Some advances in study of crack identification problems

In the present paper, the crack identification problems are investigated. This kind of problems belong to the scope of inverse problems and are usually ill-posed on their solutions. The paper includes two parts: (1) Based on the dynamic BIEM and the optimization method and using the measured dynamic information on outer boundary, the identification of crack in a finite domain is investigated and a method for choosing the high sensitive frequency region is proposed successfully to improve the precision. (2) Based on 3-D static BIEM and hypersingular integral equation theory, the penny crack identification in a finite body is reduced to an optimization problem. The investigation gives us some initial understanding on the 3-D inverse problems.

A BIEM optimization method for fracture dynamics inverse problem

In the present paper, based on the theory of dynamic boundary integral equation, an optimization method for crack identification is set up in the Laplace frequency space, where the direct problem is solved by the author's new type boundary integral equations and a method for choosing the high sensitive frequency region is proposed. The results show that the method proposed is successful in using the information of boundary elastic wave and overcoming the ill-posed difficulties on solution, and helpful to improve the identification precision.

Generalized modal identification of linear and nonlinear dynamic systems

This dissertation is concerned with the problem of determining the dynamic characteristics of complicated engineering systems and structures from the measurements made during dynamic tests or natural excitations. Particular attention is given to the identification and modeling of the behavior of structural dynamic systems in the nonlinear hysteretic response regime. Once a model for the system has been identified, it is intended to use this model to assess the condition of the system and to predict the response to future excitations.

A new identification methodology based upon a generalization of the method of modal identification for multi-degree-of-freedom dynaimcal systems subjected to base motion is developed. The situation considered herein is that in which only the base input and the response of a small number of degrees-of-freedom of the system are measured. In this method, called the generalized modal identification method, the response is separated into "modes" which are analogous to those of a linear system. Both parametric and nonparametric models can be employed to extract the unknown nature, hysteretic or nonhysteretic, of the generalized restoring force for each mode.

In this study, a simple four-term nonparametric model is used first to provide a nonhysteretic estimate of the nonlinear stiffness and energy dissipation behavior. To extract the hysteretic nature of nonlinear systems, a two-parameter distributed element model is then employed. This model exploits the results of the nonparametric identification as an initial estimate for the model parameters. This approach greatly improves the convergence of the subsequent optimization process.

The capability of the new method is verified using simulated response data from a three-degree-of-freedom system. The new method is also applied to the analysis of response data obtained from the U.S.-Japan cooperative pseudo-dynamic test of a full-scale six-story steel-frame structure.

The new system identification method described has been found to be both accurate and computationally efficient. It is believed that it will provide a useful tool for the analysis of structural response data.

Distributed Optimal Control of Cyber-Physical Systems: Controller Synthesis, Architecture Design and System Identification

The centralized paradigm of a single controller and a single plant upon which modern control theory is built is no longer applicable to modern cyber-physical systems of interest, such as the power-grid, software defined networks or automated highways systems, as these are all large-scale and spatially distributed. Both the scale and the distributed nature of these systems has motivated the decentralization of control schemes into local sub-controllers that measure, exchange and act on locally available subsets of the globally available system information. This decentralization of control logic leads to different decision makers acting on asymmetric information sets, introduces the need for coordination between them, and perhaps not surprisingly makes the resulting optimal control problem much harder to solve. In fact, shortly after such questions were posed, it was realized that seemingly simple decentralized optimal control problems are computationally intractable to solve, with the Wistenhausen counterexample being a famous instance of this phenomenon. Spurred on by this perhaps discouraging result, a concerted 40 year effort to identify tractable classes of distributed optimal control problems culminated in the notion of quadratic invariance, which loosely states that if sub-controllers can exchange information with each other at least as quickly as the effect of their control actions propagates through the plant, then the resulting distributed optimal control problem admits a convex formulation.

The identification of quadratic invariance as an appropriate means of "convexifying" distributed optimal control problems led to a renewed enthusiasm in the controller synthesis community, resulting in a rich set of results over the past decade. The contributions of this thesis can be seen as being a part of this broader family of results, with a particular focus on closing the gap between theory and practice by relaxing or removing assumptions made in the traditional distributed optimal control framework. Our contributions are to the foundational theory of distributed optimal control, and fall under three broad categories, namely controller synthesis, architecture design and system identification.

We begin by providing two novel controller synthesis algorithms. The first is a solution to the distributed H-infinity optimal control problem subject to delay constraints, and provides the only known exact characterization of delay-constrained distributed controllers satisfying an H-infinity norm bound. The second is an explicit dynamic programming solution to a two player LQR state-feedback problem with varying delays. Accommodating varying delays represents an important first step in combining distributed optimal control theory with the area of Networked Control Systems that considers lossy channels in the feedback loop. Our next set of results are concerned with controller architecture design. When designing controllers for large-scale systems, the architectural aspects of the controller such as the placement of actuators, sensors, and the communication links between them can no longer be taken as given -- indeed the task of designing this architecture is now as important as the design of the control laws themselves. To address this task, we formulate the Regularization for Design (RFD) framework, which is a unifying computationally tractable approach, based on the model matching framework and atomic norm regularization, for the simultaneous co-design of a structured optimal controller and the architecture needed to implement it. Our final result is a contribution to distributed system identification. Traditional system identification techniques such as subspace identification are not computationally scalable, and destroy rather than leverage any a priori information about the system's interconnection structure. We argue that in the context of system identification, an essential building block of any scalable algorithm is the ability to estimate local dynamics within a large interconnected system. To that end we propose a promising heuristic for identifying the dynamics of a subsystem that is still connected to a large system. We exploit the fact that the transfer function of the local dynamics is low-order, but full-rank, while the transfer function of the global dynamics is high-order, but low-rank, to formulate this separation task as a nuclear norm minimization problem. Finally, we conclude with a brief discussion of future research directions, with a particular emphasis on how to incorporate the results of this thesis, and those of optimal control theory in general, into a broader theory of dynamics, control and optimization in layered architectures.

On the identification of the early developmental stages of clupeiform fishes from Indian waters

Among about 65 species of clupeiform fishes present in the seas around India, most are distributed along both the east and west coasts and have similar spawning seasons and spawning grounds. The most difficult problem experienced with regard to the identification of their early developmental stages is the overlapping sets of diagnostic features among many species within the same genus as well as among species belonging to two or more genera. From studies carried out recently, the present paper sites a few instances wherein certain subtle characters have been used for distinguishing the early developmental stages of a few allied species.

Algorithm for identification in the v-gap metric

This paper suggests a method for identification in the v-gap metric. For a finite number of frequency response samples, a problem for identification in the v-gap metric is formulated and an approximate solution is described. It uses an iterative technique for obtaining an L2-gap approximation. Each stage of the iteration involves solving an LMI optimisation. Given a known stabilising controller and the L2-gap approximation, it is shown how to derive a v-gap approximation.

Microsatellite marker isolation and cultured strain identification in Carassius auratus gibelio

Microsatellites have become the preferred molecular markers for strain selection and genetic breeding in fish. In this study a total of 105 microsatellites were isolated and identified in gibel carp (Carassius auratus gibelio) by microsatellite sequence searches in GenBank and other databases and by screening and sequencing of positive clones from the genomic library enriched for AG and GATA repeats. Moreover, nineteen microsatellites were randomly selected to design locus-specific primer pairs, and these were successfully used to identify and discriminate different cultured strains of gibel carp including strains A, D, L, and F. Three different types of microsatellite pattern were distinguished by the number and length of fragments amplified from the 19 primer pairs, and some microsatellite primer pairs were found to produce different microsatellite patterns among strains and strain-specific fragments. In addition, some duplicated alleles were also detected in two microsatellite patterns. Therefore, the current study provides direct molecular markers to discriminate among different cultured strains for selective breeding and aquaculture practice of gibel carp.

Morphological and genetic differences of Trypanosoma in some Chinese freshwater fishes: difficulties of species identification

Blood smears and purified trypanosome from freshwater fishes yellow catfish (Pseudobagras fulvidraco) and common carp (Cyprinus carpio) captured from Niushan Lake, Hubei Province were examined to determine whether all of their trypanosomes were Trypanosoma pseudobagri, a species of supposed host specificity and widespread existence across China. Trypanosomes occurred in 16/16 blood smears, and morphometric character analysis of trypanosomes from these smears showed that there were three morphospecies, Trypanosoma sp Carpio, T. sp Pseudobagri, and T. sp. 18S rDNA sequences of trypanosomes from 16 samples revealed three genetic groups among these fish trypanosomes. Group 1 was from C. carpio containing T. sp Carpio; groups 2 and 3 were from P. fulvidraco containing T. sp Pseudobagri and T. sp, respectively. The high similarity of morphometric characters and 18S rDNA sequences showed that T. sp Carpio and T. siniperca probably were the same species. T. sp Pseudobagri was the first occurrence in China. Sequence comparison showed that T. sp Pseudobagri sequence was most similar to that of clone Marv, whereas T. sp sequence differ from those of T. sp Carpio and T. sp Pseudobagri by 5.4 and 5.8%, respectively, and tentatively identified as T. pseudobagri. It was concluded that three species of trypanosomes, at least three genotypes occur in Niushan Lake fishes, and P. fulvidraco in this region appear to contain both types, although the identification of T. pseudobagri remains a problem.

Anatomical identification of extracellularly recorded cells in large-scale multielectrode recordings.

This study combines for the first time two major approaches to understanding the function and structure of neural circuits: large-scale multielectrode recordings, and confocal imaging of labeled neurons. To achieve this end, we develop a novel approach to the central problem of anatomically identifying recorded cells, based on the electrical image: the spatiotemporal pattern of voltage deflections induced by spikes on a large-scale, high-density multielectrode array. Recordings were performed from identified ganglion cell types in the macaque retina. Anatomical images of cells in the same preparation were obtained using virally transfected fluorescent labeling or by immunolabeling after fixation. The electrical image was then used to locate recorded cell somas, axon initial segments, and axon trajectories, and these signatures were used to identify recorded cells. Comparison of anatomical and physiological measurements permitted visualization and physiological characterization of numerically dominant ganglion cell types with high efficiency in a single preparation.

Identification of pertussis-specific effector memory T cells in preschool children

Whooping cough remains a problem despite vaccination, and worldwide resurgence of pertussis is evident. Since cellular immunity plays a role in long-term protection against pertussis, we studied pertussis-specific T-cell responses. Around the time of the preschool acellular pertussis (aP) booster dose at 4 years of age, T-cell memory responses were compared in children who were primed during infancy with either a whole-cell pertussis (wP) or an aP vaccine. Peripheral blood mononuclear cells (PBMCs) were isolated and stimulated with pertussis vaccine antigens for 5 days. T cells were characterized by flow-based analysis of carboxyfluorescein succinimidyl ester (CFSE) dilution and CD4, CD3, CD45RA, CCR7, gamma interferon (IFN-γ), and tumor necrosis factor alpha (TNF-α) expression. Before the aP preschool booster vaccination, both the proliferated pertussis toxin (PT)-specific CD4⁺ and CD8⁺ T-cell fractions (CFSE^dim) were higher in aP-than in wP-primed children. Post-booster vaccination, more pertussis-specific CD4⁺ effector memory cells (CD45RA^- CCR7^-) were induced in aP-primed children than in those primed with wP. The booster vaccination did not appear to significantly affect the T-cell memory subsets and functionality in aP-primed or wP-primed children. Although the percentages of Th1 cytokine-producing cells were alike in aP- and wP-primed children pre-booster vaccination, aP-primed children produced more Th1 cytokines due to higher numbers of proliferated pertussis-specific effector memory cells. At present, infant vaccinations with four aP vaccines in the first year of life result in pertussis-specific CD4⁺ and CD8⁺ effector memory T-cell responses that persist in children until 4 years of age and are higher than those in wP-primed children. The booster at 4 years of age is therefore questionable; this may be postponed to 6 years of age.

A Fast Nonlinear Model Identification Method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

Child abuse as a complex and wicked problem: Reflecting on policy developments in the United Kingdom in working with children and families with multiple problems

In spite of significant public concern, professional efforts and financial expenditure, there has been a perceived lack of progress in reducing the incidence of child abuse, and in improving the outcomes for children in both the short and longer term. In this article the authors reflect on recent policy developments in the United Kingdom relating to children and families experiencing multiple adversities, and argue that the current conceptualisation of child abuse is flawed. In adopting a rational technical approach to the management of child abuse, there is a tendency to focus on shorter term outcomes for the child, such as immediate safety, that primarily reflect the outputs of the child protection system. However, by viewing child abuse as a wicked problem, that is complex and less amenable to being solved, then child welfare professionals can be supported to focus on achieving longer term outcomes for children that are more likely to meet their needs. The authors argue for an earlier identification of and intervention with children who are experiencing multiple adversity, such as those living with parents misusing substances and exposed to intimate partner violence.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

Convergence of the iterative algorithm for a general Hammerstein system identification

The convergence of the iterative identification algorithm for a general Hammerstein system has been an open problem for a long time. In this paper, it is shown that the convergence can be achieved by incorporating a regularization procedure on the nonlinearity in addition to a normalization step on the parameters.