Quantitative Comparative Study of the Performance of Lossless Compression Methods Based on a Text Data Model

Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.

Advanced derivative ratio filters for edge detection of potential field data

Edge location is an important information of the source,and can be obtained by the potential field data. Most edge detection methods of potential field data are the functions of horizontal and vertical derivatives.The authors provide a new strategy to establish edge detection filters that can improve the resolution to identify small bodies,which use the ratio functions of different-order derivatives to recognize the edges of the sources.The new filter is named as advanced derivative ratio( ADR) filter and balanced outputs can be produced for different forms of ADR filters. The ADR filters are tested on synthetic data and real potential field data. The advantage of the ADR filters is that they can detect the edges of the causative sources more precisely and clearly,and the model testing results show that the resolution of ADR filters is higher than other existing filters. The ADR filters were applied to real data,with more subtle details obtained.

Calculation method of the signal-to-noise ratio attribute of seismic data based on structural orientation

At present,most signal-to-noise ratio(SNR)estimation methods can only calculate the global and not the local SNR of seismic data.This paper proposes a calculation method of a structure-oriented-based seismic SNR attribute.The purpose is to characterize the temporal and spatial variation of the seismic data SNR.First,the local slope parameters of the seismic events are calculated using a plane wave decomposition filter.Then,the singular value decomposition method is used to calculate the local seismic data SNR,thereby obtaining it in time and space.The proposed method overcomes the insufficiency of a conventional global SNR to characterize any local seismic data features and uses the SNR as an attribute of seismic data to more accurately describe the signal-noise energy distribution characteristics of seismic data in time and space.The results of a theoretical model test and real data processing show that the SNR attribute can be used not only for seismic data quality evaluation but also for analysis and evaluation of denoising methods.

Minimum MSE Weighted Estimator to Make Inferences for a Common Risk Ratio across Sparse Meta-Analysis Data

The paper aims to discuss three interesting issues of statistical inferences for a common risk ratio (RR) in sparse meta-analysis data. Firstly, the conventional log-risk ratio estimator encounters a number of problems when the number of events in the experimental or control group is zero in sparse data of a 2 × 2 table. The adjusted log-risk ratio estimator with the continuity correction points based upon the minimum Bayes risk with respect to the uniform prior density over (0, 1) and the Euclidean loss function is proposed. Secondly, the interest is to find the optimal weights of the pooled estimate that minimize the mean square error (MSE) of subject to the constraint on where , , . Finally, the performance of this minimum MSE weighted estimator adjusted with various values of points is investigated to compare with other popular estimators, such as the Mantel-Haenszel (MH) estimator and the weighted least squares (WLS) estimator (also equivalently known as the inverse-variance weighted estimator) in senses of point estimation and hypothesis testing via simulation studies. The results of estimation illustrate that regardless of the true values of RR, the MH estimator achieves the best performance with the smallest MSE when the study size is rather large and the sample sizes within each study are small. The MSE of WLS estimator and the proposed-weight estimator adjusted by , or , or are close together and they are the best when the sample sizes are moderate to large (and) while the study size is rather small.

面板数据均值渐变变点的Ratio检验

为解决时间序列因观测时间较短而难以确定变点的问题,通过分析独立同分布序列面板数据,对其均值的渐变变点进行检验.假设面板数据中每条时间序列的均值在同一时刻发生变化,提出Ratio检验统计量,并分别在原假设与备择假设下给出检验统计量的极限性质.数值模拟结果表明,面板数据的检验效果优于单条时间序列,实例分析也验证了该方法的有效性与实用性.

Exploring the growth value equity valuation model with data visualization

The Growth Value Model(GVM)proposed theoretical closed form formulas consist-ing of Return on Equity(ROE)and the Price-to-Book value ratio(P/B)for fair stock prices and expected rates of return.Although regression analysis can be employed to verify these theoretical closed form formulas,they cannot be explored by classical quintile or decile sorting approaches with intuition due to the essence of multi-factors and dynamical processes.This article uses visualization techniques to help intuitively explore GVM.The discerning findings and contributions of this paper is that we put forward the concept of the smart frontier,which can be regarded as the reasonable lower limit of P/B at a specific ROE by exploring fair P/B with ROE-P/B 2D dynamical process visualization.The coefficients in the formula can be determined by the quantile regression analysis with market data.The moving paths of the ROE and P/B in the cur-rent quarter and the subsequent quarters show that the portfolios at the lower right of the curve approaches this curve and stagnates here after the portfolios are formed.Furthermore,exploring expected rates of return with ROE-P/B-Return 3D dynamical process visualization,the results show that the data outside of the lower right edge of the“smart frontier”has positive quarterly return rates not only in the t+1 quarter but also in the t+2 quarter.The farther away the data in the t quarter is from the“smart frontier”,the larger the return rates in the t+1 and t+2 quarter.

Assessment and Evaluation of Band Ratios, Brovey and HSV Techniques for Lithologic Discrimination and Mapping Using Landsat ETM⁺and SPOT-5 Data

This study aims to assess and to evaluate band ratios, brovey and HSV (Hue-Saturation-Value) techniques for discrimination and mapping the basement rock units exposed at Wadi Bulghah area, Saudi Arabia using multispectral Landsat ETM+ and SPOT-5 panchromatic data.?FieldSpec instrument is utilized to collect the spectral data of diorite, marble, gossan and volcanics, the main rock units exposed at the study area. Spectral profile of diorite exhibits very distinguished absorption features around 2.20 μm and 2.35 μm wavelength regions. These absorption features lead to lowering the band ratio values within the band-7 wavelength region. Diorite intrusions appear to have grey and dark grey image signatures on 7/3 and 7/2 band ratio images respectively. On the false color composite ratio image (7/3:R;7/2:G and 5/2:B), diorite, marble, gossan and volcanics have very dark brown, dark blue, white and yellowish brown image signatures respectively. Image fusion between previously mentioned FCC ratio image and high spatial resolution (5 meters) SPOT-5 panchromatic image is carried out by using brovey and HSV transformation methods. Visual and statistical assessment methods prove that HSV fused image yields best image interpretability results rather than brovey image. It improves the spatial resolution of the original FCC ratios image with acceptable spectral preservation.

部分多聚焦叠加方法在准噶尔盆地低信噪比地震资料中的应用

多聚焦叠加是目前已知的最佳零偏移距叠加成像方式。对多聚焦叠加方法做改进,利用得到的波场参数来提高叠前地震资料的质量。利用多聚焦波场参数做部分多聚焦叠加,对菲涅尔带内的多个相邻CMP(共中心点)道集做倾角、曲率等校正后合并为一个道集即多聚焦超道集(部分多聚焦叠加),可以补齐缺失地震道,实现叠前数据规则化,并提高信噪比,从而使得叠前道集中的同相轴尤其是来自深层的反射轴有更好的连续性,有利于识别和追踪。提高质量后的叠前道集可用于后续的速度分析、叠加、偏移等常规处理中,效果好于原始CMP道集。模型和实际地震数据的处理结果验证了该方法的正确性和有效性,该方法在低信噪比地震资料处理中将会有广阔的应用前景。

基于多源数据的长江流域1982—2022年骤旱事件时空演变

骤发性干旱(简称骤旱)是一种突发性高且强度大的极端干旱现象,会对农业生产和生态系统构成严重威胁。近年来,长江流域骤旱频发,然而其骤旱时空演变格局及规律尚不明晰。本研究基于GLEAM、GLDAS和ERA5-Land数据,以标准化蒸发胁迫比及其变化值作为识别指标,开展1982—2022年长江流域骤旱识别,全面分析长江流域骤旱空间分布和时间演变特征;并鉴于2022年旱情的严重性和特殊性,重点分析该年长江流域骤旱事件。研究结果表明:①在空间分布上,长江流域上游的金沙江水系和中下游的大型水库湖泊骤旱发生频率最高且强度最大;②在时间演变上,骤旱发生频率、平均持续时间和强度均在长江流域整体上呈现出非显著上升趋势,而有显著变化趋势的区域在2001年前后表现出明显的趋势反转现象;③2022年夏季受极端高温热浪影响,长江流域遭遇大规模骤旱事件,具有波及范围广、持续时间长的特点,且骤旱在空间上呈现出从上游向下游传递的态势。

甲状腺结节射频消融术后复发预测模型的构建与应用

目的探讨甲状腺结节射频消融术后复发的影响因素,构建列线图预测模型,以期指导临床诊治。方法选取2019年1月至2021年1月郑州大学第二附属医院收治的398例甲状腺结节作为研究对象,其中16例于生化复发前接受内分泌治疗,12例无术后2年随访信息,均予以剔除,经筛选后259例纳入训练集用于建立模型,111例纳入验证集用于验证模型,采用COX比例风险回归方程确定甲状腺结节射频消融术后复发影响因素,采用R软件可视化处理获得列线图预测模型,行内外部验证。结果术后2年,甲状腺结节射频消融术后复发率为15.14%(56/370);COX比例风险回归方程显示,体质量指数(BMI)(OR:6.873)、热休克蛋白70(HSP70)(OR:5.380)、弹性应变率比值(SR)(OR:3.872)、结节成分(OR:5.880)、结节内部血流(OR:6.944)、结节钙化(OR:3.764)是甲状腺结节射频消融术后复发影响因素(P<0.05);基于COX比例风险回归方程结果构建甲状腺结节射频消融术后复发列线图预测模型,该模型在训练集、验证集中曲线下面积(AUC)分别为0.856、0.874,且其预测效能与实际吻合较理想。结论基于SR、BMI、HSP70、结节成分、结节内部血流、结节钙化构建列线图预测模型对甲状腺结节射频消融术后复发具有良好区分度、精准度,有助于实现个体化预测,帮助临床医师识别高风险人群,确定合理防治措施。

基于映射距离比离群因子的离群点检测算法

针对基于邻近性的离群点检测方法需要花费大量时间过滤正常点,并且在检测全局离群点时难以检测出局部离群点的问题,提出一种基于映射距离比离群因子离群点检测(MDROF)算法。首先,为了减少正常点在检测过程中的时间消耗,给出了差异相似度的概念,通过定义差异相似度剪枝因子过滤掉数据集中的大部分正常点。其次,定义映射k距离,通过映射距离与可达距离的比值刻画数据对象的局部离群程度,通过可达密度刻画数据对象的全局离群程度。最后,结合数据对象相互近邻点的平均排位定义映射距离比离群因子来检测离群点。在人工数据集以及真实数据集上分别对该算法与其他经典的离群点检测算法在精确率、AUC值和离群点发现曲线上进行实验对比分析。实验结果证明MDROF算法在离群点检测的准确性和稳定性上明显优于对比算法。

融合多源数据挖掘信息的非轴对称端壁/叶身联合成型设计空间知识挖掘

为厘清联合成型设计空间各造型方法对涡轮叶栅性能提升的贡献,将基于总变差分析的全局敏感性分析方法与平行坐标系及直方图、散点图等可视化技术相结合,提出了将多源数据挖掘信息进行融合的设计空间知识挖掘框架,并利用该框架对某小展弦比叶栅端壁/叶身联合成型设计空间进行知识挖掘。结果表明:叶栅气动损失增减对基元型线微调十分敏感;非轴对称端壁所带来的损失减少量与型线微调接近,但设计空间内不同非轴对称端壁样本性能波动较小,导致其重要性被全局敏感性分析方法低估;虽然单独采用弯叶片难以整体降低小展弦比叶栅损失,但将弯、型线微调和非轴对称端壁造型相结合可获得“1+1+1>3”的性能增益;在端壁/叶身联合成型设计空间内,将基元型线朝着后加载方向微调,并采用喉部附近下凹较深的非轴对称端壁造型及反弯叶片设计,可获得气动损失较小的叶栅设计。研究结果明晰了端壁/叶身联合成型设计空间优化解特征,可为类似小展弦比叶栅设计优化提供借鉴。

某商业建筑高效制冷机房运行数据分析

制冷机房作为系统工程,其系统运行参数的变化(如冷冻水供回水温差)会对机房内不同设备的能效产生不同的影响,机房整体能效最优是机房节能运行的重要目标。针对现有建筑运行数据存储较多但对其分析较少的现状,采用数据挖掘技术,对某商业建筑制冷机房实际运行能耗、能效进行统计计算,并将其与负荷率、供回水温度、供回水温差等变量的相关性展开分析,揭示机房能效与各变量间的内在联系。研究结果显示:制冷机房单位面积制冷能耗约为16~17 kW·h/(m^(2)·a),冷水机组能耗占80%以上,冷水机组的能效表现主导了机房的整体能效表现,机房系统能效比(COPs)高于设计值6.06 kW·h/kW·h,达到了预期目标。机房整体能效与室外气温及负荷率、冷冻(却)水供回水温差、冷却水进水出水温度呈负相关关系,机房整体能效与冷冻水供回水温度主要呈正相关关系。冷水机组运行COP与机组负荷率和冷却水回水温度呈现一定相关性,且基本符合机组定冷却水温和AHRI变冷却水温工况下的选型曲线。

不平衡数据集的DC-SMOTE过采样方法

针对不平衡数据集在分类任务中表现不佳的问题,提出基于局部密度与集中度的过采样算法。针对数据集中所有的少数类样本点,分别利用高斯核函数与局部引力来计算局部密度与集中度;对于局部密度较小的部分有针对性地合成第一类新样本,解决类内不平衡问题。根据集中度的不同,区分出少数类样本的边界,有针对性地合成第二类新样本,达到强化边界的作用;同时,通过自适应生成新样本,有效解决大部分过采样算法没有明确过采样量或者盲目追求样本平衡度相等的问题。最后,在公开的12个不平衡数据集上进行了实验,实验结果表明,本算法在低不平衡数据集与高不平衡数据集上的应用均拥有良好的表现。

基于全体数据分布规律的指纹证据似然比评价方法研究

为解决千万人级数据库中指纹自动识别系统比对得分全体数据难以获取的问题,构建科学有效的指纹证据似然比模型,需要对全体数据的分布规律进行探索。以指纹查询比对得分为数据基础,通过R语言采用矩估计和极大似然估计等数理统计方法,对Beta、Norm、Weibull和Gamma 4种参数估计分布函数进行数据拟合和误差计算,以确定最佳的参数估计方法。根据最佳拟合参数方法模拟全体数据,构建似然比模型并计算同源和异源似然比对数值以直方图评估其性能。实验结果表明,Beta分布在单枚和多枚指纹的不同细节特征数量分布规律中误差值最小,拟合效果最好。分段和整体考虑二者细节特征数量所构建的模型相似,随特征数量从5至16个的增加,同源和异源得分数据的概率密度函数曲线分离程度变大,模型性能变强,尤其当细节特征数量超过12个后,同源和异源曲线几乎完全分离,直至16个细节特征时都表现出极好的区分识别能力。针对单枚指纹所分的三段模型与指纹鉴定实际工作相符,但考虑所有特征数量时,会因综合数据较多而使模型性能下降。Beta分布可较好地展现异源条件下全体指纹数据的分布规律,以此构建的似然比模型的区分识别能力较强,具有很好的实用前景,有助于推动指纹证据评价从经验走向科学。

占空比传输机制下基于协同预测的时变不确定系统递推滤波

以工业互联网为背景,研究占空比传输机制下一类时变不确定系统的滤波问题,结合协同预测方法设计了新颖的递推滤波算法,解决了占空比传输机制下滤波性能降低的问题。首先给出描述占空比传输机制的数学模型,然后提出结合协同预测方法的递推滤波方案,设计基于占空比机制的递推滤波算法,推导了滤波误差协方差矩阵的一个上界,随后分析这个上界的有界性,实现了在稀疏数据情形下提高滤波性能的目的。仿真结果验证了所提算法的高效性和有效性。

基于美国FAERS数据库索磷布韦/维帕他韦和来迪派韦/索磷布韦不良反应信号分析

目的挖掘和分析索磷布韦/维帕他韦(SOF/VEL)和来迪派韦/索磷布韦(LED/SOF)上市后的不良反应(ADR)信号,为两药临床安全合理使用提供参考。方法采用报告比值比(ROR)法和信息成分(IC)法对美国食品药品监督管理局不良事件报告系统(FAERS)数据库2017年1月1日至2021年9月30日接收的不良事件(ADE)报告进行数据挖掘,使用《监管活动医学词典》(MedDRA)术语对报告中ADE描述用语进行标准化,筛选出有信号的ADR进行对比分析。结果最终得到SOF/VEL信号16个,LED/SOF信号18个。两者ADE重叠信号7个,主要为全身、胃肠、肝胆、神经及精神系统损害,且均有HBV再激活风险。SOF/VEL较为特异的ADR有食欲增加、酒精滥用和月经出血过多;LED/SOF较为特异的ADR有非急性卟啉病、胆管上皮细胞癌和冷球蛋白血症。结论该研究有效利用ROR法和IC法挖掘出SOF/VEL和LED/SOF的ADR风险信号,为两药临床安全合理使用提供了新的证据支持。

基于长度数据的南海北部深水金线鱼资源评估

深水金线鱼(Nemipterus bathybius)是南海北部重要的底层经济鱼类,但近年来其资源呈现过度开发的态势。利用2014—2019年在南海北部底拖网调查中采集的3059尾深水金线鱼生物学数据,使用基于长度的贝叶斯生物量评估(Length-based Bayesian biomass estimation method,LBB)和基于长度的繁殖潜力比(Length-based spawning potential ratio,LBSPR)2种数据缺乏条件下的资源评估模型,对其资源状况进行了评估,为其种群科学管理和可持续利用提供技术支撑。结果表明,2014—2019年南海北部深水金线鱼的渐近体长(Linf)、相对自然死亡率(M/K)和50%性成熟体长(L50)分别为23.7 cm、2.33和11.76 cm。LBB模型评估结果显示,其种群资源量水平(B/BMSY)、50%渔获长度与最适可捕长度的比值(Lc/Lc_opt)分别为0.89、0.85,表明深水金线鱼处于轻度过度开发状态和生长型过度捕捞状态。LBSPR模型评估结果显示繁殖潜力比(SPR)为0.19,说明深水金线鱼正处于过度捕捞状态。通过先验参数的不确定性分析,发现LBB和LBSPR对参数Linf的设置极为敏感,对参数M/K的设置比较敏感,因此在使用LBB和LBSPR模型进行评估时应谨慎设置以上2种参数。

基于多尺度窗口生成器网络的抽油机噪声压制

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突发公共卫生事件下各省医疗物资供给与医疗服务综合效率评价--基于DEA-RSR模型

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