MULTITARGET STATE AND TRACK ESTIMATION FOR THE PROBABILITY HYPOTHESES DENSITY FILTER

The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existing approaches combine the data association step to solve this problem. This paper proposes an algorithm which does not need the association step. Our basic ideal is based on the clustering algorithm of Finite Mixture Models (FMM). The intensity distribution is first derived by the particle-PHD filter, and then the clustering algorithm is applied to estimate the multitarget states and tracks jointly. The clustering process includes two steps: the prediction and update. The key to the proposed algorithm is to use the prediction as the initial points and the convergent points as the es- timates. Besides, Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC) ap- proaches are used for the FMM parameter estimation.

Limited negative semantics based inductive hypotheses

The Learning method of inductive logic programming evaluates the the quality of inductive hypotheses commonlyby how well the hypotheses cover training examples extensionally. Ths kind of evalution will be completely inapplica-ble to the situation where there are non-simple or noise examples. Based on Limited Negative semantics, This paperextends induction of simple examples to that of generalized ones and presents 3 criteria on accepting inductive hypothe-ses in order to fit for different goals of induction. The relation between inductive hypotheses and background knowledgeis also established in this Paper. The conclusions in this paper are helpful to implement learning algorithms and to ex-tend application fields of ILP.

Why It Is Problematic to Calculate Probabilities of Findings Given Range Null Hypotheses

An important problem with null hypothesis significance testing, as it is normally performed, is that it is uninformative to reject a point null hypothesis [1]. A way around this problem is to use range null hypotheses [2]. But the use of range null hypotheses also is problematic. Aside from the usual issues of whether null hypothesis significance tests can be justified at all, there is an issue that is specific to range null hypotheses. It is not straightforward how to calculate the probability of the data given a range null hypothesis. The traditional way is to use the single point that maximizes the obtained p-value. The Bayesian alternative is to propose a prior probability distribution and integrate across it. Because frequentists and Bayesians disagree about a variety of issues, especially those pertaining to whether it is permissible to assign probabilities to hypotheses, and what gets lost in the shuffle is that the two camps actually come to different answers for the probability of the data given a range null hypothesis. Because the probability of the data given the hypothesis is a precursor for both camps, for drawing conclusions about hypotheses, different values for this probability for the different camps is crucial but seldom acknowledged. The goal of the present article is to bring out the problem in a manner accessible to researchers without strong mathematical or statistical backgrounds.

Two Hypotheses of “Melanization” on Electrogalvanized Steel Surface

Melanization is harmful to the lightness of galvanized steel surface and also reduces the bonding force between a paint coat and the steel sheet. Experimental results showed that the hypothesis of Zn-Pb codeposition does not hold true, while the fact that PbSO4 micelles in sulfate bath settle on the coating surface support the second hypothesis. The authors come to the conclusions that (1) Any measure to eliminate PbSO4 micelles before or after passivation of the coating will prevent melanization; (2) PbSO4 micelles adsorbed on the surface of passivated film promote the growth of oxygen-deficient Zn oxide and result in the generation of melanization.

Sequential Test of Fuzzy Hypotheses

In testing statistical hypotheses, as in other statistical problems, we may be confronted with fuzzy concepts. This paper deals with the problem of testing hypotheses, when the hypotheses are fuzzy and the data are crisp. We first give new definitions for notion of mass (density) probability function with fuzzy parameter, probability of type I and type II errors and then state and prove the sequential probability ratio test, on the basis of these new errors, for testing fuzzy hypotheses. Numerical examples are also provided to illustrate the approach.

Consistent Criteria for checking Hypotheses

This paper we study a consistent criterion for checking Hypotheses. Given definition of consistent criterion for checking hypotheses for family probability measures which were defined by Z. Zerakidze （see 5）. We prove the necessary and sufficient conditions are obtained for the existence of consistent criteria of hypotheses. For example we clearly build of a consistent criteria for checking hypotheses.

Hypotheses of ＂Harmonious Man＂： The Humanity Upgrade of Management and It＇s Sustainability Significance

Hypotheses of humanity is the logical premise and foundation of institution design and perfect. ＂Political man＂ had boosted the sprout of ancient democracy polity, ＂economic man＂ has accelerated the development of market economy in immediate and modern times, ＂free man＂ described the blueprint of future society, and ＂harmonious man＂ will promote the construction of harmonious society and the development of management to conducting oneself so as to serve others. The article explains the theory headstream, abstract ways, basic intension and sustainability effect to the hypotheses of＂harmonious man＂.

Constrained Bayesian Method for Testing the Directional Hypotheses

The paper discusses the generalization of constrained Bayesian method （CBM） for arbitrary loss functions and its application for testing the directional hypotheses. The problem is stated in terms of false and true discovery rates. One more criterion of estimation of directional hypotheses tests quality, the Type III errors rate, is considered. The ratio among discovery rates and the Type III errors rate in CBM is considered. The advantage of CBM in comparison with Bayes and frequentist methods is theoretically proved and demonstrated by an example.

Reasons for Modern Warming:Hypotheses and Facts

Two hypotheses of modern warming are considered:natural and anthro­pogenic.The probabilities of each of them are compared.It is proved that the hypothesis of natural warming is much more likely than the hypothesis of anthropogenic warming.It is shown that the displacement of the Sun from the center of mass of the solar system directly affects the temperature of the surface atmosphere in the synoptic regions of Eurasia.This result corresponds to the model of E.P.Borysenkov with variations of the solar constant or,equivalently,with variations of the Bond albedo.

周期性羽化昆虫形成机制

当昆虫类群表现为长生命周期k(k>1)年时,成虫的羽化表现为非周期性、周期性和过渡周期性3种形式。非周期性即为成虫每年均羽化,周期性即为成虫每k年才羽化1次,过渡周期性为非周期昆虫逐渐进化为羽化周期性的必经阶段,不同年份羽化的同生群在密度上产生了显著差异,形成了小同生群和优势同生群。自然界中表现出完全羽化周期性的昆虫种类是较少的,但由于其高种群密度的成虫同步性羽化现象,对比非周期性昆虫更易暴发成灾。为明确周期性昆虫演化进程并为林区虫害防控提供理论指导,总结了周期性昆虫的种类和生活特性,不同年间的气候异质性、自然灾害、扩散到未分布区域、天敌、种间和种内竞争等因素均有可能成为过渡周期现象形成的最初驱动力,生活史延长、寄主-天敌互作、低温驱动效应、天敌不敏感-捕食者饱足效应、种间和种内竞争等是促使昆虫羽化周期性形成的可能机制。在林区管理实践中,应提前评估害虫羽化周期性产生的趋势和程度。当成虫表现出完全羽化周期性,应在集中羽化年份内采取见效快的综合防控策略,降低唯一同生群密度至经济阈值以下。当成虫表现出过渡周期性,应加大优势同生群防治力度、降低小同生群防治频率,以及采取天敌林间释放和保育技术以平衡天敌对目标害虫的不同发育阶段种群的控制作用大小,遏制或减缓天敌-寄主互作驱动下的周期性演化进程,逐渐实现由过渡周期阶段向非周期性的逆转。当成虫表现出非周期性,应减少专化性天敌的释放和针对害虫特定阶段的防治措施使用频率,优先选择作用于所有发育阶段且致死率不存在显著差异的防治手段,避免因人为干扰产生的周期性演化和进一步的成灾。

语料库翻译研究背景下的“translationese”与“翻译共性”刍议

近年来,在基于语料库的翻译研究背景下,翻译语言特征描写中的两个关键词——“translationese”与“翻译共性”备受关注,两者之间既有区别,亦有交织重叠的地方。在国内翻译研究领域,“translationese”的译名和定义也不尽一致。本研究梳理了语料库研究方法背景下两者的概念和研究模式的发展,进而探讨了translationese的连续性特征以及它与翻译共性假设的关系问题,提出以“特征译语”为译名以突出其特点,并阐明了本研究在理论与实践方面的意义。

子空间不确定下多重假设AMF、Rao与Wald检测方法

由于目标多秩子空间大小的不确定性会导致多重假设检测发生,传统目标自适应二元检测方法不再适用。针对此问题,文章提出了子空间不确定下多重假设AMF、Rao与Wald检测方法。首先,基于Kullback-Leibler信息准则,建立了目标多秩子空间存在多种假设下的目标检测模型;然后,基于AMF、Rao和Wald检测准则,设计多重假设检测器,并优化估计未知参数与计算惩罚项。最后,通过仿真实验验证了所提检测器的性能,并分析了惩罚项对各检测器性能的影响。实验结果表明,相比传统检测器,所提检测器在一定情况下具有更优的检测性能。

Island biogeography theory and the habitat heterogeneity jointly explain global patterns of Rhododendron diversity

Mountain biodiversity is of great importance to biogeography and ecology.However,it is unclear what ecological and evolutionary processes best explain the generation and maintenance of its high levels of species diversity.In this study,we determined which of six common hypotheses(e.g.,climate hypotheses,habitat heterogeneity hypothesis and island biogeography theory)best explain global patterns of species diversity in Rhododendron.We found that Rhododendron diversity patterns were most strongly explained by proxies of island biogeography theory(i.e.,mountain area)and habitat heterogeneity(i.e.,elevation range).When we examined other relationships important to island biogeography theory,we found that the planimetric area and the volume of mountains were positively correlated with the Rhododendron diversity,whereas the‘mountains-to-mainland’distance was negatively correlated with Rhododendron diversity and shared species.Our findings demonstrate that Rhododendron diversity can be explained by island biogeography theory and habitat heterogeneity,and mountains can be regarded as islands which supported island biogeography theory.

A NEW DATA ASSOCIATION ALGORITHM USING PROBABILITY HYPOTHESIS DENSITY FILTER

Probability Hypothesis Density (PHD) filtering approach has shown its advantages in tracking time varying number of targets even when there are noise,clutter and misdetection. For linear Gaussian Mixture (GM) system,PHD filter has a closed form recursion (GMPHD). But PHD filter cannot estimate the trajectories of multi-target because it only provides identity-free estimate of target states. Existing data association methods still remain a big challenge mostly because they are com-putationally expensive. In this paper,we proposed a new data association algorithm using GMPHD filter,which significantly alleviated the heavy computing load and performed multi-target trajectory tracking effectively in the meantime.

Analysis of Krashen's Affective Filter Hypothesis

Affective filter hypothesis is one of Krashen's five hypotheses on Second Language Acquisition (SLA), and it states that a number of affective variables play a very important role in Second Language Acquisition. Based on the introduction of affective filter hypothesis, this paper will analyze the significance and shortcomings of this hypothesis.

Statistical Tests of Hypothesis Based Color Image Retrieval

This paper proposes a novel method based on statistical tests of hypotheses, such as F-ratio and Welch’s t-tests. The input query image is examined whether it is a textured or structured. If it is structured, the shapes are segregated into various regions according to its nature;otherwise, it is treated as textured image and considered the entire image as it is for the experiment. The aforesaid tests are applied regions-wise. First, the F-ratio test is applied, if the images pass the test, then it is proceeded to test the spectrum of energy, i.e. means of the two images. If the images pass both tests, then it is concluded that the two images are the same or similar. Otherwise, they differ. Since the proposed system is distribution-based, it is invariant for rotation and scaling. Also, the system facilitates the user to fix the number of images to be retrieved, because the user can fix the level of significance according to their requirements. These are the main advantages of the proposed system.

THE PROBABILITY HYPOTHESIS DENSITY FILTER WITH EVIDENCE FUSION

The original Probability Hypothesis Density (PHD) filter is a tractable algorithm for Multi-Target Tracking (MTT) in Random Finite Set (RFS) frameworks. In this paper,we introduce a novel Evidence PHD (E-PHD) filter which combines the Dempster-Shafer (DS) evidence theory. The proposed filter can deal with the uncertain information,thus it forms target track. We mainly discusses the E-PHD filter under the condition of linear Gaussian. Research shows that the E-PHD filter has an analytic form of Evidence Gaussian Mixture PHD (E-GMPHD). The final experiment shows that the proposed E-GMPHD filter can derive the target identity,state,and number effectively.

结构化分析方法应用的局限与反思

[目的/意义]休厄提出的结构化分析方法是国外比较流行的一种情报研究方法并受到国内情报学界的广泛推介。然而,结构化分析方法在应用中存在的问题与局限却并未得到国内情报学者的足够重视。[方法/过程]介绍结构化分析方法的源起与发展,分析国内结构化分析方法相关研究的主题与局限,阐述结构化分析方法近年来在国外受到的诸多理论与实践上的质疑与批判。[结果/结论]情报研究方法的借鉴与运用不可脱离情报任务场景,而需因地制宜、因人而异。我国情报工作者应注意避免对国外流行的情报研究方法如结构化分析方法的简单照搬,应当重视针对具体的情报任务场景运用合适的分析工具,探索并构建符合国情和专业领域情报工作特点与要求的情报研究方法。

基于分层贝叶斯模型的图像修复方法

针对当前图像修复方法存在的运算复杂、修复后图像质量下降等问题,提出一种基于分层贝叶斯模型的图像修复方法,在高斯先验假设的基础上,利用Gaussian-Wishart分布对均值和协方差矩阵的概率分布进行建模,将问题正则化的同时提升参数估计的稳定性,采用变分贝叶斯期望最大(Variational Bayesian Expectation Maximum,VBEM)算法对模型进行求解,得到最小均方误差准则下的最优重构。结果表明,该算法在图像修复时间、峰值信噪比和结构相似度等方面相对于对比方法均有较为明显提升,具有较高的应用前景。

目标跨像元展宽“分裂”退化模型与检测研究

采用大规模红外探测器的遥感仪器对以恒星为主的点目标成像时,受点目标跨像元效应、探测器读出带宽不足等影响,目标信号展宽,在多抽头间隔采样下出现“分裂点”现象,空间红外遥感仪器定位精度、目标辨识能力下降。针对此问题,本文构建了点目标“分裂”退化模型,并根据分裂点之间的强相关性,设计了预检测-能量维度迭代-模型更新-后检测的目标检测流程,通过形态学滤波与多假设关联跟踪对“分裂点”目标各观测进行独立跟踪,获取“分裂点”统计信息。然后利用已知恒星成像的质心矫正退化模型并映射到检测图像,修正目标质心。测试结果表明,该方法能有效区分多目标和“分裂点”造成的假目标,平均定位精度由0.5像元以上提升到0.15像元内。