In this paper, the proposed fault detection method detected outliers of the pseudo-range which are one of the problems in tight coupling relative positioning. The tight coupling relative positioning is a technique to calculate a relative position using the velocity vector of the each antennas traveling, and the pseudo-range was calculated by the propagation delay time from the satellite observed from two GNSS antennas. The proposed fault detection of pseudo-range considers an innovation process to be one signal in the tight coupling using the motion of GNSS antenna state and tries to isolate the signal by using the stationarity state. The proposed method judge the stationarity state to have an effect such as the cycle-slip or the multi-path when a signal was able to separate and handle the signals for outliers. The effect of the proposed method was inspected by using a vehicle. As a result, the proposed method proved that the technique was particularly to effective in the area where there were few common visible satellite number. On the other hand, the proposed method had less efficient in the area where there were many common visible satellites, thus it was necessary to devise a more suitable techniques such as combining plural techniques.

A vacuum pump is always using in semiconductor manufacturing equipment. Anomaly of a vacuum pump leads to troubles such as stoppage of equipment. Therefore, anomaly detection of a vacuum pump is very important. We considered a vacuum pump state estimation using thin AE (Acoustic Emission) sensor and machine learning. However, anomaly data is not always available in a production line. Anomaly detector is necessary to learn from only normal data. There is an anomaly detection using autoencoder in recent years. In autoencoder, normal data is reconstructed and anomaly data is not reconstructed. As a result, anomaly detection can using reconstruction error. In this paper, we propose anomaly detection for rotary vacuum pump by thin AE sensor and reconstruction error of autoencoder. Performance of the proposed method is evaluated by detect exhaust anomaly of a vacuum pump.

In this paper, load frequency control problem for power system is discussed. The control is conducted to retain power demand supply balance by controlling output of generators in power network. However, it becomes harder to retain the balance because of large volume injection of renewable energy whose output is uncertain. To resolve the problem, some approaches are considered in this research field. One is to use dispersion type power sources like battery in addition to conventional generators like thermal power generator and hydroelectric generator. Another is to apply new control theories and methods. The other is the load frequency control considering demand response by using a controllable load on the consumer side. Especially among them, in this paper, power system which holds large volume wind turbine generator, turbine generator and battery is focused on and new control method is proposed. Concretely, load frequency control based moving horizon estimation and robust model predictive control is proposed in order to satisfy the constraints of state and control input.

This paper deals with a power demand-supply management based on negawatt trading, and the purpose of this research is to keep demand supply balance and to minimize the social cost of negawatt trading, balancing generator, and power flow. In this research, consumer and Independent System Operator (ISO) participate in the electricity market, and each consumer acts based on Regret Matching which is one of the learning algorithms. Furthermore, as the incentive design of negawatt trading, we consider strategy-proofness and individual rationality using VCG mechanism design. Then, we propose a novel negawatt trading algorithm based on Regret Matching and VCG mechanism design. Finally, simulation results show the effectiveness of the proposed algorithm.