In this paper, a visual analytics system that are used to analyze the causal relation- ship between fishing catches data in adjacent sea areas is proposed. By using the proposed system, the distribution of sea surface temperature is visualized so that the sea areas can be divided accordingly. Next, the daily fishing efficiency in each sea area are calculated and the convergent cross mapping is used to analyze the causality between the data in adjacent sea areas. The experimental results show that the possible causation between the data in two of the divided sub sea areas are detected. In addition, the causal variable can be further identified by referring to the prediction skills.

In this study, a logistics network simulator based on the similarity between logistic and electric behaviors was considered. To reduce the complexity of practical electrical circuit development, the developed logistics model was converted to an electrical circuit model for analysis. Basic logistics networks such as centralized, de-centralized, and complex models were simulated using the well-defined electrical circuit models. The results show that theoretical logistical behavior can be simulated using electrical circuit models and converted simulation block models, confirming the similarity between flow of logistics and current distribution in electrical circuit.

We derive a Fokker Planck Equation (FPE) governing probability density functions (PDFs) of an impulsively controlled 1-D diffusion process in seasonal population management problems. Two interventions are considered: perfect (completely controllable) and imperfect interventions (not completely controllable). The FPE is an initial- and boundary-value problem subject to a non-local boundary condition along a moving boundary. We show that an finite volume method (FVM) with a domain transformation realizes a conservative discretization for the FPE. We demonstrate that the computed PDFs with the FVM and those with a Monte Carlo method agree well.

The effects of traffic flow improvements were compared in terms of real traffic data by using the level of congestion, throughput, and average duration time criteria. A current situation and a situation with smart tolling were considered in order to ensure the reliability of the results. The simulation consisted of eight scenarios, the error rates were checked with 30 replications, and the error rates were noted to be lower than 0.5%. The scenarios were categorized by vehicle speeds that could affect subsequent vehicles. The smart tolling model was better than the current model overall. In particular, the results of comparing lifelike scenario 1 and scenario 8 with smart tolling are key. The results showed that the level of congestion and average duration time could be decreased to approximately 46%. With regard to the results, there are significant impacts on improving traffic flow while maintaining five lanes, unlike in the current model.

A helmet for disaster prevention must be worn to prevent secondary disasters in rescue and evacuation activities when a disaster occurs. Helmet antennas have been widely developed as wearable antennas. In this paper, we investigated the effect of ohmic loss due to the material of the hemispherical conductor. In addition, the validity of the simulation results was verified through experiment. The loading position of the antenna element was examined to further improve the gain. As a result, the maximum gain of −2.6 dBi and the local specific absorption rate value of 0.07 W/kg was achieved at 150 MHz.

Individuals with severe disabilities typically suffer from lack of muscle power and deformation of their hands. This significantly affects their ability to operate an Electric Wheelchair (EW) especially when using joystick controllers because one operation of a joystick affects both the straight and turning motions of an EW. Thus, individuals with severe disabilities typically exhibit difficulties in operating an EW, especially in the cases that require fine steering capabilities. In contrast to autonomous driving, the study proposes a novel shared control method using reinforcement learning considering utilizing users' residual physical functions. With the use of online learning, the shared controller provides proper assistance based on the driving environments and each user's input characteristics. The effectiveness and the characteristics of the novel shared controller are discussed via simulations.