Constrained Target Clustering for Military Targeting Process

Constrained target clustering (CTC) is proposed to support the targeting decision-making in the network centric warfare environment. When area targets are detected by sensors, it is required to decide the points at which a missile or bomb is aimed to achieve operational goals. CTC can determine the optimal numbers and positions of aiming points by transforming the targeting problem into clustering-based optimisation problems. The CTC formulations include objective functions and constraints in consideration of area targets, protected objects, target-level background information, lethal radius, and required damage rate. The numerical example shows how to apply the CTC formulation given a sample data set. In order to compare the effects of different constraints, the demonstration explores from an unconstraint problem to constrained problems by adding constraints. The results show that CTC can effectively decide the aiming points with consideration of both targets and capabilities of friendly weapons, and serve as a targeting decision support system in the network centric warfare environment

Data driven TRL Transition Predictions for Early Technology Development in Defence

This paper proposes the framework of TRL (Technology Readiness Level) transition predictions for early technology development in defense. Though predicting future TRLs is an important planning tool, it has been studied less actively than the other critical issues on TRL, and previous studies mostly have resorted to domain experts. The proposed framework is data-driven and utilises both explanatory and predictive modelling techniques. As a case study, the proposed framework is applied to real technology development data from DTiMS (Defense Technology InforMation Service) which is identified as a key resource. The result of explanatory modelling shows that the two predictor variables, TRL before R&D and project cost, are statistically significant for future TRLs. Also, popular predictive models are fitted and compared with various performance indices using 10-fold cross validation. The two selected predictive models are linear regression and support vector machine models with the lowest prediction errors

The Wnt Receptor Ryk Reduces Neuronal and Cell Survival Capacity by Repressing FOXO Activity During the Early Phases of Mutant Huntingtin Pathogenicity

The Wnt receptor Ryk is an evolutionary-conserved protein important during neuronal differentiation through several mechanisms, including γ-secretase cleavage and nuclear translocation of its intracellular domain (Ryk-ICD). Although the Wnt pathway may be neuroprotective, the role of Ryk in neurodegenerative disease remains unknown. We found that Ryk is up-regulated in neurons expressing mutant huntingtin (HTT) in several models of Huntington's disease (HD). Further investigation in Caenorhabditis elegans and mouse striatal cell models of HD provided a model in which the early-stage increase of Ryk promotes neuronal dysfunction by repressing the neuroprotective activity of the longevity-promoting factor FOXO through a noncanonical mechanism that implicates the Ryk-ICD fragment and its binding to the FOXO co-factor β-catenin. The Ryk-ICD fragment suppressed neuroprotection by lin-18/Ryk loss-of-function in expanded-polyQ nematodes, repressed FOXO transcriptional activity, and abolished β-catenin protection of mutant htt striatal cells against cell death vulnerability. Additionally, Ryk-ICD was increased in the nucleus of mutant htt cells, and reducing γ-secretase PS1 levels compensated for the cytotoxicity of full-length Ryk in these cells. These findings reveal that the Ryk-ICD pathway may impair FOXO protective activity in mutant polyglutamine neurons, suggesting that neurons are unable to efficiently maintain function and resist disease from the earliest phases of the pathogenic process in HD. © 2014 Tourette et al

Affordance-based modeling of a human-robot cooperative system for area exploration

The cooperation of humans and robots is ubiquitous in modern systems, while human ability to cooperate has been limitedly investigated in terms of systems theory. A formal model is proposed to describe the human capability for the cooperation based on the finite state automata (FSA) and the affordances theory. Unlike the previous studies focused on conceptual approaches, real and virtual experiments are conducted to investigate human actions in a cooperative system with a human and a robot. A modeling scheme is provided to implement agent-based simulations for the cooperative system using the proposed affordance-based FSA. The developed simulation for the cooperation problem can reproduce the patterns of the actual experiments in terms of affordances and supervisory capability. The modular architecture of the agent-based framework allows establishing open-ended algorithms for action selections with their isolated effects under physical constraints, which need to be revised to deal with human-involved cooperation problems

Performance assessment in an interactive call center workforce simulation

In this paper a new performance assessment methodology for human-in-the-loop call center systems at the level of customer-agent interactions (CAI) is proposed We develop a team-in-the-loop simulation test bed to analyze CAI-level performance of a service system using a temporal performance measure with time windows The proposed framework should allow researchers to collect and analyze individual as well as team performance at a finer granularity than current call center efforts which focus on queue-centered analysis The software framework is object-oriented and has been designed to be configurable A sample simulation study in different scenarios is illustrated to provide the usages and advantages of the proposed method with index of Interactive Service Performance.close

Environmental sustainability evaluation of additive manufacturing using the NIST test artifact

To identify which elements of 3D printers influence the environment, this paper compares four 3D printers: Material-jetting (PJ), powder-bed-fusion of a large-bed-size (LSa), powder-bed-fusion of a small-bed-size (LSb) and material-extrusion (FDM), when printing the NIST test artifact. The elements consist of the input of the life cycle inventory. Our results show that the 3D printer with the lowest environmental impact is LSb, then LSa, and FDM, while PJ has the largest impact amongst the four. For PJ, LSa and LSb, the dominant elements are ???power for printing??? while it is ???additional material??? for FDM. However, during high-volume-production the dominant elements become ???additional material??? for LSa and ???object material??? for PJ, LSb, and FDM. The most influential element of each 3D printer also varies according to the part-orientation. Overall, it is found that LSb is the least harmful to the environment for low-volume-production, while LSa is the least harmful to the environment for the high-volume-production

Aerial multi-spectral AI-based detection system for unexploded ordnance

Unexploded ordnance (UXO) poses a threat to soldiers operating in mission areas, but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard. Recent technological advancements in artificial intelligence (AI) and small unmanned aerial systems (sUAS) present an opportunity to explore a novel concept for UXO detection. The new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral (MS) sensor on sUAS. This paper explores feasibility of AI-based UXO detection using sUAS equipped with a single (visible) spectrum (SS) or MS digital electro-optical (EO) sensor. Specifically, it describes the design of the Deep Learning Convolutional Neural Network for UXO detection, the development of an AI-based algorithm for reliable UXO detection, and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery