Describing phenomena of interest as a system is valuable because system science methodologies are applicable for the analysis. This paper presents a model to describe and analyze phenomena which present (1) no definable boundaries, (2) multiple hierarchical levels, and (3) necessity of diverse viewpoints to understand the phenomena. The proposed framework, the hypernetwork model, is applied to describe the lifestyle disease and the music composition process. The hypernetwork model homogenizes boundaries, hierarchical levels, and different viewpoints to be treated as instances of relationship.
This paper deals with passivity-based stability analysis of electricity market trading with dynamic pricing. In the deregulated electricity market, power consumers and generators will participate in market trading as market players. For such a new kind of the market trading system, the dynamic pricing procedure has to be taken into account of the intermittent participation of power consumers and generators. Then, this paper discusses the stability of the electricity market trading system including power flow using passivity analysis. This paper also shows that the optimal power demand, supply and electricity prices are asymptotically stable and these values are derived in a distributed manner through market trading.
This paper deals with the problem of secure state estimation in an adversarial environment with the presence of bounded noises. The problem is given as min-max optimization, that is, the system operator seeks an optimal estimate which minimizes the worst-case estimation error due to the manipulation by the attacker. To derive the optimal estimate, taking the reach set of the system into account, we first show that the feasible set of the state can be represented as a union of polytopes, and the optimal estimate is given as the Chebyshev center of the union. Then, for calculating the optimal state estimate, we provide a convex optimization problem that utilizes the vertices of the union. On the proposed estimator, the estimation error is bounded even if the adversary corrupts any subset of sensors. For the sake of reducing the calculation complexity, we further provide another estimator which resorts to the interval hull approximation of the reach set and properties of zonotopes. This approximated estimator is able to reduce the complexity without degrading the estimation accuracy sorely. Numerical comparisons and examples finally illustrate the effectiveness of the proposed estimators.
This paper focuses on the aircraft landing problem (ALP) and proposes an optimization method for ALP which addresses both the landing routes of multiple aircraft and their landing sequence. The difficulty of solving ALP is to optimize both the landing route and landing sequence of multiple aircraft, even the landing routes of the aircraft may be in conflict. To address this issue, this paper proposes a novel efficient search optimization method based on novelty search, which considers a tradeoff relationship between distance-minimality (i.e., a short route of an aircraft) and diversity (i.e., a variety of landing routes of an aircraft). Through the intensive simulation on a simplified model of Haneda Airport in ALP, the following implications have been revealed: (1) The proposed method succeeded to minimize the total distances of all aircraft while optimizing the landing routes of each aircraft with keeping a diversity of its landing routes, in comparison with the single objective method based on the fitness as distance-minimality or the novelty as diversity: and (2) ven in the aircraft crowded situation, our proposed method could find feasible solutions in all trials, while the fitness- or novelty-based single objective methods could not find them.
This paper proposes numerical solutions of Hamilton-Jacobi inequalities based on constrained Gaussian process regression. While Gaussian process regression is a tool to estimate an unknown function from its input and output data conventionally, the proposed method applies it to solving a known partial differential inequality. This is done by generating sample data pairs of states and corresponding values of the unknown function satisfying the inequality. A formal algorithm to execute such a procedure to obtain the probability of a solution to the Hamilton-Jacobi inequality is proposed. In addition, a nonstationary covariance function is introduced to increase the accuracy of the solutions and to reduce the computational cost. Furthermore, its hyper parameters are optimized using an empirical gradient method.