Application of Road Information in Ground Moving Target Tracking

AbstractA new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification (DMM VS-IMM for short). Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads

Quantitative Study of Exfoliation Corrosion: Exfoliation of Slices in Humidity Technique

A new technique, exfoliation of slices in humidity (ESH), was developed for the determination of exfoliation corrosion (EFC) susceptibility and quantification of EFC kinetics. Two AA7178 plates taken from the wingskin of a retired KC135 airplane were used as test samples. Slices of the plate were pretreated by potentiostatic polarization in chloride solution to develop localized corrosion sites. Subsequent exposure to high humidity after pretreatment of properly oriented and unconstrained samples resulted in the development of EFC at the edges of the slices. The EFC kinetics were determined by measuring the width of the central unattacked region of the samples. The ESH results were representative of the different EFC behavior of the two plates during outdoor exposure. These results show the capability of the ESH test to discriminate between plates of varying susceptibility and to determine EFC rates quantitatively. The different susceptibility of the two plates to EFC was attributed to differences in microstructure and grain boundary chemistry.The authors acknowledge the support of the Aging Aircraft Division of ASC in support of the Aeronautical Enterprise Structures Strategy with a contract through S&K Technologies. The material was provided by W. Abbott from Battelle, who also did the atmospheric exposure testing

Global Constitutionalism as a Method in International Economic Law

Constitutionalism was traditionally a domestic law doctrine contending the institutionalization of power, the protection of fundamental (human) rights of individuals as citizens, and the rule of law. Given its value in promoting good governance in democratic societies, constitutionalism has been broadly discussed by international law scholars as a means to promote good global governance. Originally this was limited to human rights, but later extended to other shared values like the fight against nationalism and protectionism. This chapter focuses on the impact of constitutionalism on international economic law (IEL). We consider the role constitutionalism played in the development of international economic law, as well as current criticisms concerning its application in this area. Scholars have tried to link the legal and philosophical ideas of constitutionalism with economic theories, in particular ordo-liberalism which has led to particularly harsh criticism (as it is common in economic theory when it comes to the role of markets and price mechanisms as well as the role of enforceable individual rights [of capital owners and entrepreneurs]). We argue that despite this highly political controversy, constitutionalism had considerably contributed to the development and judicialization of international economic law and will continue to exert an effect on this matter

Testing a Dual Path Framework of the Boomerang Effect: Proattitudinal versus Counterattitudinal Messages

This dissertation aims to differentiate two types of boomerang effects on belief and attitude change: a boomerang effect under a proattitudinal message and a boomerang effect under a counterattitudinal message. By employing a 2 (Message valence: anti-policy vs. pro-policy) × 2 (Issues: legal age for drinking vs. legal age of marriage) × 2 (Threat to freedom: low threat vs. high threat) × 2 (Argument quality: low quality vs. high quality) plus 2 (Control groups: no-message control for the two issues) cross-sectional factorial design (N = 458), antecedents and mediators that bring about the two types of boomerang effect were examined. Under a counterattitudinal message, both argument quality and prior belief strength predicted a boomerang effect: Those receiving a low-quality argument or those with a strong prior belief, as compared with the control group, exhibited a boomerang on belief and attitude. The dominant mechanism that explained the relationship between argument quality and belief position boomerang was counterarguing (vs. anger). Under a proattitudinal message, there was an indirect effect of trait reactance on belief boomerang through anger (vs. negative cognitions). But the perceived threat to attitudinal freedom did not predict a boomerang effect. These results contribute to attitude change research by empirically separating cognitive and affective mechanisms for boomerang effects. Furthermore, this study refines the construct of negative cognitions and integrates reactance theory and the cognitive response perspective on boomerang effects. Both structural equation models and confirmatory factor analysis suggested that counterarguments and nonrefutational thoughts were two distinct types of negative cognitions. The two constructs were caused by different sets of antecedents and had different outcomes: Poor argument quality caused counterarguments, whereas perceived threat and trait reactance caused nonrefutational thoughts. Only counterarguments mediated the effects of argument quality on the boomerang effects for belief (e.g., the extent to which the legal drinking age should be decreased on a magnitude scale) and belief position (e.g., the legal age for drinking), which subsequently predicted the boomerang effect on attitude (e.g., the extent to which the legal drinking age is liked). This dissertation expands the theoretical scope of belief and attitude change research. Future research should explore the persuasive appeals for mitigating the cognitive or affective process resulting in a boomerang effect. Among those who are more prone to boomerang on certain issues, a boomerang appeal can be employed to persuade

Multimodal Emotion Recognition Model using Physiological Signals

As an important field of research in Human-Machine Interactions, emotion recognition based on physiological signals has become research hotspots. Motivated by the outstanding performance of deep learning approaches in recognition tasks, we proposed a Multimodal Emotion Recognition Model that consists of a 3D convolutional neural network model, a 1D convolutional neural network model and a biologically inspired multimodal fusion model which integrates multimodal information on the decision level for emotion recognition. We use this model to classify four emotional regions from the arousal valence plane, i.e., low arousal and low valence (LALV), high arousal and low valence (HALV), low arousal and high valence (LAHV) and high arousal and high valence (HAHV) in the DEAP and AMIGOS dataset. The 3D CNN model and 1D CNN model are used for emotion recognition based on electroencephalogram (EEG) signals and peripheral physiological signals respectively, and get the accuracy of 93.53% and 95.86% with the original EEG signals in these two datasets. Compared with the single-modal recognition, the multimodal fusion model improves the accuracy of emotion recognition by 5% ~ 25%, and the fusion result of EEG signals (decomposed into four frequency bands) and peripheral physiological signals get the accuracy of 95.77%, 97.27% and 91.07%, 99.74% in these two datasets respectively. Integrated EEG signals and peripheral physiological signals, this model could reach the highest accuracy about 99% in both datasets which shows that our proposed method demonstrates certain advantages in solving the emotion recognition tasks.Comment: 10 pages, 10 figures, 6 table