All in Good Team: Optimising Team Personalities for Different Dynamic Problems and Task Types

Predictive maintenance, as one of the core components of Industry 4.0, takes a proactive approach to maintain machines and systems in good order to keep downtime to a minimum and the airline maintenance industry is not an exception to this. To achieve this goal, practices in Structural Health Monitoring (SHM) complement the existing Non-Destructive-Testing (NDT) have been established in the last decades. Recently, the increasing computational capability such as utilization of a graphical processing unit (GPU) in combination with advanced machine learning techniques such as deep learning has been one of the main drivers in the advancement of predictive analytics in condition monitoring.In our previous work, we proposed a novel approach using deep learning for guided wave based structural health called DeepSHM. As a study case, we treated an ultrasonic signal from guided Lamb wave SHM with a convolutional neural network (CNN). In that work, we only considered a single central frequency excitation. This led to a single governing wavelength which is normally good for the detection of a single damage size.In classical signal processing, applying a broader excitation frequency poses an analysis and interpretation nightmare because it contains more complex information and thus is difficult to understand. This problem can be overcome with deep learning; however, it creates another problem: while deep learning typically results in a more accurate result prediction, it is specifically made for solving only certain types of tasks. While many papers have already introduced deep learning for diagnostics, many of these works are only proposing novel predictive techniques, however the mathematical formalization is lacking, and we are not informed about why we should treat acoustic signal with deep learning. So, the basis of ‘explainable AI’ for SHM and NDT is currently lacking.For this reason, in this paper, we would like to extend our previous work into a more generalized. Rather than focusing on a novel technique, we propose a plausible theoretical perspective inspired from neuroscience for signal representation of deep learning framework to model machine perception in structural health monitoring (SHM), especially because SHM typically involves multiple sensory input from different sensing locations. To do this, we created a set of artificial data from a finite element model (FEM) and represented DeepSHM in two different ways: 1). Perpetual representation of observation and 2). Hierarchical structure of entities that is decomposable in a smaller sub-entity. Consequently, we assume two plausible models for DeepSHM: 1). Either it behaves as a single deciding actor since the observation is regarded as perpetual, and 2). Or it acts as a multiple actor with independent outputs since multiple sensors can form different output probabilities. These artificial data were split into several different input representations, classified into several damage scenarios and then trained with commonly used deep learning training parameters. We compare the performance metrics of each perception model to describe the training behavior of both representations.

Cardiovascular response patterns during choice reaction time

Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

A study was conducted to compare dynamic lifting task with a simulated static lifting task. 17 subjects 12 males and 5 females volunteered to participate in this study. The independent variables were the type of task (static and dynamic), frequency of lift (2, 4, and 6 lifts/min), and muscle group (back lift, leg lift, and arm lift). The psychophysical methodology was used in this study. The results showed highly significant differences between maximum acceptable static effort (MASE) and maximum acceptable dynamic effort (MADE) for both males and females. The frequency was also statistically significant. The muscle groups were significant for females and not significant for males. On the average MADE values were 45% of those of MASE values for females and 38% for males. The ratio of female to male data, on the average, were 39% for MASE values and 44% for MADE values. Regression models for the prediction of maximum of MADE from MASE and other task and worker variables were developed.

The motor system (partially) deceives body representation biases in absence of visual correcting cues

The dynamic routing problem for multiple destination networks is considered. The minimum time rather than total delay cost functional is employed. The problem is solved through an iterative link-by-link optimization. Each link capacity is optimally partitioned by examining the upper bounds for the evacuation time imposed through different capacity allocations for each origin/destination pair traffic. The computational complexity per iteration is polynomial in the number of network nodes. This is due to the examination of origin/destination pairs rather then destinations alone as in previous work where a similar approach led to exponential complexity. Sufficient conditions for the convergence of the iterative algorithm to the optimum are given. If these are not satisfied supplementary steps are described which conduct the algorithm to the desired solution. These involve exponential computational complexity.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, we consider the problem of inverse dynamic games: given the observed behaviour of players during a dynamic game in equilibrium, how can we determine the underlying objective functions of the game? Whereas previous work in the literature has focused on inverse static games, our work focuses on inverse dynamic games. In particular, we address the problem of estimating the unknown parameters of the objective function of a two-player zero-sum dynamic game in open-loop Nash equilibrium. We exploit necessary conditions for equilibrium in a two-player zero-sum dynamic game to develop sufficient conditions for solving the two-player zero-sum inverse dynamic game problem. The sufficient conditions hold under assumptions on the control constraints and convexity of the game dynamics, and transform the inverse two-player zero-sum dynamic game problem into the problem of solving a system of linear equations. We apply our results to a linear quadratic two-player zero-sum game, and illustrate the recovery of objective function parameters from state and control equilibrium trajectories.

FEM based Cutting Velocity Selection for Thin Walled Part Machining

Active disturbance rejection controller(ADRC) is a model-independent controller. It has good robustness and adaptability for the controlled systems with nonlinearity, large time delay, and high uncertainty. We notice that, although the previous work has focused mainly on its application in industrial engineering, the basic idea of ADRC also reflects very naturally the rational behavior of the decision maker in the face of dynamic optimization problems in economics and management science. The classical economic growth model is used as an example to demonstrate how to take into account the characteristic of economics so as to provide an effective mathematical tool for the analysis of a large class of dynamic problems in economics and management science.

The need for persistent patrol and detection arises in many contexts such as crime prevention, search and rescue, post-conflict stability operations, and peace keeping. In these situations, military or police units are not only effective deterrents to would-be adversaries but also a speedy task force to intercept any trespassers or provide swift security and assistance. With recent advances in technology, unmanned aerial vehicles (UAVs) are well-suited for these tasks because they possess a large bird’s-eye view and are unhindered by ground obstacles. The path planning algorithms used in such missions play a critical role in minimizing the required resources, and maximizing the quality of provided service. Moreover, in many of these mission scenarios, if a patrol pattern is regular or predictable, adversaries can plan a counter strategy to the patrolling effort. In other words, unpredictability is one of the key features of planning in these circumstances. In this work, we propose and analyze the Persistent Patrol and Detection Problem (PPDP), a generic mathematical model for UAVs with limited sensors performing such a mission in a stochastic environment. Incidents occur dynamically and stochastically according to a general renewal process with known time intensity and spatial distribution in a planar region. The UAV is modeled as a point mass traveling at a constant speed with unbounded acceleration. The UAV detects incidents within the footprint of its onboard sensors, i.e., within its visibility range. We want to minimize the expected waiting time between the occurrence of an incident, and its detection epoch. Furthermore, we prefer stochastic trajectories so that would-be adversaries cannot predict the UAV’s motion based on past observations to plan for evasion. Related research focuses on search and rescue missions in which the number of searched objects is known at the beginning of the missions.1,2, 3 In other words, these works present static problems in terms of the number of objects to be found. In contrast, incidents of interest in the PPDP arrive continuously with unknown arrival times, and therefore the search effort must be persistent and preventive. This inherent difference between the PPDP and previous works has made the PPDP a dynamic problem in term of the number of incidents. ∗V. A. Huynh is with the Laboratory of Information and Decision Systems, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139. vuhuynh@mit.edu †J. J. Enright is with Kiva Systems, 225 Wildwood Ave, Woburn, MA 01801. jenright@kivasystems.com ‡E. Frazzoli is with the Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139. frazzoli@mit.edu

Current and future parallel programming models need to be portable and efficient when moving to heterogeneous multi-core systems. OmpSs is a task-based programming model with dependency tracking and dynamic scheduling. This paper describes the OmpSs approach on scheduling dependent tasks onto the asymmetric cores of a heterogeneous system. The proposed scheduling policy improves performance by prioritizing the newly-created tasks at runtime, detecting the longest path of the dynamic task dependency graph, and assigning critical tasks to fast cores. While previous works use profiling information and are static, this dynamic scheduling approach uses information that is discoverable at runtime which makes it implementable and functional without the need of an oracle or profiling. The evaluation results show that our proposal outperforms a dynamic implementation of Heterogeneous Earliest Finish Time by up to 1.15x, and the default breadth-first OmpSs scheduler by up to 1.3x in an 8-core heterogeneous platform and up to 2.7x in a simulated 128-core chip.

Using domain knowledge to decompose difficult control problems is a widely used technique in robotics. Previous work has automated the process of identifying some qualitative behaviors of a system, finding a decomposition of the system based on that behavior, and constructing a control policy based on that decomposition. We introduce a novel method for automatically finding decompositions of a task based on observing the behavior of a preexisting controller. Unlike previous work, these decompositions define reparameterizations of the state space that can permit simplified control of the system

It has been already proofed and discussed in our previous works that thanks to its computational as well as implemental simplicity evolutionary multi-agent system (EMAS) approach can be easily adjusted and widely used for solving almost any type of multi-objective optimization task. Unfortunately, results obtained by distributed and decentralized agent-based evolutionary heuristic approach applied for solving multi-objective optimization problems (MOOPs) can be limited by such factors, as stagnation of agents' evolution or problems with distributing agents evenly over the whole approximation of the Pareto frontier. In the course of this paper three main shortcomings of EMAS-based approach are diagnosed and ideas of possible solutions of identified problems with the use of "flock-based" mechanisms are presented. Next, one of many possible realization of flock-based ideas i.e. so-called densityplus realization of floEMAS paradigm is discussed. Finally, experimental results obtained by floEMAS system in solving noisy multi-objective optimization tasks are presented. The goal of this paper is: to discuss shortcomings identified in applying EMAS for solving MOOPs, to present flock-based mechanisms allowing for overcoming such shortcomings, to discuss one of possible realization of floEMAS approach and to present the behavior of proposed floEMAS realization in noisy environment - since in the case of EMAS-based approaches there is no need to introduce any additional mechanisms to solve efficiently noisy problems.

Tourette’s syndrome (TS): Inhibitory performance in adults with uncomplicated TS.

Sequence generation tasks, such as neural machine translation (NMT) and abstractive summarization, usually suffer from exposure bias as well as the error propagation problem due to the autoregressive training and generation. Many previous works have discussed the relationship between error propagation and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accuracy drop</i> problem (i.e., the right part of the generated sentence is often worse than its left part in left-to-right decoding models). In this paper, taking NMT as a typical sequence generation task, we measure the accuracy of the generated sentence with various metrics and conduct a series of analyses to deeply understand the accuracy drop problem. We obtain several interesting findings. First, The role of error propagation on accuracy drop is overstated in the literature, although it is indeed a cause to the accuracy drop problem. Second, Characteristics of a language play a more important role in causing the accuracy drop problem: the left part of the generated sentence in a right-branching language (e.g., English) is more likely to be more accurate than its right part, while the right part is more accurate for a left-branching language (e.g., Japanese). Our discoveries are also confirmed on other generation tasks (e.g., image captioning, abstractive summarization and language modeling) with multiple left/right-branching languages, as well as in various model structures.