Generating locally optimal trajectories for an automatically driven car

The test-drive of an automobile along a given test-course can be modeled by formulating a suitable optimal control problem. However, if the length of the course is very long or if it has a very complicated structure, the numerical solution of the optimal control problem becomes very difficult. Therefore a moving horizon technique is employed, which splits the optimal control problem into a sequence of local optimal control problems that are combined by suitable continuity conditions. This approach yields a reference trajectory. A controller and differential GPS are integrated in a real-world car and allows a reference trajectory to be followed in real-time. A benefit of this approach is the very high accuracy obtained in reproducing the reference trajectory. Hence, it can be used for testing different setups of cars under the same conditions while excluding the comparatively large influence of a real-world driver. In this article, we will focus on a method for generating the reference trajectory and report our experiences with this algorithm. The method allows an locally optimal solution to be computed for various handling courses in a robust way.     

An adaptive radial basis algorithm (ARBF) for expensive black-box mixed-integer constrained global optimization

Response surface methods based on kriging and radial basis function (RBF) interpolation have been successfully applied to solve expensive, i.e. computationally costly, global black-box nonconvex optimization problems. In this paper we describe extensions of these methods to handle linear, nonlinear, and integer constraints. In particular, algorithms for standard RBF and the new adaptive RBF (ARBF) are described. Note, however, while the objective function may be expensive, we assume that any nonlinear constraints are either inexpensive or are incorporated into the objective function via penalty terms. Test results are presented on standard test problems, both nonconvex problems with linear and nonlinear constraints, and mixed-integer nonlinear problems (MINLP). Solvers in the TOMLAB Optimization Environment () have been compared, specifically the three deterministic derivative-free solvers rbfSolve, ARBFMIP and EGO with three derivative-based mixed-integer nonlinear solvers, OQNLP, MINLPBB and MISQP, as well as the GENO solver implementing a stochastic genetic algorithm. Results show that the deterministic derivative-free methods compare well with the derivative-based ones, but the stochastic genetic algorithm solver is several orders of magnitude too slow for practical use. When the objective function for the test problems is costly to evaluate, the performance of the ARBF algorithm proves to be superior.     

A new spanning tree-based genetic algorithm for the design of multi-stage supply chain networks with nonlinear transportation costs

The design of configuration and the transportation planning are crucial issues to the effectiveness of multi-stage supply chain networks. The decision makers are interested in the determination the optimal locations of the hubs and the optimal transportation routes to minimize the total costs incurred in the whole system. One may formulate this problem as a 0-1 mixed integer non-linear program though commercial packages are not able to efficiently solve this problem due to its complexity. This study proposes a new spanning tree-based Genetic Algorithm (GA) using determinant encoding for solving this problem. Also, we employ an efficient heuristic that fixes illegal spanning trees existing in the chromosomes obtained from the evolutionary process of the proposed GA. Our numerical experiments demonstrate that the proposed GA outperforms the other previously published GA in the solution quality and convergence rate.     

An entangling-probe attack on Shor’s algorithm for factorization

We investigate how to attack Shor’s quantum algorithm for factorization with an entangling probe. We show that an attacker can steal an exact solution of Shor’s algorithm outside an institute where the quantum computer is installed if he replaces its initialized quantum register with entangled qubits, namely the entangling probe. He can apply arbitrary local operations to his own probe. Moreover, we assume that there is an unauthorized person who helps the attacker to commit a crime inside the institute. He tells garbage data obtained from measurements of the quantum register to the attacker secretly behind a legitimate user’s back. If the attacker succeeds in cracking Shor’s algorithm, the legitimate user obtains a random answer and does not notice the attacker’s illegal acts. We discuss how to detect the attacker. Finally, we estimate a probability that the quantum algorithm inevitably makes an error, of which the attacker can take advantage.     

Integrating grey sequencing with the genetic algorithm–immune algorithm to optimise touch panel cover glass polishing process parameter design

The touch panel cover glass is one of the important parts and components that determine touch panel quality. The quality requirement of touch panel cover glass emphasises the stability of glass thickness. As this factor directly influences the induction effect and touch of the touch panel, the parameter conditions for the cover glass polishing process have significant impact. This study integrated grey sequencing with the Genetic algorithm–Immune algorithm to optimise the parameter design for the touch panel cover glass polishing process. The experimental measurement value was the thickness value of the processed glass, and the uniformity of glass thickness after processing was discussed. The optimum processing combination influencing the process conditions is as follows: the ambient temperature is 22 (°C), the processing pressure is 0.04 (Mpa), the processing time is 30 (min), the machine speed is 70 (rpm), the polishing solution concentration is 1.4 (g/cm³), the central particle size of polishing powder is 1.4 (um) and the process capability C_pk is 1.75, which is better than the process capability of C_pk 1.41 of the response surface methodology and the process capability of C_pk 1.37 of the Taguchi experimental design.     

Functionalised gold nanoparticles for selective induction of in vitro apoptosis among human cancer cell lines

The interaction of citrate- and polyethylene imine (PEI)-functionalised gold nanoparticles (GNP) with cancer cell lines with respect to the cellular response was studied. It was found that GNP/citrate nanoparticles were able to induce apoptosis in human carcinoma lung cell lines A549, but GNP/PEI did not show any reduction in the viability of the cells in human breast cancer cell line MCF-7 and A549 cell lines. FACS data confirmed that the number of apoptotic cells increased with increase in the concentration of GNP/citrate nanoparticles. Decline in cellular expansion and changes in the nuclear morphology were noted after the treatment of GNP/citrate nanoparticles on A549 cell lines, which itself is a direct response for stress induction. The induction of cellular apoptosis was further confirmed by DNA fragmentation assay. These data confirm the potential of GNP/citrate nanoparticle to evoke cell-specific death response in the A549 cell lines.     

Fabrication of Cryogenic Readout Circuits with n-type GaAs-JFETs for Low Temperature Detectors

We present the design and fabrication of cryogenic readout integrated circuits (ROICs) for Superconducting Tunneling Junctions using the SONY n-type GaAs-JFETs, which have good current-voltage characteristics and low noise performance even at <1 K. In order to fabricate the ROICs, we have designed simple GaAs-JFET amplifiers based on the measurement results of the GaAs-JFETs at <4.2 K: source follower amplifier, common source amplifier, and two types of cascode amplifiers. The obtainable gain of the cascode active load amplifiers is >100. These amplifiers were fabricated with the other circuit elements as an integrated circuit. We also show initial test results of the cascode active load amplifier.     

Reference Bayesian methods for recapture models with heterogeneity

In the context of capture–recapture experiments heterogeneous capture probabilities are often perceived as one of the most challenging features to be incorporated in statistical models. In this paper we propose within a Bayesian framework a new modeling strategy for inference on the unknown population size in the presence of heterogeneity of subject characteristics. Our approach is attractive in that parameters are easily interpretable. Moreover, no parametric distributional assumptions are imposed on the latent distribution of individual heterogeneous propensities to be captured. Bayesian inference based on marginal likelihood by-passes some common identifiability issues, and a formal default prior distribution can be derived. Alternative default prior choices are considered and compared. Performance of our formal default approach is favorably evaluated with two real data sets and with a small simulation study.     

An integrated neural network–simulation algorithm for performance optimisation of the bi-criteria two-stage assembly flow-shop scheduling problem with stochastic activities

This paper presents an integrated computer simulation and Artificial Neural Network (ANN) algorithm for a stochastic Two-Stage Assembly Flow-Shop Scheduling Problem (TSAFSP) with setup times under a weighted sum of makespan and mean completion time (MCT) criteria, known as bi-criteria. Significantly, it should be noted that there is no mathematical model to analyse the stochastic model, therefore simulation is used to solve the problem. The simulation model enables decision makers to consider the influence of job scheduling on machines in order to examine both criteria simultaneously. Since it is not possible to evaluate all sequence combinations using the simulation model in a reasonable time, multilayered neural network meta-models have been trained and used to estimate objective function values composed of both makespan and mean completion time criteria for the stochastic TSAFSP. To the best of the authors’ knowledge, this is the first study that considers stochastic machine breakdown, processing times, setup times, makespan and mean completion time as objectives concurrently. The TSAFSP is modelled by Visual SLAM simulation software. The simulation output results are then given to the ANN as inputs to build the meta-model. This meta-model is then used to obtain the results with the optimum values. The advantage of these meta-model applications is a reduction in the number of simulation runs and consequently a reduced run time. Also, this is the first study that introduces an intelligent and flexible algorithm for handling stochastic TSAFSP.     

An efficient and effective l0–l2 minimisation algorithm for compressive imaging

Compressive imaging has been intensively studied during the past few years, capable of reconstructing high-resolution images with sampling ratios far below the Nyquist rate. In contrast to previous works, a new l₀–l₂ minimisation approach is proposed for compressive imaging in this paper, regularised by sparsity constraints in three complementary frames. The new approach stems from the observation that images of practical interest may consist of different morphological components (e.g. point singularities, oscillating textures, curvilinear edges), and therefore, cannot be sparsely represented in one single frame. The alternating split Lagrangian method is further exploited to resolve the l₀–l₂ minimisation problem, leading to an efficient iteration scheme for compressive imaging from partial Fourier data. In addition, we analyse the convergence properties of the proposed algorithm and compare its performance against several recently proposed methods. Numerical simulations on natural and magnetic resonance images show that the proposed approach achieves state-of-the-art performance.     

Minimality and the Rauzy – Veech algorithm for interval exchange transformations with flips

Oriented interval exchange transformations (i.e.t.s) admitting infinite orbit of the Rauzy–Veech operator are minimal. In this paper, we analyse what happens for non-oriented i.e.t.s. In particular, we prove an analogous result for i.e.t.s with flips (Theorem A). We also show that the behaviour of i.e.t.s with flips is different to the one of oriented i.e.t.s, see Proposition 11.     

An integrated CPU–GPU heuristic inspired on variable neighbourhood search for the single vehicle routing problem with deliveries and selective pickups

Environmental issues have become increasingly important to industry and business in recent days. This trend forces the companies to take responsibility for product recovery, and proper recycling and disposal, moving towards the design of sustainable green supply chains. This paper addresses the backward stream in transportation of products, by means of reverse logistics applied to vehicle routing. This problem, called single vehicle routing problem with deliveries and selective pickups, consists in finding a route that starts from the depot and visits all delivery customers. Some pickup customers may also be visited, since the capacity of the truck is not exceeded, and there is also a revenue associated with each pickup. We develop an algorithm inspired on the variable neighbourhood search metaheuristic that explores the power of modern graphics processing unit (GPU) to provide routes in reasonable computational time. The proposed algorithm called four-neighbourhood variable neighbourhood search (FN-VNS) includes a novel high-quality initial solution generator, a CPU–GPU integrated perturbation strategy and four different neighbourhood searches implemented purely in GPU for the local search phase. Our experimental results show that FN-VNS is able to improve the quality of the solution for 51 instances out of 68 instances taken from the literature. Finally, we obtained speedups up to 14.49 times, varying from 17.42 up to 76.84 for each local search, measured over a set of new large-size instances.     

Parametric semi-blind deconvolution algorithm with Huber–Markov regularization for passive millimeter-wave images

Passive millimeter-wave (PMMW) images often suffer common problems of noise and blurring. A new method is proposed to estimate the instrument response function (IRF) and desired image simultaneously. The proposed variational model integrates the adaptive weight data term, image smooth term, and IRF smooth term. The major novelty of this work is that Huber–Markov regularization is adopted for PMMW image restoration, which can preserve structural details as well as suppress noise effectively. The IRF is parametrically formulated as a Gaussian-shaped function based on experimental measurements through the utilized PMMW imaging system. The alternation minimization iterative method is applied to achieve the IRF width and desired image. Comparative experimental results with some real PMMW images reveal that the proposed approach can effectively suppress noise, reduce ringing artifacts, and improve the spatial resolution.     

Optimal age replacement scheduling for a random work system with random lead time

System maintenance and spare parts are two closely related logistics activities since maintenance generates the demand for spare parts. Most studies on integrated models of preventive replacement and inventory of spare parts have focused on age replacement scheduling, while random replacement policy, which is sensible and necessary in practice, is rarely discussed and applied. The purpose of this paper is to present a generalised age replacement policy for a system which works at random time and considers random lead time for replacement delivery. To model an imperfect maintenance action, we consider that the system undergoes minimal repairs at minor failures and corrective replacements at catastrophic failures. Before catastrophic failures, the system is replaced preventively at age T or at the completion of a working time, whichever occurs first. The main objective is to determine an optimal schedule of age replacement that minimises the mean cost rate function of the system in a finite time horizon. The existence and uniqueness of optimal replacement policy are derived analytically and computed numerically. It can be seen that the proposed model is a generalisation of the previous works in maintenance theory.     

Development of Micro-SQUID Magnetometers for Investigation of Quantum Tunneling of Magnetization in Nanometer-Size Magnetic Materials

We report development of micro superconducting quantum interference device (μ-SQUID) magnetometers for investigation of quantum tunneling of magnetization in μm- and nm-size magnetic materials. Both high- and low-temperature superconductor (HTS and LTS) based μ-SQUID magnetometers were fabricated and a three dimensional magnetic coil system was constructed for this purpose. The HTS-μ-SQUIDs with a hole of 4×9 μm² work at temperatures between 4.2 and 70 K and in magnetic fields up to 120 mT. A magnetization measurement of a ferrimagnetic micro-crystal was carried out at 35 K with an accuracy of 10^?9 emu. The development of LTS-μ-SQUIDs has been started in order to study much smaller magnetic materials in a mK temperature range. We present a preliminary result on the LTS-μ-SQUID with a hole of 1×1 μm². The critical current as a function of applied magnetic field shows the SQUID modulation at 4.2 K and up to 30 mT. The heat release associated with the present measurement method is estimated to be on the order of several microwatts.     

Output feedback control with optimal gains for a one‐link flexible robot arm

Abstract

An output feedback control law is proposed to control a flexible robot arm with co‐located actuator and sensors. The design of the control law is based on a truncated model of the flexible robot arm. The feasible directions method, an optimization technique, is used to synthesize the feedback gains of proposed control law. The proposed control law is simple and easy to implement, it is robust to modal truncation and to parameter variations. Numerical simulation shows that the performance of the controlled system is satisfactory.     

Boundary Integral Equation for Eddy-Current Problems with Dirichlet Boundary Condition

A boundary integral equation (BIE) is developed for the eddy-current (EC) problems with Dirichlet boundary condition by considering the difference between the field without cracks and the one with cracks. Once the field and its normal derivative are given for the structure in the absence of cracks, normal derivative of the scattered field on the surface can be calculated by solving this integral equation numerically. For infinite-domain problems, this equation is more efficient than the conventional BIE due to a smaller computational region needed. Four kinds of two-dimensional EC problems have been solved using this integral equation. The surface impedance for different cases is presented in this paper. Numerical results are compared with analytical solutions and published numerical results. There are good agreements between them. Also, this concept can be extended to three-dimensional problems with other boundary conditions.     

An imprinted organic–inorganic hybrid sorbent for selective separation of copper ion from aqueous solution

A new organic–inorganic hybrid sorbent was prepared by a double-imprinting approach for the selective separation of Cu(II) from aqueous solution. In the prepared hierarchically imprinted sorbent, both Cu(II) and surfactant micelles (cetyltrimethylammonium bromide) were used as templates. The sorbent was prepared through self-hydrolysis, self-condensation, and co-condensation of the crosslinking agent (tetraethoxysilane) and the functional precursor (3-aminopropyltrimethoxysilane) in an alkaline media followed by gelation. The adsorption property and selective recognition ability of the sorbents were studied by equilibrium-adsorption method. Results showed that in the presence of Zn(II) the biggest selectivity coefficient of the imprinted sorbents for Cu(II) was over 500, which is much higher than those of non-imprinted sorbents. The largest relative selectivity coefficient (k′) of the ion-imprinted functionalized sorbent between Cu(II) and Zn(II) was over 500. The uptake capacities and the selectivity coefficients of the hierarchically imprinted sorbent were much higher than those of the sorbent prepared without CTAB template. Furthermore, the new imprinted sorbent possessed a fast kinetics for the removal of Cu(II) from aqueous solution with the saturation time less than 10 min, and could be used repeatedly. This sorbent has been successfully applied to the separation and determination of the trace Cu(II) in real water samples and those spiked with standards. This new sorbent can be used as an effective solid-phase extraction material for the highly selective preconcentration and separation of Cu(II) in environmental samples.