Enhancing the Efficiencies of Organic Photovoltaic and Organic Light-Emitting Diode Devices by Regular Nano-Wrinkle Patterns

Organic photovoltaic (OPV) cells and organic light-emitting diodes (OLEDs) are energy harvesting and generation devices that have attracted great attention these years because of their low costs, thin film structures, flexibility and environment-friendly manufacturing processes. For such thin film devices, photon management methods that increase the light absorption of OPV cells and the light extraction from OLEDs are highly desirable. Here, we report the experimental efforts to fabricate geometrically tunable and spontaneously formed nano-wrinkle structures with large areas, and the theoretical results on photon management with the nano-wrinkles.     

The Analysis of Angle Resolution of Stress Vector Sensor Based on Optical Fiber Sensing Cable for High Speed Railway Traffic

With the development of high speed railway traffic, the structure health monitoring for high-speed rail is necessary due to the safety issue. Optical fiber sensing technology is one of the options to solve it. Stress vector information is the important index to make more reasonable judgments about railway safety. However, information sensed by lots of commercial optical sensors is scalar. According to the stress filed distribution of rail, this paper proposes a new type of stress vector sensor based on optical fiber sensing cable (OFSC) with a symmetrical seven optical fibers structure and analyzes the relations between angle resolution and distance between adjacent of optical fibers through finite-element software (ANSYS) simulation. Through reasonable distance configuration, the angle resolution of the OFSC can be improved, and thus stress vector information, including the stress magnitude and the angle of stress, can be more accurately obtained. The simulation results are helpful to configure OFSC for angle resolution improvement in actual practice, and increase the safety factor in high speed railway structure health monitoring.     

Decision Model for Market of Performing Arts with Factorization Machine

Performing arts and movies have become commercial products with high profit and great market potential. Previous research works have developed comprehensive models to forecast the demand for movies. However, they did not pay enough attention to the decision support for performing arts which is a special category unlike movies. For performing arts with high-dimensional categorical attributes and limit samples, determining ticket prices in different levels is still a challenge job faced by the producers and distributors. In terms of these difficulties, factorization machine (FM), which can handle huge sparse categorical attributes, is used in this work first. Adaptive stochastic gradient descent (ASGD) and Markov chain Monte Carlo (MCMC) are both explored to estimate the model parameters of FM. FM with ASGD (FM-ASGD) and FM with MCMC (FM-MCMC) both can achieve a better prediction accuracy, compared with a traditional algorithm. In addition, the multi-output model is proposed to determine the price in multiple price levels simultaneously, which avoids the trouble of the models’ repeating training. The results also confirm the prediction accuracy of the multi-output model, compared with those from the general single-output model.     

Research on Equipment Support Activity Process Simulation Based on Monte Carlo Method

The influencing factors of the equipment support activity process have the characteristics of nonlinearity, high dimension, many constraints, random uncertainty and fuzzy uncertainty. Monte Carlo method can solve the above problems commendably. This paper analyzes the main equipment support activity process and establishes the sampling plan and simulation model of the medium maintenance process based on Monte Carlo method, and the simulation result verifies a fact that the medium maintenance time can be effectively reduced when parallel operation on some procedures is used. It has a practical value and can give good advice to achieve the capability of equipment supportability.     

Damage Detection for Simply Supported Bridge with Bending Fuzzy Stiffness Consideration

The benchmark of a simply supported beam with damage and bending fuzzy stiffness consideration is established to be utilized for damage detection. The explicit expression describing the Rotational Angle Influence Lines (RAIL) of the arbitrary section in the benchmark is presented as the nonlinear relation between the moving load and the RAIL appeared, when the moving load is located on the damage area. The damage detection method is derived based on the Difference of the RAIL Curvature (DRAIL-C) prior to and following arbitrarily section damage in a simply supported beam with bending fuzzy stiffness consideration. The results demonstrate that the damage position can be located by the DRAIL-C graph and the damage extent can be calculated by the DRAIL-C curve peak. The simply supported box girder as a one-dimensional model and the simply supported truss bridge as a three-dimensional model with the bending fuzzy stiffness are simulated for the validity of the proposed method to be verified. The measuring point position and noise intensity effects are discussed in the simply supported box girder example. This paper provides a new consideration and technique for the damage detection of a simply supported bridge with bending fuzzy stiffness consideration.     

Research on Motion Estimation Algorithm of Star Point Based on Nonlinear Gaussian Fitting

The energy distribution model of motion blurred star point is analyzed. The distribution of the star point approximates to a two-dimensional (2D) Gaussian distribution under degeneration. Two multi-parameter nonlinear Gaussian fitting methods (GFMs) are proposed, and the relationship between fitting parameters and motion blur parameters is analyzed. Estimation of the parameters of motion blur by fitting parameters is calculated to realize the error compensation of the motion blur. The simulation results show the effectiveness and accuracy.     

An SNN Ontology Based Environment Monitoring Method for Intelligent Irrigation System

In order to realize high precision of environment parameters detection in irrigation applications, a sensor and sensor network (SSN) ontology based data fusion method is proposed. An SSN sub-ontology for soilstate monitoring is revised, which includes the sensing devices hierarchies and measurement properties selection according to the detection feature interests. As for sensor data processing, a tuning data method by data pool filtering and clustering is adopted, as well as a useful data fusion method for multi-sensor system. The testing results show that both the accuracy and efficiency of the proposed method are higher after related filtering and clustering process, which enables a thorough monitoring for intelligent irrigation systems and can be extended into environment monitoring and control applications.     

Discretization Algorithm Based on Particle Swarm Optimization and Its Application in Attributes Reduction for Fault Data

In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matrix-based reduction method depends on whether the numerical attributes can be properly discretized or not. So a discretization algorithm based on particle swarm optimization (PSO) is proposed. Moreover, hybrid weights are adopted in the process of particles evolution. Comparative calculations for certain equipment are completed to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has better performance than other popular algorithms such as class-attribute interdependence maximization (CAIM) discretization method and entropy-based discretization method.     

A New Fusion Chemical Reaction Optimization Algorithm Based on Random Molecules for Multi-Rotor UAV Path Planning in Transmission Line Inspection

A fusion chemical reaction optimization algorithm based on random molecules (RMCRO) is proposed to meet the special demand of power transmission line inspection. This new algorithm improves the shortcomings of chemical reaction algorithm by merging the idea of repellent-attractant rule and accelerates convergence by using difference algorithm. The molecules in this algorithm avoid obstacles and search optimal path of transmission line inspection by using sensors on multi-rotor unmanned aerial vehicle (UAV). The option of optimal path is based on potential energy of molecules and cost function without repeated parameter adjustment and complicated computation. By compared with an improved particle swarm optimization (IMPSO) in different circumstances of simulation, it can be concluded that the new algorithm presented not only can obtain more optimal path and avoid to trap in local minimum, but also can keep related sensors in a more stable status.     

Economic Dispatch of Islanded Microgrid Considering a Cooperative Strategy Between Diesel Generator and Battery Energy Storage System

For the impact of intermittent resources’ high penetration on the economic dispatch of islanded microgrid, a new economic dispatch method is presented to minimize the overall generating cost for islanded microgrid, considering a cooperative strategy between diesel generator (hereinafter referred to as DE) and battery energy storage system (BESS). The optimum economic operation range of DE and the optimal set-point between DE and BESS are presented in the cooperative dispatch strategy, in which BESS is used fully to enable DE in a lower cost and higher efficient way. The results are analyzed under various operation conditions and also prove the validity of the proposed method.