Prediction of starting performance of synchronous motor by DC decaytesting method with the rotor in any arbitrary position

This paper presents a novel method to predict the starting performance of synchronous motors. This proposed strategy is based on a simple standstill response testing method using a small-capacity DC power supply unit, tentatively named the DC decay testing method in this paper, and can be applied to standstill synchronous motors with the rotor in any arbitrary position. The proposed testing method is carried out on a 10 kW, 200 V, 31.9 A, 50 Hz four-pole laminated salient-pole synchronous motor with damper windings. The results measured by on-load test and those predicted by the proposed method on starting performance clearly show the validity of the proposed method     

Reorganizing knowledge in neural networks: an explanatory mechanismfor neural networks in data classification problems

We propose an explanatory mechanism for multilayered neural networks (NN). In spite of the effective learning capability as a uniform function approximator, the multilayered NN suffers from unreadability, i.e., it is difficult for the user to interpret or understand the "knowledge" that the NN has by looking at the connection weights and thresholds obtained by backpropagation (BP). This unreadability comes from the distributed nature of the knowledge representation in the NN. In this paper, we propose a method that reorganizes the distributed knowledge in the NN to extract approximate classification rules. Our rule extraction method is based on the analysis of the function that the NN has learned, rather than on the direct interpretation of connection weights as correlation information. More specifically, our method divides the input space into "monotonic regions" where a monotonic region is a set of input patterns that belongs to the same class with the same sensitivity pattern. Approximate classification rules are generated by projecting these monotonic regions.     

Experimental measurement on tangential momentum accommodation coefficient in a single microtube

The tangential momentum accommodation coefficient (TMAC) was investigated experimentally from the mass flow rate through a single microtube under the slip flow and the early part of the transition regime. The measurements were carried out by the constant-volume method under the mean Knudsen number smaller than 0.3, which is based on the mean pressure of the inlet and the outlet of the microtube, to apply the second-order slip boundary condition. To measure TMACs on various materials, quite large microtube was employed, which require the reduction in leakage. TMAC was obtained from the slip coefficient determined by the relation of the mass flow rate to the mean Knudsen number. The obtained mass flow rate was well explained by the theoretical equation. TMACs of deactivated-fused silica with argon, nitrogen, and oxygen were measured, showing the tangential momentum was not accommodated completely to the surface, and the values showed good agreement with previous studies. From the comparison between microtubes with different inner diameter, it is showed that TMAC is determined mainly by gas species and surface material.     

Improvement of a neural network-based motion generator with bimanual coordination for upper limb prosthesis

We propose to redesign a neural network used as a motion generator with bimanual coordination for upper limb prosthesis in order to improve its learning capability. We assumed that the wearer of the prosthesis was a unilateral amputee. In our previous work, we proposed a prosthesis control system using a neural network that learned bimanual coordination in order to implement smooth motion with both hands. However, the previously proposed system has the problem that a neural network cannot generate the desired motion of the prosthesis in special cases. The reason is that the motion generator calculates the desired posture of the prosthesis from the current posture of the healthy arm only, regardless of the current posture of the prosthesis. We propose to use the current posture of both the healthy arm and the prosthesis as neural network inputs in order to solve this problem. In this article, we show that a single neural network whose input was the current posture of both arms could learn the relationships of the coordinated motions of holding boxes of different sizes, and the newly proposed system can calculate the desired motion of the prosthesis in special cases through computer simulations.     

Parametric polynomial spectral factorization using the sum of roots and its application to a control design problem

This paper presents an algebraic approach to polynomial spectral factorization, an important mathematical tool in signal processing and control. The approach exploits an intriguing relationship between the theory of Gröbner bases and polynomial spectral factorization which can be observed through the sum of roots, and allows us to perform polynomial spectral factorization in the presence of real parameters. It is discussed that parametric polynomial spectral factorization enables us to express quantities such as the optimal cost in terms of parameters and the sum of roots. Furthermore an optimization method over parameters is suggested that makes use of the results from parametric polynomial spectral factorization and also employs two quantifier elimination techniques. This proposed approach is demonstrated in a numerical example of a particular control problem.     

Vibration control of load for rotary crane system using neural network with GA-based training

A neuro-controller for vibration control of load in a rotary crane system is proposed involving the rotation about the vertical axis only. As in a nonholonomic system, the vibration control method using a static continuous state feedback cannot stabilize the load swing. It is necessary to design a time-varying feedback controller or a discontinuous feedback controller. We propose a simple three-layered neural network as a controller (NC) with genetic algorithm-based (GA-based) training in order to control load swing suppression for the rotary crane system. The NC is trained by a real-coded GA, which substantially simplifies the design of the controller. It appeared that a control scheme with performance comparable to conventional methods can be obtained by a relatively simple approach. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Unravel four hairpins!

DNA machines consisting of consecutive hairpins, which we have previously described, have various potential applications in DNA computation. In the present study, a 288-base DNA machine containing four consecutive hairpins was successfully constructed by ligation and PCR. PAGE and fluorescence spectroscopy experiments verified that all four hairpins were successfully opened by four opener oligomers, and that hairpin opening was dependent on the proper openers added in the correct order. Quantitative analysis of the final results by fluorescence spectroscopy indicated that all four hairpins were open in about 1/4 to 1/3 of the DNA machines.     

A Face Identification System Based on the Mahalanobis–Taguchi System

In this paper, we propose a face identification system based on the Mahalanobis–Taguchi System (MTS). The MTS is one of the pattern recognition methods frequently used in quality engineering, and can perform robust pattern recognition by using training data, including noise. It is likely that this advantage will allow the effective implementation of a robust face identification system against lighting and face position fluctuations. Moreover, the MTS can optimize the number of attributes required for identification by using the orthogonal array and the signal/noise (SN) ratio. The face identification system has to deal with many users and the amount of data in a facial image is large. Therefore, the time required for identification can be decreased and the amount of data in the facial image database can be reduced by performing the optimization. We confirmed the effectiveness of the proposed system through practical experiments. The experimental results revealed that the MTS was an effective method for robust face identification, and could effectively reduce the number of attributes required for identification.     

Generating Instances for MAX2SAT with Optimal Solutions

A test instance generator (an instance generator for short) for MAX2SAT is a procedure that produces, given a number n of variables, a 2-CNF formula F of n variables (randomly chosen from some reasonably large domain), and simultaneously provides one of the optimal solutions for F. We propose an outline to design an instance generator using an expanding graph of a certain type, called here an "exact 1/2-enlarger". We first show a simple algorithm for constructing an exact 1/2-enlarger, thereby deriving one concrete polynomial-time instance generator GEN. We also show that an exact 1/2-enlarger can be obtained with high probability from graphs randomly constructed. From this fact, we propose another type of instance generator RGEN; it produces a 2-CNF formula with a solution which is optimal for the formula with high probability. However, RGEN produces less structured formulas and a much larger class of formulas than GEN. In fact, we prove the NP-hardness of MAX2SAT over the set of 2-CNF formulas produced by RGEN.