Advanced Automation in the Heat Treat Industry

Over the last 20 years, automation and robotics have become standard in production centers all around the world. In contrast, heat treatment processes are still typically manual and employee task oriented. In this presentation, we will review the latest developments and integration processes to improve the tact time of furnaces and guarantee process stability load after load, part after part. We will expand on how to use robotics for automatically loading and unloading a variety of parts on fixtures, and how automation can be utilized for checking mechanical and dimensional property before and after heat treatment. In addition, we will discuss how recipes can be automatically uploaded and full reports generated with details such as compliance and tolerances.

Deep Reinforcement Learning-Based Path Planning for Multi-Arm Manipulators with Periodically Moving Obstacles

In the workspace of robot manipulators in practice, it is common that there are both static and periodic moving obstacles. Existing results in the literature have been focusing mainly on the static obstacles. This paper is concerned with multi-arm manipulators with periodically moving obstacles. Due to the high-dimensional property and the moving obstacles, existing results suffer from finding the optimal path for given arbitrary starting and goal points. To solve the path planning problem, this paper presents a SAC-based (Soft actor–critic) path planning algorithm for multi-arm manipulators with periodically moving obstacles. In particular, the deep neural networks in the SAC are designed such that they utilize the position information of the moving obstacles over the past finite time horizon. In addition, the hindsight experience replay (HER) technique is employed to use the training data efficiently. In order to show the performance of the proposed SAC-based path planning, both simulation and experiment results using open manipulators are given.

Analyzing the thermal and hygral behavior of wool and its impact on fabric dimensional stability for wool processing and garment manufacturing

Wool is one of the most moisture sensitive natural fibers. This paper investigated changes of wool fiber diameter, fabric dimensions and fabric dimensional properties, as a function of moisture regain, temperature and pH. Experiments were conducted on fabrics with different weave structures as well as on fabrics with and without a permanent set. Results showed that the fabrics tended to contract when they were subjected to increased temperature at saturated regain. The degree of contraction appeared to depend on the weave structure of the fabrics and permanent setting treatments. Dimensions of the wool fabrics were also found to be dependent on the pH. Greater fabric dimensions were observed at pH 7.2 than at pH 2.1. The contraction effect was almost reversible when unset fabric samples were measured in pH 2.1. The reasons for the changes of dimensional property were analyzed in terms of changes in wool fiber swelling, yarn crimp and polymer relaxation phenomena with changes in regain, temperature and pH. Industrial implications from outcomes of this research to practical wool processing are discussed in the paper.

On the Unified Design of Accelerated Gradient Descent

Nowadays, different kinds of problems such as modeling, optimal control, and machine learning can be formulated as an optimization problem. Gradient descent is the most popular method to solve such problem and many accelerated gradient descents have been designed to improve the performance. In this paper, we will analyze the basic gradient descent, momentum gradient descent, and Nesterov accelerated gradient descent from the system perspective and it is found that all of them can be formulated as a feedback control problem for tracking an extreme point. On this basis, a unified gradient descent design procedure is given, where a high order transfer function is considered. Furthermore, as an extension, both a fractional integrator and a general fractional transfer function are considered, which resulting in the fractional gradient descent. Due to the infinite-dimensional property of fractional order systems, numerical inverse Laplace transform and Matlab command stmcb() are used to realize a finite-order implementation for the fractional gradient descent. Besides the simplified design procedure, it is found that the convergence rate of fractional gradient descent is more robust to the step size by simulating results.

Revising order or challenging the balance of military power? An alternative typology of revisionist and status-quo states

Unimensional accounts of revisionism – those that align states along a single continuum from supporting the status quo to seeking a complete overhaul of the international system – miss important variation between a desire to alter the balance of military power and a desire to alter other elements of international order. We propose a two-dimensional property space that generates four ideal types: status-quo actors, who are satisfied with both order and the distribution of power; reformist actors, who are fine with the current distribution of power but seek to change elements of order; positionalist actors, who see no reason to alter the international order but do aim to shift the distribution of power; and revolutionary actors, who want to overturn both international order and the distribution of capabilities. This framework helps make sense of a number of important debates about hegemony and international order, such as the possibility of revisionist hegemonic powers, controversies over the concept of ‘soft balancing’, and broader dynamics of international goods substitution during power transitions.

A New Look at the Fractional Initial Value Problem: The Aberration Phenomenon

A typical phenomenon of the fractional order system is presented to describe the initial value problem from a brand-new perspective in this paper. Several simulation examples are given to introduce the named aberration phenomenon, which reflects the complexity and the importance of the initial value problem. Then, generalizations on the infinite dimensional property and the long memory property are proposed to reveal the nature of the phenomenon. As a result, the relationship between the pseudo state-space model and the infinite dimensional exact state-space model is demonstrated. It shows the inborn defects of the initial values of the fractional order system. Afterward, the pre-initial process and the initialization function are studied. Finally, specific methods to estimate exact state-space models and fit initialization functions are proposed.

Probing Protein Allostery as a Residue-specific Concept via Residue Perturbation Maps

Allosteric regulation is a well-established phenomenon classically defined as conformational or dynamical change of a small number of allosteric residues of the protein upon allosteric effector binding at a distance. Here, we developed a novel approach to delineate allosteric effects in proteins. In this approach, we applied robust machine learning methods, including Deep Neural Network and Random Forest, on extensive molecular dynamics (MD) simulations to distinguish otherwise similar allosteric states of proteins. Using PDZ3 domain of PDS-95 as a model protein, we demonstrated that the allosteric effects could be represented as residue-specific properties through two-dimensional property-residue maps, which we refer as “residue perturbation maps”. These maps were constructed through two machine learning methods and could accurately describe how different properties of various residues are affected upon allosteric perturbation on protein. Based on the “residue perturbation maps”, we propose allostery as a residue-specific concept, suggesting all residues could be considered as allosteric residues because each residue “senses” the allosteric events through perturbation of its one or multiple attributes in a quantitatively unique way. The “residue perturbation maps” could be used to fingerprint a protein based on the unique patterns of residue perturbations upon binding events, providing a novel way to systematically describe the protein allosteric effects of each residue upon perturbation.Author SummaryAllostery is protein regulation at distance. A perturbation at one site of the protein could distantly affect another site. The residues involved in these sites are considered as allosteric residues. The allostery concept has been widely used to understand protein mechanisms and to design allosteric drugs. It is long believed only a small number of residues are allosteric residues. Here, we argue that all residues in a protein are allosteric residues. Upon the perturbation of the allosteric events, the different properties of each residue are affected at the distinct extend. We used hybrid models including molecular dynamics simulations and machine learning components to reveal that not only many properties of residues are affected upon ligand binding, but also each residue is affected through perturbation of its various properties, which makes the residue distinguishable from other residues. According to our findings in a model protein, we defined a “residue perturbation map” as a two-dimensional map that fingerprint a protein based on the extent of perturbation in different properties of all its residues in a quantitative fashion. This “residue perturbation map” provides a novel way to systematically describe the protein allosteric effects of each residue upon perturbation.

Infrared Aerial Small Target Detection with NSCT and Two-Dimensional Property Histogram

A novel algorithm is presented based on non-subsampled contourlet transform (NSCT) and two dimension property histogram in order to realize the aerial small target detection of infrared imaging under complex background. First, this method transforms the infrared image from space domain to NSCT domain. In high frequency bandpass domain, this method describes the sub-band coefficients according to Gaussian scale mixture model based on Bayesian estimation and estimates the center coefficient with the local neighbor’s in order to predict the high frequency background. On the other hand, this method predicts the low frequency background with self-adaption median filter in low frequency lowpass domain. Subsequently, the reversing NSCT is done and the complex background is estimated. By means of subtracting the estimated background image from the source image, the complex background is suppressed and the outstanding small target is acquired. Second, constructing the target’s property set according to the priori knowledge, this method defines the corresponding two-dimensional property histogram which is applied into calculating the segmenting threshold on basis of the maximum entropy method. Subsequently, the infrared image whose complex background is suppressed will be segmented into binary image by the threshold. Finally, infrared small target is detected by the pipeline filter algorithm which makes use of the relativity of the target movement between frames. The experimental results prove the presented method’s effectiveness which can detect the small target whose signal noise ratio (SNR) value is above 2 steadily.