Traditional simultaneous localization and mapping (SLAM) system tends to operate in small-area static environments, and its performance might degrade when moving objects appear in a highly dynamic environment. To address this issue, this paper proposes a dynamic object-aware visual SLAM algorithm specifically designed for dynamic indoor environments. The proposed method leverages a semantic segmentation architecture called U-Net, which is utilized in the tracking thread to detect potentially moving targets. The resulting output of semantic segmentation is tightly coupled with the geometric information extracted from the corresponding SLAM system, thus associating the feature points captured by images with the potentially moving targets. Finally, filtering out the moving feature points can greatly enhance localization accuracy in dynamic indoor environments. Quantitative and qualitative experiments were carried out on both the Technical University of Munich (TUM) public dataset and the real scenario dataset to verify the effectiveness and robustness of the proposed method. Results demonstrate that the semantics-guided approach significantly outperforms the ORB SLAM2 framework in dynamic indoor environments, which is crucial for improving the robustness and reliability of the SLAM system.

In view of the known problems of parameter sensitivity, local optimum, and slow convergence in the ant colony optimization (ACO), we aim to improve the performance of the ACO. To solve the traveling salesman problem (TSP) quickly with accurate results, we propose a fully parallel ACO (FP‐ACO). Based on the max–min ant system (MMAS), we initiate a compensation mechanism for pheromone to constrain its value, guarantee the correctness of results and avoid a local optimum, and further enhance the convergence ability of ACO. Moreover, based on the compute unified device architecture (CUDA), the ACO is implemented as a kernel function on a graphics processing unit (GPU), which shortens the running time of massive iterations. Combined with the roulette wheel selection mechanism, FP‐ACO has powerful search capabilities and is committed to obtaining better solutions. The experimental results show that, compared with the effective strategies ACO (ESACO) that runs on CPU, the speed‐up ratio of the proposed algorithm reaches 35, and the running time is less than that of the max–min ant system‐roulette wheel method‐bitmask tabu (MMAS‐RWM‐BT) that runs on GPU. Furthermore, our algorithm outperforms the other two algorithms in the speed‐up ratio and less runtime, proving that the proposed FP‐ACO is more suitable for solving TSP.

Background: Detection technology is a product development technique that serves as a basis for quality assurance. As electric energy meters (EEMs) are measurement instruments whose use is mandatory in several nations, their accuracy, which directly depends on their reliability and proper functioning, is paramount. In this study, to eliminate electromagnetic interference, a device is developed for testing a set of EEMs under a constant magnetic field interference. The detection device can simultaneously test 6 electric meters; moreover, in the future, it will be able to measure the influence of magnetic field strength on the measurement accuracy of EEMs, thereby improving the production efficiency of electric meter manufacturers. Methods: In this study, we first design a 3D model of the detection device for a single meter component; then, we establish a network, which includes a control system, and perform the planning of the path of a block that generates a constant magnetic field. Finally, we control the three-axis motion and rotation of the block using a PLC to implement detection for the five sides of the EEM. Results & Discussion: The proposed device can accurately determine whether an EEM can adequately function, within the error range prescribed by a national standard, under electromagnetic interference; this can enable reliable, automatic testing and fault detection for EEMs. Experiments show that our device can decrease the labor cost for EEM manufacturers.

With the continuous deepening of the application of educational OA, massive educational data has been produced. Hence, the application of teaching big data (TBD) has a certain theoretical basis, practical methods, and research methods in the field of education. How to fully play the leading role of education on TBD in professional education, guide and recommend students to carry out personalized learning, change the teaching mode, enrich the teaching evaluation, then further improve the quality of talent training is a current issue, which has yet to be solved. Based on the analysis and mining of big data, this paper uses the spectral clustering algorithm to construct the curriculum association classification model and realizes the clustering of core courses. Then, through the analysis of the academic achievements of previous professional core courses, we can master the current situation of students' learning, construct the model as the portrait of students' individual, curriculum, and professional characteristics through deep learning, so as to realize the precise referral of personalized learning courses, provide students with targeted academic guidance, and further dynamically adjust the teaching syllabus, including the teaching methods and teaching means. Vice versa, we can improve the core courses clustering to further feedback on the curriculum association classification model by analyzing the job position technical requirements. Experiments show that the proposed model using a spectrum clustering algorithm could be better provided strong technical support for the decision-making of precision education in colleges and universities.

Robots complete tasks in dangerous and extreme environments can significantly enhance people's life safety. Given the complexity and variability of indoor fire accidents, the traditional artificial potential field (APF) method faces challenges in executing the task of path planning. As two types of deep reinforcement learning (DRL) with APF methods, in this paper we leverage an on‐value method deep Q‐learning network (DQN–APF) and an on‐policy method deep deterministic policy gradient (DDPG‐APF) to address the path planning problem. For the purpose of validation upon both the algorithm's efficiency and generalisation, we take Harbin Geographic Information Industrial Park in Heilongjiang Province of China as our research area and incorporate Microsoft HoloLens devices as APF builders for simulating potential fields. Experiments show that both optimisation algorithms can significantly enhance the capabilities on path planning in complex environments. In six random obstacles environments, the DDPG‐APF is better than the DQN–APF method, with a 14.4% higher efficiency. Furthermore, for the generalisation of the algorithm, DDPG‐APF requires less time to plan a more efficient path than DQN–APF, respectively. The experimental results indicate that both optimisation algorithms effectively enable path planning for robots in indoor fire accidents and demonstrate good generalisation abilities.