Block-based neural networks

This paper presents a novel block-based neural network (BBNN) model and the optimization of its structure and weights based on a genetic algorithm. The architecture of the BBNN consists of a 2D array of fundamental blocks with four variable input/output nodes and connection weights. Each block can have one of four different internal configurations depending on the structure settings, The BBNN model includes some restrictions such as 2D array and integer weights in order to allow easier implementation with reconfigurable hardware such as field programmable logic arrays (FPGA). The structure and weights of the BBNN are encoded with bit strings which correspond to the configuration bits of FPGA. The configuration bits are optimized globally using a genetic algorithm with 2D encoding and modified genetic operators. Simulations show that the optimized BBNN can solve engineering problems such as pattern classification and mobile robot control.     

NearFar: A computer program for nearside-farside decomposition of heavy-ion elastic scattering amplitude

The NearFar program is a package for carrying out an interactive nearside-farside decomposition of heavy-ion elastic scattering amplitude. The program is implemented in Java to perform numerical operations on the nearside and farside angular distributions. It contains a graphical display interface for the numerical results. A test run has been applied to the elastic scattering at Elab=1503 MeV.

Program summary

Title of program: NearFarCatalogue identifier: ADYP_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADYP_v1_0Program obtainable from: CPC Program Library, Queen's University of Belfast, N. IrelandLicensing provisions: noneComputers: designed for any machine capable of running Java, developed on PC-Pentium-4Operating systems under which the program has been tested: Microsoft Windows XP (Home Edition)Program language used: JavaNumber of bits in a word: 64Memory required to execute with typical data: case dependentNo. of lines in distributed program, including test data, etc.: 3484Number of bytes distributed program, including test data, etc.: 142 051Distribution format: tar.gzOther software required: A Java runtime interpreter, or the Java Development Kit, version 5.0Nature of physical problem: Interactive nearside-farside decomposition of heavy-ion elastic scattering amplitude.Method of solution: The user must supply a external data file or PPSM parameters which calculates theoretical values of the quantities to be decomposed.Typical running time: Problem dependent. In a test run, it is about 35 s on a 2.40 GHz Intel P4-processor machine.     

Investigation of point-to-point performance test of touch trigger probes on coordinate-measuring machines

A major factor contributing to the total measuring error of coordinate measuring machines (CMMs) is the performance of the probing sub-system. Probing test methods are typically used to detect errors due to the probing sub-system. The probe performance evaluation method specified in the ANSI B89 standard is investigated in this paper. The sampling plan associated in the probe performance evaluation was tested by using experimental probing data from a CMM. Research findings indicate that the performance of touch trigger probes is overestimated due to a systematic bias in the vertical direction of the best-fit reference ball center in the probe performance test. A two-latitude sampling plan synthesis method based on a pretravel model for touch trigger probes is proposed in this paper. The proposed method can be used to accurately identify the reference ball center in the performance test of touch trigger probes.     

A connection rule for alpha-carbon coarse-grained elastic network models using chemical bond information

A sparser but more efficient connection rule (called a bond-cutoff method) for a simplified alpha-carbon coarse-grained elastic network model is presented. One of conventional connection rules for elastic network models is the distance-cutoff method, where virtual springs connect an alpha-carbon with all neighbor alpha-carbons within predefined distance-cutoff value. However, though the maximum interaction distance between alpha-carbons is reported as 7 angstroms, this cutoff value can make the elastic network unstable in many cases of protein structures. Thus, a larger cutoff value (>11 angstroms) is often used to establish a stable elastic network model in previous researches. To overcome this problem, a connection rule for backbone model is proposed, which satisfies the minimum condition to stabilize an elastic network. Based on the backbone connections, each type of chemical interactions is considered and added to the elastic network model: disulfide bonds, hydrogen bonds, and salt-bridges. In addition, the van der Waals forces between alpha-carbons are modeled by using the distance-cutoff method. With the proposed connection rule, one can make an elastic network model with less than 7 angstroms distance cutoff, which can reveal protein flexibility more sharply. Moreover, the normal modes from the new elastic network model can reflect conformational changes of a given protein better than ones by the distance-cutoff method. This method can save the computational cost when calculating normal modes of a given protein structure, because it can reduce the total number of connections. As a validation, six example proteins are tested. Computational times and the overlap values between the conformational change and infinitesimal motion calculated by normal mode analysis are presented. Those animations are also available at UMass Morph Server (http://biomechanics.ecs.umass.edu/umms.html).     

Capacitive humidity sensors based on a newly designed interdigitated electrode structure

This paper presents a study of capacitive humidity sensors constructed based on a newly designed interdigitated electrode (IDE) structure with a polyimide (PI) sensing layer fabricated using micro-electro-mechanical system (MEMS) technology. The humidity sensors use an IDE with increased height, formed by the surface micromachining of a silicon substrate. The fabricated sensors showed higher sensitivity in variable ambient relative humidity (RH) when compared with the humidity sensors of a conventional IDE since the horizontal electric field lines, generated between the thick electrodes, are confined to the PI sensing layer. The effect of the thickness of the PI layer was also investigated. The fabricated sensors with a 4-μm-thick PI layer showed a higher sensitivity of 37.1 fF/%RH when compared with those of a 2- and 3-μm-thick PI layers, indicating that the properties of the sensors depend on the thickness of the PI layer. This is because the amount of the fringing electric field lines passing through the PI is determined by the PI thickness. In addition, the sensor with the 4-μm-thick PI showed very slight hysteresis with a maximum of 2.87%RH and displayed high stability with a variation range of 0.06 pF.     

Measuring the success of retention management models built on churn probability,retention probability,and expected yearly revenues

We claim that often marketers have not all the information to develop various marketing campaign models. For example, marketers may have sufficient information to build a model for predicting possible churners, while they may have no clues of which customers are most likely to accept a retention campaign. In this paper, we first show that the information useful for a successful churner prediction model alone is not sufficient to develop a successful retention marketing program. In such a case, we claim that only theory-based simulation approach is feasible. In particular, it is claimed that optimal retention management models should consider not only churn probability but also retention probability and expected revenues from target customers. To validate our claim, we develop and compare five retention management models based on churn probability, retention probability, expected revenues, and combination of these models along with different evaluation metrics. Our experimental results show that the retention management model with the highest accuracy in predicting possible churners is not necessarily optimal because it does not consider the probability of accepting retention promotions. In contrast, the retention management model based on both churn and retention probability is the best in terms of predicting customers who are most likely to positively respond to retention promotions. Ultimately, the model based on expected yearly revenue of customers accrues the highest revenues across most target points, making it the best model out of five churn management models.     

Solid-State Control-Low Voltage Heating of Motors

In recent years, industrial plants have been utilizing more electric motors as drivers because of their high efficiency and reliability and lower maintenance costs. Electrified continuous process plants are using electric motors for both primary and standby drives. When these standby motors are not operating during normal plant runs, their windings collect moisture. Moisture attacks insulation and causes insulation failure. A breakdown of motor insulation may occur when a standby motor is suddenly energized to take the place of the primary motor. When it is not in operation, directly heating the motor winding with a low voltage system, coupled with automatic control, will guard the motor from moisture attack and extend its service life. The experience with low voltage heating of motor windings as described by M. H. Yuen in his article [1] has proved its effectiveness. However, conventional relays, timers, contactors, etc., were utilized in the circuits.     

Controlled density of vertically aligned carbon nanotubes in a triode plasma chemical vapor deposition system

We report on the growth mechanism and density control of vertically aligned carbon nanotubes using a triode plasma enhanced chemical vapor deposition system. The deposition reactor was designed in order to allow the intermediate mesh electrode to be biased independently from the ground and power electrodes. The CNTs grown with a mesh bias of + 300 V show a density of ∼ 1.5 μm^− 2 and a height of ∼ 5 μm. However, CNTs do not grow when the mesh electrode is biased to − 300 V. The growth of CNTs can be controlled by the mesh electrode bias which in turn controls the plasma density and ion flux on the sample.     

Fabrication of Er3+/Pr3+ Co-doped soda-lime glass thin films using RF magnetron sputtering method and optical property characterization

Er^3+/Pr³⁺ co-doped soda-lime glass thin films have been fabricated using RF magnetron sputtering method and their structural and optical properties have been studied. Deposition rate, crystallinity, and composition of glass thin films were investigated by scanning electron microscopy, transmission electron microscopy, and electron probe micro area analysis. Refractive index, birefringence and binding characteristics have been investigated using a prism coupler and X-ray photoelectron spectroscopy. Er³⁺/Pr³⁺ co-doped soda lime glass thin films were prepared by changing substrate temperature (room temp. ∼550^∘C), RF power (90 W–130 W), and Ar/O₂ gas flow ratio at processing pressure of 4 mTorr. Glass thin films could be obtained at the optimized processing condition at 350^∘C, RF power of 130 W, and gas flow of Ar:O₂ = 40:0 with maximum deposition rate of 1.6 μm/h. Refractive index and birefringence increased from 1.5614 to 1.5838 and from 0.000154 to 0.000552, respectively, as the content of Pr³⁺ increased. Binding energy of Pr3d also increased as the content of Pr³⁺ increased.