In this paper we develop a general method for the robust stabilization of dynamic graphs. The proposed approach is based on linear matrix inequalities, and can accommodate a broad range of uncertainties and information structure constraints. It is shown that control laws of this type can be applied to graphs in which both the nodes and the edges exhibit time-varying behavior. In all cases, the control design can be formulated as a convex optimization problem.
The VLF (Very Low Frequency) / LF (Low Frequency) receiving network has been established in Japan, which is composed of seven observing stations (Moshiri (Hokkaido), Chofu (Tokyo, UEC, University of Electro-Communications), Tateyama (Chiba), Shimizu (Shizuoka), Kasugai (Aichi), Maizuru (Kyoto) and Kochi (Kochi)), and three additional foreign stations have been established in Kamchatka, Taiwan and Indonesia. At each station we observe simultaneously several VLF/LF transmitter signals (two Japanese transmitters with call signals fo JJY (Fukushima), JJI (Miyazaki)), and foreign VLF transmitters (NWC (Western Australia, Australia), NPM (Hawaii, USA), NLK (Washington, USA)). This Japanese VLF/LF network is used to study the ionospheric perturbations associated with earthquakes, and we present two recent results; (1) a statistical result on the correlation between VLF/LF propagation anomalies and earthquakes, and (2) the latest results during the last six months on the two particular propagation paths; JJY-Moshiri and JJY-Taiwan. Then, we discuss the correlation of ionospheric perturbations with earthquakes in the sense of a possibility of earthquake prediction by means of VLF propagation anomalies.
The Modular Network Self-Organizing Map (mnSOM) is a generalization of the SOM, where each node represents a parametric function such as a multi-layer perceptron or another SOM. Since given datasets are, in general, fewer than nodes, some nodes never win in competition and have to update their parameters from the winners in the neighborhood. This is a process that can be regarded as interpolation. This study derives the interpolation curve between winners in simple cases and discusses the distribution of winners based on the neighborhood function.
In this paper, stability of an incompressible 2D channel flow is investigated. This problem has many practical applications such as mixing fuel and air in combustion engines. However, since the flow dynamics is described by a nonlinear partial differential equation, its stability analysis is a challenging problem. Most of existing results obtained in analytical ways are conservative and less flexible. In this paper, we attempt to derive less conservative and easily checkable criteria for the problem via the sum of squares relaxation technique.
There are several problems which discourage an organization from achieving tasks, e.g., partial observation, credit assignment, and concurrent learning in multi-agent reinforcement learning. In many conventional approaches, each agent estimates hidden states, e.g., sensor inputs, positions, and policies of other agents, and reduces the uncertainty in the partially-observable Markov decision process (POMDP), which partially solve the multiagent reinforcement learning problem. In contrast, people reduce uncertainty in human organizations in the real world by autonomously dividing the roles played by individual agents. In a framework of reinforcement learning, roles are mainly represented by goals for individual agents. This paper presents a method for generating internal rewards from manager agents to worker agents. It also explicitly divides the roles, which enables a POMDP task for each agent to be transformed into a simple MDP task under certain conditions. Several situational experiments are also described and the validity of the proposed method is evaluated.
DC-DC converters by nature present hybrid behavior, which is described by a set of discrete modes associated with continuous dynamics. The control objective is accomplished by switching among the discrete modes. This paper presents a new model predictive control approach to optimize the performance of DC-DC converters. The proposed control algorithm is tailored to DC-DC converters, making full use of the fact of finite number of modes to transform the performance optimization problem into a combinatory optimization task. This approach has made the on-line computation very simple and effective for implementation.
For international combustion engines, due to the combustion cyclic nature, the intake-to-power stroke delay is inherent that causes additional difficulties in control design and validation phases. In this paper, a nonlinear speed control scheme is proposed based on the proportional feedback control method. From the consideration of improving the transient performance, a reference model is introduced to design the feedback controller. Then, the speed controller is formulated as a designed feedback control law connecting with a model-based feedforward compensation. The asymptotic convergence to the desired speed is guaranteed under the presented conditions of the feedback gains, which include the cases of using a speed-depended gain function and a constant gain, respectively. For the stability analysis of the proposed delayed control system, an initial method is presented via Lyapunov-Krasovskii functional stability theorem. Experimental results on the transition speed control are shown to demonstrate the control scheme.
Intertransaction association rules have been reported to be useful in many fields such as stock market prediction, but still there are not so many efficient methods to dig them out from large data sets. Furthermore, how to use and measure these more complex rules should be considered carefully. In this paper, we propose a new intertransaction class association rule mining method based on Genetic Network Programming (GNP), which has the ability to overcome some shortages of Apriori-like based intertransaction association methods. Moreover, a general classifier model for intertransaction rules is also introduced. In experiments on the real world application of stock market prediction, the method shows its efficiency and ability to obtain good results and can bring more benefits with a suitable classifier considering larger interval span.
This paper presents a novel blockage detection method using a flexible piezoelectric film sensor. This sensor is made of oriented aluminum nitride (AlN) thin film, and the total thickness is less than 40 µm. The thin thickness makes this sensor sensitive to stress in the length direction, and in the meanwhile flexibility facilitates installing this sensor to the external surfaces of pipes. With this sensor, pressure pulsation in fluid can be measured non-invasively and a blockage detection method is developed using a transfer function analysis. In experiments, linearity and response characteristics of the AlN film sensor in the length direction is studied. Then experiments of partial blockage detection are conducted using a test pipe system. Validation of the proposed method is confirmed through the experimental results.