An instrument to measure hepatic function was developed as a medical application of optoelectronic technology. The study of Indocyanine green (ICG) clearance in blood is clinically used for the estimation of hepatic function. Multiple blood samplings, however, subject the patients to mental and physical stress. Furthermore, although it is necessary with this method to correctly measure the blood sampling time after ICG injection, it is difficult to accurately measure this time in an actual test. Thus, a measuring method without blood sampling has been desired. Although some preliminary studies employing a non-invasive method were undertaken, there were still problems for clinical usage. Therefore, in this paper, a description of an instrument for the non-invasive measurement of ICG clearance using an optical sensor attached to the fingertip is presented, and a calibration method before ICG injection is proposed to solve the problem of changing blood volume. Next, the results of the clinical evaluation of 470 cases are presented. Using this newly developed instrument, the values of ICG tests correlated well with the values calculated from the conventional blood sampling method.
The position measurement methods for navigation of robotic vehicles have been proposed by emitting various laser beams into various fixed retroreflectors. We have developed a measurement system that detect the position and heading of vehicle by emitting only one laser beam into a double corner-cube retroreflector. When we set the double corner-cube retroreflector on a standard point to form a reference axis in a job site and emitting a laser beam captures the double corner-cube retroreflector, the position and heading of vehicle can be determined based on the direction of the beam and the angle between the beam and reference axis. In this paper, the algorithm of this new measurement method is introduced and the measurement precision is examined.
This paper models a cellular mobile communication system as synergetics from a macroscopic point of view. When the system becomes near saturation in call-flows, a cooperative action arises between each flow and the synergetics appears in the network. Under such a state, there is a microscopic interaction among call-flows in order to keep the macroscopic relationships. It is mathematically shown that the interaction is related to parameters representing with the graphtheoretical degree of freedom in the network.
In this paper, a method is described which improves a performance in learning by introducing partial fitness (PF) into the genetic algorithm (GA). The method divides a chromosome in the GA into several parts, the PFs of which are evaluated. Each part in chromosomes independently performs a selection and a crossover. Such a technique improves the perfomance in learning of the GA. This paper applies the method to a rotated coin recognition problem to examine its effectiveness. As a result, it is shown that it is better than the conventional GA on convergence in learning, makes a smaller network in size, and gives better generalization ability.
The genetic algorithm (GA), an optimization technique based on evolution, suffers often from a phenomenon called the premature convergence. That is, the system often loses the diversity of the population at an early stage of searching. In this paper, the authors propose a novel method called the ThermoDynamical Genetic Algorithm (TDGA), which adopts concepts of the temperature and entropy suggested from the simulated annealing (SA) to maintain the diversity of the population. Further, the computational complexity of TDGA is evaluated, and comparative study of TDGA with the Simple GA is carried out taking a knapsack problem as an example.