Earticle

This paper investigates the tracking control problem of high-speed trains, and a direct robust adaptive control strategy is designed to deal with the uncertainties and time-varying properties occurring in the train dynamics. The longitudinal dynamics of the train is a typical uncertain nonlinear presentation, whose parameters, especially the coefficients of the resistance cannot be acquired precisely and varying due to the complexity running conditions, and the impacts of these uncertainties are becoming even more serious as the speed increases. Although adaptive algorithm can improve a controller performance with parameterized uncertainties where the plant parameters are unknown but constant for all time, the stability properties can no longer be guaranteed when the plant parameters are varying with time even the speed of the variations are sufficiently small. In this paper, a traditional adaptive controller is designed based on the time-invariant assumption, and then, a kind of robust adaptive law is designed to improve the robustness when the plant parameters are varying slowly with time. Both the rigorous analysis and the simulation results are all verified the effectiveness of the proposed control scheme.

The ability to detect and assess information of blood flow in color Doppler imaging (CDI) has played one of the most important roles in a modern ultrasound imaging system. However, since CDI requires large amount of data and computations, it has been mainly implemented on custom-designed hardware. Recent trend of programmable approach offers the advantages of flexibility and quick implementation. Specifically, general-purpose GPUs have been emerged as excellent accelerators across a wide range of applications. For best exploiting outstanding computational power, high memory bandwidth and SIMD architecture of a GPU, this paper explores the design space of CDI on GPU architecture platform and presents a high performance implementation of CDI on the GPU platform using CUDA API. The performance analysis shows our GPU-based CDI can achieve a frame rate of 152 for 800 range samples and 200 scan lines with an ensemble size of 12. Speedup of 19.8x can be obtained when compared with that on a CPU platform.

White is the color the human eye realizes when it looks at light containing all the wavelengths of the visible spectrum. In this paper, we propose an LED system to realize the white light by combining multiple LEDs used in emitting spectra of multiple colors to improve color renderings. When a light-emitting diode is switched on, electrons recombine with holes within the device, releasing energy in the form of photons. This effect is called electroluminescence and the color of the light (corresponding to the energy of the photon) is determined by the energy band gap of the semiconductor. The color of an LED is determined by the semiconductor material. The proposed system is composed of voltage generator, LED emitter, and LED luminaire. The sensor for detecting the color is well adopted in order to detect the light colors received from the LED emitter, with steady-current controller designed using operational amplifier and transistor. We took advantage of steady-current through the LED emitter in order that several forms of light composed of different wavelengths or frequencies could be adopted producing white color of all frequencies.

Determining the temperature of the cutting tool is important due to its influence on the quality and cost of the production process. However, the process of modeling the temperature of the tool is a complex process; this due to the nonlinear relationship between the system variables. In this paper, a symbolic regression approach via genetic programming (GP) is used to model a cutting machine temperature and compared to other approaches which based on estimating the parameters of the nonlinear regressive curve of the cutting tool. The developed GP model shows an promising results compared to models developed based on parameter estimation such as Least Squares regression (LS) , Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).

Mobile Wireless Sensor Networks (MWSNs) is one of the rising technologies for numerous areas of application, such as environmental monitoring, industrial sensing and diagnostics and battlefield awareness. Such wireless sensor nodes are often deployed in remote locations. Thus, energy efficient and reliable data forwarding is important because each node has limited resources. In this paper we propose a position-based energy-efficient greedy forwarding scheme to reduce the energy consumption while increasing the packet delivery ratio for Mobile Wireless Sensor Networks (MWSNs).In our paper, each node decides whether or not to be a relay node by itself considering the remaining energy and mobility. Simulation results show that the energy consumption and the average end-to-end delay can be reduced by selecting relay node considering the energy and mobility.

With the development of computer science, the numerical simulation of oil reservoirs has been advanced and widely applied. In this paper, according to the surfactant flooding mechanism, a mathematical model for three dimension two-phase three-component surfactant flooding is established. The numerical difference equation is solved using implicit method for pressure and concentration terms and explicit method for saturation terms. A new method is used to treat the relation between the adsorption of the surfactant and the relative permeability curve, which reduces the discrepancy between the mathematical model and the data from actual production. The model is programmed into software using FOR90 language. The software is used to study the surfactant displacement in Chao 61 district of Daqing oilfield. An optimization is done for the injection plan of the surfactant. The oilfield experiment shows that the optimal scheme reduces the threshold pressure of low permeability reservoirs and increases the water absorption rate. The reservoir production ratio increases as well.

This paper examines the use of geographical tracking system as a method of detecting juvenile behaviors, while providing an extension to existing a mobile service which has about 30000 users in Korea. The degree of juvenile behaviors has gotten more severe recently with the invention and proliferation of smart phones; they are easily exposed to harmful contents using smart phones. However, we could solve this problem by detecting unusual behaviors, because people exhibit certain behaviors when using devices and contents. In Mobile, since social media is one of the most popular services, we chose it as the research target. In addition, we propose both data visualization and geo-social tracking system for user profiling to examine the underlying meanings of individuals’ activities at the social media with spatiotemporal information. With social media becoming more ‘geographical’, many social networks, including Facebook, provide location based services to assist users’ daily lives. In experimental results, we show our visualization methods and weight functions for plotting on the map. Moreover, at the end of this work, we propose how our work could be integrated as an add-on to existing services. Lastly, we discuss our data visualization methods and the constraints we encountered during information gathering. Experimental results in this research are going to contribute to other researches attempting to profile individuals on mobile phones.

To analyze responses under open fault conditions in switching devices, a performance investigation and condition monitoring scheme are presented through an integrated simulation study of a permanent magnet synchronous generator (PMSG) based variable-speed grid-connected wind power system. Among various faults in power electronics components, the open fault in switching devices may arise due to the destruction of switches by an accidental over current or the forced disconnection for protection. When the switch open fault occurs in the generator-side converter, the grid-side inverter as well as the generator-side converter does not operate normally, resulting in an increase of the current harmonics and a reduction in generated output and efficiency. As an effective way of the condition monitoring for the generation system by online basis without requiring any diagnostic apparatus, the estimation schemes for the generated voltage, flux linkage, and stator resistance are proposed in this paper and the validity of the proposed scheme is proved through comparative simulations.

Mobile communication systems have been adopting link adaptive transmission schemes such as adaptive modulation and coding (AMC) and hybrid-ARQ (HARQ). Several wireless mobile communication systems have considered highly efficient incremental redundancy (IR) hybrid ARQ scheme. However IR-HARQ with LDPC codes has not been specified yet. In this paper, we propose an IR-HARQ scheme with rate compatible LDPC codes which adopts the notion of k-SR node and the propagation rate of soft output. Based on the design rule of rate compatible LDPC codes, we define the transmission priority of code bits and propose the sub-packet construction rule. We present the throughput performance of IR-HARQ with various modulation and coding and multi-antenna modes. Consequently, the proposed scheme provides the improvement of system throughput by elaborate link adaptation with CQI information.