Most Popular Books by Tao Lin

Editor Biography: Tao Lin received the B.S.E.E., M.E., and PhD degrees from Huazhong University of Science and Technology, Wuhan, China, in 1991, 1994, and 1997, respectively. He joined Central China Power Group Co., Wuhan, China, as an R and D Engineer in 1997. From May 2000 to July 2005, he was with Nagasaki University, Japan, University of Bath, U.K., and University of Florida, USA. From 2005, he has been a full professor at the School of Electrical Engineering, Wuhan University, Wuhan, 430072, China. His research fields are power system operation and control, power system relaying, power quality, distributed generation and microgrid. Book Description: This book systematically discusses (a) Distributed Generation (DG), which operates in a single, stand-alone controllable system mode, and (b) the Microgrid (MG) powered by DG, along with the technical concepts, the impact on power systems, control and optimization techniques, and their applications. It includes ten chapters that focus on the following five aspects: 1) An overview of distributed generation is introduced in Chapter One, and the technical concept of the microgrid is introduced in Chapter Eight with detail. 2) As the main element of distributed generation (DG), a smart inverter system for the control of active and reactive power in a grid-tied mode, which is treated as an interface between grid and the RES (Renewable Energy System), is studied concretely in Chapter Two. 3) The influence of distributed generation on power systems, including the impact of DG on the planning and operation of power systems, the impact of DG on power quality, and power system protection are concretely described and analyzed in Chapters Three, Four and Five, respectively. 4) The control and optimization technologies for DG and MG. These techniques include: the Economic Model Predictive Control (EMPC) strategy for the solution of pricing management in community-based microgrids (MGs), which consider economic benefits as the control and optimization objects; the distributed control and optimization techniques for islanded microgrids (MGs) that consider stability as the control and optimization objects; the intelligent load shedding for stability enhancement in an autonomous microgrid; and the recovery (restoration) control after a contingency situation. These are all investigated in Chapters Six, Seven, Eight and Nine, respectively. 5) The applications of renewable energy technology, such as efficient artisanal light fishing technologies that exploit lake light physics and light-fish interactions, are specifically presented in Chapter Ten. Target Audience: This book can be used as a teaching reference for professors and researchers, or as a reference book for university seniors and graduate students to carry out their research work. It can also be used to guide engineering applications, for example, and as a reference book for engineers and on-site personnel in electrical power systems.

Chemicals, Vol. 1 is a collection of writingsby authors under the influenceof a mind altering substance.All proceeds will be donatedto the Red Cross helping in Japan.

Abstract: "Recently a probability interpretation of moments was proposed as a compromise between the Elmore delay and higher order moment matching for RC timing estimation [5]. By modeling RC impulses as time-shifted incomplete gamma distribution functions, the delays could be obtained via table lookup using a gamma integral table and the first three moments of the impulse response. However, while this approximation works well for many examples, it struggles with responses when the metal resistance becomes dominant, and produces results with impractical time shift values. In this paper the probability interpretation is extended to the circuit homogeneous response, without requiring the time shift parameter. The gamma distribution is used to characterize the normalized homogeneous portion of the step response. For a generalized RC interconnect model (RC tree or mesh), the stability of the homogeneous-gamma distribution model is guaranteed. It is demonstrated that when a table model is carefully constructed, the h-gamma approximation provides for excellent improvement over the Elmore delay in terms of accuracy, with very little additional cost in terms of CPU time."

Objectives: Identifying patients who may benefit from therapy or not in early psychotherapy is important to improve treatment outcome. This study aimed to investigate the trajectories of symptom change and working alliance in early psychotherapy and examine their predictions of treatment outcome. Methods: Growth mixture model (GMM) was performed to examine trajectories of symptom change and working alliance in the first five sessions. The association of early symptom trajectories and alliance trajectories with treatment outcome were examined. Results: The present study identified two symptom trajectories, high symptom/steady change (63.2%) and early improving (36.8%), and four alliance development patterns: undeveloped alliance (40.1%), strengthening moderate alliance (31.6%), optimal alliance (17.3%), repaired alliance (11%) in early psychotherapy. The early symptom change trajectories and alliance development patterns both independently and interactively predicted treatment outcome. Optimal alliance generally led to the best outcome. The effect of repaired alliance on treatment outcome was moderated by symptom change trajectories: For high symptom/steady change subgroup, repaired alliance produced better treatment outcome, whereas for medium symptom/slow change subgroup, repaired alliance resulted in worse outcome. Conclusion: Patients showed heterogeneous responses regarding symptom reduction and alliance development in early psychotherapy. Combining early symptom trajectories and alliance trajectories simultaneously can facilitate routine outcome monitoring and contribute to the prediction of treatment outcome.

The AMR effect, evidence for a proximity induced magnetic moment in Pt/YIG bilayer, was previously found absent in experiments with a rotating magnetic field applied at room temperature. Instead, the observed magnetoresistance was attributed to a new type of effect called SMR that is caused by magnetization-dependent spin current reflection at the interface. In both Pd/YIG and Pt/YIG bilayers, however, we have observed that SMR and AMR coexist over a wide range of temperatures with AMR dominating at low temperatures. Detailed experimental results were shown of the temperature dependence and field dependence of the two types of magnetoresistance, to disentangle AMR and SMR quantitatively. While AMR is likely caused by the proximity induced magnetization in NM that saturates at relatively low fields, at higher magnetic fields when additional interfacial magnetic moments are gradually aligned, the spin current reflection effect and consequently SMR are greatly enhanced. The detailed analysis and discussions will be presented in Chapter IV.

The Bayesian network model forms the basis for probabilistic reasoning in this exposition. A Bayesian network consists of a directed acyclic graph, and a set of conditional probability tables. The directed acyclic graph encodes the conditional independencies of the domain variables. The product of the conditional probability tables defines a joint probability distribution representing the domain knowledge. In order to build a Bayesian network, one may construct the directed acyclic graph based on the causal relationships of the variables involved. However, in many applications it may be necessary to construct the directed acyclic graph from the conditional independency information supplied by the experts.

This paper offers a continuous time, general equilibrium model where a risky asset is traded among risk-averse overconfident investors. Two kinds of overconfidence are introduced: investors exhibit relative overconfidence if each investor believes her model is better than others'' and aggregate overconfidence if they believe signals have more information content than those in the true model. Relative overconfidence creates market price underreaction, while aggregate overconfidence generates overreaction. Excess trading volume is generated from relative overconfidence but not aggregate overconfidence. A high equity premium and excess price change volatility result from aggregate overconfidence in the early stages of investors'' extrapolation of information, but not in the steady state.

It has been widely debated whether producing ethanol from corn is sustainable in the long term. Environmentally, the major concern is that producing ethanol from corn involves intensive water and energy consumption. Economically, recent fluctuations in petroleum, ethanol, and corn prices have driven several large producers of ethanol into bankruptcy. The ethanol industry is vulnerable to periods of economic weakness because its product value varies with oil prices but its raw material (corn) varies with food prices. To improve the economic sustainability of corn-to-ethanol production, several modified dry grind processes had been developed at the lab scale. The Quick-germ / Quick-fiber (QQ) process is one of them. However, there has been no analysis of the QQ process that provides detailed information related to the energy, water, and economic performance at a commercial scale. To determine the both environmental and economic performance, a process simulation model was developed on the SuperPro Designer® platform to simulate the QQ process, and compared to the conventional dry grind model. Results indicate that germ and fiber recovery as done in the QQ process improves the process capacity of a conventional dry grind ethanol facility by approximately 24%. Because of germ and fiber recovery at the front end, the ethanol concentration has been increased to 15% (w/w) as compared to 10.9 (w/w) in the conventional dry grind process. The QQ process reduces the energy and water consumption by 32% and 17.8%, respectively. The QQ process produces more value-added coproducts, including corn germ, corn fiber, and a modified distillers dried grains with solubles (DDGS), but has a lower ethanol yield rate due to some starch losses to the recovered germ and fiber fractions at the front end. A detailed cost and benefit analysis of the QQ process, based on the market prices in April 2009, shows that despite its higher capital investment costs, the QQ process reduces the payback period to 6.5 years, compared to 9.2 years for the conventional dry grind process. Increased ethanol production, more value-added coproducts, as well as significant reduced utility costs are three major contributors to improve the economic performance of the QQ process. This work lays the foundation for the similar studies on the sustainability performance for other modified dry grind ethanol processes.

Iceberg system is a departmental database system. It is built for the graduate advisors of the CIS department. It stores the graduate student''s information, such as background, bridge requirement and transcript. The graduate advisors can process the student''s records using the graphic user interface of Iceberg system. Iceberg system is an example of the powerful Java language. We use the latest Java technologies to build a flexible system, which is easily extended. The system consists of Iceberg client, Iceberg server and Oracle data source. The Iceberg client is a web-based applet, which can be easily accessed using a browser. The Iceberg server runs on a fast UNIX machine, providing service to the Iceberg client through RMI. The most interesting feature of Iceberg system is the component architecture of the Iceberg client. The Iceberg client is consisted of visual components that have no knowledge of each other at compile time. They are assembled together at run time, following the instruction of a script file. Since the container component can hold any components the Iceberg system is readily extendable.

This dissertation presents theoretical and computational approaches for estimating sound-speed in the ocean under different conditions. The first part of the dissertation discusses a fast approach for solving the inverse problem of estimating sediment sound-speed based on the Deift-Trubowitz trace formula. Under certain assumptions, this algorithm can recover the sound speed profile in the seabed using pressure field measurements in the water column at low frequencies. The inversion algorithm requires solving a non-linear integral equation. In the past, Stickler and Zhou employed a first order Born approximation for solving the integral equation. This work introduces two new methods. The first is a modified Born approximation that improves upon a standard first order approximation, yet it is easy to implement; the second one is an approximation based on interpolating the integrand. It is shown that these methods work well with synthetic data in the numerical simulations. Results are compared to those of previously developed methods and demonstrate improvement especially at sharp changes in sound speed. Although the methods are stable and effective with noise-free data, problems arise when noise is considered. Regularization methods are developed to remedy this problem. Finally, we recognize that some assumptions necessary for this algorithm to work may not be realistic; several possibilities are presented to relax these limitations. In the second part, a method is developed for the estimation of source location and sound speed in the water column relying on linearization. The Jacobian matrix, necessary for the proposed linearization approach, includes derivatives with respect to Empirical Orthogonal Function coefficients instead of sound speed directly. First, the inversion technique is tested on synthetic arrival times, using Gaussian distributions for the errors in the considered arrival times. The approach is efficient, requiring a few iterations, and produces accurate results. Probability densities of the estimates are calculated for different levels of noise in the arrival times. Subsequently, particle filtering is employed for the estimation of arrival times from signals recorded during the Shallow Water 06 experiment. It has been shown in the past that particle filtering can be employed for the successful estimation of multipath arrival times from short-range data and, consequently, in geometry, bathymetry, and sound speed inversion. Here, probability density functions of arrival times computed via particle filtering are propagated backwards through the proposed inversion process. Inversion estimates are consistent with values reported in the literature for the same quantities. Lastly, it is shown that the results and estimates from fast simulated annealing applied to the same arrival times are very similar.