Traction-energy balancing adaptive control with slip optimization for wheeled robots on rough terrain

On rough terrain, excessive wheel slippage is easily generated by changes of surface conditions such as soil types and geometries. It induces considerable loss of wheel traction and battery energy. To prevent this, wheeled robots should consistently recognize the current situation generated between wheel and surface. And also wheeled robots are required to optimally control wheel motion in limited wheel traction and battery capacity. Therefore, this paper proposes a novel wheel control algorithm based on slip optimization of traction and energy, which is adaptive to change of surface condition. Proposed wheel control algorithm is called Traction-Energy Balancing Adaptive Control (TEB) in this paper and TEB assigns optimized rotation speed to each wheel by observing wheel slip ratio which is a key parameter of TEB. As functions of TEB, TEB is largely divided into three main parts; (1) slip optimizer (2) slip controller (3) SC-compensator. In the slip optimizer, two optimal slip models were derived as a function of slip ratios regarding maximum traction and tractive efficiency using experimental data about wheel-terrain interaction in three types of soil (grass, gravel and sand). And the optimal slip models were employed in order to determine a desired slip value of wheel with observation of a change in actual robot velocity as control input in the slip controller. For optimal slip control, the proposed slip controller is based on conventional PID controller with compensating disturbance in the controller (SC-compensator) which occurs by change of surface shapes. In the SC-compensator, radial function networks (RBFN) was applied in the slip controller and RBFN was of help to readjust previously set PID gains depending on occurred slip error. Finally, TEB was experimentally verified by controlling a real robot having four wheels on various terrain types.     

Two-Stage maximum likelihood estimation in the misspecified restricted latent class model

The maximum likelihood classification rule is a standard method to classify examinee attribute profiles in cognitive diagnosis models (CDMs). Its asymptotic behaviour is well understood when the model is assumed to be correct, but has not been explored in the case of misspecified latent class models. This paper investigates the asymptotic behaviour of a two-stage maximum likelihood classifier under a misspecified CDM. The analysis is conducted in a general restricted latent class model framework addressing all types of CDMs. Sufficient conditions are proposed under which a consistent classification can be obtained by using a misspecified model. Discussions are also provided on the inconsistency of classification under certain model misspecification scenarios. Simulation studies and a real data application are conducted to illustrate these results. Our findings can provide some guidelines as to when a misspecified simple model or a general model can be used to provide a good classification result.     

Subjective,physiological, and behavioural responses towards evaluatively conditioned stimuli

Evaluative Conditioning (EC) is commonly defined as the change in liking of a stimulus (conditioned stimulus, CS) due to its pairings with an affective unconditioned stimulus (US). In Experiment 1, we investigated effects of repeated stimulus pairings on affective responses, i.e. valence and arousal ratings, pupil size, and duration estimation. After repeatedly pairing the CSs with affective USs, a consistent pattern of affective responses emerged: The CS_negative was rated as being more negative and more arousing, resulted in larger pupils, and was temporally overestimated compared to the CS_neutral. In Experiment 2, the influence of a mere instruction about the contingency between a CS and US on affective responses was examined. After mere instruction about upcoming pairings between the CS and US, subjective ratings also changed, but there was neither evidence for differential pupillary responses nor for differential temporal processing. The results indicate that EC via pairings or instructions can change the affective responses towards formerly neutral stimuli and introduce pupil size as a physiological measure in EC research. However, Experiment 2 suggests that there might be moderating factors based on the type of EC procedure involved.     

Adaptive working memory training reveals a negligible effect of emotional stimuli over cognitive processing

Here we analyze how performance differences in an adaptive cognitive training regime based on the n-back task interact with emotional stimuli (scenes and faces) varying in their valence (negative, positive, and neutral). One hundred and three participants completed four training sessions across 2 weeks showing remarkable improvements from time to time. Results revealed similar results for faces and scenes regarding accuracy levels across increased complexity levels. However, reaction times (RTs) were sensitive to emotional conditions to some extent. Observed faster RTs to negative faces (disgust) were consistent with the negativity bias phenomenon, but this effect vanished for the highest levels of processing complexity. It is suggested that emotional information contents fail to interact with cognition when there are no cognitive resources left after the primary task is addressed.     

The estimation of vehicle speed and stopping distance by pedestrians crossing streets in a naturalistic traffic environment

The ability to estimate vehicle speed and stopping distance accurately is important for pedestrians to make safe road crossing decisions. In this study, a field experiment in a naturalistic traffic environment was conducted to measure pedestrians’ estimation of vehicle speed and stopping distance when they are crossing streets. Forty-four participants (18–45 years old) reported their estimation on 1043 vehicles, and the corresponding actual vehicle speed and stopping distance were recorded. In the speed estimation task, pedestrians’ performances change in different actual speed levels and different weather conditions. In sunny conditions, pedestrians tended to underestimate actual vehicle speeds that were higher than 40 km/h but were able to accurately estimate speeds that were lower than 40 km/h. In rainy conditions, pedestrians tended to underestimate actual vehicle speeds that were higher than 45 km/h but were able to accurately estimate speeds ranging from 35 km/h to 45 km/h. In stopping distance estimation task, the accurate estimation interval ranged from 60 km/h to 65 km/h, and pedestrians generally underestimated the stopping distance when vehicles were travelling over 65 km/h. The results show that pedestrians have accurate estimation intervals that vary by weather conditions. When the speed of the oncoming vehicle exceeded the upper bound of the accurate interval, pedestrians were more likely to underestimate the vehicle speed, increasing their risk of incorrectly deciding to cross when it is not safe to do so.     

Empirical underidentification in estimating random utility models: The role of choice sets and standardizations

A standard approach to distinguishing people’s risk preferences is to estimate a random utility model using a power utility function to characterize the preferences and a logit function to capture choice consistency. We demonstrate that with often-used choice situations, this model suffers from empirical underidentification, meaning that parameters cannot be estimated precisely. With simulations of estimation accuracy and Kullback–Leibler divergence measures we examined factors that potentially mitigate this problem. First, using a choice set that guarantees a switch in the utility order between two risky gambles in the range of plausible values leads to higher estimation accuracy than randomly created choice sets or the purpose-built choice sets common in the literature. Second, parameter estimates are regularly correlated, which contributes to empirical underidentification. Examining standardizations of the utility scale, we show that they mitigate this correlation and additionally improve the estimation accuracy for choice consistency. Yet, they can have detrimental effects on the estimation accuracy of risk preference. Finally, we also show how repeated versus distinct choice sets and an increase in observations affect estimation accuracy. Together, these results should help researchers make informed design choices to estimate parameters in the random utility model more precisely.     

Estimation and visualization of confusability matrices from adaptive measurement data

We present a simple but effective method based on Luce’s choice axiom [Luce, R.D. (1959). Individual choice behavior: A theoretical analysis. New York: John Wiley & Sons] for consistent estimation of the pairwise confusabilities of items in a multiple-choice recognition task with arbitrarily chosen choice-sets. The method combines the exact (non-asymptotic) Bayesian way of assessing uncertainty with the unbiasedness emphasized in the classical frequentist approach.We apply the method to data collected using an adaptive computer game designed for prevention of reading disability. A player’s estimated confusability of phonemes (or more accurately, phoneme-grapheme connections) and larger units of language is visualized in an easily understood way with color cues and explicit indication of the accuracy of the estimates. Visualization of learning-related changes in the player’s performance is considered.The empirical validity of the choice axiom is evaluated using the game data itself. The axiom appears to hold reasonably well although a small systematic violation is observable for the smallest choice-set sizes.     

A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.     

Adapting to test structure: Letting testing teach what to learn

We propose that we encode and store information as a function of the particular ways we have used similar information in the past. More specifically, we contend that the experience of retrieval can serve as a powerful cue to the most effective ways to encode similar information in comparable future learning episodes. To explore these ideas, we did two studies in which all participants went through study–test cycles of single category lists while we manipulated the nature of the recognition tests. The recognition tests either included only same-category lures or only different-category lures. The experience of repeated testing leads participants to avoid conceptual-based strategies but only when conceptual knowledge was poorly diagnostic for recognition (i.e., in the same-category lures condition). In a second study with a similar manipulation, we showed that repeated testing with lures from the same category as study items improved performance in a final recall surprise test compared to conditions in which different-category lures were used. Such a difference is akin to the one obtained when encoding instructions focus on distinctive item features compared to cases in which the focus is on relational processing. We suggest that testing requirements lead to adaptive changes at encoding.     

《心理统计学》教学内容的新探索

本文在对当前国内外主要心理统计学教材进行比较的基础上,指出与上个世纪八十年代的心理统计学教材内容相比较,在内容上的新探索主要体现在（1）由“假设检验”的内容中发展出“统计检验力”和“效果大小”的统计指标和估计方法;（2）引进一般线性模型来统合方差分析和回归分析这两种统计方法;（3）适度增加一些“多元统计分析”的内容等三个方面.本文对前两个方面的新内容作了简要评述,并对教材内容的编排方面提出了新的思路.