Sequential Probabilistic Ratio Test for the Scale Parameter of the P -Norm Distribution

We consider a series of independent observations from a P -norm distribution with the position parameter μ and the scale parameter σ . We test the simple hypothesis H 0 : σ = σ 1 versus H 1 : σ = σ 2 . Firstly, we give the stop rule and decision rule of sequential probabilistic ratio test (SPRT). Secondly, we prove the existence of h σ which needs to satisfy the specific situation in SPRT method, and the approximate formula of the mean sample function is derived. Finally, a simulation example is given. The simulation shows that the ratio of sample size required by SPRT and the classic Neyman–Pearson N − P test is about 50.92 % at most, 38.30 % at least.

When Should I Stop? Dysphoria Leads to Impaired Task Persistence via Negative Mood

Based on reported motivational deficits in depression – and on persistence deficits in particular – the present study examined whether dysphoric individuals benefit from task contexts that favor longer task persistence. Undergraduates worked on an item-generation task with different stop rules: “Is this a good time to stop?” ( enough rule), “Do I feel like continuing?” ( enjoy rule), or no specific rule. Results revealed that, independent of the stop rule, participants with high depression scores stopped earlier and generated fewer items than participants with low depression scores – an effect that was mediated by current mood state. Thus, contrary to experimentally induced negative mood, the enough-rule intervention was ineffective in eliminating the persistence deficits of dysphoric individuals. Implications for task disengagement and performance outcomes are discussed.

Two-Step Adaptive Augmented Unscented Kalman Filter for Roll Angles of Spinning Missiles Based on Magnetometer Measurements

It is critical to measure the roll angle of a spinning missile quickly and accurately. Magnetometers are commonly used to implement these measurements. At present, the estimation of roll angle parameters is usually performed with the unscented Kalman filter algorithm. In this paper, the two-step adaptive augmented unscented Kalman filter algorithm is proposed to calibrate the biaxial magnetometer and circuit measurements quickly, which allows accurate estimates of the missile roll angle. Unlike the existing algorithms, the state vector of the algorithm is based on the missile roll angle parameters and the error factors caused by the magnetometer and the measurement circuit errors. Next, a two-step fast fitting algorithm is used to fit the initial value. After satisfying the stop rule, the state vector of the filter is configured to estimate the roll angle parameters and the calibration parameters. This method is evaluated by running numerous simulations. In the experiment, the algorithm completes the calibration of the magnetometer and the measurement circuit 1 s after the missile launches, with a sampling rate of 1 ms and an output roll attitude angle with a 0.0015 rad precision. The conventional unscented Kalman filter algorithm requires more time to achieve such a high accuracy. The simulation results demonstrate that the proposed two-step augmented unscented Kalman filter outperforms the conventional unscented Kalman filter in its estimation accuracy and convergence characteristics.

Optimized Active Learning for User’s Behavior Modelling based on Non-Intrusive Smartphone

In order to protect the data in the smartphone, there is some protection mechanism that has been used. The current authentication uses PIN, password, and biometric-based method. These authentication methods are not sufficient due to convenience and security issue. Non-Intrusive authentication is more comfortable because it just collects user’s behavior to authenticate the user to the smartphone. Several non-intrusive authentication mechanisms were proposed but they do not care about the training sample that has a long data collection time. This paper propose a method to collect data more efficient using Optimized Active Learning. The Support Vector Machine (SVM) used to identify the effect of some small amount of training data. This proposed system has two main functionalities, to reduce the training data using optimized stop rule and maintain the Error Rate using modified model analysis to determine the training data that fit for each user. Finally, after we done the experiment, we conclude that our proposed system is better than Threshold-based Active Learning. The time required to collect the data can reduced to 41% from 17 to 10 minutes with the same Error Rate.

DIFFERENCE IN THE BEHAVIOR OF RIGHT-TURNING VEHICLES AND THEIR EFFECT ON SAFETY AT UNSIGNALIZED INTERSECTIONS IN MALAYSIA

Intersections are more prone to accidents as compared to straight road segments and vehicles that make right-turning maneuver are the ones which are more likely to be involved in an angle collision. Therefore, this study investigates their behavior at unsignalized intersections in Malaysia. The aim of this paper is to evaluate the compliance with the stop rule, use of turning indicator and right-turning behavior of minor road vehicles. All the behavioral observations were made with respect to two vehicle types which were ‘motorcycles’ and ‘others’. Descriptive analysis was presented and χ²-test was performed to investigate the association between the variables. It was found that most motorcyclists in Malaysia do not abide by the stopping rule at the intersection before making a right-turn. Moreover they seldom use their turning indicators and tend to make the indigenous ‘Weaving Merging Right-Turn’ (WMRT) more often as compared to other vehicles. Not complying with the stopping rule and keeping the indicator switched off while making a right-turn was found to be hazardous and resulted in the decrease in the safety of intersection and increase in the risk of accident. However, WMRT was found to be a safer maneuver as compared to the conventional right-turn. For vehicles other than motorcycles, the analysis concluded the same results. It is recommended that the methodology proposed in this research should be extended to other studies with a larger sample size.

Worry, Mood and Stop Rules in Young Adolescents: Does the Mood-as-Input Theory Apply?

The present study used a mixed methods approach to test the mood-as-input theory of perseverative worry in young adolescents. In an experiment young adolescents were randomized into four groups (positive or negative mood, each with ‘as many as can’ or ‘feel like continuing’ stop rules). However, there was no impact of mood and/or stop rules on perseveration of worry. Some evidence for the mood as input theory was provided by adolescents' qualitative reports of using mood and stop rules as information when deciding to stop worrying. Furthermore, cross-sectional data concurred with adult studies, suggesting trait worry is associated with ‘as many as can’ stop rules and initial negative mood. It is proposed that worry status might interact in complex ways when both mood and stop rules are manipulated and that developmental issues might have impacted on the participants' ability to follow the stop rule allocated.