Neuro-Fuzzy Control of a Tracked Vehicle Featuring Semi-Active Electro-Rheological Suspension Units

This paper presents vibration control of a tracked vehicle installed with electro-rheological suspension units (ERSU). As a first step, an in-arm type ERSU is designed, and its spring and damping characteristics are evaluated with respect to the intensity of electric fields. Subsequently, a 16 degree-of-freedom model for a tracked vehicle equipped with the proposed ERSU is established followed by the formulation of a neuro-fuzzy controller. This controller takes account for both ride quality and steering stability by adopting a weighting parameter between two performance requirements. The parameter is appropriately determined by employing a fuzzy algorithm associated with two fuzzy variables: the vertical speed of the body and the rotational angular speed of the wheel. Control performances to isolate unwanted vibration from bump and random road excitations are evaluated through computer simulations. In addition, maximum speed of the vehicle with 6 Watt power absorption is investigated with respect to the road roughness.     

Optimum design of tilting bogie frame in consideration of fatigue strength and weight

This paper addresses the use of finite-element (FE) analysis to calculate the fatigue strength of a bogie frame, for the development of tilting trains in Korea. A multi-body dynamic analysis was performed to extract the load condition by tilting on curves. Using the results of the multi-body dynamic analysis and the load scenario setout in the UIC standard, FE analysis was performed to obtain the stress distribution and to calculate the fatigue strength. An attempt was made to minimize the weight of the bogie frame using a back-propagation artificial neural network (ANN). The results of this study reveal that the stresses at some nodes are near the fatigue limit in the Goodman diagram and by using back-propagation ANN, the weight of the bogie frame could be reduced by 4.7%.     

Seabed scene simulation and its realization in extending Vega

Realistic simulation of underwater scene is always difficult because of the special and complex vision effects in underwater space. Seabed is an important part of underwater environment. This paper describes the methods for seabed scene simulation based on OpenGL. It includes construction of fluetuant terrain based on the random sinusoidal algorithm, simulation of seabed flicker effect by means of circular texture mapping and generation of turbidity effect by using fog techniques. For the application based on the leading high level 3D development environment-Vega, underwater scene simulation is still a difficulty since there is no module for it. Based on the analysis of Vega software and the research on seabed scene simulation methods, a Vega extending module named “Underwater Space” was created through developing module class and extending lynx interface. The module class was designed through developing DLL written in C . The Lynx was extended through developing keyword configure file, GUI configure file and lynx plug-in DLL. The problem that Vega can‘t simulate underwater space, is elementarily resolved. The results show that this module is efficient, easy using, and the seabed scene images am vivid.     

Identity-based threshold decryption on access structure

For the applied limitation of the existing threshold decryption schemes based on the (t, n) structure, an identity-based threshold decryption scheme which can be applied on the access structure is proposed through designing a special distribution algorithm of the private key shares. The generation and distribution of private key shares, the encryption, the decryption and the combination are introduced in detail. The validity and security of the scheme are proved and analyzed. Comparisons with the existing schemes show that the proposed scheme is more flexible.     

Tool sequence optimization based on three logical modes of robot control via CPLEX optimization

Cluster tools have advantages of shorter cycle times, faster process development, and better yield for less contamination. The sequence of dual-arm cluster tools is a complex logistics process during the semiconductor production. Efficient use of cluster tools is naturally very significant to competitive fab operations. Generating an optimized sequence in a computationally efficient manner and assessing the quality of the requirements to improve the fab production are the key factors for semiconductor manufacturing productivity. The Petri net modeling is introduced to minimize the makespan of the process for the three different logical modes and select a better mode after comparing the makespan among the three logical modes. The tool sequence optimization problem is formulated as optimization firing transition sequences based on the Petri net and then the formulation is converted to be linearly solved by the branch-and-cut method in the standard commercial solver CPLEX. Special methods for the linear conversion are highlighted. Due to the limited calculation time requirement for the real production and the large scale of the problem, special methods for the efficiency tuning are applied according to the characteristics of the problem. Numerical testing is supported by one of the most advanced semiconductor enterprises and the computational results show significant improvement compared with the traditional manual sequence results.     

Poisson noise removal based on nonlocal total variation with Euler’s elastica pre-processing

An enhancement-based Poisson denoising method for photon-limited images is presented. The noisy image is firstly pre-processed for enhancing incomplete object information, and then it is denoised while preserving the restored structural details. A variational regularization model based on Euler’s elastica (EE) is proposed for image enhancement pre-processing. A nonlocal total variation (NLTV) regularization model is then employed in the second stage of image denoising. The above two optimization problems are solved by the alternating direction method of multipliers (ADMM). For Poissonian images with low image peak values, experiments demonstrate the validity and efficiency of the proposed method for both restoring geometric structure and removing noise.     

Semiparametric specification of the utility function in a travel mode choice model

We propose a semiparametric approach that can capture the nonlinearity of deterministic components of the utility functions in discrete choice models and demonstrate it by analyzing travel mode choice behaviour for an interregional trip. The proposed smoothing spline-based specification method can be used to make ex ante evaluations regarding the parametric specifications of the deterministic utility functions in discrete choice models.     

Effective method for knock signal denoising in internal combustion engine

Uncontrolled expansion of combustion wave in spark ignited internal combustion engine causes knock effect which seriously degrades efficiency and lifetime of the engine. Thus, accurate knock detection and control are essential for obtaining a desired performance from the engine. Usually, knock sensor is used to detect this phenomenon but it has limited accuracy especially at engine high-speed rotations because of natural vibration and external noises. In this study an effective method based on Non-Local Mean (NLM) algorithm has been proposed to improve the knock detection accuracy. This method is evaluated based on four different indicators and four engine cylinders. The results show 52.9 % improvement in knock detection. Also feasibility of real time execution of this method based on embedded hardware has been studied.