The Use of a Population Balance Model in the Study of Inoculation of Soybean Seeds in a Spouted Bed

The coating of soybean seeds with bacteria and micronutrients favours the vigorous growth of the plant, dispensing with the use of ammoniacal fertilizers. The optimum thickness of the coating should allow the fundamental gaseous interchanges for the germination and the ideal conditions for the activity of the bacteria. The objective of this work was to simulate dynamic mass distribution of soybean seeds covered with bacteria and macronutrients in spouted bed through a specific program developed in Maple V^®. The validation of the model is done through 16 experiments carried out in a spouted bed with spray at the top.     

Monolithiation of 5,5′‐Dibromo‐2,2′‐bithiophene Using Flow Microreactors: Mechanistic Implications and Synthetic Applications

The lithiation of 5,5′‐dibromo‐2,2′‐bithiophene with one equivalent of an alkyllithium such as n‐BuLi or s‐BuLi was studied by varying the residence time in flow microreactors. With a short residence time, the product 2,2′‐bithiophene (3) derived from dilithiation was obtained preferentially and a significant amount of the starting material 5,5′‐dibromo‐2,2′‐bithiophene remained unchanged. An increase in the residence time caused a higher yield of the product 5‐bromo‐2,2′‐bithiophene derived from monolithiation with expense in the yields of 2,2′‐bithiophene and 5,5′‐dibromo‐2,2′‐bithiophene. The lithiation using MeLi gave the product 5‐bromo‐2,2′‐bithiophene preferentially even with a very short residence time.     

Intelligent combinatorial auctions of decentralized task assignment for AGV with multiple loading capacity

The need to have an efficient transportation system has attracted worldwide attention. Although there is increasing demand to implement distributed control system for industrial applications, there is still an unexplored potential of deploying distributed transportation system. This paper focuses on dynamic assignment of transportation requests to a fleet of vehicles in real time. We introduce an improved combinatorial auction methodology to accommodate the distributed task assignment procedure. Based on a multiagent architecture, each vehicle is represented by an intelligent agent that bids for task and plans its own schedule. On the other hand, the auctioneer has the objective of minimizing transportation tardiness. An automated guided vehicle (AGV) has been selected as the case study, and numerical experiments have been carried out. The result obtained shows that the improved task assignment approach is able to produce performance competitive to a conventional task assignment. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

New integral experiments for large angle scattering cross section data benchmarking with DT neutron beam at JAEA/FNS

A new integral experiment with a deuteron–triton fusion (DT) neutron beam started in order to validate scattering cross section data. First the DT neutron beam was constructed with a collimator. The performance of the collimator system and the characteristics of the DT neutron beam were measured. Second a new integral experiment for type 316 stainless steel (SS316) was carried out with this DT neutron beam. The DT neutron beam of 3.5 cm in diameter was injected to the front surface center of an SS316 cylindrical assembly. Reaction rates of the ⁹³Nb(n,2n)^92mNb reaction in the assembly were measured with the activation foil method and were calculated with the Monte Carlo transport calculation code. The measurement points were located at three positions, on the center of the beam axis and at 15 cm and 30 cm apart from the axis. The ratio of calculation to experiment of the ⁹³Nb(n,2n)^92mNb reaction rate became smaller than 1 with the distance from the beam axis. Then, the dependency of each reaction rate on scattering angle was calculated. It was proved that at off-axis positions, where C/E is smaller than 1, 90° scattering contribute relatively larger than at on-axis positions and backward scattering made little contribution to the results in this experiment. The reasons of the discrepancy between the measured and calculated data will be investigated.     

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

     

Synthesis of optimal controllers for piecewise affine systems with sampled-data switching

This paper discusses the optimal control problem of the continuous-time piecewise affine (PWA) systems with sampled-data switching, where the switching action is executed based upon a condition on the state at each sampling time. First, an algebraic characterization for the problem to be feasible is derived. Next, an optimal continuous-time controller is derived for a general class of PWA systems with sampled-data switching, for which the optimal control problem is feasible but whose subsystems in some modes may be uncontrollable in the usual sense. Finally, as an application of the proposed approach, the high-speed and energy-saving control problem of the CPU processing is formulated, and the validity of the proposed methods is shown by numerical simulations.     

A new learning method using prior information of neural networks

In this paper, we present a new learning method using prior information for three-layer neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering interrelated weights values. Thus, the training results are usually not good. The reason for this in that each parameter has its influence on others during learning. To overcome this problem, we first give an exact mathematical equation that describes the relation between weight values given a set of data conveying prior information. The we present a new learning method that trains part of the weights and calculates the others using these exact mathematical equations. This method often a priori keeps the given mathematical structure exactly the same during learning; in other words, training is done so that the network follows a predetermined trajectory. Numerical computer simulation results are provided to support this approach. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999.     

Self-reconfigurable robots

In this article, the concept of a cellular robot that is capable of reconfiguring itself is reviewed. This "self-reconfigurable (SR) robot" exemplifies a new trend in robotics, indeed, we can now build various kinds of SR robots with off-the-shelf technologies of processors, actuators, and sensors. These SR robots, based on modern mechatronics, are still not as adaptable as the liquid metal robot in The Terminator 2 but are just as flexible as any conventional robots