On commutativity based Edge Lean search

The problem of state space search is fundamental to many areas of computer science, such as, e.g., AI and formal methods. Often, the state space to be searched is huge, so optimizing the search is an important issue. In this paper, we consider the problem of visiting all states in the setting where transitions between states are generated by actions, and the (reachable) states are not known in advance. Some of the actions may commute, i.e., they result in the same state for every order in which they are taken. We show how to use commutativity to achieve full coverage of the states, while traversing a relatively small number of edges.     

Human-like behavior of robot arms: general considerations and the handwriting task—Part I: mathematical description of human-like motion: distributed positioning and virtual fatigue

This two-part paper is concerned with the analysis and achievement of human-like behavior by robot arms (manipulators). The analysis involves three issues: (i) the resolution of the inverse kinematics problem of redundant robots, (ii) the separation of the end-effector's motion into two components, i.e. the smooth (low accelerated) component and the fast (accelerated) component, and (iii) the fatigue of the motors (actuators) of the robot joints. In the absence of the fatigue, the human-like performance is achieved by using the partitioning of the robot joints into “smooth” and “accelerated” ones (called distributed positioning—DP). The actuator fatigue is represented by the so-called “virtual fatigue” (VF) concept. When fatigue starts, the human-like performance is achieved by engaging more the joints (motors) that are less fatigued, as does the human arm. Part I of the paper provides the theoretical issues of the above approach, while Part II applies it to the handwriting task and provides extensive simulation results that support the theoretical expectations.     

Dissecting learning tactics in MOOC using ordered network analysis

Background

Select and enact appropriate learning tactics that advance learning has been considered a critical set of skills to successfully complete highly flexible online courses, such as Massive open online courses (MOOCs). However, limited by analytic methods that have been used in the past, such as frequency distribution, sequence mining and process mining, we lack a deep, complete and detailed understanding of the learning tactics used by MOOC learners.

Objectives

In the present study, we proposed four major dimensions to better interpret and understand learning tactics, which are frequency, continuity, sequentiality and role of learning actions within tactics. The aim of this study was to examine to what extent can a new analytic technique, the ordered network analysis (ONA), deepen the understanding of MOOC learning tactics compared to using other methods.

Methods

In particular, we performed a fine-grained analysis of learning tactics detected from more than 4 million learning events in the behavioural trace data of 8788 learners who participated in a large-scale MOOC ‘Flipped Classroom’.

Results and Conclusions

We detected eight learning tactics, and then chose one typical tactic as an example to demonstrate how the ONA technique revealed all four dimensions and provided deeper insights into this MOOC learning tactic. Most importantly, based on the comparison with different methods such as process mining, we found that the ONA method provided a unique opportunity and novel insight into the roles of different learning actions in tactics which was neglected in the past.

Takeaway

In summary, we conclude that ONA is a promising technique that can benefit the research on learning tactics, and ultimately benefit MOOC learners by strengthening the strategic support.     

Stability properties of reset systems

Stability properties for a class of reset systems, such as systems containing a Clegg integrator, are investigated. We present Lyapunov based results for verifying L₂ and exponential stability of reset systems. Our results generalize the available results in the literature and can be easily modified to cover L_p stability for arbitrary p∈[1,∞]. Several examples illustrate that introducing resets in a linear system may reduce the L₂ gain if the reset controller parameters are carefully tuned.     

Parallel structured milling machines with long X travel

The paper presents research and development results of one parallel structured milling machine with long X travel. The developed milling machine experimental prototype is based on a newly developed 3-DOF spatial parallel mechanism. In comparison with similar developed machines, it has several advantages such as: rather regular shape of the workspace similar to serial machines, greater stiffness by nature of strut arrangement and good force and speed ratio through the entire machine's workspace.     

H 2 optimal control for a wide class of discrete-time linear stochastic systems

In this article, the problem of H ₂-control of a discrete-time linear system subject to Markovian jumping and independent random perturbations is considered. Different H ₂ performance criteria (often called H ₂-norms) are introduced and characterised via solutions of some suitable linear equations on certain spaces of symmetric matrices. Some aspects specific to the discrete-time framework are revealed. The problem of optimisation of H ₂-norms is solved under the assumption that full state vector is available for measurements. One shows that among all stabilising controllers of higher dimension, the best performance is achieved by a zero-order controller. The corresponding feedback gain of the optimal controller is constructed based on the stabilising solution of a system of discrete-time generalised Riccati equations.     

Heuristic Modelling of the Water Resources Management in the Guadalquivir River Basin, Southern Spain

A model comprising blocks of artificial neural networks (ANNs) combined in sequence was used to simulate the inflow and outflow in a water resources system under a shortage of water. We assessed the selection of appropriate input data using linear and non-linear cross-correlation functions and sensitivity analysis. The potential model inputs were flow, precipitation and temperature data from various gauging stations throughout the upper watershed of the ‘Guadiana Menor’ River (southern Spain), and the model considered various input time lags. The ANNs based on the selected inputs were effective relative to those with no relevant inputs, and produced more parsimonious models. We also investigated conceptual analogies inherent in the ANN models by analyzing the response profiles of the modelled variables (inflow and outflow) in relation to each of the selected input data. The results demonstrate that the neural approach approximated the behaviour of various components of the water resources system in terms of various hydrologic cycle processes and management rules. Our findings suggest that in dry periods a mean temperature increase of 1°C in low altitude locations of the region will result in a mean decrease of approximately 2% in the inflow to the water resources system, and a mean increase of approximately 12% in the outflow requirements for irrigation purposes.     

Additive Spanners and Distance and Routing Labeling Schemes for Hyperbolic Graphs

δ-Hyperbolic metric spaces have been defined by M. Gromov in 1987 via a simple 4-point condition: for any four points u,v,w,x, the two larger of the distance sums d(u,v)+d(w,x),d(u,w)+d(v,x),d(u,x)+d(v,w) differ by at most?2δ. They play an important role in geometric group theory, geometry of negatively curved spaces, and have recently become of interest in several domains of computer science, including algorithms and networking. In this paper, we study unweighted δ-hyperbolic graphs. Using the Layering Partition technique, we show that every n-vertex δ-hyperbolic graph with δ≥1/2 has an additive O(δlog?n)-spanner with at most O(δn) edges and provide a simpler, in our opinion, and faster construction of distance approximating trees of δ-hyperbolic graphs with an additive error O(δlog?n). The construction of our tree takes only linear time in the size of the input graph. As a consequence, we show that the family of n-vertex δ-hyperbolic graphs with δ≥1/2 admits a routing labeling scheme with O(δlog?² n) bit labels, O(δlog?n) additive stretch and O(log?₂(4δ)) time routing protocol, and a distance labeling scheme with O(log?² n) bit labels, O(δlog?n) additive error and constant time distance decoder.     

Intelligent control of DC motor driven electromechanical fin actuator

In this paper modeling, simulation and control of an electromechanical actuator (EMA) system for aerofin control (AFC) with permanent magnet brush DC motor driven by a constant current driver are investigated. Nonlinear model of the EMA-AFC system has been developed and experimentally verified in actuator test bench. Model has been used as the starting point for PID position controller synthesis. To improve performances of the system, computational intelligence has been applied. Genetic PID optimization, genetic algorithm (GA) optimized fuzzy supervisory PID control and finally GA optimized nonlinear PID algorithm modification are proposed. Improved transient response and system behavior have also been experimentally validated.