An evolutionary neural network approach for module orientationproblems

A novel neural network approach called “Evolutionary Neural Network (ENN)” is presented for the module orientation problem. The goal of this NP-complete problem is to minimize the total wire length by flipping circuit modules with respect to their vertical and/or horizontal axes of symmetry. In order to achieve high quality VLSI systems, it is strongly desired to solve the problem as quickly as possible in the design cycle. Based on the concept of the genetic algorithm, the evolutionary initialization scheme on neuron states is introduced so as to provide a high quality solution within a very short time. The performance of ENN is compared with three heuristic algorithms through simulations on 20 examples with up to 500 modules. The results show that ENN can find the best solutions in the shortest time     

A control strategy for leveling load power fluctuations with a successive learning fuzzy-neural network based on prediction of average load power

The effective usage of power facilities can be realized by leveling the fluctuating active power and compensating the reactive power. A fuzzy control strategy of superconducting magnetic energy storage (SMES) has been proposed for this purpose. The control results depend on the values of the scaling factors in fuzzy reasoning. Therefore, to obtain better control results, the scaling factor should be successively adjusted according to the load power fluctuations. In this paper, a control strategy based on autotuning of scaling factors and a fuzzy singleton reasoning method using backpropagation in a neural network is proposed for leveling load fluctuations. The prediction and revision of the teaching signal in terms of the energy of the SMES is proposed. The learning rate and the revision of the teaching signal are discussed. Better leveling of load power fluctuation is shown to be achievable by using fuzzy logic and neural networks. © 1997 Scripta Technica, Inc. Electr Eng Jpn, 120(2): 72–81, 1997     

VLSI reliability challenges: from device physics to wafer scalesystems

The philosophical and practical differences between Japanese and US IC industries in regard to VLSI reliability, as well as recent research topics and new analysis methods such as wafer scale testing, are discussed. It is suggested that a new approach to VLSI reliability is needed in response to the paradigm shift being brought about by simple scaling limitations, increased process complexity and application of VLSI to advanced systems. An example of this shift is the movement from simple failure analysis by sampling the output of a manufacturing line to the building-in-reliability approach. To introduce and expand on the building-in reliability approach in VLSIs, the authors discuss the required deeper physical understanding of such important processes as hot-carrier effects, dielectrics and metallization     

Revisiting sex differences in the expression of depression.

251 male and 390 female college students indicated the degree to which they would engage in a number of behaviors or cognitions if depressed and completed the Beck Depression Inventory. The rated items were gathered using procedures suggested by the behavioral-analytic model. Factor analyses were employed to categorize items into interpretable variables. Stepwise discriminant function analyses were performed to determine maximum separation of male and female groups. Men and women showed distinct differences in the nature of their interpersonal behaviors as well as in cognitive styles for coping with depression. Findings suggest that there are sex differences in the expression of depression even in predominantly normal populations and reinforce the value of investigating theories of depression in the context of a continuity between normal mood states and depressive phenomena. (25 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

PCR detection of Bacillus and Staphylococcus in various foods

A broad-range PCR assay for the detection of bacteria belonging to Bacillus and Staphylococcus genera was developed. Primers targeting the bacterial 16S rRNA gene were newly designed and used in a PCR assay. To determine the specificity of the assay, 81 different bacterial strains (of 50 genera), 2 fungi, 3 animals, and 4 plants were tested. Results were positive for every tested Bacillus, Staphylococcus, or Aerococcus strain. In addition, the result for Listeria grayi was positive with lower PCR product. For all other bacterial strains and eukaryotes tested, results were negative. Bacterial DNA was prepared with the use of achromopeptidase and Chelex 100 resin from culture after growth in brain heart infusion medium. To test the sensitivity of this PCR assay for Bacillus or Staphylococcus genus, either Bacillus cereus or Staphylococcus aureus was inoculated into various foods with undetectable levels of endogenous microbial contamination as an indicator. Inoculation of bacteria at 10 to 30 CFU/g of food was followed by a 5-h enrichment culture step after which the PCR assay allowed the detection of bacterial cells. When the inoculation (B. cereus or S. aureus) of 10 to 90 CFU/g into noodle foods containing endogenous microflora (10(3) to 10(5) CFU/g) was followed by a 6-h enrichment culture step, the PCR assay detected the bacteria. Including the enrichment culture step, the entire PCR detection process can be completed within 8.5 h.     

A gradual neural-network approach for frequency assignment insatellite communication systems

A novel neural-network approach called gradual neural network (GNN) is presented for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The frequency assignment problem in the satellite communication system is efficiently solved by GNN as the typical problem of this class. The goal of this NP-complete problem is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate the increasing demands. The GNN consists of NxM binary neurons for the N-carrier-M-segment system with the gradual expansion scheme of activated neurons. The binary neural network achieves the constrain satisfaction with the help of heuristic methods, whereas the gradual expansion scheme seeks the cost optimization. The capability of GNN is demonstrated through solving 15 instances in practical size systems, where GNN can find far better solutions than the existing algorithm.     

A parallel algorithm for broadcast scheduling problems in packetradio networks

A parallel algorithm based on an artificial neural network model for broadcast scheduling problems in packet radio networks is presented. The algorithm requires n×m processing elements for an n-mode-m-slot radio network problem. The algorithm is verified by simulating 13 different networks     

A Comparison of Electric Power Smoothing Control Methods for Distributed Generation Systems

Renewable forms of energy such as solar energy have attracted attention as alternative sources of fossil fuel. The output power of photovoltaic generation systems changes steeply. Changes in output power have an effect on the electric power quality of the power system. For that reason, a system which can smooth fluctuations of output power is required. In this paper, the moving average, modified moving average, single exponential smoothing, and double exponential smoothing methods are applied to electric power smoothing control schemes for distributed generation systems with photovoltaic generation. The reduction rate of power fluctuations and the maximum stored energy of electric double layer capacitors are adopted as system evaluation measures. To confirm the effectiveness of the power smoothing control methods, a distributed generation power system with photovoltaic generation systems is simulated with the power electronics circuit simulation software PSIM. The methods are then compared by evaluating the reduction in the capacity of electric double layer capacitors without loss in the power smoothing effect.