RESUMEN
Recently, autonomous mobile robots (AMRs) have begun to be used in the delivery of goods, but one of the biggest challenges faced in this field is the navigation system that guides a robot to its destination. The navigation system must be able to identify objects in the robot's path and take evasive actions to avoid them. Developing an object detection system for an AMR requires a deep learning model that is able to achieve a high level of accuracy, with fast inference times, and a model with a compact size that can be run on embedded control systems. Consequently, object recognition requires a convolutional neural network (CNN)-based model that can yield high object classification accuracy and process data quickly. This paper introduces a new CNN-based object detection system for an AMR that employs real-world vehicle datasets. First, we create original real-world datasets of images from Banda Aceh city. We then develop a new CNN-based object identification system that is capable of identifying cars, motorcycles, people, and rickshaws under morning, afternoon, and evening lighting conditions. An SSD Mobilenetv2 FPN Lite 320 × 320 architecture is employed for retraining using these real-world datasets. Quantitative and qualitative performance indicators are then applied to evaluate the CNN model. Training the pre-trained SSD Mobilenetv2 FPN Lite 320 × 320 model improves its classification and detection accuracy, as indicated by its performance results. We conclude that the proposed CNN-based object detection system has the potential for use in an AMR.
RESUMEN
The application of Scirpus grossus (SG) fiber as a sound absorber is important to reduce the level of noise affected the physical and mental wellbeing of people. The sound absorption coefficient (SAC) and noise reduction coefficient (NRC) of the SG specimen were evaluated based on a typical model-based design using the data analysis with MATLAB. The results showed that SG specimen with a thickness of 20 mm coated with the perforated aluminum sheet (PAS) compared to that without coating can improve the capability of sound absorption by 14% at the frequency of 4000 Hz. SG specimen coated with PAS that has a NRC value of 0.39 can absorb 39% of sound and thus reflects 61% of sound wave while SG specimen without coating that has a NRC value of 0.23 absorbs 23% of sound and can reflect 77% of sound wave. The sound absorption class of D for SG specimen coated with PAS should be better that of E for SG specimen without coating, which permits us to get better understanding on the applications of SG fiber as a sound adsorber in the future.