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Video-based learning has successfully become an effective alternative to face-to-face instruction. In such situations, modeling or predicting learners' flow experience during video learning is critical for enhancing the learning experience and advancing learning technologies. In this study, we set up an instructional scenario for video learning according to flow theory. Different learning states, i.e., boredom, fit (flow), and anxiety, were successfully induced by varying the difficulty levels of the learning task. We collected learners' electrocardiogram (ECG) signals as well as facial video, upper body posture and speech data during the learning process. We proposed classification models of the learning state and regression models to predict flow experience by utilizing different combinations of the data from the four modalities. The results showed that the model performance of learning state recognition was significantly improved by the decision-level fusion of multimodal data. By using the selected important features from all data sources, such as the standard deviation of normal to normal R-R intervals (SDNN), high-frequency (HF) heart rate variability and mel-frequency cepstral coefficients (MFCC), the multilayer perceptron (MLP) classifier gave the best recognition result of learning states (i.e., mean AUC of 0.780). The recognition accuracy of boredom, fit (flow) and anxiety reached 47.48%, 80.89% and 47.41%, respectively. For flow experience prediction, the MLP regressor based on the fusion of two modalities (i.e., ECG and posture) achieved the optimal prediction (i.e., mean RMSE of 0.717). This study demonstrates the feasibility of modeling and predicting the flow experience in video learning by combining multimodal data. [ABSTRACT FROM AUTHOR]

This article introduces a new method for balancing the state of charge (SOC) in a dual-bus battery system architecture. The system consists of multiple battery cells or modules connected in series to provide high voltage output. Additionally, low-power flyback converters are connected in series with each battery cell or module at the inputs, and their outputs are connected in parallel to provide lower voltage output. The SOC balancing algorithm ensures that the lower voltage output remains at a desired reference value by adjusting the average duty cycle of each power converter, while also balancing the rate of charge or discharge of each battery cell or module. This SOC balancing process does not affect the normal operation of the high voltage power output. In other words, the dual output (high voltage and low voltage) of the battery system can function independently, and the balancing current only flows through the low voltage power path. Experimental results from a prototype are provided and discussed to validate the proposed dual-bus battery system and controller. [ABSTRACT FROM AUTHOR]

Surmounting obstacles during continuously climbing in a complex environment is an important issue for pole-climbing robots. An obstacle-surmounting strategy is presented for a pole-climbing robot. The force and moment applied on the pole-climbing robot in static status were analyzed, and the analysis of pole-climbing robot’s upward vertical climbing was conducted. The climbing execution has four steps: loosening the lower gripper, curling up, striding forward, and clamping the upper gripper. To obtain the information of obstacle crossing accurately, the obstacle-surmounting conditions were analyzed in detail. We modeled the striding linkage with thickness and obtained the Denavit–Hartenberg coordinates of each vertex. The model of the grippers with thickness was proposed and the Denavit–Hartenberg coordinates of each vertex of the grippers were obtained. Then single-step negotiating an obstacle and multistep negotiating an obstacle were proposed. Experiments were conducted to verify the effectiveness of the obstacle-surmounting strategy.

The popularity of science videos is critical for the dissemination of knowledge, and predicting the popularity of a video is a hot topic among researchers. The existing research is mainly based on the videos' content (e.g., theme) or video-related external information (e.g., comments). However, videos with different popularity can bring different learning states and viewing experiences (emotional awakening, flow experiences). In this paper, we utilize participants' learning states when they watch videos to predict the popularity of the videos, depending on two modal indicators, i.e., contact physiological indicators recorded by a Biopac MP150 polygraph and noncontact gesture indices recorded by a Kinect V2 body camera which can obtain data of head position. We propose two classification prediction models with each modality of indicator and filter out the indicators that make sense for modeling. Results show that the meaningful indicators in the physiological modality through stepwise logistic regression are the standard deviation of normal to normal R-R intervals (SDNN) and high-frequency heart rate variability (HF). We find that participants had higher SDNN and lower HF when watching science video with high popularity (compared with low popularity one), and the accuracy rate of classification model is 81.6%. In a same way, the selected meaningful indicators in the gesture aspect are the maximum, the standard deviation (SD) of distance between the participants' head and Kinect. We find that the maximum and SD of head distance are smaller when participants studied highly popular science videos (contrary to the less popular videos) and the accuracy rate is 73.7%. Combining the four indicators for modeling by the direct input method, the accuracy rate is 78.9%, and the SD of head distance is probably the most important indicator for predicting popularity of videos. These results indicate that it is feasible to predict video popularity by learning states. [ABSTRACT FROM AUTHOR]