TRACKING PERFORMANCE OF A SWEPT-WING FIGHTER WITH A DIRECTORTYPE RADAR FIRE-CONTROL SYSTEM AND SCOPE PRESENTATION

Tracking performance of f-86d aircraft with radar fire-control syste

Determination of stores pointing error due to wing flexibility under flight load

The in-flight elastic wing twist of a fighter-type aircraft was studied to provide for an improved on-board real-time computed prediction of pointing variations of three wing store stations. This is an important capability to correct sensor pod alignment variation or to establish initial conditions of iron bombs or smart weapons prior to release. The original algorithm was based upon coarse measurements. The electro-optical Flight Deflection Measurement System measured the deformed wing shape in flight under maneuver loads to provide a higher resolution database from which an improved twist prediction algorithm could be developed. The FDMS produced excellent repeatable data. In addition, a NASTRAN finite-element analysis was performed to provide additional elastic deformation data. The FDMS data combined with the NASTRAN analysis indicated that an improved prediction algorithm could be derived by using a different set of aircraft parameters, namely normal acceleration, stores configuration, Mach number, and gross weight

The Empirical Modeling of an Ecosystem

The authors have endeavored to create a verified a-posteriori model of a planktonic ecosystem. Verification of an empirically derived set of first-order, quadratic differential equations proved elusive due to the sensitivity of the model system to changes in initial conditions. Efforts to verify a similarly derived set of linear differential equations were more encouraging, yielding reasonable behavior for half of the ten ecosystem compartments modeled. The well-behaved species models gave indications as to the rate-controlling processes in the ecosystem

Right Ventricular Compression Observed in Echocardiography from Pectus Excavatum Deformity

Pectus excavatum exists as varying anatomic deformities and compression of the right heart by the chest wall can lead to patient symptoms including dyspnea and chest pain with exertion. Echocardiography can be difficult but is critical to the evaluation and diagnosis of this patient population. Modifying standard views such as biplane transthoracic and 3-D transesophageal views may be necessary in some patients due to limitations from the abnormal anatomy of the deformed anterior chest wall. Apical four-chamber views when seen clearly can usually visualize any extrinsic compression to the right ventricle of the heart

KUCHEN: An experiment to evaluate decoupling in high-aspect-ratio cavities

It has been argued that even if cavity-decoupled nuclear explosions are a theoretical evasion scenario, the size of the cavity required may be so large as to preclude their use, except possibly in salt. For example, to obtain a decoupling factor of 50 or more would require a cavity radius of at least 20 m/kt. Various theoretical studies have shown, however, that spherical cavities may not be necessary, and that ratios of length-to-span of 10-20 might be used without significant loss of decoupling capability so long as the volume is maintained. This means, for example, that if a tunnel with cylindrical cross section were employed to decouple a 1 kt explosion, the tunnel radius would decrease from 20 m to 8.1 m with an aspect (length-to-diameter) ratio of 10 and to 6.4 m with an aspect ratio of 20. At NTS, we intend to take advantage of the readiness effort activities and funding to perform mid-scale chemical-explosion decoupling experiments in an event called KUCHEN that is scheduled for the spring of 1995. We have identified an 8 ft-diameter hole, 3 50 ft deep in area 9 (U9cu) that is available for these experiments. Our plan is to conduct two tamped shots and at least one decoupling shot in this hole. The explosive charge will be on the order of 50 kg and the aspect ratio will be in the range 10-15. Details of the proposed experiments are discussed

Effects of perturbations on estuarine microcosms

Microcosms containing planktonic communities from Chesapeake Bay responded to enrichment with sewage by developing larger standing crops of phytoplankton and zooplankton. Data suggest that increased productivity would be reflected up the food chain but might increase existing problems with dissolved oxygen and might lead to qualitative changes in the composition of the zooplankton. Either phosphorus or nitrogen was removed more rapidly from solution depending on where and when the experimental water was obtained. Increases in standing crop of algae were associated with loss of nitrogen from solution in two experiments and losses of both nitrogen and phosphorus from solution in one experiment

Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition

There has been recent interest in the use of machine learning (ML) approaches within mathematical software to make choices that impact on the computing performance without affecting the mathematical correctness of the result. We address the problem of selecting the variable ordering for cylindrical algebraic decomposition (CAD), an important algorithm in Symbolic Computation. Prior work to apply ML on this problem implemented a Support Vector Machine (SVM) to select between three existing human-made heuristics, which did better than anyone heuristic alone. The present work extends to have ML select the variable ordering directly, and to try a wider variety of ML techniques. We experimented with the NLSAT dataset and the Regular Chains Library CAD function for Maple 2018. For each problem, the variable ordering leading to the shortest computing time was selected as the target class for ML. Features were generated from the polynomial input and used to train the following ML models: k-nearest neighbours (KNN) classifier, multi-layer perceptron (MLP), decision tree (DT) and SVM, as implemented in the Python scikit-learn package. We also compared these with the two leading human constructed heuristics for the problem: Brown's heuristic and sotd. On this dataset all of the ML approaches outperformed the human made heuristics, some by a large margin.Comment: Accepted into CICM 201