The Effect of Strengthening Asking Skills with the Guided Inquiry Learning Model on Students Learning Outcomes on Rotation Dynamics

This study aims to determine the effect of strengthening questioning skills with the guided inquiry learning model on student learning outcomes. This type of research is quasi-experimental. The population in the study were all students of class XI semester II with a sample of class XI IPA 3 and class XI IPA 4, each of which consisted of 36 students. Sampling in this study was carried out randomly. The research design was a two group pre-test post-test design. The instrument used in the study was a multiple choice test consisting of 20 questions and post-test result with an average score of 76.94 for the experimental class and 70.13 for the control class. The result of the t-test stated that there was a significant influence between the learning outcomes of students who used strengthening asking skills with the guided inquiry learning model and learning without being given reinforcement of asking skills in class XI semester II students at Madrasah Aliyah.This study aims to determine the effect of strengthening questioning skills with the guided inquiry learning model on student learning outcomes. This type of research is quasi-experimental. The population in the study were all students of class XI semester II with a sample of class XI IPA 3 and class XI IPA 4, each of which consisted of 36 students. Sampling in this study was carried out randomly. The research design was a two group pre-test post-test design. The instrument used in the study was a multiple choice test consisting of 20 questions and post-test result with an average score of 76.94 for the experimental class and 70.13 for the control class. The result of the t-test stated that there was a significant influence between the learning outcomes of students who used strengthening asking skills with the guided inquiry learning model and learning without being given reinforcement of asking skills in class XI semester II students at Madrasah Aliyah

Cooperative Line Formation Control of Multi-Agent Systems Based on Least Squares Estimation

In this paper, we consider the problem of multi-agent systems where each agent aims to establish a line formation in a distributed manner. In constructing an efficient line formation, finding a line with the closest total distance from every agent is essential. We propose a formation control using least squares estimation (LSE) performed by each agent with only the local information that consists of the corresponding agent’s and neighbors’ positions. Each agent calculates the local cost function, which is the squared distance from the LSE line to the related agent’s and its neighbors’ positions. Our goal is to minimize the global cost function, which is the sum of these local cost functions. To achieve this, we employ distributed optimization to the global cost function of the overall system using the subgradient method performed by each agent locally. We evaluate our proposed method using numerical simulation, and the result complies with our goal of this pape

PFL-LSTR: A privacy-preserving framework for driver intention inference based on in-vehicle and out-vehicle information

Intelligent vehicle anticipation of the movement intentions of other drivers can reduce collisions. Typically, when a human driver of another vehicle (referred to as the target vehicle) engages in specific behaviors such as checking the rearview mirror prior to lane change, a valuable clue is therein provided on the intentions of the target vehicle's driver. Furthermore, the target driver's intentions can be influenced and shaped by their driving environment. For example, if the target vehicle is too close to a leading vehicle, it may renege the lane change decision. On the other hand, a following vehicle in the target lane is too close to the target vehicle could lead to its reversal of the decision to change lanes. Knowledge of such intentions of all vehicles in a traffic stream can help enhance traffic safety. Unfortunately, such information is often captured in the form of images/videos. Utilization of personally identifiable data to train a general model could violate user privacy. Federated Learning (FL) is a promising tool to resolve this conundrum. FL efficiently trains models without exposing the underlying data. This paper introduces a Personalized Federated Learning (PFL) model embedded a long short-term transformer (LSTR) framework. The framework predicts drivers' intentions by leveraging in-vehicle videos (of driver movement, gestures, and expressions) and out-of-vehicle videos (of the vehicle's surroundings - frontal/rear areas). The proposed PFL-LSTR framework is trained and tested through real-world driving data collected from human drivers at Interstate 65 in Indiana. The results suggest that the PFL-LSTR exhibits high adaptability and high precision, and that out-of-vehicle information (particularly, the driver's rear-mirror viewing actions) is important because it helps reduce false positives and thereby enhances the precision of driver intention inference.Comment: Submitted for presentation only at the 2024 Annual Meeting of the Transportation Research Boar