingly eternal imbalance between production and demand.Salt rock underground energy storage,for one,is widely applied in both traditional and renewable energy fields;and this particular technique can be used to store natural gas,hydrogen,and compressed air.However,resource diversification and structural complexity make the supply system increasingly uncertain with the passing years,leading to great challenges for energy storage facilities in the present,and perhaps going into the future as well.Hence,it is necessary to research the operation stability of underground energy storage further.In this paper,a stability evaluation index system of Underground Gas Storage(UGS)is constructed with natural gas as the main medium,according to FLAC 3D cavity creep simulation software,along with fuzzy membership function to comprehensively determine the impact factor scoring model;the subjective weight is calculated based on the improved Analytic Hierarchy Process(AHP),the objective weight is calculated by the Entropy Weight Method(EWM),the combined constant weight is obtained by combining the variance maximization theory,and introducing the variable weight theory to obtain a more accurate combined variable weight.Finally,with this all being considered and accounted for,and with the four different conditions designed for UGS deployment case analysis and verification taken into consideration,the combined variable weight evaluation achieved excellent results;compared with the traditional constant weight method,in fact,the new evaluation results are more rigorous and objective.

基于变权模糊综合评判的信息安全等级保护测评模型(英文)

Nowadays,clear evaluation models and methods are lacking in classified protection of information system,which our country is making efforts to promote.The quantitative evaluation of classified protection of information system security is studied.An indicators system of testing and evaluation is established.Furthermore,a model of unit testing and evaluation and a model of entirety testing and evaluation are presented respectively.With analytic hierarchy process and two-grade fuzzy comprehensive evaluation,the subjective and uncertain data of evaluation will be quantitatively analyzed by comprehensive evaluation.Particularly,the variable weight method is used to model entirety testing and evaluation.It can solve the problem that the weights need to be adjusted because of the relationship role which enhances or reduces security of information system.Finally,the paper demonstrates that the model testing and evaluation can be validly used to evaluate the information system by an example.The model proposed in this paper provides a new valuable way for classified protection of information system security.

Robust Dataset Classification Approach Based on Neighbor Searching and Kernel Fuzzy C-Means

Dataset classification is an essential fundament of computational intelligence in cyber-physical systems(CPS).Due to the complexity of CPS dataset classification and the uncertainty of clustering number,this paper focuses on clarifying the dynamic behavior of acceleration dataset which is achieved from micro electro mechanical systems(MEMS)and complex image segmentation.To reduce the impact of parameters uncertainties with dataset classification,a novel robust dataset classification approach is proposed based on neighbor searching and kernel fuzzy c-means(NSKFCM)methods.Some optimized strategies,including neighbor searching,controlling clustering shape and adaptive distance kernel function,are employed to solve the issues of number of clusters,the stability and consistency of classification,respectively.Numerical experiments finally demonstrate the feasibility and robustness of the proposed method.

Risk Analysis Technique on Inconsistent Interview Big Data Based on Rough Set Approach

Rough set theory is relativly new to area of soft computing to handle the uncertain big data efficiently. It also provides a powerful way to calculate the importance degree of vague and uncertain big data to help in decision making. Risk assessment is very important for safe and reliable investment. Risk management involves assessing the risk sources and designing strategies and procedures to mitigate those risks to an acceptable level. In this paper, we emphasize on classification of different types of risk factors and find a simple and effective way to calculate the risk exposure.. The study uses rough set method to classify and judge the safety attributes related to investment policy. The method which based on intelligent knowledge accusation provides an innovative way for risk analysis. From this approach, we are able to calculate the significance of each factor and relative risk exposure based on the original data without assigning the weight subjectively.

Icing tolerance envelope protection based on variable-weighted multiple-model predictive control

Multiple-model predictive control(MMPC) is a fundamental icing tolerance envelope protection(ITEP) design method that can systematically handle nonlinear and time-varying constraints. However, few studies have addressed the envelope protection failure that results from the inaccurate prediction of multiple linear predictive models when actual conditions deviate from design conditions. In this study, weights that vary with icing conditions and flight parameters are considered to develop an effective and reliable envelope protection control strategy. First, an ITEP structure based on variable-weighted MMPC was implemented to improve the protection performance with condition departure information. Then, a variable-weighted rule was proposed to guarantee the stability of variable-weighted MMPC. A design approach involving a variable-weighted function that uses icing conditions and flight parameters as arguments was also developed with the proposed rules. Finally, a systematic ITEP design method on variable-weighted MMPC was constructed with additional design criteria for other normal control parameters.Simulations were conducted, and the results show that the proposed method can effectively enhance ITEP performance.

Existence of <i>T</i>-<i>ν</i>-<i>p</i>(<i>x</i>)-Solution of a Nonhomogeneous Elliptic Problem with Right Hand Side Measure

Using the theory of weighted Sobolev spaces with variable exponent and the L1-version on Minty’s lemma, we investigate the existence of solutions for some nonhomogeneous Dirichlet problems generated by the Leray-Lions operator of divergence form, with right-hand side measure. Among the interest of this article is the given of a very important approach to ensure the existence of a weak solution of this type of problem and of generalization to a system with the minimum of conditions.

Weighted Estimates of Variable Kernel Fractional Integral and Its Commutators on Vanishing Generalized Morrey Spaces with Variable Exponent

In this paper,the authors obtain the boundedness of the fractional integral operators with variable kernels on the variable exponent generalized weighted Morrey spaces and the variable exponent vanishing generalized weighted Morrey spaces.And the corresponding commutators generated by BMO function are also considered.

Using the Inverse of Expected Error Variance to Determine Weights of Individual Ensemble Members： Application to Temperature Prediction

The inverse of expected error variance is utilized to determine weights of individual ensemble members based on the THORPEX （The Observing System Research and Predictability Experiment） Interactive Grand Global Ensemble （TIGGE） forecast datasets. The weights of all ensemble members are thus calculated for summer 2012, with the NCEP final operational global analysis （FNL） data as the truth. Based on the weights of all ensemble members, the variable weighted ensemble mean （VWEM） of temperature of summer 2013 is derived and compared with that from the simple equally weighted ensemble mean. The results show that VWEM has lower root-mean-square error （RMSE） as well as absolute error, and has improved the temperature prediction accuracy. The improvements are quite notable over the Tibetan Plateau and its surrounding areas; specifically, a relative improvement rate of RMSE of more than 24% in 2-m temperature is demonstrated. Moreover, the improvement rates vary slightly with the pre- diction lead-time （24-96 h）. It is suggested that the VWEM approach be employed in operational ensemble predic- tion to provide guidance for weather forecasting and climate prediction.

A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption

Oil is an important strategic material and civil energy.Accurate prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production,oil import and export,and optimize the allocation of social resources.Therefore,a new grey model FENBGM(1,1)is proposed to predict oil consumption in China.Firstly,the grey effect of the traditional GM(1,1)model was transformed into a quadratic equation.Four different parameters were introduced to improve the accuracy of the model,and the new initial conditions were designed by optimizing the initial values by weighted buffer operator.Combined with the reprocessing of the original data,the scheme eliminates the random disturbance effect,improves the stability of the system sequence,and can effectively extract the potential pattern of future development.Secondly,the cumulative order of the new model was optimized by fractional cumulative generation operation.At the same time,the smoothness rate quasi-smoothness condition was introduced to verify the stability of the model,and the particle swarm optimization algorithm(PSO)was used to search the optimal parameters of the model to enhance the adaptability of the model.Based on the above improvements,the new combination prediction model overcomes the limitation of the traditional grey model and obtains more accurate and robust prediction results.Then,taking the petroleum consumption of China's manufacturing industry and transportation,storage and postal industry as an example,this paper verifies the validity of FENBGM(1,1)model,analyzes and forecasts China's crude oil consumption with several commonly used forecasting models,and uses FENBGM(1,1)model to forecast China's oil consumption in the next four years.The results show that FENBGM(1,1)model performs best in all cases.Finally,based on the prediction results of FENBGM(1,1)model,some reasonable suggestions are put forward for China's oil consumption planning.

Load balance in hierarchical routing network

In this article,the problem of load balance in hierarchical routing network is studied.Since conventional shortest path first（SPF） algorithm over aggregated topology in hierarchical routing network may result in worse routing performance,a traffic sharing path selection algorithm and a variable weight scheme are put forward for hierarchical routing network,which can equilibrate the utilities of link resources and reduce the blocking probability of connections with the improvement on survivability.Simulations are conducted to evaluate proposed variable weight and traffics balance（VWTB） algorithm,which combines traffic sharing and variable weight.From the simulation results,it can be found that the proposed VWTB algorithm can balance the traffics and equilibrate the utilities of link resources significantly.

SCC-UEFAS, an urban-ecological-feature based assessment system for sponge city construction

An effective sponge city construction evaluation system plays a crucial role in evaluating sponge city construction schemes.The construction of a sponge city evaluation system still faces challenges related to incomplete index selection and unscientific weight division.Limited studies have focused on the comprehensive assessment of sponge city construction in the early stages.This study constructed a scientific assessment indicator system and a quantitative indicator weight at all levels by literature review and statistical analysis methods from an objective perspective.To demonstrate how to utilize our evaluation methods,three construction schemes randomly generated by MATLAB were evaluated under evaluation states of constant weight and variable weight,respectively.Scheme 3 had the highest score of 0.638 under the constant weight assessment,but it cannot practically be the final construction scheme due to the imbalance between indicators.Compared to the constant weight assessment,a variable weight assessment can effectively balance the states of the evaluation index with changes in the decision variable.Among the three schemes,Scheme 2 is the best choice with a value of 0.0355 under variable weight evaluation due to punishment and incentives in the variable weight method.The concept of“punishing”a disadvantageous indicator and“motivating”an advantageous indicator increases the relative advantages of the indices,ultimately affecting the assessment results of schemes and leading to a more balanced state.This study provides reasonable analysis and decision-making mechanisms to support decision-making and guide the scientific selection of a construction scheme.