As the need for efficient warehouse logistics has increased in manufacturing systems, the use of automated guided vehicles (AGVs) has also increased to reduce travel time. The AGVs are controlled by a system using laser sensors or floor-embedded wires to transport pallets and their loads. Because such control systems have only predefined palletizing strategies, AGVs may fail to engage incorrectly positioned pallets. In this study, we consider a vision sensor-based method to address this shortcoming by recognizing a pallet’s position. We propose a multi-task deep learning architecture that simultaneously predicts distances and rotation based on images obtained from a visionary sensor. These predictions complement each other in learning, allowing a multi-task model to learn and execute tasks impossible with single-task models. The proposed model can accurately predict the rotation and displacement of the pallets to derive information necessary for the control system. This information can be used to optimize a palletizing strategy. The superiority of the proposed model was verified by an experiment on images of stored pallets that were collected from a visionary sensor attached to an AGV.

As the scale of high-performance computing systems (HPC) continues to expand, the frequency of failures within the system also increases. The follow-up steps in the event of a failure are necessary because efficient prevention measures are absent. These actions, however, result in decreased operating efficiency as the system normalizes. To solve this problem, recent research has utilized deep learning to predict failures in advance by examining log messages. One common method uses log ID extracted from log messages as input data. However, these methods could lead to information loss in log messages and may not accurately capture log message properties. In this study, we propose the prior failure detection in large-scale systems using language models (FALL) that reflects the properties of log messages. The FALL uses log messages as text data and only uses normal data for training. The FALL makes use of sharpening, which concentrates on the minimal lexical change between normal and abnormal log messages. In addition, by leveraging the characteristics of log messages with limited vocabularies, the FALL utilizes only some tokens for anomaly detection. The experiments' results from log data from real industry-based HPC systems show that the FALL achieves superior performance in early failure detection.

In real-time strategy (RTS) games, to defeat their opponents, players need to choose and implement the correct sequential actions. Because RTS games like StarCraft II are real-time, players have a very limited time to choose how to develop their strategy. In addition, players can only partially observe the parts of the map that they have explored. Therefore, unlike Chess or Go, players do not know what their opponents are doing. For these reasons, applying generally used artificial intelligence models to forecast sequential actions in RTS games is a challenge. To address this, we propose depthwise separable convolution-based multimodal deep learning (DESEM) for forecasting sequential actions in the game StarCraft II. DESEM performs multimodal learning using high-dimensional frames and action labels simultaneously as inputs. We use a depthwise separable convolution as the backbone network for extracting features from high-dimensional frames. In addition, we propose a weighted loss function to resolve class imbalances. We use 1,978 StarCraft II replays where the Terrans win in a Terran vs. Protoss game. The experimental results show that the proposed depthwise separable convolution is superior to the conventional convolution. Furthermore, we demonstrate that multimodal learning and the weighted loss function contribute significantly to improving forecasting performance.