Adaptive state construction for reinforcement learning and its application to robot navigation problems

This paper applies our state construction method by ART neural network to robot navigation problems. Agents in this paper consist of ART neural network and contradiction resolution mechanism. The ART neural network serves as a mean of state recognition which maps stimulus inputs to a certain state and state construction which creates a new state when a current stimulus input cannot be categorized into any known states. On the other hand, the contradiction resolution mechanism (CRM) uses agents' state transition table to detect inconsistency among constructed states. In the proposed method, two kinds of inconsistency for the CRM are introduced: &#34;Different results caused by the same states and the same actions&#34; and &#34;Contradiction due to ambiguous states.&#34; The simulation results on the robot navigation problems confirm the effectiveness of the proposed method</p

Prediction Of Lateral Vibration Behavior Of Integrally Geared Centrifugal Compressor During Synchronous Motor Startup By Transient Torsional-Lateral Coupled Analysis

LectureStartup transients of both torsional and lateral vibration behaviors of an integrally geared centrifugal compressor driven by a synchronous motor are examined by transient torsional-lateral coupled analyses, and the numerical calculation results are evaluated using the field measurements as a benchmark. Since linear bearing coefficients are employed in the numerical simulation instead of more sophisticated nonlinear bearing model, bilinear stiffness is additionally considered to reflect the effects of the rotor confinement within the bearing clearance. Moreover, temporary teeth separation of the gear meshing and engagement at the backside during torque reversal is also considered in the numerical calculation. The transient lateral vibration behavior of the pinion rotor during the synchronous motor’s startup is successfully replicated. Both (a) bilinear stiffness of the pinion rotor bearings due to rotor restraint within the bearing clearance, and (b) effect of temporary teeth separation within the backlash and engagement at the backside because of torque reversal, are found to strongly influence the numerical predictions

An incremental state-segmentation method for reinforcement learning using ART neural network

In this paper, we propose a new incremental state segmentation method by utilizing information of the agents' state transition table which consists of a tuple of (state; action, state) in order to reduce the effort of designers and which is generated using the ART neural network. In the proposed method, if an inconsistent situation in the state transition table is observed, agents refine their map from perceptual inputs to states such that inconsistency is resolved. We introduce two kinds of inconsistency, i.e., different results caused by the same states and the same actions, and contradiction due to ambiguous states. Several computational simulations on cart-pole problems confirm the effectiveness of the proposed method</p

Application of Long-Range Surface Plasmon Resonance for ABO Blood Typing

In this study, we demonstrate a long-range surface plasmon resonance (LR-SPR) biosensor for the detection of whole cell by captured antigens A and B on the surface of red blood cells (RBCs) as a model. The LR-SPR sensor chip consists of high-refractive index glass, a Cytop film layer, and a thin gold (Au) film, which makes the evanescent field intensity and the penetration depth longer than conventional SPR. Therefore, the LR-SPR biosensor has improved capability for detecting large analytes, such as RBCs. The antibodies specific to blood group A and group B (Anti-A and Anti-B) are covalently immobilized on a grafting self-assembled monolayer (SAM)/Au surface on the biosensor. For blood typing, RBC samples can be detected by the LR-SPR biosensor through a change in the refractive index. We determined that the results of blood typing using the LR-SPR biosensor are consistent with the results obtained from the agglutination test. We obtained the lowest detection limits of 1.58 × 105 cells/ml for RBC-A and 3.83 × 105 cells/ml for RBC-B, indicating that the LR-SPR chip has a higher sensitivity than conventional SPR biosensors (3.3 × 108 cells/ml). The surface of the biosensor can be efficiently regenerated using 20 mM NaOH. In summary, as the LR-SPR technique is sensitive and has a simple experimental setup, it can easily be applied for ABO blood group typing