The purpose of this study is to examine the relationship between staff members' daily chats in a refresh room during break times and their knowledge sharing in the workplace, using a subjective questionnaire survey and statistical analysis relying on a social psychological methodology. The data was gathered from staff in a Japanese hospital and a hierarchical regression analysis and a simple slope analysis were conducted. As a result, it was found that among those who seldom participate in refresh room chats, staff members who frequently talk about positive job experiences when they do occasionally participate are likely to evaluate their knowledge sharing better than others who do not often talk so. In addition, among those who do not discuss positive job experiences, staff members who often participate in chats are likely to evaluate their knowledge sharing better than those who participate less often. Therefore, the development of measures both to promote participation in chats and to enable and encourage staff to talk about positive job experiences will be recommended in order to foster effective knowledge sharing.
The selfish routing game is a mathematical model to represent the behavior of selfish players who select a path in a congested network. In the equilibrium solution search on the selfish routing game, the amount of flow passing through each path is designated as the decision variable. Therefore, it is difficult to obtain the equilibrium solution of the selfish routing game in large-scale networks with a vast number of paths in a realistic time. In many cases, flows pass through a few part of the paths only and no flow passes through the other paths in the equilibrium solution of the selfish routing game in large-scale networks. If some of the paths which are zero-flow paths in the equilibrium solution can be removed from the decision variables in advance, the efficiency of the equilibrium solution search is expected to be improve. This paper proposes a new solution search method to improve the efficiency of the equilibrium solution search by removing redundant paths which can be detected in advance by considering a condition with respect to the equilibrium solution. The effectiveness of the proposed method is confirmed through numerical experiments.
Recently, an orthodox decomposition method for optimization problems by pricing mechanism based on Lagrange multipliers method has been interested as a distributed optimization approach for electric power supply problems with an electricity trading market. However, the approach can be applied to a convex case only, where the objective function is convex and the constraint set is convex, that is, Lagrange multipliers method cannot be used to nonconvex cases theoretically. In this paper, we propose to utilize the augmented Lagrangian method available to nonconvex cases. However, it is impossible to separate the augmented Lagrangian into mutually independent sub-objective functions, because squared penalty terms are added to the Lagrangian. Therefore, we regard a group of the mutually dependent sub-problems with interfered objective functions as a game problem, and present a new decomposition method in which Nash equilibrium as a rational solution is required. Availability of the presented decomposition method is verified by applying to simple energy flow problems with non-convexity, for example, electric power flow interchanging through nonlinear converter from gas energy and also market is allocated on the power flow.
The authors are developing a talking robot based on the physical model of human vocal organs in order to reproduce human speech mechanically. This study focuses on developing a real-time interface to control and visualize talking behavior. The talking robot has been trained using a self-organizing map (SOM) to reproduce human sounds; however, due to the nonlinear characteristics of sound dynamics, automatic generation of human-like expressive speech is difficult. It is important to visualize its performance and manually adjust the motions of the artificial vocal system to get a better result, especially when it learns to vocalize a new language. Therefore, a real- time interactive control for the talking robot is designed and developed to fulfill this task. A novel formula about the formant frequency change due to vocal tract motor movements is derived from acoustic resonance theory. In the first part of the paper, the construction of the talking robot is briefly described, followed by the real-time interaction system using Matlab Graphic User Interface (GUI) together with the strategy to interactively modify the speech articulation based on the formant frequency comparison.
A driver is still needed in case of emergency for automated driving systems. Since it is difficult for human to just monitor the system, it is necessary to develop methods for drivers to maintain the attention appropriately when an automated driving system is active. This paper proposes to involve a driver in a tactical decision making. We focus on lane-change decision making in this paper. An experiment with a medium-fidelity driving simulator is demonstrates the effectiveness of involving drivers in lane-change decision making. The results support that the drivers are able to maintain their situation awareness in an appropriate manner.
This paper proposes an electricity market model comprising farm owners, an electricity company, and a persuasive dialogue system, to dynamically adjust electricity price by controlling owners' proclivity toward selling their electricity. Several numerical examples confirm that the proposed model facilitates price adjustment.