Masahiro Tokumitsu

Asymmetric Interactions between Cooperators and Defectors for Controlling Self-repairing

In an information network composed of selfish agents pursuing their own profits, undesirable phenomena such as spam mail occur if the profit sharing and other game structures permit such equilibriums. This note focuses on applying the spatial Prisoners Dilemma to control a network of selfish agents by allowing each agent to cooperate or to defect. Cooperation and defection respectively correspond to repair (using the self resource) and not repair (thus saving the resource) in a self-repair network. Without modifying the payoff, the network will be absorbed into the state where all the agents become defectors and abnormal. Similarly to kin selection, agents favor survival of neighbors in organizing these two actions to prevent the network from being absorbed if payoffs are measured by summing all the neighboring agents. Even with this modification, the action organization exhibits spatial and temporal adaptability to the environment.

Prediction of the electron flux environment in geosynchronous orbit using a neural network technique

In this study, a neural network technique is adopted to predict the electron flux in a geosynchronous orbit using several items of solar wind data obtained by ACE spacecraft and magnetic variations observed on the ground as input parameters. Parameter tuning for the back-propagation learning method is attempted for the feed-forward neural network. As a result, the prediction using the combined data of solar wind and ground magnetic data shows a highest prediction efficiency of 0.61, which is enough to adapt to the actual use of the space environment prediction.

Adaptive sensing based on profiles for sensor systems.

This paper proposes a profile-based sensing framework for adaptive sensor systems based on models that relate possibly heterogeneous sensor data and profiles generated by the models to detect events. With these concepts, three phases for building the sensor systems are extracted from two examples: a combustion control sensor system for an automobile engine, and a sensor system for home security. The three phases are: modeling, profiling, and managing trade-offs. Designing and building a sensor system involves mapping the signals to a model to achieve a given mission.

A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network

This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

An adaptive sensor network for home intrusion detection by human activity profiling

An adaptive sensor network for home intrusion detection is proposed. The sensor network combines profile-based anomaly detection and adaptive information processing based on hidden Markov models (HMM) that allow the system to train and tune the profiles automatically. The trade-off between miss-alarms and false alarms has been studied experimentally. Several types of hypothetical intrusion have been tested and successfully detected. However, hypothetical anomalies such as supposing that a resident has fallen down due to sudden illness have been difficult to detect.

An adaptive control technique for a connection weight of agents in a self-repairing network

Cooperation among agents is a crucial problem in autonomous distributed systems composed of selfish agents pursuing their own profits. An earlier study of a self-repairing network revealed that a systemic payoff made the selfish agents cooperate with other agents and was similar to kin selection. We study the relationship between the systemic payoff and kin selection more deeply. This paper considers the systemic payoff that involves a connection weight representing strength of relationship among the agents. We found that the performance of the self-repairing network changes by varying the connection weight. The connection weight appropriate to the environments would elicit the good performance of the self-repairing network. This paper proposes an adaptive control technique for the connection weight among the agents in the systemic payoff. The technique changes the connection weight dynamically. In simulations, the proposed technique showed the good performance in the dynamic environments.

Toward Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Data: An Example of Space Weather Forecasting☆

Abstract This paper attempts to construct a resilient sensor network model for space weather forecasting. The proposed model is based on a dynamic relational network. A space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient for failures of sensors/missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates an example of the space weather forecasting involving the missing of the observation in a test case. In this example, the sensor network of the space weather forecasting continues a diagnosis by replacing faulted sensors with imaginary ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspend of hardware failures or technical reasons.

Adaptive Forecasting of High-Energy Electron Flux at Geostationary Orbit Using ADALINE Neural Network

High-energy electron flux increases in the recovery phase after the space weather events such as a coronal mass ejection. High-energy electrons can penetrate circuits deeply and the penetration could lead to deep dielectric charging. The forecast of high-energy electron flux is vital in providing warning information for spacecraft operations. We investigate an adaptive predictor based on ADALINE neural network. The predictor can forecast the trend of the daily variations in high-energy electrons. The predictor was trained with the dataset of ten years from 1998 to 2008. We obtained the prediction efficiency approximately 0.6 each year except the first learning year 1998. Furthermore, the predictor can adapt to the changes for the satellites location. Our model succeeded in forecasting the high-energy electron flux 24 hours ahead.

Using the spatial dilemma strategies to model agents’ commitments for a coalition formation

In spatial strategies of a spatial prisoner’s dilemma (Ishida and Mori (2005) Artif Life Robotics 9:139–143), it is possible to involve not only the geographical configuration of countries, but also many other relations such as economic relations, historical relations, military relations, and so on if they can be expressed by a network. This article explores the possibility of modeling an agent’s commitments using spatial strategies. Several types of spatiotemporal strategy are discussed in a context of coalition formation in international communities.

Self-Repairing Network in a Dynamic Environment with a Changing Failure Rate

We considered a self-repair network by an autonomous and strategic repair. The network is assumed to be composed of agents with a failure rate. This paper further assumes that the failure rate changes dynamically, hence modelling a dynamic environment. When the failure rate oscillates with a fixed amplitude and cycle, computer simulations indicated that there is a threshold of not only the amplitude but the cycle. When the failure rate changes with the cycle not exceeding the threshold, the strategic repair adapts to the environment and exhibits a reasonable performance.