Identification of Military-related Science and Technology

A proof-of-principle demonstration for extracting military-related technologies from a country's total technology publications has been performed, and applied to the Indian science and technology literature#. The method is general and can be applied to the extraction of any meta-category (e.g., intelligence-relevanttechnologies, infrastructure-relevant technologies, etc) which is not easily obtained from document clustering or factor analysis. The methodology for identifying relevant literature on military science appears to provide credible results. The volume of literature retrieved will vary depending on how strongly relevant is the desired literature. For the same definitions of 'military relevant', the volume of India's literature in the Ei Compendex database was an order of magnitude less than that of the USA or China.Defence Science Journal, 2010, 60(3), pp.259-270, DOI:http://dx.doi.org/10.14429/dsj.60.35

Dynamic Optimization Algorithms for Baseload Power Plant Cycling under Variable Renewable Energy

The growing deployment of variable renewable energy (VRE) sources, such as wind and solar, is mainly due to the decline in the cost of renewable technologies and the increase of societal and cultural pressures. Solar and wind power generation are also known to have zero marginal costs and fuel emissions during dispatch. Thereby, the VRE from these sources should be prioritized when available. However, the rapid deployment of VRE has heightened concerns regarding the challenges in the integration between fossil-fueled and renewable energy systems. The high variability introduced by the VRE as well as the limited alignment between demand and wind/solar power generation led to the increased need of dispatchable energy sources such as baseload natural gas- and coal-fired power plants to cycle their power outputs more often to reliably supply the net load. The increasing power plant cycling can introduce unexpected inefficiencies into the system that potentially incur higher costs, emissions, and wear-and-tear, as the power plants are no longer operating at their optimal design points. In this dissertation, dynamic optimization algorithms are developed and implemented for baseload power plant cycling under VRE penetration. Specifically, two different dynamic optimization strategies are developed for the minute and hourly time scales of grid operation. The minute-level strategy is based on a mixed-integer linear programming (MILP) formulation for dynamic dispatch of energy systems, such as natural gas- and coal-fired power plants and sodium sulfur batteries, under VRE while considering power plant equipment health-related constraints. The hourly-level strategy is based on a Nonlinear Multi-objective dynamic real-time Predictive Optimization (NMPO) implemented in a supercritical pulverized coal-fired (SCPC) power plant with a postcombustion carbon capture system (CCS), considering economic and environmental objectives. Different strategies are employed and explored to improve computational tractability, such as mathematical reformulations, automatic differentiation (AD), and parallelization of a metaheuristic particle swarm optimization (PSO) component. The MILP-based dynamic dispatch framework is used to simulate case studies considering different loads and renewable penetration levels for a suite of energy systems. The results show that grid flexibility is mostly provided by the natural gas power plant, while the batteries are used sparingly. Additionally, considering the post-optimization equivalent carbon analysis, the environmental performance is intrinsically connected to grid flexibility and the level of VRE penetration. The stress results reinforce the necessity of further considering and including equipment health-related constraints during dispatch. The results of the NMPO successfully implemented for a large-scale SCPC-CCS show that the optimal compromise is automatically chosen from the Pareto front according to a set of weights for the objectives with minimal interaction between the framework and the decision maker. They also indicate that to setup the optimization thresholds and constraints, knowledge of the power system operations is essential. Finally, the market and carbon policies have an impact on the optimal compromise between the economic and environmental objectives

Harmony Search Method: Theory and Applications

The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm, which has been employed to cope with numerous challenging tasks during the past decade. In this paper, the essential theory and applications of the HS algorithm are first described and reviewed. Several typical variants of the original HS are next briefly explained. As an example of case study, a modified HS method inspired by the idea of Pareto-dominance-based ranking is also presented. It is further applied to handle a practical wind generator optimal design problem

Exploratory Boosted Feature Selection and Neural Network Framework for Depression Classification

Depression is a burdensome psychiatric disease common in low and middle income countries causing disability, morbidity and mortality in late life. In this study, we demonstrate a novel approach for detection of depression using clinical data obtained from the on-going Mysore Studies of Natal effects on Ageing and Health (MYNAH), in South India where the members have undergone a comprehensive assessment for cognitive function, mental health and cardiometabolic disorders. The proposed model is developed using machine learning approach for classification of depression using Meta-Cognitive Neural Network (McNN) classifier with Projection-based learning (PBL) to address the self-regulating principles like how, what and when to learn. XGBoost is used for feature selection on the available data of assessments with improved confidence. To improve the efficiency of McNN-PBL classifier the best parameters are found using Particle Swarm Optimization (PSO) algorithm. The results indicate that the McNNPBL classifier selects appropriate records to learn and remove repetitive records which improve the generalization performance. The study helps the clinician to identify the best parameters to analyze the patient

Advances and applications in high-dimensional heuristic optimization

“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or a high number of objectives, effectively limiting the range of scenarios to which they may be applied. This research introduces mechanisms to improve the runtime complexity of many multiobjective evolutionary algorithms, achieving state-of-the-art performance, as compared to many prominent methods from the literature. Further, the investigations here presented demonstrate the capability of multiobjective evolutionary algorithms in a complex, large-scale optimization scenario. Showcasing the approach’s ability to intelligently generate well-performing solutions to a meaningful optimization problem. These investigations advance the concept of multiobjective evolutionary algorithms by addressing a key limitation and demonstrating their efficacy in a challenging real-world scenario. Through enhanced computational efficiency and exhibited specialized application, the utility of this powerful heuristic strategy is made more robust and evident”--Abstract, page iv

Reactive approach for automating exploration and exploitation in ant colony optimization

Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations

Review of automated time series forecasting pipelines

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting

Robust Hand Motion Capture and Physics-Based Control for Grasping in Real Time

Hand motion capture technologies are being explored due to high demands in the fields such as video game, virtual reality, sign language recognition, human-computer interaction, and robotics. However, existing systems suffer a few limitations, e.g. they are high-cost (expensive capture devices), intrusive (additional wear-on sensors or complex configurations), and restrictive (limited motion varieties and restricted capture space). This dissertation mainly focus on exploring algorithms and applications for the hand motion capture system that is low-cost, non-intrusive, low-restriction, high-accuracy, and robust. More specifically, we develop a realtime and fully-automatic hand tracking system using a low-cost depth camera. We first introduce an efficient shape-indexed cascaded pose regressor that directly estimates 3D hand poses from depth images. A unique property of our hand pose regressor is to utilize a low-dimensional parametric hand geometric model to learn 3D shape-indexed features robust to variations in hand shapes, viewpoints and hand poses. We further introduce a hybrid tracking scheme that effectively complements our hand pose regressor with model-based hand tracking. In addition, we develop a rapid 3D hand shape modeling method that uses a small number of depth images to accurately construct a subject-specific skinned mesh model for hand tracking. This step not only automates the whole tracking system but also improves the robustness and accuracy of model-based tracking and hand pose regression. Additionally, we also propose a physically realistic human grasping synthesis method that is capable to grasp a wide variety of objects. Given an object to be grasped, our method is capable to compute required controls (e.g. forces and torques) that advance the simulation to achieve realistic grasping. Our method combines the power of data-driven synthesis and physics-based grasping control. We first introduce a data-driven method to synthesize a realistic grasping motion from large sets of prerecorded grasping motion data. And then we transform the synthesized kinematic motion to a physically realistic one by utilizing our online physics-based motion control method. In addition, we also provide a performance interface which allows the user to act out before a depth camera to control a virtual object