Presence Of Multiple Constraints Research Articles

On the search for efficient face recognition algorithm subject to multiple environmental constraints

From literature, majority of face recognition modules suffer performance challenges when presented with test images acquired under multiple constrained environments (occlusion and varying expressions). The performance of these models further deteriorates as the degree of degradation of the test images increases (relatively higher occlusion level). Deep learning-based face recognition models have attracted much attention in the research community as they are purported to outperform the classical PCA-based methods. Unfortunately their application to real-life problems is limited because of their intensive computational complexity and relatively longer run-times. This study proposes an enhancement of some PCA-based methods (with relatively lower computational complexity and run-time) to overcome the challenges posed to the recognition module in the presence of multiple constraints. The study compared the performance of enhanced classical PCA-based method (HE-GC-DWT-PCA/SVD) to FaceNet algorithm (deep learning method) using expression variant face images artificially occluded at 30% and 40%. The study leveraged on two statistical imputation methods of MissForest and Multiple Imputation by Chained Equations (MICE) for occlusion recovery. From the numerical evaluation results, although the two models achieved the same recognition rate (85.19%) at 30% level of occlusion, the enhanced PCA-based algorithm (HE-GC-DWT-PCA/SVD) outperformed the FaceNet model at 40% occlusion rate, with a recognition rate of 83.33%. Although both Missforest and MICE performed creditably well as de-occlusion mechanisms at higher levels of occlusion, MissForest outperforms the MICE imputation mechanism. MissForest imputation mechanism and the proposed HE-GC-DWT-PCA/SVD algorithm are recommended for occlusion recovery and recognition of multiple constrained test images respectively.

On multiple imputation-based reconstruction of degraded faces and recognition in multiple constrained environments

Recognition of degraded frontal face images acquired under occlusion constraints remain challenging despite the plethora of reconstruction mechanisms. Though recent works have leveraged on some imputation mechanisms in this regard, their robustness in multiple constrained environments may not be guaranteed and may be affected by the choice of pre-processing mechanism. This paper proposes enhancement mechanisms that augment or complement the use of three (3) multiple imputation mechanisms for facial reconstruction in the presence of multiple constraints (10% and 20% occlusions and varying facial expressions). Specifically, we propose the use of a Discrete Cosine Transform-based (DCT) denoising or a Discrete Wavelet-based denoising following Histogram Equalization (HE-DWT) of the reconstructed face images prior to recognition. Experimental results showed that the proposed augmented enhancements improved significantly the recognition rates (90.63% & 91.15% and 86.98% & 85.94% for DCT and HE-DWT at 10% and 20% occlusion levels respectively for Missforest de-occluded face images) as compared with DWT in recognizing degraded frontal face images under moderately low levels of occlusions and varying expressions.

Deep reinforcement learning for rendezvous guidance with enhanced angles-only observability

This research studies the application of reinforcement learning in spacecraft angles-only rendezvous guidance in the presence of multiple constraints and uncertainties. To apply a standard reinforcement learning framework, a stochastic optimal control problem is constructed as a time-discrete Markov decision process, and the observability of angles-only navigation is included in the construction of a reward function. A proximal policy optimization algorithm is used to train a deep neural network (DNN) to map observations of spacecraft state to an optimal control strategy. The trained guidance control network provides a robust nominal orbit and the corresponding closed-loop guidance law, which has the potential to improve the performance of closed-loop navigation, guidance and control. Numerical simulation results are given for an example of far-rendezvous approach guidance. The influence of different weights of the observability term in the reward function on the optimal control strategy is studied. The robustness of the obtained closed-loop control law is evaluated and verified through Monte Carlo simulations in disturbed scenarios.

Minimization of Nonproductive Time in Drilling: A New Tool Path Generation Algorithm for Complex Parts

In computerized tool path programming, the operator/user can generate the tool path based on the shape and geometry of the part to be produced by choosing from a set of predefined strategies available in the library of Computer Aided Manufacturing (CAM) software. These tool paths are typically not optimum, specifically for complex geometries. This paper employed Travelling Salesman Problem (TSP) as a foundation to propose a new tool path optimization algorithm for drilling to minimize the tool path length and subsequently reduce the time spent on nonproductive movements. The proposed algorithm was solved using local search approach in the presence of multiple constraints including geometric obstacles and initial location of tool origin. The outcome was a near-optimum tool path for drilling operations with no collision with workpiece features. The computational efficiency of the proposed algorithm was also compared with other methods in available literature using a standard workpiece as a benchmark. The results confirmed that for given examples, the near-optimum collision-free tool paths using the developed model in this paper were almost 50% shorter than the tool path generated by a commercial CAM software.

Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints

Many architectures of face recognition modules have been developed to tackle the challenges posed by varying environmental constraints such as illumination, occlusions, pose, and expressions. These recognition systems have mainly focused on a single constraint at a time and have achieved remarkable successes. However, the presence of multiple constraints may deteriorate the performance of these face recognition systems. In this study, we assessed the performance of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transform (DWT-PCA/SVD) for preprocessing face recognition algorithm on multiple constraints (partially occluded face images acquired with varying expressions). Numerical evaluation of the study algorithm gave reasonably average recognition rates of 77.31% and 76.85% for left and right reconstructed face images with varying expressions, respectively. A statistically significant difference was established between the average recognition distance of the left and right reconstructed face images acquired with varying expressions using pairwise comparison test. The post hoc analysis using the Bonferroni simultaneous confidence interval revealed that the significant difference established through the pairwise comparison test was mainly due to the sad expressions. Although the performance of the DWT-PCA/SVD algorithm declined as compared to its performance on single constraints, the algorithm attained appreciable performance level under multiple constraints. The DWT-PCA/SVD recognition algorithm performs reasonably well for recognition when partial occlusion with varying expressions is the underlying constraint.

Safety-guaranteed adaptive neural motion control for a hovercraft with multiple constraints

This paper develops a safety-guaranteed adaptive neural motion controller for an underactuated hovercraft in the presence of multiple constraints and model uncertainties. First, a surge controller is designed based on a new improved integral barrier Lyapunov function (iBLF) to guarantee that the surge speed is above the resistance hump speed to achieve better course stability. Second, the constraint on the drift angle is transformed into one on the sway speed, and then the virtual sway controller is designed to constrain the sway speed for the purpose of confining the drift angle. Third, a time-varying iBLF is constructed to constrain the yaw angular velocity to inside of safety boundary relating to the surge speed to ensure a safe turning motion at high speed. Neural networks (NNs) are incorporated into each controller to handle the model uncertainties. It is proved that all the tracking errors are ultimately uniformly bounded. Finally, the results from numerous simulations demonstrate the effectiveness of the proposed control approaches.

The impact, identification and management of dispersive soils in rainfed cropping systems

AbstractDispersive soils limit crop growth and significantly impact world food production. Although numerous reviews have examined soil dispersion, many focus on irrigated systems and fail to differentiate the approaches required for rainfed agriculture. This review seeks to fill this gap by focusing on the impact, identification and management of dispersive soils in rainfed areas. Dispersive soils can have large impacts on crop production because of their adverse physical, chemical and biological effects, with this impact particularly large in rainfed systems where irrigation water is unavailable to supplement crop water supply and assist with amelioration. However, the identification of these soils is challenging and tests that can reliably relate soil characteristics to crop performance are lacking. Recent work has found that first identifying consistently lower yielding locations (using yield mapping or proximal/remote sensing) and then using traditional soil testing to identify the potential cause/s of the yield loss may be a promising approach, although this requires refinement. Knowledge of the type of dispersive soil (e.g., saline/non‐saline, acidic/alkaline/neutral) and where constraints occur in the profile (surface or subsoil) must also be determined during identification as this will affect management approaches, particularly where multiple constraints need to be treated together to achieve yield increase. Improved understanding of how to economically use ameliorants and combine them to achieve maximum benefit in the presence of multiple constraints is needed. Greater appreciation of how to use agronomic management to improve crop growth in the presence of dispersive behaviour is also likely to increase profitability in rainfed systems where amelioration is often impractical or uneconomical. Dispersive soils are a major challenge for rainfed cropping, and so the refinement of our management approach can help improve profitability and productivity.Highlights Dispersive soils limit crop growth and significantly impact world food production We examine the impact, identification and management of dispersive soils in rainfed agriculture Improvements in the identification of dispersive soils are required to improve management Refinement of ameliorant use and agronomic management will improve profitability and productivity

Evolutionary Algorithm Embedded With Bump-Hunting for Constrained Design Optimization

Abstract Real-world design optimization problems commonly entail constraints that must be satisfied for the design to be viable. Mathematically, the constraints divide the search space into feasible (where all constraints are satisfied) and infeasible (where at least one constraint is violated) regions. The presence of multiple constraints, constricted and/or disconnected feasible regions, non-linearity and multi-modality of the underlying functions could significantly slow down the convergence of evolutionary algorithms (EA). Since each design evaluation incurs some time/computational cost, it is of significant interest to improve the rate of convergence to obtain competitive solutions with relatively fewer design evaluations. In this study, we propose to accomplish this using two mechanisms: (a) more intensified search by identifying promising regions through “bump-hunting,” and (b) use of infeasibility-driven ranking to exploit the fact that optimal solutions are likely to be located on constraint boundaries. Numerical experiments are conducted on a range of mathematical benchmarks and empirically formulated engineering problems, as well as a simulation-based wind turbine design optimization problem. The proposed approach shows up to 53.48% improvement in median objective values and up to 69.23% reduction in cost of identifying a feasible solution compared with a baseline EA.

Single-period newsvendor modelling of flu vaccination order quantities

The objective of this paper is to determine optimal inventory levels of flu vaccines at one medical facility using the newsvendor model while also maintaining customer service levels and maximising profit. Using four years of empirical evidence, we illustrate the optimal ordering quantity of flu vaccine by healthcare managers to provide proper medical coverage. This research provides an initial step for ordering quantities of perishable biopharmaceuticals. The research also develops a framework for the logistics of ordering in the presence of multiple constraints throughout the supply chain network. An important contribution of this work is demonstrating the value of the posited logistics model within the context of a healthcare application that has the potential to influence practice.

A modified Covariance Matrix Adaptation Evolution Strategy with adaptive penalty function and restart for constrained optimization

In the last decades, a number of novel meta-heuristics and hybrid algorithms have been proposed to solve a great variety of optimization problems. Among these, constrained optimization problems are considered of particular interest in applications from many different domains. The presence of multiple constraints can make optimization problems particularly hard to solve, thus imposing the use of specific techniques to handle fitness landscapes which generally show complex properties. In this paper, we introduce a modified Covariance Matrix Adaptation Evolution Strategy (CMA-ES) specifically designed for solving constrained optimization problems. The proposed method makes use of the restart mechanism typical of most modern variants of CMA-ES, and handles constraints by means of an adaptive penalty function. This novel CMA-ES scheme presents competitive results on a broad set of benchmark functions and engineering problems, outperforming most state-of-the-art algorithms as for both efficiency and constraint handling.

Cognition in Wireless Sensor Networks: A Perspective

Wireless Sensor Networks are believed to be the enabling technology for Ambient Intelligence. They hold the promise of delivering to a smart communication paradigm which enables setting up an intelligent network capable of handling applications that evolve from user requirements. Cognitive agents capable of making proactive decisions based on learning, reasoning and information sharing when interspersed in sensor networks, may help achieve end-to-end goals of the network even in the presence of multiple constraints and optimization objectives. Cognitive radio at the physical layer of such agents may enable the opportunistic use of the heterogeneous wireless environment. However, research efforts have been discrete and cognitive techniques have focused on improving specific aspects of the network or benefiting specific applications. The main contribution of this paper is providing the vision and advantage of a holistic approach to cognition in sensor networks, which can be achieved by incorporating learning and reasoning in the upper layers, and opportunistic spectrum access at the physical layer. Rather than providing an ostensive survey of cognitive architectures applicable to sensor networks, this paper provides the reader with a framework based on knowledge and cognition that can help achieve end-to-end goals of application-specific sensor networks.

Cluster-aware iterative improvement techniques for partitioning large VLSI circuits

Move-based iterative improvement partitioning (IIP) methods, such as the Fiduccia-Mattheyses (FM) algorithm [Fidducia and Mattheyses 1982] and Krishnamurthy's Look-Ahead (LA) algorithm [Krishnamurthy 1984], are widely used in VLSI CAD applications, largely due to their time efficiency and ease of implementation. This class of algorithms is of the "local/greedy improvement" type, and they generate relatively high-quality results for small and medium-size circuits. However, as VLSI circuits become larger, these algorithms suffer a rapid deterioration in solution quality. We propose new IIP methods CLIP and CDIP that select cells to move with a view to moving clusters that straddle the two subsets of a partition, into one of the subsets. The new algorithms significantly improve partition quality while preserving the advantage of time efficiency. Experimental results on 25 medium to large-size ACM/SIGDA benchmark circuits show up to 70% improvement over FM in mincut, and average mincut improvements of about 35% over all circuits and 47% over large circuits. They also outperform state-of-the-art non-IIP techniques, the quadratic-programming-based method Paraboli [Reiss et al. 1994] and the spectral partitioner MELO [Alpert and Yao 1995], by about 17% and 23%, respectively, with less CPU time. This demonstrates the potential of sophisticated IIP algorithms in dealing with the increasing complexity of emerging VLSI circuits. We also compare CLIP and CDIP to hMetis [Karypis et al. 1997], one of the best of the recent state-of-the-art partitioners that are based on the multilevel paradigm (others include ML c [Alpert et al. 1997] and LSR/MFFS [Cong et al. 1997]). The results show that one scheme of hMetis is 8% worse than CLIP/CDIP and the other two schemes are only 2--4% better. However, CLIP/CDIP have advantages over hMetis and other multilevel partitioners that outweigh these minimal mincut improvements. The first is much faster times-to-solution (for example, one of our best schemes CLIP-LA2 is 6.4 and 11.75 times faster than the two best hMetis schemes) and much better scalability with circuit size (e.g., for the largest circuit with about 162K nodes, CLIP-LA2 is 10.4 and and 21.5 times faster and obtains better solution qualities than the two best hMetis schemes). Second, CLIP/CDIP are "flat" partitioners, while multilevel techniques perform a sequence of node clustering/coarsening before partitioning the circuit. In complex placement applications such as timing-driven placement in the presence of multiple constraints, such circuit coarsening can hide crucial information needed for good-quality solutions, thus making the partitioning process oblivious to them. This, however, is not a problem with flat partitioners like CLIP/CDIP that can take all important parameters into account while partitioning. All these advantages make CLIP/CDIP suitable for use in complex physical design problems for large, deep-submicron VLSI circuits.

Evolutionary structural optimisation (ESO) for combined topology and size optimisation of discrete structures

The single most researched and analysed topic in the field of structural optimisation in the last 40 years has been that of size optimisation for pin- and rigid-jointed frames. Much of the effort has been in developing the search engines that will find a global weight/volume minimum in the presence of multiple constraints, not all of which may be active at the minimum. Most of the algorithmic developments have followed formal mathematical programming techniques. Recently more heuristic methods have emerged for structural optimisation such as genetic algorithms, simulated biological growth and evolutionary algorithms. The authors have been researching the latter mentioned evolutionary structural optimisation (ESO) method for the last six years and have found it to be most efficacious for the full range of structural situations; topology, shape and size optimisation with stress, stiffness, frequency, stability constraints in 2D and 3D with single or multiple loads and support conditions. In the present work is a summary of some of our ESO research for pin and rigid-jointed 2D and 3D frames with or without multiple loads. The paper presents the simple evolutionary algorithm and gives examples covering the range of applications. Attention is also given to benchmarking the algorithms and to their reliability and robustness.

On utility of inductive learning in multiobjective robust design

Most engineering design problems involve optimizing a number of often conflicting performance measures in the presence of multiple constraints. Traditional vector optimization techniques approach these problems by generating a set of Pareto-optimal solutions, where any specific objective can be further improved only at the cost of degrading one or more other objectives. The solutions obtained in this manner, however, are only single points within the space of all possible Pareto-optimal solutions and generally do not indicate to designers how small deviations from predicted design parameters settings affect the performance of the product or the process under study.In this paper we introduce a new approach to robust design based on the concept of inductive learning with regression trees. Given a set of training examples relating to a multiobjective design problem, we demonstrate how a multivariate regression tree can utilize an information-theoretic measure of covariance complexity to capture optimal, tradeoff design surfaces. The novelty of generating design surfaces as opposed to traditional points in the design space is that now designers are able to easily determine how the responses of a product or process vary as design parameters change. This ability is of paramount importance in situations where design parameter settings need to be modified during the lifetime of a product/process due to various economic or operational constraints. As a result, designers will be able to select optimal ranges for design parameters such that the product's performance indices exhibit minimal or tolerable deviations from their target values. To highlight the advantages of our methodology, we present a multiobjective example that deals with optimum design of an electric discharge machining (EDM) process.