Capacity planning with ant colony optimization for TFT-LCD array manufacturing

Array manufacturing in thin film transistor-liquid crystal display (TFT-LCD) production network is characterized as a capital-intensive and capacity-constrained production system with re-entrance and batch operations. Effectively using associated machines through optimal capacity planning and order scheduling decisions is a critical issue for array manufacturing. This study develops a capacity planning system (CPS) for TFT-LCD array manufacturing. CPS uses information including master production schedule, order due date, process routing, processing time, and number of machines. In addition, CPS derives the order release time, estimated machine start and finish time, machine allocation, and order completion time to maximize machine workload, improve lateness, and eliminate setup time. This research also develops ant colony optimization (ACO) to seek the optimal order release schedule to maximize a combination of the above objectives. The preliminary experiments are first applied to identify the optimal tuning parameters of the ACO algorithm. Computational experiments are then conducted to evaluate the significance and the robustness of the proposed algorithm compared with other competitive algorithms by full factorial experimental design.     

A comparison of text weighting schemes on sentiment analysis of government policies: a case study of replacement of national examinations

Multimedia Tools and Applications - The National Examination (UN) is a system of evaluation of education standards for elementary and secondary schools conducted nationally and is also used to...     

Composite learning adaptive backstepping control using neural networks with compact supports

The ability to learn is crucial for neural network (NN) control as it is able to enhance the overall stability and robustness of control systems. In this study, a composite learning control strategy is proposed for a class of strict‐feedback nonlinear systems with mismatched uncertainties, where raised‐cosine radial basis function NNs with compact supports are applied to approximate system uncertainties. Both online historical data and instantaneous data are utilized to update NN weights. Practical exponential stability of the closed‐loop system is established under a weak excitation condition termed interval excitation. The proposed approach ensures fast parameter convergence, implying an exact estimation of plant uncertainties, without the trajectory of NN inputs being recurrent and the time derivation of plant states. The raised‐cosine radial basis function NNs applied not only reduces computational cost but also facilitates the exact determination of a subregressor activated along any trajectory of NN inputs so that the interval excitation condition is verifiable. Numerical results have verified validity and superiority of the proposed approach.     

Identification of coal seams suitability for carbon dioxide sequestration with enhanced coalbed methane recovery: a case study in South Sumatera Basin,Indonesia

Clean Technologies and Environmental Policy - Sequestering carbon dioxide (CO2) with enhanced coalbed methane recovery (ECBM) is a promising clean coal technology in Indonesia, which can reduce CO2...     

Path tracking controller design of four wheel independent steering automatic guided vehicle

This paper presents a new type of four wheel independent steering automatic guided vehicle (4WIS-AGV) for carrying heavy baggage and proposes a controller for the 4WIS-AGV to track reference trajectories. To do this task, the followings are done. Firstly, a 4WIS-AGV is designed and manufactured for experimental purpose. Secondly, a kinematic modeling for the 4WIS-AGV is introduced based on a single track vehicle model. Thirdly, based on the modeling, a controller is designed based on Backstepping method for the 4WIS-AGV to track reference trajectories. Fourthly, a control system is developed using industrial PC and AVR ATmega128 microcontrollers to implement the designed controller. Finally, simulations and experiments are conducted to verify the effectiveness and performances of the proposed controller in tracking two types of reference trajectories: a trajectory with sharp edges for parallel steering maneuver and a circular trajectory for zero-sideslip maneuver. The results show that the proposed controller can make the 4WIS-AGV track the trajectory with sharp edges and the circular trajectory very well.     

Positioning and obstacle avoidance of automatic guided vehicle in partially known environment

This paper presents positioning and obstacle avoidance of Automatic Guidance Vehicle (AGV) in partially known environment. To do this task, the followings are done. Firstly, the system configuration of AGV is described. Secondly, mathematical kinematic modeling of the AGV is presented to understand its characteristics and behavior. Thirdly, the Simultaneous Localization and Mapping (SLAM) algorithm based on the laser measurement system and encoders is proposed. The encoders are used for detecting the motion state of the AGV. In a slippery environment and a high speed AGV condition, encoder positioning method generates big error. Therefore, Extended Kalman Filter (EKF) is used to get the best position estimation of AGV by combining the encoder positioning result and landmark positions obtained from the laser scanner. Fourthly, to achieve the desired coordinate, D* Lite algorithm is used to generate a path from the start point to the goal point for AGV and to avoid unknown obstacles using information obtained from laser scanner. A backstepping controller based on Lyapunov stability is proposed for tracking the desired path generated by D* Lite algorithm. Finally, the effectiveness of the proposed algorithms and controller are verified by using experiment. The experimental results show that the AGV successfully reaches the goal point with an acceptable small error.     

Long-term knowledge acquisition using contextual information in a memory-inspired robot architecture

In this paper, we present a novel cognitive framework allowing a robot to form memories of relevant traits of its perceptions and to recall them when necessary. The framework is based on two main principles: on the one hand, we propose an architecture inspired by current knowledge in human memory organisation; on the other hand, we integrate such an architecture with the notion of context, which is used to modulate the knowledge acquisition process when consolidating memories and forming new ones, as well as with the notion of familiarity, which is employed to retrieve proper memories given relevant cues. Although much research has been carried out, which exploits Machine Learning approaches to provide robots with internal models of their environment (including objects and occurring events therein), we argue that such approaches may not be the right direction to follow if a long-term, continuous knowledge acquisition is to be achieved. As a case study scenario, we focus on both robot–environment and human–robot interaction processes. In case of robot–environment interaction, a robot performs pick and place movements using the objects in the workspace, at the same time observing their displacement on a table in front of it, and progressively forms memories defined as relevant cues (e.g. colour, shape or relative position) in a context-aware fashion. As far as human–robot interaction is concerned, the robot can recall specific snapshots representing past events using both sensory information and contextual cues upon request by humans.