A wheel loader is a construction machine that consists of a four-wheel driving mechanism and a two-degree-of-freedom working mechanism of boom and bucket. This is mainly used to excavate sediment and crushed stones and load them into a dump truck. Currently, at civil engineering and quarry sites where construction machines are used, problems such as manpower shortages and worker hazards have made automation and remote operation of construction equipment desirable. Against this background, the objective of this study is to design an efficient excavation motion based on mathematical optimization, aiming at the automation of excavation by construction machinery. In particular, we used a Bayesian optimization approach that alternates modeling and optimization of experimental and input-output data. Based on the idea that it may be possible to tolerate deviation from the designed target trajectory and actively utilize “weak” tracking, we propose a flexible excavate operation strategy. Based on the above policy, the excavation motion design problem for a wheel loader is formulated as follows: the design variables are the parameters of the parametrized trajectory and the tracking accuracy of each drive element with respect to the reference trajectory, and the objective function is a linear combination of excavation volume and workload with a certain weight. Then, we investigated the effectiveness of this method by comparing it with track following control using an excavation simulation. As a result, we found that the proposed method can improve the excavation volume and workload depending on the conditions.

In individual air conditioning environments, automatic control of air conditioning has not been implemented until now, and control has been manually conducted based on individual sensations like feeling hot or cold. This has become a problem in terms of both comfort for all occupants and energy consumption. In this research, we aim to create a comfortable thermal environment based on data obtained from a group of IoT devices, such as room temperature. By utilizing machine learning, we will develop a system that estimates the state in which occupants feel comfortable and automatically controls the air conditioning environment.

As the frequency and types of cyber-attacks on control systems are increasing, it is important to recover from cyber-attacks in addition to detecting and preventing cyber-attacks. The objective of this paper is to design the sequence of operations to return the control system to a normal state as recovery control in the event of cyber-attacks. Representing control system behaviors in terms of finite automata, this paper recasts the design of recovery operations as a path finding problem. In this case, it is important to avoid secondary damages due to the obtained recovery operation, such as collisions between field devices. Then, this paper also considers the safety of the recovery operation. The basic idea of the safety is the specification of the normal control in which the collisions are avoid. The proposed method evaluates whether the recovery operation satisfies the specification of the normal control.