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Addressing issues such as high hardware costs, low recognition accuracy, and the inability to achieve fine-grained equipment classification, a non-invasive load fine-grained recognition system based on FPGA was developed and tested on a Linux system for online training. A three-dimensional (3D) image construction method based on color coding of voltage–current (V-I) trajectories is proposed to preprocess the collected voltage and current data, allowing for the distinction of features of various electrical equipment in multiple dimensions. First, high-frequency sampling data is preprocessed to extract the V-I trajectory and higher harmonic characteristics of the load. Then, the V-I trajectory is processed using RGB color coding and fused with higher-order harmonic features to construct a 3D image. This results in a 3D color V-I trajectory image that incorporates both color and harmonic features. Finally, the improved ResNet50 network is employed to identify the load characteristics, and the method is validated using the PLAID dataset and measured data. The load identification method achieves an accuracy rate of over 98%, enhancing the information conveyed by the V-I trajectory and improving the uniqueness of load characteristics, thereby enabling fine-grained equipment identification. This advancement holds significant implications for energy conservation and emission reduction in household electricity consumption, as well as for eliminating potential safety hazards associated with electrical equipment. [ABSTRACT FROM AUTHOR]

Addressing issues such as high hardware costs, low recognition accuracy, and the inability to achieve fine-grained equipment classification, a non-invasive load fine-grained recognition system based on FPGA was developed and tested on a Linux system for online training. A three-dimensional (3D) image construction method based on color coding of voltage–current (V-I) trajectories is proposed to preprocess the collected voltage and current data, allowing for the distinction of features of various electrical equipment in multiple dimensions. First, high-frequency sampling data is preprocessed to extract the V-I trajectory and higher harmonic characteristics of the load. Then, the V-I trajectory is processed using RGB color coding and fused with higher-order harmonic features to construct a 3D image. This results in a 3D color V-I trajectory image that incorporates both color and harmonic features. Finally, the improved ResNet50 network is employed to identify the load characteristics, and the method is validated using the PLAID dataset and measured data. The load identification method achieves an accuracy rate of over 98%, enhancing the information conveyed by the V-I trajectory and improving the uniqueness of load characteristics, thereby enabling fine-grained equipment identification. This advancement holds significant implications for energy conservation and emission reduction in household electricity consumption, as well as for eliminating potential safety hazards associated with electrical equipment.

Color and spatial layout are important factors that affect users’ icon cognition and play a huge role in the visual search process of icons. Guided by the user’s interactive needs, this paper aims to improve the visual search efficiency of mobile map navigation icons. The mixed design within and between subjects is adopted through the combination of theoretical and experimental research, and the subjective questionnaire method is used to explore the research. This paper explores the visual search problem of mobile map navigation icons based on color coding and layout coding. The experimental results mainly include reaction time, accuracy rate, user experience, and statistical and variance analysis. The results show that the layout of the mobile map navigation icons significantly impacts the user’s visual search. The navigation icons that use color for layout coding have the highest visual search efficiency and better user experience. Among the icons, the layout with regular color distribution and a larger area of the same color has the highest visual search efficiency for users and the best user experience; the visual search efficiency of navigation icons using color for layout coding is significantly higher than that of mobile map navigation icons.Relevance to industry: The user scale of mobile information maps is huge and the usage rate is high, but the large number of navigation icons increases the burden of user information identification and acquisition. As a result, the efficiency of user information acquisition is low, and the user experience is reduced. A clear, easy-to-search navigation icon design can enhance the user experience of the entire product. The results of this research provide theoretical support and practical guidance for the design optimization and improvement of mobile map navigation icons.

In the traditional non-invasive load monitoring (NILM) algorithms, the identification accuracy is enhanced with the increased network scale while sacrificing the calculation speed, which restricts the efficiency of the load identification. In this study, a multi-feature (active/reactive power and current peak-to-peak value) fusion algorithm is proposed, which can achieve enhanced identification accuracy with a smaller network scale while maintaining the calculation speed. The features of the power and current amplitudes of the loads are transformed into the values of red-green-blue (RGB) color channels by color coding and then fused into the V-I trajectory features. After that, the true-color feature image with higher discrimination is generated and input into the convolutional neural network (CNN). The testing results on the PLAID data set indicate that in comparison with the traditional load identification algorithm, the algorithm proposed in this study performs higher identification accuracy with a smaller neural network parameter scale, which significantly improves the identification efficiency.

In the present study, we tested the effectiveness of color coding on the programming learning of students who were learning from video lectures. Effectiveness was measured using multimodal physiological measures, combining eye tracking and electroencephalography (EEG). Using a between-subjects design, 42 university students were randomly assigned to two video lecture conditions (color-coded vs. grayscale). The participants' eye tracking and EEG signals were recorded while watching the assigned video, and their learning performance was subsequently assessed. The results showed that the color-coded design was more beneficial than the grayscale design, as indicated by smaller pupil diameter, shorter fixation duration, higher EEG theta and alpha band power, lower EEG cognitive load, and better learning performance. The present findings have practical implications for designing slide-based programming learning video lectures; slides should highlight the format of the program code using color coding. [ABSTRACT FROM AUTHOR]

In the present study, we tested the effectiveness of color coding on the programming learning of students who were learning from video lectures. Effectiveness was measured using multimodal physiological measures, combining eye tracking and electroencephalography (EEG). Using a between-subjects design, 42 university students were randomly assigned to two video lecture conditions (color-coded vs. grayscale). The participants’ eye tracking and EEG signals were recorded while watching the assigned video, and their learning performance was subsequently assessed. The results showed that the color-coded design was more beneficial than the grayscale design, as indicated by smaller pupil diameter, shorter fixation duration, higher EEG theta and alpha band power, lower EEG cognitive load, and better learning performance. The present findings have practical implications for designing slide-based programming learning video lectures; slides should highlight the format of the program code using color coding.