Evaluation of vector magnetic property in a three-phase induction motor model core with visualization of streamline

In this paper, a streamline display method for magnetic flux density and magnetic field strength vectors in a three-phase induction motor model core is presented, and the relationship between streamlines and magnetic properties is investigated. It is possible to visualize the vector magnetic properties distribution by representing the magnetic flux density and the magnetic field strength vector with the streamlines. In addition, the time variation of the streamlines of the magnetic flux density and magnetic field strength vectors, and magnetic energy was compared. It was clear that the magnetic energy did not necessarily increase in the areas where the magnetic flux density and magnetic field strength increased because these vectors have the spatial phase difference.     

Development of fault cause estimation method for distribution line

We are planning to develop the new Advanced Distribution Automation System (ADAS) equipped with optical communication network and sectionalizer with sensor for more grid reliability and efficiency on maintenance operation. The new system is featured with the function of fault cause or damaged part estimation. The new sectionalizer captures the waveform of electrical line surge originated by fault and the system analyzes the waveform for this function. This paper mainly introduces the system configuration of ADAS and an early stage examination of fault cause estimation technology.     

Prototyping and modeling of the centrifugal casting process for paraffin waxes

Centrifugal casting is a technology used for manufacturing hybrid rocket paraffin grains. This technology helps avoiding voids formation inside the solid paraffin as it cools. Voids are formed because of air bubbles being entrapped while pouring and because the liquid wax shrinks by 17–19% upon cooling. In this work, the centrifugal casting process for the manufacturing of paraffin cylinders was prototyped at two different scales considering critical casting issues. The effects of process parameters (rotational speed, melt temperature, and flow rate) on the tensile properties of the manufactured grains were analyzed. The results of the optimization conducted at the lower scale (2.5?kg) were up scaled to manufacture 25?kg grains. The resulting mechanical properties complied with the design specifications, and they were better than those characterized from the gravity cast wax. A numerical model of growth and dissolution of bubbles during the process was then developed to predict the quality of the castings. The numerical results showed how increasing the mold rotational speed up to 1800?rpm reduced the removal time. However, compared to grains solidification time, the predicted removal times were much shorter, proving the advantage of centrifugal casting in counteracting voids formation.     

Virtual segment: Store–carry–forward relay-based support for wide-area non-real-time data exchange

We have proposed the concept of the virtual segment (VS), in which a global communication service is provided by combining a store–carry–forward scheme using vehicles with broadband wireless/wired network infrastructures along roads connected to the Internet. The VS can be a practical framework for non-real-time, asynchronous message transfer (especially for large messages) in a cost-effective manner. In this study, a critical implementation design issue, the message forward scheduling, in the VS approach is discussed and investigated through computer simulation by our developed VS simulator that has reflected the results of the field experiment for realistic performance evaluation.     

Codevelopmental learning between human and humanoid robot using a dynamic neural-network model.

This paper examines characteristics of interactive learning between human tutors and a robot having a dynamic neural-network model, which is inspired by human parietal cortex functions. A humanoid robot, with a recurrent neural network that has a hierarchical structure, learns to manipulate objects. Robots learn tasks in repeated self-trials with the assistance of human interaction, which provides physical guidance until the tasks are mastered and learning is consolidated within the neural networks. Experimental results and the analyses showed the following: 1) codevelopmental shaping of task behaviors stems from interactions between the robot and a tutor; 2) dynamic structures for articulating and sequencing of behavior primitives are self-organized in the hierarchically organized network; and 3) such structures can afford both generalization and context dependency in generating skilled behaviors.     

A programmable editor for developing structured documents based on bidirectional transformations

This paper presents an application of bidirectional transformation to the design and implementation of a novel editor supporting interactive refinement in the development of structured documents. The user performs a sequence of editing operations on a view of the document, and the editor automatically derives an efficient and reliable document source and a transformation that produces the document view. The editor is unique in its programmability, in the sense that transformations can be obtained through editing operations. It uses the view-updating technique developed in the database community and a new bidirectional transformation language that can describe not only the relationship between the document source and its view, but also the data dependency in the view.     

Compact electrostatic levitator for diffraction measurements with a two axis diffractometer and a laboratory x-ray source

A compact electrostatic levitator was developed for the structural analysis of high-temperature liquids by x-ray diffraction methods. The size of the levitator was 200 mm in diameter and 200 mm in height and can be set up on a two axis diffractometer with a laboratory x-ray source, which is very convenient in performing structural measurements of high-temperature liquids. In particular, since the laboratory x-ray source allows a great amount of user time, preliminary or challenging experiments can be performed with trial and error, which prepares and complements synchrotron x-ray experiments. The present small apparatus also provides the advantage of portability and facility of setting. To demonstrate the capability of this electrostatic levitator, the static structure factors of alumina and silicon samples in their liquid phases were successfully measured.     

Selectivity and stability via dendritic nonlinearity

Inspired by recent studies regarding dendritic computation, we constructed a recurrent neural network model incorporating dendritic lateral inhibition. Our model consists of an input layer and a neuron layer that includes excitatory cells and an inhibitory cell; this inhibitory cell is activated by the pooled activities of all the excitatory cells, and it in turn inhibits each dendritic branch of the excitatory cells that receive excitations from the input layer. Dendritic nonlinear operation consisting of branch-specifically rectified inhibition and saturation is described by imposing nonlinear transfer functions before summation over the branches. In this model with sufficiently strong recurrent excitation, on transiently presenting a stimulus that has a high correlation with feed- forward connections of one of the excitatory cells, the corresponding cell becomes highly active, and the activity is sustained after the stimulus is turned off, whereas all the other excitatory cells continue to have low activities. But on transiently presenting a stimulus that does not have high correlations with feedforward connections of any of the excitatory cells, all the excitatory cells continue to have low activities. Interestingly, such stimulus-selective sustained response is preserved for a wide range of stimulus intensity. We derive an analytical formulation of the model in the limit where individual excitatory cells have an infinite number of dendritic branches and prove the existence of an equilibrium point corresponding to such a balanced low-level activity state as observed in the simulations, whose stability depends solely on the signal-to-noise ratio of the stimulus. We propose this model as a model of stimulus selectivity equipped with self-sustainability and intensity-invariance simultaneously, which was difficult in the conventional competitive neural networks with a similar degree of complexity in their network architecture. We discuss the biological relevance of the model in a general framework of computational neuroscience.     

Multicomponent diffusion in phase-separating polymer blends with different frictional interactions: a mean-friction model

We present a compact formula for describing the mean frictional forces acting on a molecule in multicomponent systems. The friction-based diffusion theory of Zielinski and Hanley was extended to newly include the friction-average molar velocity as a reference frame. The results showed that the previous diffusion theories are unified by the friction-average concept by properly choosing the average velocity. The present model based on the diffusivity-related molar average velocity provides better predictions for the diffusive flux in a ternary miscible liquid compared to the other existing theories. The application of the model in phase-separating ternary systems revealed that the introduction of a highly diffusive third component into demixing polymer blends promotes a particular enhancement of the spinodal decomposition due to the difference in the frictional interactions between polymers.