氧化三甲胺(Trimethylamine N-oxide,TMAO)是水产品中的一种天然鲜味成分,但在不同条件的影响下也会分解为有害物质,对人体健康造成影响.为研究鱿鱼上清液在热处理过程中TMAO的降解对氨基酸和还原糖的影响,测定了反应前后鱿鱼上清液中甲醛(Formaldehyde,FA)、二甲胺(Dimethylamine,DMA)、三甲胺(Trimethylamine,TMA)以及氨基酸和还原糖含量的变化.结果显示:加热时间越长,鱿鱼上清液中的TMAO分解速度越快,相应的FA、DMA和TMA生成量越多,且随着温度升高,半乳糖含量显著减少,大部分氨基酸的含量呈下降趋势,表明氧化三甲胺的降解与氨基酸和还原糖有相关性,为进一步研究氨基酸和还原糖对TMAO热分解的机制奠定了基础.

挥发性盐基氮(TVB-N)是反映水产品新鲜度的主要指标,但其测定方法不易在冷链运输中进行,因此利用各种技术设计了一套水产品冷链物流鲜度预测系统,通过水产品产生的硫化氢、氨气以及三甲胺气体浓度来预测TVB-N的浓度,并首次在水产品质量检测中引入随机森林对水产品的鲜度进行预测.针对现有随机森林的缺点,利用随机搜索法、网格搜索法以及交叉验证对随机森林参数进行优化,建立了基于随机森林的水产品冷链物流鲜度预测模型.将优化后的随机森林模型与决策树、最小二乘法和支持向量机模型进行对比分析,通过比较均方误差、平均绝对误差以及决定系数,发现优化的随机森林模型具有最低的均方误差和平均绝对误差与最高的决定系数,验证了此模型能更准确预测TVB-N的浓度,更具有实际应用价值.

In order to suppress the mixed noise composed of salt & pepper noise and Gauss noise in a digital image, a filter algorithm for suppressing mixed noise based on grey relevance is proposed.The algorithm classifies the noise point in the center of the filtering window, calculates the correlation coefficient of non-pepper and salt noise points in window by using the grey relational degree, and performs the weighted operation to replace the salt & pepper noise point.For Gaussian noise point, the algorithm uses the mean of non-pepper and salt noise points in the filtering window to replace.The experiments results show that the algorithm can suppresses the mixed noise in images, and improves the sharpness of the image, which is better than traditional filtering algorithms.

为了有效缓解城市区域交通拥堵,以区域路网总行程时间最小为目标,引入饱和度、有效绿灯时长、周期时长和相位差作为约束,构建了一种城市区域交通信号控制与诱导协同的一体化模型,并提出了一种基于MapReduce的遗传算法对协同模型进行求解.通过VISSIM仿真实例验证所提出的协同模型和求解算法,实验结果表明:实施协同以后,区域路网中各路段的流量得到了均衡,减少了区域路网的总行程时间,提高了模型求解的效率.

定量构效关系(QSAR)是预测鱼类急性毒性的常用手段,其中多维构效关系为非线性回归问题,在处理这种非线性问题时往往存在一定的局限性.为了更加准确地预测鱼类急性毒性,本文利用了在非线性情况下表现良好的支持向量机(SVM)进行预测,提出了一种基于人工蜂群算法(ABC)优化支持向量机的鱼类毒性LC50预测模型,并与SVM、GA-SVM、PSO-SVM模型的结果进行对比.结果 表明,ABC-SVM模型的预测准确率已达到了85.166％,在迭代多次的情况下,运行时间仅46.518秒,具有最高的预测精度、最小的误差,是相对高效的一种鱼类毒性预测方法.

为揭示海鲈鱼在低温贮藏过程中ATP及相关物质降解规律及其肌苷酸降解过程中酸性磷酸酶活性的变化,研究了0℃和4℃贮藏条件下海鲈鱼体内ATP关联化合物含量、K值、酸性磷酸酶(ACP)活性和水分分布.结果显示,贮藏初期ATP迅速降解,ADP和AMP含量较低,IMP降解致使HxR和Hx积累,以致K值上升.ACP活性呈现明显差异.相比之下,0℃条件下鱼片的水分损失较小,K值较低.

针对水质预警指标复杂冗余问题,提出基于关联属性重要度的水质指标属性约简算法.以属性依赖度和属性重要性为基础,根据每个条件属性对决策属性影响程度,引入属性关联重要性概念进行约简操作.通过计算属性关联重要性,挖掘水质指标之间的相互影响程度,进一步判别决策属性对条件属性的分类强度,并以鲈养殖水质为实例进行算法解析,以UCI数据集进行实验验证,结果表明此算法在冗余属性约简方面是有效可行的.

为了有效去除数字图像中的椒盐噪声,结合灰色理论和MTM滤波原理,提出了一种基于MTM和灰色关联的去除椒盐噪声的滤波算法.算法采用开关滤波原则,如果滤波窗口中心点为噪声点,统计滤波窗口内非噪声点的像素值集合,以集合中像素中值M为中心,δ为阈值选取灰度区间[M-δ,M+δ],将落在区间内的像素值确定为比较序列,然后计算这些像素的中值,将中值作为参考序列,采用均值化方法对比较序列和参考序列进行无量纲化处理,利用灰色关联分析法计算各比较序列对应元素的关联系数,将它们作为对应像素的权值系数进行加权运算,将最终的加权结果作为滤波窗口的滤波输出.如果滤波窗口中心点不是噪声点,则保持该点原始像素值不变.从实验结果可以得出,该算法和标准中值滤波算法、极值中值滤波算法、MTM滤波算法相比,具有较强的抑制椒盐噪声性能,且边缘保护效果良好.

In data clustering, density based algorithms are well known for the ability of detecting clusters of arbitrary shapes. DBSCAN is a widely used density based clustering approach, and the recently proposed density peak algorithm has shown significant potential in experiments. However, the DBSCAN algorithm may misclassify border data points of small density as noises and does not work well with large density variance across clusters, and the density peak algorithm has a large dependence on the detected cluster centers. To circumvent these problems, we make a study of these two algorithms and find that they have some complementary properties. We then propose to combine these two algorithms to overcome their problems. Specifically, we use the DP algorithm to detect cluster centers and then determine the parameters for DBSCAN adaptively. After DBSCAN clustering, we further use the DP algorithm to include border data points of small density into clusters. By combining the complementary properties of these two algorithms, we manage to relieve the problems of DBSCAN and avoid the drawbacks of the density peak algorithm in the meanwhile. Our algorithm is tested with synthetic and real datasets, and is demonstrated to perform better than DBSCAN and density peak algorithms, as well as some other clustering algorithms.

系统研究了干法消化-分光光度法测定食品中铝含量的最佳反应条件,建立了快速测定食品中铝含量的方法.研究了槲皮素用量、铝用量、缓冲溶液浓度、缓冲溶液pH值及反应时间对络合物吸光度的影响以及生成络合物的稳定性,并对不同食品中铝含量进行了测定.在乙酸盐的缓冲介质中,铝离子和槲皮素生成黄色络合物,测定波长为425 nm.缓冲液浓度为0.02～0.10 mol/L;缓冲溶液的pH为4.5～6.0;反应时间为20 min.吸光度与铝含量呈现良好的线性关系,根据曲线方程测定了食品中铝含量,样品中铝含量从高到低的顺序为海蜇皮、油条和面包.相对标准差为1.64％～3.76％,回收率为94.90％～100.60％.此方法测定食品中的铝含量简单快速,灵敏度高,稳定性好.

在传统中值、均值滤波算法的基础上借鉴灰色关联理论,提出了一种有效抑制混合噪声的滤波算法.算法采用窗口自适应策略,先统计3×3滤波窗口内椒盐噪声点数目,如果数目大于7,则扩大窗口至5×5.计算各点关联系数,将滤波窗内非椒盐噪声点的灰度值作为比较序列,它们的中值或均值作为参考序列,对于椒盐噪声的滤除参考序列使用中值,对于高斯噪声的滤除使用均值作为参考序列.然后将各非噪声点灰度值与关联系数加权运算,得出的灰度值替换噪声点像素值.通过实验验证了几种算法的性能差别,证实了算法具有较强的去噪能力和边缘保护效果.

The density peak based clustering algorithm is presented by assuming that cluster centers are local density peaks, and utilizes local density relationship to detect cluster centers. This algorithm has been shown to be effective and efficient in some experiments. However, by studying the clustering mechanism in depth, we find that it may not be appropriate to treat density peaks as cluster centers in some cases. On one hand, the cluster centers obtained this way are often inconsistent with human intuition. On the other hand, local density difference across clusters is likely to influence the cluster center identification result. To relieve this problem, we present centerness as an alternative criterion of cluster center detection. The centerness criterion reflects to which degree the neighborhood of one data is filled with the nearest neighbors evenly, and is calculated with a histogram based method in our approach. By selecting cluster centers from centerness peaks, the clustering can be accomplished in a similar way as density peak algorithm. Our approach relieves the aforementioned problems of density peak algorithm, and performs well in experiments with synthetic and real datasets.

The dominant sets clustering algorithm has some interesting properties and has achieved impressive results in experiments. However, with the data represented as feature vectors, we need to estimate data similarity and the regularization parameter influences the clustering results and number of clusters significantly. To obtain a specified number of clusters efficiently with the dominant sets algorithm, we present a target dominant set clustering algorithm. Our algorithm detects clusters in the first step, and then extracts dominant sets around the cluster centers based on a specially designed game dynamics. In addition, we show that this game dynamics can be utilized to reduce the computation and memory load significantly. Experiments show that our algorithm performs favorably to the original dominant sets algorithm in clustering quality with much smaller computation load than the latter.

A new algorithm for removing salt and pepper noise is proposed based on grey theory and median filtering algorithm.The algorithm uses switching strategy,if the filter window center is noise point,adjusts the window size adaptively according to the number of non-noise,and uses these non-noise points gray and their mean value as comparison sequence and reference sequence to calculate correlation degree,then weighted these non-noise points gray together with their correlation degree,if the result is normal gray value,replaces the center noise point gray,otherwise,uses the median gray of non-noise points in the filter window to replace center noise point gray.The experimental results show that the proposed algorithm has better denoising ability and edge protection effect compared with other algorithms.

Recently, a clustering algorithm is proposed by treating local density peaks as cluster centers. This algorithm proposes to describe the data to be clustered with local density and the distance of one data to the nearest data of larger local density. This description highlights the uniqueness of cluster centers and is utilized to determine cluster centers. With the assumption that one data and the nearest data of larger local density are in the same cluster, the non-center data are assigned labels efficiently. By studying the clustering process of this algorithm in depth, we find that the local density is not very effective in highlighting the uniqueness of cluster centers. As a result, this algorithm is dependent on the parameters in local density calculation. We discuss this problem and find that it is the role of density peaks, but not the absolute local density, that highlights the uniqueness of cluster centers. Based on this observation, we introduce the concept of subordinate and use the amount of subordinates to replace the local density in cluster center identification. Together with a new density kernel, this new criterion is shown to be effective in experiments and comparisons.

As a clustering approach with significant potential, the density peak (DP) clustering algorithm is shown to be adapted to different types of datasets. This algorithm is developed on the basis of a few simple assumptions. While being simple, this algorithm performs well in many experiments. However, we find that local density is not very informative in identifying cluster centers and may be one reason for the influence of density parameter on clustering results. For the purpose of solving this problem and improving the DP algorithm, we study the cluster center identification process of the DP algorithm and find that what distinguishes cluster centers from non-density-peak data is not the great local density, but the role of density peaks. We then propose to describe the role of density peaks based on the local density of subordinates and present a better alternative to the local density criterion. Experiments show that the new criterion is helpful in isolating cluster centers from the other data. By combining this criterion with a new average distance based density kernel, our algorithm performs better than some other commonly used algorithms in experiments on various datasets.

Based on the grey relevance theory and the idea of switching filter,an algorithm for removing salt and pepper noise based on grey relation is proposed.The algorithm uses switching strategy,for the noise point,selects the non-noise pixels in the filtering window with size of 3X3,calculated the weighting coefficient of each pixel by grey value,and used the non-noise pixel weighted to replace the noise pixels.The simulation results show that the algorithm not only suppresses the salt and pepper noise in the image,but also maintains the edge detail of the image,and the filtering effect is better than the traditional filtering algorithms.

To solve the problem of traffic state identification for regional road network which includes large amount of data and difficult calculation, improve every link of the fuzzy C-means clustering algorithm, a traffic state identification method for urban road network was proposed combined with genetic algorithm and MapReduce parallel programming model of cloud computing. A typical road network environment with twelve intersections was set up in VISSIM simulate software, and the model was verified by the simulated data. Experiment results show that the proposed model can accurately identify the traffic state of the road network, and greatly improve the efficiency of traffic state identification.

Although there are a lot of clustering algorithms available in the literature, existing algorithms are usually afflicted by practical problems of one form or another, including parameter dependence and the inability to generate clusters of arbitrary shapes. In this paper we aim to solve these two problems by merging the merits of dominant sets and density based clustering algorithms. We firstly apply histogram equalization to eliminate the parameter dependence problem of the dominant sets algorithm. Noticing that the obtained clusters are usually smaller than the real ones, a density threshold based cluster growing step is then used to improve the clustering results, where the involved parameters are determined based on the initial clusters. This is followed by the second cluster growing step which makes use of the density relationship between neighboring data. Data clustering experiments and comparison with other algorithms validate the effectiveness of the proposed algorithm.

Analysis on cell morphology in medical image processing mainly depends on the technology of edge detection,according to the characteristics of medical cell image,this paper proposed a new edge detection algorithm based on gradient algorithm principle.The algorithm uses 5 × 5 detection window,calculates the difference of pixels gray average of four strip sub-windows in horizontal and vertical direction separately,gets their obsolutions,and the maxium of them is as direction gradent,uses the maxium direction gradient as the gradient of central point of detection window.At last,it thines the gradient image and extracts the image edge.The experiments show that the extracted cell edge has good continuity and high accuracy,and has a certain noise suppression ability,and the performance is better than the traditional detection algorithm.