Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

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As a special plant for train maintenance in northern Taiwan, the Taipei Railway Workshop was founded in 1885 and moved in 2011, reflecting the changes in Taiwan’s history, transportation, and industrial technology. Now, it is planned to change the maintenance plant into a railway museum in the form of an in situ site. This study briefly introduces the historical background and present situation of the Taipei Railway Workshop and takes its forging workshop as the object for investigation and exhibition planning. According to the preservation and maintenance methods of the cultural heritage of the museum, the investigation process proposed includes four steps: Site exploration, object registration, object research, and exhibition planning. The work area in the plant is divided into shaping and forging areas, as based on the categories of the machines on the site of the forging workshop. In this study, a total of 85 industrial relics in the forging workshop are registered for systematic research. The working conditions, including machine parts for train maintenance, manufacturing processes of parts, and the relationship between in-line on-site machines and tools, of the forging workshop before closing are restored, as based on the principles of machine manufacturing, literature, and retired workers’ oral histories. Finally, an in situ exhibition plan of the forging workshop is put forward based on the results of the object research.

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With the rapid development of information technology and widespread use of the Internet of Things, machine intelligence will undoubtedly emerge as a leading research topic in the future. The main purpose of the present research is to incorporate an image recognition system into a robotic arm motion to achieve automatic classification. First, we upload captured images to a PC for classification process and use chess patterns to conduct a sampling test. Next, when the system identifies these patterns as proper chess patterns, the robotic arm grabs the objects and moves them to designated locations. The project is divided into two main sections: image recognition and robotic arm motion. In the image recognition section, we use Keras and the Tensorflow open source learning machine to build a convolutional neural network model. Then, we use a learning model network that is a considerably more compact variant of the VGGNet network in the image recognition system. With this model, we achieve a recognition accuracy of 95%. In the robotic arm section, we use a five-axis robotic arm and an Arduino Uno board as the controller. We design the Denavit–Hartenberg parameters of the arm and calculate the direct (inverse) kinematics parameters to plan its trajectory. Thereafter, we use MATLAB software to simulate prototype processes, such as grabbing, moving, and placing. Finally, we import the program into the controller so that the robotic arm can execute classification based on the chess pattern.