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Cutoff frequency ƒT characteristics for AlGaAs/GaAs HBTs with planar structures are studied, by two‐dimensional simulation, with an emphasis placed on their dependences on the collector parameters. It is shown that the sub‐collector resistance becomes an important factor to achieve a higher ƒT in the high current region, and so it should be made as low as possible. Effects of introducing semi‐insulating external collectors are also studied. It is shown that the introduction of semi‐insulating layer is effective to improve the ƒT characteristics provided that it is slightly away from the intrinsic collector region.

I‐V characteristics of GaAs n‐i‐n structures are calculated by considering impact ionization of carriers. Impact ionization at reverse‐biased n‐i junction becomes a cause of steep current rise when an acceptor density in the i‐layer is high. It is shown that an optimum acceptor density exists to keep a good isolation. Photoconduction transients of GaAs n‐i‐n structures are also simulated, and are shown to be strongly affected by existence of n‐i junctions.

In this paper, we describe an energy transport simulation method for graded AlGaAs/GaAs HBTs, which uses Al composition‐, doping density‐, and energy‐dependent transport parameters estimated by a Monte Carlo method. For several representative Al composition x and doping densities, parameters such as electron mobility, energy relaxation time and upper valley fraction are evaluated as a function of electron energy. For the other x, these are determined by linear extraporation. The problem of this extraporation method is also described. Calculated cutoff frequency characteristics and electron velocity profiles are compared with those by using more simplified approaches, indicating the importance of using adequate transport parameters.

A computer program for the solution of the single carrier semiconductor equations in GaAs has been developed to simulate charge storage and transfer in GaAs charge‐coupled devices. An uncoupled Newton method is used to solve the steady state problem, and a stable, uncoupled method is used for the transient solution. Using transient simulation, the transfer of a charge packet from well to well can be simulated over time. By comparing the size of the charge packet before and after the transfer, information on the charge transfer inefficency can be derived.

Numerical device simulation is developed to study the steady‐state and transient current‐voltage characteristics of double heterostructure AlGaAs/GaAs PNPN electro‐photonic device when its performance is influenced by the presence of interface and bulk recombination mechanism. The simulation results show that the holding current and voltage and the breakover point are strongly affected by varying the minority carrier lifetime at outer heterojunctions. Numerical results also indicate that shortening the minority carrier lifetime in the inner PN homojunction region only increases the OFF‐state current. These results are in agreement with experimental data on AlGaAs/GaAs PNPN devices. The numerical modelling approach taken in this study is shown to be essential in the design and optimization of PNPN switch.

An energy balance equation model coupled with drift‐diffusion transport equations are solved in heterojunction p‐i‐n diodes with embedded single quantum well to model hot electron effects. A detailed formulation of hot electron transport is presented. In the well, the carrier energy levels are estimated from the analytical expressions applied to a quantum well with finite height. Both bound and free carriers are modeled by Fermi‐Dirac statistics. Both size quantization and the two dimensional density of states in the well are considered. Thermionic emission is applied to the heterojunctions and quantum wells boundary. Energy transfer among the charge carriers and crystal lattice is modeled by an energy relaxation lifetime. Two sets of devices are simulated. First, the simulated kinetic energy and carrier density profiles were compared with published Monte Carlo results on an GaAs n⁺/n/n⁺ diode. Second, the current‐voltage characteristics of an embedded single quantum well AlGaAs/GaAs p‐i‐n structure was compared with measured data. Both comparisons are satisfactory and demonstrate the usefulness of the model for studying quantum well structures.

The purpose of this paper is to present an approach to improve the accuracy of estimating feature points of human body on a vision‐based motion capture system (MCS) by using the variable‐density self‐organizing map (VDSOM).

The VDSOM is a kind of self‐organizing map (SOM) and has an ability to learn training samples incrementally. The authors let VDSOM learn 3D feature points of human body when the MCS succeeded in estimating them correctly. On the other hand, one or more 3D feature point could not be estimated correctly, the VDSOM is used for the other purpose. The SOM including VDSOM has an ability to recall a part of weight vector which have learned in the learning process. This ability is used to recall correct patterns and complement such incorrect feature points by replacing such incorrect feature points with them.

Experimental results show that the approach is effective for estimation of human posture robustly compared with the other methods.

The proposed approach is interesting for the collaboration between an MCS and an incremental learning.

With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed.

After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization.

Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence.

This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown.

IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum.

This research provides an efficient scheduling method for solving the FJSP problem.

This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

In recent decades, development of effective methods for optimizing a set of conflicted objective functions has been absorbing an increasing interest from researchers. This refers to the essence of real-life engineering systems and complex natural mechanisms which are generally multi-modal, non-convex and multi-criterion. Until now, several deterministic and stochastic methods have been proposed to cope with such complex systems. Advanced soft computational methods such as evolutionary games (cooperative and non-cooperative), Pareto-based techniques, fuzzy evolutionary methods, cooperative bio-inspired algorithms and neuro-evolutionary systems have effectively come to the aid of researchers to build up efficient paradigms with application to vector optimization. The paper aims to discuss this issue.

A novel hybrid algorithm called synchronous self-learning Pareto strategy (SSLPS) is presented for the sake of vector optimization. The method is the ensemble of evolutionary algorithms (EA), swarm intelligence (SI), adaptive version of self-organizing map (CSOM) and a data shuffling mechanism. EA are powerful numerical optimization algorithms capable of finding a global extreme point over a wide exploration domain. SI techniques (the swarm of bees in our case) can improve both intensification and robustness of exploration. CSOM network is an unsupervised learning methodology which learns the characteristics of non-dominated solutions and, thus, enhances the quality of the Pareto front.

To prove the effectiveness of the proposed method, the authors engage a set of well-known benchmark functions and some well-known rival optimization methods. Additionally, SSLPS is employed for optimal design of shape memory alloy actuator as a nonlinear multi-modal real-world engineering problem. The experiments show the acceptable potential of SSLPS for handling both numerical and engineering multi-objective problems.

To the author’s best knowledge, the proposed algorithm is among the rare multi-objective methods which fosters the use of automated unsupervised learning for increasing the intensity of Pareto front (while preserving the diversity). Also, the research evaluates the power of hybridization of SI and EA for efficient search.

Flexible job-shop scheduling is significant for different manufacturing industries nowadays. Moreover, consideration of transportation time during scheduling makes it more practical and useful. The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem (MOFJSP) considering transportation time.

A hybrid genetic algorithm (GA) approach is integrated with simulated annealing to solve the MOFJSP considering transportation time, and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.

The performance of the proposed algorithm is tested on different MOFJSP taken from literature. Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution, especially when the number of jobs and the flexibility of the machine increase.

Most of existing studies have not considered the transportation time during scheduling of jobs. The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs. Meanwhile, GA is one of primary algorithms extensively used to address MOFJSP in literature. However, to solve the MOFJSP, the original GA has a possibility to get a premature convergence and it has a slow convergence speed. To overcome these problems, a new hybrid GA is developed in this paper.