Self-optimizing Method Research Articles

The Research of Power Generating Self-Optimization Mode Based on the Objective Function Optimization

The self-optimization method for power generation proposed in this paper is an initial allocation of power energy. Every unit is according to the same principles of utilization hours in the same province. Under the premise of unchanged total power of each power generation unit and the security of the power system as well as the discharge standards, this method can achieve the minimum social resource consumption and the minimum discharge meet the demand of power energy at the same time by adjusting of autonomy optimization among internal different units. It is an important means and effective way to carry out energy generation scheduling work.

Improvement of Chaotic Signals De-Noising with the Self-Optimizing Method of Wavelet Threshold

For the purpose of improving adaptive performance of chaotic signals de-noising with wavelet transform, a method of Memetic-algorithm-based adaptive wavelet de-noising (MAWD) is presented. The MAWD based on generalized cross validation (GCV) is competent to obtain the global optimum thresholds and to raise the efficiency of adaptive searching computation. The de-noising results of simulative Lorenz time series are presented. The results show that the chaotic signals de-noised by MAWD can remove the white noise more effectively than the signals de-noised by using standard soft threshoding method (STM) and genetic-algorithm-based adaptive wavelet de-noising (GAWD), and the advantages are more apparent under the condition of lower SNR. The Lorenz time series with lower SNR de-noised by MAWD and GAWD respectively are predicted by Volterra adaptive filters, and the results show that the prediction absolute error of Lorenz time series de-noised by MAWD is nearly nine times smaller than that by GAWD. This method has a promising prospect in practical Chaotic signals de-noising.

Direct sampling of multiple single-molecular rupture dominant pathways involving a multistep transition

We report a novel single-molecular rupture mechanism revealed by direct sampling of the dominant pathway using a self-optimized path sampling method. Multiple dominant pathways involving multistep transitions are identified. The rupture may take place via a direct unfolding from the native state to the unfolding state, or through a two-step pathway bypassing a distinct intermediate metastable state (IMS). This scenario facilitates us to propose a three-state kinetic model, which can produce a nonlinear dependence of the rupture time on pulling forces similar to the ones reported in the literature. In particular, molecule conformations in the IMS maintain an elongation of the tail at one terminal, by which external pulling will enhance the relative stability of IMS. Consequently, even though the overall transition rate of the multistep pathway is relatively small, the molecule still has to be ruptured via the multistep pathway rather than the direct pathway. Thus, our work demonstrates an IMS trapping effect induced rupture mechanism involving an abnormal switching from a fast dominant pathway to a slow one.

Sequence characterization and computational analysis of the non-ribosomal peptide synthetases controlling biosynthesis of lipopeptides, fengycins and bacillomycin D, from Bacillus amyloliquefaciens Q-426

Lipopeptides secreted by bacteria attract interest because of their uses in biomedicine, biotechnology and food technology; however, harnessing their megasynthases (non-ribosomal peptide synthetases, NRPSs) has met with some difficulties in heterologous expression and crystallization. Here, we used similarity and phylogenetic analysis of NRPS sequences, including the fengycin and iturin family synthetases from Bacillus spp., and have developed a novel approach for delineating the length and boundaries of NRPS domains from Bacillus amyloliquefaciens strain Q-426. The sequences were further characterized (including specific residues and conserved motifs) that gave insight into the basis of the substrate specificity. Data from the prediction of the NRPS domains, obtained by the self-optimized prediction method with Alignment program, showed they are all structurally unstable, making it difficult to determine their crystal structures.

Wavelet Based De-Noising Using Self-Optimizing Method for ECG Signal

This paper presents a new ECG demising algorithm based on the self-optimizing method. This paper discusses the optimum threshold concept and the optimum threshold is decided by signal and threshold function. Based on the concept, this paper provides the ECG optimal method concrete steps. Through the dead value at high frequency phenomenon that is observed by experiment can identify the terminal value. Experiments show that the proposed method improves the signal-to-noise ratios. Moreover, the de-noising signals have a smooth and visual appearance.

ER<i>β</i> interacting protein ERAP 140 shows age dependent alteration in its interaction and expression in male mouse brain

Estrogen mediates multiple functions in the brain through the interaction of estrogen receptor (ER)α and ERβ with a host of nuclear proteins that regulate specific gene transcription. We have identified ERAP 140, AIB 1, Trk A, Src, pCREB and CREB as ERβ interacting proteins in the mouse brain. Earlier we showed that the interaction of ERβ with ERAP 140 decreased whereas its expression increased with aging in the brain of female mice. Here we report that the pattern of interaction and expression is different in male mice as compared to females. The interaction of ERAP 140 with ERβ decreased in adult male mouse brain as compared to young and remained almost similar in old whereas its expression was higher in adult than young and old, which were almost similar. Further in silico secondary structure analysis by self-optimized prediction method alignment (SOPMA) and PSIPRED revealed that ERβ interacting proteins were rich in alpha helices and coils. Such findings might help to design ER modulators which can regulate specific functions of estrogen in the brain during aging and degenerative diseases.

Parameter estimation of a rate-dependent damage constitutive model for damage-tolerant brittle composites by Self-OPTIM analyses

This article demonstrates a novel parameter identification of a rate-dependent damage constitutive model using self-optimizing inverse method. In the self-optimizing inverse method, an implicit–explicit objective function is formulated as a function of two sets of full-field stresses/strains (implicit non-measurable variables) from two nonlinear finite element analyses, that is, force-driven and displacement-driven simulations, respectively, and global boundary displacements and forces (explicit measurable variables) from experimental tests. The self-optimizing inverse method can self-correct the damage parameter set through optimization procedures referring to global responses measured in laboratory tests. A micromechanics and fracture mechanics based damage constitutive law that accounts for the microcrack nucleation and growth is adopted. Synthetic data from impact tension test simulations were used to demonstrate successful performances of the self-optimizing inverse method in identifying the nonlinear constitutive and damage-related parameters. Comparative studies were conducted using two different optimization techniques – the simplex method and the steady-state genetic algorithm. The identified parameters proved to be identical to the reference values. Finally, in order to further verify the inverse identification method, self-optimizing inverse method analyses were conducted to identify the damage parameter set based on real experimental data from impact tension tests at different strain rates.

Parametric investigation of load-induced structure remodeling in the proximal femur

The process of adaptive bone remodeling can be simulated with a self-optimizing finite element method. The basic remodeling rules attempt to obtain a constant value for the strain energy per unit bone mass, by adapting density. The precise solution is dependent on the loads, initial conditions, and the parameters of the remodeling rule. While there are several investigations on developing algorithms leading to the bone density distribution in the proximal femur, these algorithms often require a large number of iterations. The aim of this study was to develop a more efficient adaptive bone remodeling algorithm, and to identify how the bone density distribution of the proximal femur was affected by parameters that govern the remodeling process. The forces at different phases of the gait cycle were applied as boundary conditions. The bone density distributions from these forces were averaged to estimate the density distribution in the proximal femur. The effect of varying the initial bone density, spatial influence function, non-linear order of the adaptive algorithm, and the influence range on the converged solution were investigated. The proposed procedure was shown to converge in a fewer number of iterations and requiring less computational time, while still generating a realistic bone density distribution. It was also shown that varying the identified parameters within reasonable upper and lower bounds had very little impact on the qualitative form of the converged solution. In contrast, the convergence rate was affected to a greater degree by variation of these parameters. In all cases, the solutions obtained are comparable with the actual density in the proximal femur, as measured by Dual-energy X-ray absorptiometry (DEXA) scans.

Kernel Self-optimized Locality Preserving Discriminant Analysis for feature extraction and recognition

We propose Kernel Self-optimized Locality Preserving Discriminant Analysis (KSLPDA) for feature extraction and recognition. The procedure of KSLPDA is divided into two stages, i.e., one is to solve the optimal expansion of the data-dependent kernel with the proposed kernel self-optimization method, and the second is to seek the optimal projection matrix for dimensionality reduction. Since the optimal parameters of data-dependent kernel are achieved automatically through solving the constraint optimization equation, based on maximum margin criterion and Fisher criterion in the empirical feature space, KSLPDA works well on feature extraction for classification. The comparative experiments show that KSLPDA outperforms PCA, LDA, LPP, supervised LPP and kernel supervised LPP.

Autonomous neighbor relation detection and handover optimization in LTE

Handovers require serving cells to have knowledge about the existence of neighboring cells. This information is stored in neighbor relation tables (NRTs), which can be automatically generated during a procedure called automatic neighbor relation (ANR) by leveraging measurements carried out by terminals. We show, using simulations, how fast the NRT converges with respect to the number of terminals and their speeds, as well as how incomplete NRTs influence the handover drop rate. By means of a field trial, we demonstrate that ANR works successfully in a real-life environment. In addition, we investigate solutions for problematic handover scenarios and self-optimization of relevant handover parameters based on decentralized approaches. We show that some handover parameters have more impact on network performance than others. Two promising self-optimization methods are discussed, based on either a cost function or a genetic programming technique which searches for a suitable algorithm that determines the parameters.

Self-Optimization of femtocell coverage to minimize the increase in core network mobility signalling

Femtocells are low-cost, low-power cellular base stations that are deployed by the end user to supplement macrocellular coverage and provide high data rates in the customer's premises. In femtocell deployments, leakage of the pilot signal to the outside of a house can result in a highly increased signalling load to the core network, as a result of the higher number of mobility events caused by passing users. In this paper, the impact of these mobility events on the core network signalling load is investigated for urban areas. It is shown that without further optimization, the resulting increase in signalling is unacceptably high and as a result prevents the large-scale deployment of femtocells. Different self-optimization methods for residential femtocell coverage are investigated that can significantly reduce the resulting mobility events. First, mobility event based pilot power self-optimization is proposed for femtocells with a single antenna that matches the coverage to the size of the house and thereby can provide both good indoor coverage and a reduced level of mobility events. It is shown that the proposed coverage optimization can significantly outperform simpler methods that aim to achieve a constant cell radius. Then, this concept is further refined by using a low-cost switched pattern multi-element antenna solution. Antenna gain pattern measurements of a prototype with two patch and two inverted F antennas are presented and a corresponding feeder network is discussed. Self-optimization methods are proposed that jointly select an appropriate antenna pattern and optimize the pilot power. Finally, a second multi-element antenna solution is investigated where lobes in different directions, generated by several patch antennas, can be individually attenuated to shape the coverage. Both multi-element antenna solutions allow for a better match of femtocell coverage to the shape of each individual house, and result in a further improvement of both indoor coverage and core network signalling resulting from mobility events.

Self-optimization and auto-stabilization of receiver in DPSK transmission system

We propose a self-optimization and auto-stabilization method for a 1-bit DMZI in DPSK transmission. Using the characteristics of eye patterns, the optical frequency transmittance of a 1-bit DMZI is thermally controlled to maximize the power difference between the constructive and destructive output ports. Unlike other techniques, this control method can be realized without additional components, making it simple and cost effective. Experimental results show that error-free performance is maintained when the carrier optical frequency variation is approximately 10% of the data rate.

VISION BASED MOTION CONTROL AND TRAJECTORY TRACKING FOR MICROASSEMBLY ROBOTS

This paper presents a vision based motion control and trajectory tracking strategies for microassembly robots including a self-optimizing visual servoing depth motion control method and a novel trajectory snake tracking strategy. To measure micromanipulator depth motion, a normalized gray-variance focus measure operator is developed using depth from focus techniques. The extracted defocus features are theoretically distributed with one peak point which can be applied to locate the microscopic focal depth via self-optimizing control. Tracking differentiators are developed to suppress noises and track the features and their differential values without oscillation. Based on the differential defocus signals a coarse-to-fine self-optimizing controller is presented for micromanipulator to precisely locate focus depth. As well as a novel trajectory snake energy function of robotic motion is defined involving kinematic energy, curve potential and image potential energy. The motion trajectory can be located through searching the converged energy distribution of the snake function. Energy weights in the function are real-time adjusted to avoid local minima during convergence. To improve snake searching efficiency, quadratic-trajectory least square estimator is employed to predict manipulator motion position before tracking. Experimental results in a microassembly robotic system demonstrate that the proposed strategies are successful and effective.

IP-based Clustering for Peer-to-Peer Overlays

The efficiency of overlay networks built on top of the IP network is often threatened by the mismatch between the topologies of the overlay and the underlying IP network, resulting in unnecessary traffic and increased latencies. Substantial improvement can be achieved by optimizing the logical links between overlay nodes to better match the IP network topology. In this paper, we propose a new method for selfoptimization of a DHT-based peer-to-peer overlay. Our method has no need for active measurement of inter-node latencies, thus minimizing network traffic costs of node insertion and topology maintenance. We verify our method by means of analysis of large data sets of latency measurements between arbitrary nodes on the Internet, proving correlation among common IP prefix length of communicating nodes and latency.

Self-optimizing window flow control in high-speed data networks

The critical parameter in a window flow control scheme is the window size, which represents the maximum number of packets that can be in transit at a time. In this paper, we consider the problem of selecting the optimum window size in high-speed data networks. A self-optimizing method is proposed to adapt the window size to network conditions. The scheme employs a cross-correlation technique for process identification where the perturbation signal is random. Simulation results are presented for the isarithmic flow control to show the convergence rate and stability characteristics of the self-optimizing scheme.