基于分类计算法的规划水资源论证需水总量预测研究——以连云港市徐圩新区为例

为确保江苏省连云港市徐圩新区经济社会发展规划与水资源条件相匹配、产业布局与水资源条件相适应,实现以水定产、以水定城,提高规划编制的科学性和规划实施的可行性,开展了规划阶段需水总量预测研究。根据现有资料及徐圩新区的实际情况,采用分类计算法预测徐圩新区需水总量,同时对江苏省工业园区实际用水量指标法、城市单位建设用地综合用水量指标法、单位用地用水量指标法进行综合比较。结果表明,分类计算法计算结果与江苏省工业园区实际用水量预测结果基本一致,预测需水量基本合理;城市单位建设用地综合用水量指标法和单位用地用水量指标法推算的需水量不合理,不适用于徐圩新区。

水污染经济损失计算方法述评

简述水污染经济损失的概念 ,对分类计算法、计量经济学法和恢复费用法等现有的水污染经济损失计算方法进行评述 .研究表明 ,今后的水污染经济损失计算应根据当地的水资源情况 ,因地制宜采用不同的方法 ,如对流域可采用水污染经济损失函数法 ,对河流可采用经验公式法 ,对湖泊可采用水污染损失率法等 ,以获得较为准确的计算结果 .指出当前研究中存在的不足之处和有待进一步研究的方向 .

计算机计算占用床日的两种特殊计算方法

目的 探讨占用床日计算机的简便特殊算法。方法 采用分类计算汇总法和剩余天数计算法两种方法用计算机计算占用床日。结果 两种方法与传统计算方法比具有较大优越性 ,两种方法比各有特点。结论 这两种特殊方法是值得推广和非常有实际应用价值的方法 [1 ] 。

交通碳排放测算方法与影响因素应用研究

通过对比分析城市交通碳排放计算方法,包括燃料分类排放计算法、行驶里程排放计算法、交通出行分担碳排放计算法,阐述Kaya恒等式、STIRPAT模型和LMDI模型等碳排放因素分解模型的优缺点及适用范围,讨论这些模型在识别碳排放源、交通评估、政策决策支持、政策效果仿真以及碳排放监测报告等方面的应用。研究成果为城市交通的碳排放计量、影响因素分析、制定相应的低碳政策和措施等提供方法支撑。

Reconstructing bubble profiles from gas-liquid two-phase flow data using agglomerative hierarchical clustering method

The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved computational fluid dynamics (CFD) simulations. To obtain this information, an efficient bubble profile reconstruction method based on an improved agglomerative hierarchical clustering (AHC) algorithm is proposed in this paper. The reconstruction method is featured by the implementations of a binary space division preprocessing, which aims to reduce the computational complexity, an adaptive linkage criterion, which guarantees the applicability of the AHC algorithm when dealing with datasets involving either non-uniform or distorted grids, and a stepwise execution strategy, which enables the separation of attached bubbles. To illustrate and verify this method, it was applied to dealing with 3 datasets, 2 of them with pre-specified spherical bubbles and the other obtained by a surface-resolved CFD simulation. Application results indicate that the proposed method is effective even when the data include some non-uniform and distortion.

Analyzing Motion Patterns in Crowded Scenes via Automatic Tracklets Clustering

Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.

EEG classification based on probabilistic neural network with supervised learning in brain computer interface

Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.

Research on the Multimedia Data Mining and Classification Algorithm based on the Database Optimization Techniques

In this research article, we analyze the multimedia data mining and classification algorithm based on database optimization techniques. Of high performance application requirements of various kinds are springing up constantly makes parallel computer system structure is valued by more and more common but the corresponding software system development lags far behind the development of the hardware system, it is more obvious in the field of database technology application. Multimedia mining is different from the low level of computer multimedia processing technology and the former focuses on the extracted from huge multimedia collection mode which focused on specific features of understanding or extraction from a single multimedia objects. Our research provides new paradigm for the methodology which will be meaningful and necessary.

中国居民膳食生物素平均每日摄入量评估

目的采用食物分类计算法评估中N居民膳食生物素平均摄入量。方法448种食物，采用微生物法测定生物素含量后，根据2002年中国居民营养与健康状况调查报告分为31类，计算各类食物算术均值和几何均值，乘以相应的食物消费量加和计算城乡居民和2岁以上各年龄段人群生物素平均摄入量。结果全国居民总体生物素摄入量（算术均值）为40．0μg／d，城乡范围36．8～48．9bμg／d，几何均值计算结果比算术均值约低9btg／d。30岁前生物素摄入量随年龄增加，到30～45岁时达到最高；其中女性（38．7μg／d）低于男性（43．8μg／d）；45岁后随老龄化生物量摄入逐渐下降，70岁时女性仅为31．2μg／d（算术均值）。结论我N城乡居民膳食生物素平均摄入量基本可以达到中国营养学会推荐营养素适宜摄入量（AI），育龄妇女和老年妇女生物素摄入量相对偏低。

Estimation of multipath parameters in wireless communications using multi-way compressive sensing

This paper addresses the problem of joint angle and delay estimation(JADE) in a multipath communication scenario. A low-complexity multi-way compressive sensing(MCS) estimation algorithm is proposed. The received data are firstly stacked up to a trilinear tensor model. To reduce the computational complexity,three random compression matrices are individually used to reduce each tensor to a much smaller one. JADE then is linked to a low-dimensional trilinear model. Our algorithm has an estimation performance very close to that of the parallel factor analysis(PARAFAC) algorithm and automatic pairing of the two parameter sets. Compared with other methods, such as multiple signal classification(MUSIC), the estimation of signal parameters via rotational invariance techniques(ESPRIT), the MCS algorithm requires neither eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also does not require the channel fading information, which means the proposed algorithm is blind and robust, therefore it has a higher working efficiency.Simulation results indicate the proposed algorithm have a bright future in wireless communications.

Network-based naive Bayes model for social network

Naive Bayes(NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are becoming increasingly accessible, due to the fast development of various social network services and websites. By contrast, data generated by a social network are most likely to be dependent. The dependency is mainly determined by their social network relationships. Then, how to extend the classical NB method to social network data becomes a problem of great interest. To this end, we propose here a network-based naive Bayes(NNB) method, which generalizes the classical NB model to social network data. The key advantage of the NNB method is that it takes the network relationships into consideration. The computational efficiency makes the NNB method even feasible in large scale social networks. The statistical properties of the NNB model are theoretically investigated. Simulation studies have been conducted to demonstrate its finite sample performance.A real data example is also analyzed for illustration purpose.