Beyond correlation: Towards matching strategy for causal inference in Information Science

Correlation has become a fundamental method for information science. However, correlations are limited in making concrete decisions. In this article, we detail how causal inference could be utilised in the field of information science. There are six main steps of implementing matching for causal inference, namely, selecting candidate control variables, determining control variables, calculating similarities among all samples, forming control group, examining the performance of control group and estimating causal effects. As an example, this article applies causal inference to investigate whether Nobel Physics award increases the after-award citations. The method is presented in a step-by-step manner so that researchers can reproduce our analysis in the future.

Learning compositional models of robot skills for task and motion planning

The objective of this work is to augment the basic abilities of a robot by learning to use sensorimotor primitives to solve complex long-horizon manipulation problems. This requires flexible generative planning that can combine primitive abilities in novel combinations and, thus, generalize across a wide variety of problems. In order to plan with primitive actions, we must have models of the actions: under what circumstances will executing this primitive successfully achieve some particular effect in the world? We use, and develop novel improvements to, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the constraints on skill effectiveness from small numbers of expensive-to-collect training examples. In addition, we develop efficient adaptive sampling methods for generating a comprehensive and diverse sequence of continuous candidate control parameter values (such as pouring waypoints for a cup) during planning. These values become end-effector goals for traditional motion planners that then solve for a full robot motion that performs the skill. By using learning and planning methods in conjunction, we take advantage of the strengths of each and plan for a wide variety of complex dynamic manipulation tasks. We demonstrate our approach in an integrated system, combining traditional robotics primitives with our newly learned models using an efficient robot task and motion planner. We evaluate our approach both in simulation and in the real world through measuring the quality of the selected primitive actions. Finally, we apply our integrated system to a variety of long-horizon simulated and real-world manipulation problems.

Phasor-Based Control for Scalable Integration of Variable Energy Resources

We propose an innovative framework termed phasor-based control (PBC) to facilitate the integration of heterogeneous and intermittent distributed energy resources (DER) on the electric grid. PBC presents a unified approach that is agnostic to optimization criteria and to the particular characteristics of participating resources. It is enabled by synchronized, high-precision voltage phasor measurements that allow stating control objectives in grid-specific, rather than resource-specific, terms. We present qualitative justification and examine the general feasibility of this control approach, including the behavior of candidate control algorithms in simulation. Initial results suggest that PBC has significant potential to support stable and resilient grid operations in the presence of arbitrarily high penetrations of DER.

Fusion of inverse optimal and model predictive control strategies

In this study, model predictive control (MPC) and inverse optimal control (IOC) approaches are merged with each other and a new control strategy is evolved. The key feature in this strategy is to solve the IOC problem repeatedly for each receding horizon of the model predictive control approach. From another perspective, MPC structure is inserted to IOC problem and thus, IOC problem is solved repeatedly using different initial conditions at the beginning of each receding horizon. In the solution phase of IOC, the parameters of the candidate control Lyapunov function matrix are estimated using the global evolutionary Big Bang-Big Crunch (BB-BC) optimization algorithm in an on-line manner. Thus, the proposed control structure solves the optimal control problem in classical MPC approach to the search of an appropriate candidate control Lyapunov function matrix for each control horizon. The comparison of the proposed method with the other related control methods are performed on the ball and beam system via simulations and real-time applications.

Elections with Few Voters: Candidate Control Can Be Easy

We study the computational complexity of candidate control in elections with few voters, that is, we consider the parameterized complexity of candidate control in elections with respect to the number of voters as a parameter. We consider both the standard scenario of adding and deleting candidates, where one asks whether a given candidate can become a winner (or, in the destructive case, can be precluded from winning) by adding or deleting few candidates, as well as a combinatorial scenario where adding/deleting a candidate automatically means adding or deleting a whole group of candidates. Considering several fundamental voting rules, our results show that the parameterized complexity of candidate control, with the number of voters as the parameter, is much more varied than in the setting with many voters.

Stereo Matching Algorithm Based on Pyramid Double Dynamic Programming

A stereo matching algorithm was proposed based on pyramid algorithm and dynamic programming. High and low resolution images was computed by pyramid algorithm, and then candidate control points were stroke on low-resolution image, and final control points were stroke on the high-resolution images. Finally, final control points were used in directing stereo matching based on dynamic programming. Since the striking of candidate control points on low-resolution image, the time is greatly reduced. Experiments show that the proposed method has a high matching precision.

Option Study on a Steam Pressure Control Logic for SMART

Design features of SMART such as a large coolant inventory with a relatively low flow rate and the existence of a once-through steam generator require new steam control logic capable of coping with a prompt load change without inducing severe operational parameter fluctuations. A new MMS SMART model was developed to study the load-following capability and the system parameter manageability of three candidate control logics: the reactor leading, the turbine leading, and the feedwater leading logics. The MMS SMART model was composed of several interacting MMS modules with numerical data, each of which represented a component of the SMART plant and control logic. The Reactor Coolant System, and the Steam and Power Conversion System with their control logics were modeled using default modules such as a pipe, a pump, and a tank. The candidate control logics had been implemented in the model and their dynamic characteristics for the case of a 100%-50%-100% load-following operation with a 25%/min rate were examined. With the reactor-leading control logic implemented, the turbine power was changed with a considerable time delay, which was mainly due to coolant temperature signal retardation to the feedwater controller. The steam pressure variation was very limited for the reactor-leading control logic. With the turbine-leading control logic, the turbine power was manipulated well to match the reference value, whereas relatively large fluctuations of the steam pressure and the coolant temperature occurred. The steam pressure swung with a comparatively large amplitude and the peak value of the fluctuation was not reduced even with larger gain values of the PI controller. This steam pressure swing was considerably decreased with the feedwater leading control logic, while the reactor power and the coolant temperatures had similar trends to those of the turbine leading control logic.