Combining GA and iterative searching DOA estimation for CDMA signals

This paper deals with direction-of-arrival (DOA) estimation based on iterative searching technique for code-division multiple access signals. It has been shown that the iterative searching technique is more likely to converge to a local maximum, causing errors in DOA estimation. In conjunction with a genetic algorithm for selecting initial search angle, we present an efficient approach to achieve the advantages of iterative DOA estimation with fast convergence and more accuracy estimate over existing conventional spectral searching methods. Finally, several computer simulation examples are provided for illustration and comparison.     

Electroplating of nickel films at ultra low electrolytic temperature

In this paper, we have demonstrated a successful electroplating process of nickel (Ni) in a sulfamate electrolyte bath at ultra low electrolytic temperatures of 273–278 K. The potentiostatic mode is essential for the electroplating process rather than galvanostatic mode. The reason is that diffusion-limited current can be easily obtained by applying a specific potential which is higher than reduction potential. On the contrary, galvanostatic mode can not identify the diffusion-limited current and the reduction will suspend while the setting current is higher than diffusion-limited current ranged in hundreds of μA. The microstructure, morphology and hardness of the Ni electrodeposits were characterized using grazing incidence x-ray diffractometer, atomic force microscopy and nano-indentation test were performed. The hardness of Ni film was much enhanced to around 6.37 GPa at 273 K to 6.18 GPa at 278 K compared with that around 4.11 GPa at 288 K to 4.01 GPa at 293 K. The normal hardness of pure nickel is about 4 GPa. The enhanced hardness of Ni at ultra low temperature is attributed to both mechanisms of reduced grain size strengthening and residual compressive stress hardening.     

Molding and hot forming techniques for fabricating plastic aspheric lenses with high blue-light transmittance

The purpose of this paper is to use micro-electroforming and hot-embossing technology as an alternative to high-cost precision cutting or traditional injection molding in the fabrication of plastic aspheric lenses with high Blu-Ray transmittance. The female dies for the aspheric lenses are fabricated from UV-cured SU-8 polymer via electrostatic attraction. However, SU-8 has a 405 nm Blu-Ray transmittance of only roughly 40–50%, which is not appropriate for use in high-density optical pickup systems. This paper, therefore, uses low stress, low surface roughness, nickel micro-electroforming and molding technologies and employs a micro hot-embossing system to form aspheric lenses with high Blu-Ray transmittance from COC plastic (transmittance: 88% at 405 nm). The resulting lenses have a clear aperture of approximately 1 mm and a numerical aperture of roughly 0.6. The electroforming mold has a roughness of approximately 8 nm as measured by AFM. The roughness of COC (n = 1.53) aspheric lenses after hot-embossing is approximately 146 nm (300 µm × 220 µm) as measured by white light interferometer (WYKO). The shape precision of the hot-embossing COC and original SU-8 (n = 1.67) aspheric lenses can be controlled with approximately 2.638% error. The spot size of the hot-embossed COC and original SU-8 aspheric lenses can be controlled with approximately 11% error. This error should account for the material refractive index difference and the shape error. The roughness and spot size were also tested using different pressing temperatures and forces. This technology could be developed to fabricate lenses in Blu-Ray 405 nm micro-optical systems.     

Performance analysis of the IEEE 802.11 DCF in the presence of the hidden stations

In this paper, we present an analytical model to evaluate the hidden station effect on the performance of the IEEE 802.11 Distributed Coordination Function (DCF) in both non-saturation and saturation condition. DCF is a random channel-access scheme based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) method and the exponential backoff procedure. DCF is widely used and can support both wireless network with an access point and ad hoc wireless network because of its random channel-access method. On the other hand, this method unavoidably suffers the hidden station effect that causes significant performance degradation. As shown in this paper, hidden stations occur frequently in real-world settings, and the performance impact on the 802.11 DCF is a significant concern, but it has not been adequately studied. We study this problem through a spatial–temporal analysis and a Markov chain model. Our model generalizes the existing work on the performance modeling of 802.11 DCF for both non-saturation and saturation conditions. The scenario of no hidden station can be considered as a special case in our model. The performance of our model is evaluated by comparison with ns-2 simulations.     

Design of a sliding mode controller for an automatic guided vehicle and its implementation

In this paper, a control scheme that combines a kinematic controller and a sliding mode dynamic controller with external disturbances is proposed for an automatic guided vehicle to track a desired trajectory with a specified constant velocity. It provides a method of taking into account specific mobile robot dynamics to convert desired velocity control inputs into torques for the actual mobile robot. First, velocity control inputs are designed for the kinematic controller to make the tracking error vector asymptotically stable. Then, a sliding mode dynamic controller is designed such that the mobile robot’s velocities converge to the velocity control inputs. The control law is obtained based on the backstepping technique. System stability is proved using the Lyapunov stability theory. In addition, a scheme for measuring the errors using a USB camera is described. The simulation and experimental results are presented to illustrate the effectiveness of the proposed controller.     

Semi-supervised multi-class Adaboost by exploiting unlabeled data

Semi-supervised learning has attracted much attention in pattern recognition and machine learning. Most semi-supervised learning algorithms are proposed for binary classification, and then extended to multi-class cases by using approaches such as one-against-the-rest. In this work, we propose a semi-supervised learning method by using the multi-class boosting, which can directly classify the multi-class data and achieve high classification accuracy by exploiting the unlabeled data. There are two distinct features in our proposed semi-supervised learning approach: (1) handling multi-class cases directly without reducing them to multiple two-class problems, and (2) the classification accuracy of each base classifier requiring only at least 1/K or better than 1/K (K is the number of classes). Experimental results show that the proposed method is effective based on the testing of 21 UCI benchmark data sets.     

Optimization of multi-task turning operations under minimal tool waste consideration

Most of the literatures on machining economics problems tend to focus on single cutting operations. However, in reality most parts that need to be machined require more than one operation. In addition, machining technology has been developed to the point that a single computer numerical control (CNC) machine is capable of performing multiple operations, even simultaneously, employing multiple spindles and cutting tools. When several operations are performed on a CNC turning machine, various tools are required for the cutting operations. Determining the life of these cutting tools under different machining conditions is an arduous task for the operators. They usually replace the tools based on their experience or according to the specific cutting tool handbook. Frequent tool replacements may result in wasted tools and tool utilization, while infrequent tool replacements may result in poorly machined parts. In this study we propose a mathematical model in which several different turning operations (turning, drilling, and parting) with proper constraints are performed. The issue of tool replacement is taken into account in the proposed cutting model. In addition, an evolutionary strategy (ES)-based optimization approach is developed to optimize the cutting conditions of the multiple turning-related operations while taking into account the minimizing unit cost criteria under the economical tool replacement strategy.     

Activity-based divergent supply chain planning for competitive advantage in the risky global environment: A DEMATEL-ANP fuzzy goal programming approach

Supply chain management allows modern enterprises to relax their own capacities and produce in a more flexible manner for diversified consumer demands. However, for an enterprise with divergent supply chain (DSC) and multiple product lines, to plan the production allocation for higher competitive advantage in the risky global market is a challenging problem. The existing literature still has not address this problem very well. This paper is aimed to treat this problem by using an integrated approach of activity based costing (ABC) and management, five forces analysis, risk and value-at-risk analysis, decision making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and fuzzy goal programming (FGP). The proposed model can effectively incorporate the key factors of precise costing, managerial constraints, competitive advantage analysis, and risk management into DSC forecasting and multi-objective production planning. A case study of a consumer-oriented cell phone DSC is also presented. The sensitivity analysis shows that identifying and relaxing crucial constraints can play an important role in DSC planning for higher competitive advantage and lower risk.     

An efficient fuzzy weighted average algorithm for the military UAV selecting under group decision-making

The fuzzy weighted average (FWA), which is a function of fuzzy numbers and is useful as an aggregation method in engineering or management science based on fuzzy sets theory. It provides a discrete approximate solution by α-cuts level representation of fuzzy sets and interval analysis. Since the FWA method has an exponential complexity, thus several researches have focused on reducing this complexity. This paper also presents an enhanced fuzzy weighted average approach to achieve the objective of reducing the complexity. This proposed approach is through an improved initial solution for original FWA algorithm, and a two-phase concept by extending and applying both the algorithms of Chang et al. [4] and Guu [14]. Although the complexity of the proposed FWA algorithm is O(n) the same as Guu [14] which is the best level achieved to date. But from the experimental results appear that the proposed algorithm is more efficient, which only needs a few evaluated numbers and spend much less overall CPU time than Guu [14] and other FWA algorithms. In order to demonstrate the usefulness of this study, a practical example for unmanned aerial vehicle (UAV) selecting under military requirement has illustrated. Additionally, a computer-based interface, which helps the decision maker make decisions more efficiently, has been developed.