一种改进的同步侦破和识别过失误差的方法

将线性组合技术引入过失误差同步估计法 ,用于确定候选过失误差子集。以一个循环网为例 ,对两种算法的性能进行了比较。对一个蒸汽系统的稳态数据校正仿真结果证明了该方法的有效性     

Cognitive Processing of Emotional Information in Depression, Panic, and Somatoform Disorder.

Emotional Stroop tasks (subliminal/supraliminal exposures), implicit memory tasks (tachistoscopic word identification), and explicit memory tasks (free recall after incidental learning) with 4 word types (physical threat, positive, negative, and neutral words) were administered to patients with major depressive disorder (n = 30), panic disorder (n = 33), somatoform disorder (n = 25), and healthy control participants (n = 33). On the Stroop task, panic patients showed subliminal interferences for physical threat and negative words, depressive patients showed supraliminal interferences for negative words, and somatoform patients showed supraliminal interferences for physical threat words. No patient groups demonstrated implicit memory biases. On the explicit memory task, depressive and panic patients showed memory biases for negative words; somatoform patients showed biases for physical threat words. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An agent-based debiasing framework for investment decision-support systems

Though researchers agree on the role of psychological forces on individuals' decision-making and emphasise the need for developing decision-support systems (DSS) that make individuals aware of these forces, a framework that can guide us in building such systems is still non-existent. In this article, we attempt to bridge this gap by proposing an agent-based debiasing framework for developing investment DSS. Identifying the primary characteristics of major biases influencing investment decisions through a thorough literature review, we propose a taxonomy to categorise them as cognitive, affective or conative. Cognitive biases are information-processing biases. Affective biases involve general moods and emotions. Conative biases are relatively stable personality traits such as overconfidence and inertia. We then outline debiasing strategies for each of these bias categories and identify decision-support characteristics necessary in software agents to carry out the appropriate debiasing tasks. An agent-based DSS architecture is then proposed, and a detailed trading example triggering a sample bias detection and debiasing algorithm is discussed to demonstrate the feasibility of the proposed system.     

Nonlinear unbiased minimum-variance filter for Mars entry autonomous navigation under large uncertainties and unknown measurement bias

High-precision navigation algorithm is essential for the future Mars pinpoint landing mission. The unknown inputs caused by large uncertainties of atmospheric density and aerodynamic coefficients as well as unknown measurement biases may cause large estimation errors of conventional Kalman filters. This paper proposes a derivative-free version of nonlinear unbiased minimum variance filter for Mars entry navigation. This filter has been designed to solve this problem by estimating the state and unknown measurement biases simultaneously with derivative-free character, leading to a high-precision algorithm for the Mars entry navigation. IMU/radio beacons integrated navigation is introduced in the simulation, and the result shows that with or without radio blackout, our proposed filter could achieve an accurate state estimation, much better than the conventional unscented Kalman filter, showing the ability of high-precision Mars entry navigation algorithm.     

Univariate and default standard unit biases in estimation of body weight and caloric content.

College students estimated the weight of adult women from either photographs or a live presentation by a set of models and estimated the calories in 1 of 2 actual meals. The 2 meals had the same items, but 1 had larger portion sizes than the other. The results suggest: (a) Judgments are biased toward transforming the example in question to the size and/or properties of a “standard” unit. For estimates of body weight, students assigned weights assuming a standard height, even though height information was provided in the photographs or directly present with live models. (b) There is an inclination to focus on 1 aspect or dimension of the stimulus (e.g., for female figures, their width, for meals the identity of the components as opposed to their size) and either devalue or completely ignore another parameter critical for accurate judgment (height, for the case of body weight estimations). That is, students defaulted to a normative unit size and thus treated the stimulus as a representative, categorical, and unvarying example, and focused on only 1 dimension (univariate bias) in making judgments. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于玻尔兹曼的微机械陀螺温度误差补偿

使用最优拟合的方法,在一定的温度范围内,对微机械陀螺仪温度误差建模,通过温度误差模型对陀螺仪输出进行温度误差补偿。应用线性拟合和玻尔兹曼拟合方法对实验数据进行按拟合修正,该方法减小了温度对微机械陀螺仪的影响,并验证玻尔兹曼误差模型的正确性与可实用性。补偿后的数据较陀螺原始数据零偏,提高了1~2个数量级。在工程实际中有一定的实用参考价值。     

Development of quantitative reasoning and gender biases.

One hundred twenty-seven 7-, 9-, and 11-year-old children were presented large or small samples of own-gender enhancing or other-gender enhancing observations. Children read arguments based on the observations, rated argument intelligence, judged the number of other children to whom the observations could be generalized, and provided verbal justifications for their judgments. Own-gender reasoning biases declined with age; these declines were, however, partially accounted for by declines in the strength of self-reported gender affiliations. Reasoning biases-demonstrated by problem-to-problem shifts in reasoning quality-were constrained by sample size, indicating a modest degree of rationality even among 7-year-olds. Specifically, biases co-existed with reasonably extensive generalization from large samples of other-gender evidence. Children were thus able to satisfy motivations for own-gender favoritism and reason in accord with the law of large numbers. Several explanations of the findings-based on changes in the salience of gender, multiple classification skills, and the ability to reason independently from beliefs-are offered. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

What very small numbers mean.

This article presents a theoretical and experimental framework for assessing the biases associated with the interpretation of numbers. This framework consists of having participants convert between different representations of quantities. These representations should include both variations in numerical labels that symbolize quantities and variations in displays in which quantity is inherent. Five experiments assessed how people convert between relative frequencies, decimals, and displays of dots that denote very low proportions (i.e., proportions below 1%). The participants demonstrated perceptual, response, and numerical transformation biases. Furthermore, the data suggest that relative frequencies and decimals are associated with different abstract representations of amount. (PsycINFO Database Record (c) 2010 APA, all rights reserved)