Integrated optimal topology design and shape optimization using neural networks

In this paper, neural network- and feature-based approaches are introduced to overcome current shortcomings in the automated integration of topology design and shape optimization. The topology optimization results are reconstructed in terms of features, which consist of attributes required for automation and integration in subsequent applications. Features are defined as cost-efficient simple shapes for manufacturing. A neural network-based image-processing technique is presented to match the arbitrarily shaped holes inside the structure with predefined features. The effectiveness of the proposed approach in integrating topology design and shape optimization is demonstrated with several experimental examples.     

A MIP based heuristic for capacitated MRP systems

Although Material Requirements Planning (MRP) is the most widely used production planning tool in today’s manufacturing companies, its inability to perform an exhaustive capacity planning, lack of a comprehensive and integrated shop floor extension and using constant and inflated lead times necessitate intelligent methods for developing cost effective production plans. A single optimization model might be employed to overcome these limitations, but it would be intractable to use it in large manufacturing systems. Hence, in this paper, we propose a heuristic method called Capacity Allocater and Scheduler, CAS, to eliminate drawbacks of MRP systems and provide solutions for large-scale instances. The CAS procedure, based on iteratively solving relaxed Mixed Integer Programming (MIP) models, is built on a lot sizing and scheduling framework, which considers both supply alternatives and lot size restrictions simultaneously. Finally, we give a detailed numerical example to demonstrate how CAS may be used in practice, and provide our concluding remarks.     

A multiagent framework for coordinated parallel problem solving

Today’s organizations, under increasing pressure on the effectiveness and the increasing need for dealing with complex tasks beyond a single individual’s capabilities, need technological support in managing complex tasks that involve highly distributed and heterogeneous information sources and several actors. This paper describes CoPSF, a multiagent system middle-ware that simplifies the development of coordinated problem solving applications while ensuring standard compliance through a set of system services and agents. CoPSF hosts and serves multiple concurrent teams of problem solving contributing both to the limitation of communication overheads and to the reduction of redundant work across teams and organizations. The framework employs (i) an interleaved task decomposition and allocation approach, (ii) a mechanism for coordination of agents’ work, and (iii) a mechanism that enables synergy between parallel teams.     

Asynchronous privacy-preserving iterative computation on peer-to-peer networks

Privacy preserving algorithms allow several participants to compute a global function collaboratively without revealing local information to each other. Examples of applications include trust management, collaborative filtering, and ranking algorithms such as PageRank. Most solutions that can be proven to be privacy preserving theoretically are not appropriate for highly unreliable, large scale, distributed environments such as peer-to-peer (P2P) networks because they either require centralized components, or a high degree of synchronism among the participants. At the same time, in P2P networks privacy preservation is becoming a key requirement. Here, we propose an asynchronous privacy preserving communication layer for an important class of iterative computations in P2P networks, where each peer periodically computes a linear combination of data stored at its neighbors. Our algorithm tolerates realistic rates of message drop and delay, and node churn, and has a low communication overhead. We perform simulation experiments to compare our algorithm to related work. The problem we use as an example is power iteration (a method used to calculate the dominant eigenvector of a matrix), since eigenvector computation is at the core of several practical applications. We demonstrate that our novel algorithm also converges in the presence of realistic node churn, message drop rates and message delay, even when previous synchronized solutions are able to make almost no progress.     

Visual enhancement of underwater images using Empirical Mode Decomposition

Most underwater vehicles are nowadays equipped with vision sensors. However, it is very likely that underwater images captured using optic cameras have poor visual quality due to lighting conditions in real-life applications. In such cases it is useful to apply image enhancement methods to increase visual quality of the images as well as enhance interpretability and visibility. In this paper, an Empirical Mode Decomposition (EMD) based underwater image enhancement algorithm is presented for this purpose. In the proposed approach, initially each spectral component of an underwater image is decomposed into Intrinsic Mode Functions (IMFs) using EMD. Then the enhanced image is constructed by combining the IMFs of spectral channels with different weights in order to obtain an enhanced image with increased visual quality. The weight estimation process is carried out automatically using a genetic algorithm that computes the weights of IMFs so as to optimize the sum of the entropy and average gradient of the reconstructed image. It is shown that the proposed approach provides superior results compared to conventional methods such as contrast stretching and histogram equalizing.     

Multi-valued autoepistemic logic

We generalize Moore's autoepistemic logic to multi-valued autoepistemic logic, where the set of truth-values can be any complete lattice. Multi-valued autoepistemic extensions can be characterized by admissible belief interpretations which are the conrete approximations of extensions and are appropriate to be computed and manipulated. We prove that multi-valued autoepistemic extensions are exactly the theories of maximal multi-valued Kripke models. The class of stratified theories is investigated and it is shown that stratified theories have exactly one multi-valued autoepistemic extension. Finally we present a sequent calculus for multi-valued logic which serves as a tool for a decision procedure for multi-valued autoepistemic logic.     

Investigation deuteron-induced reactions on cobalt

The excitation functions of deuteron-induced reactions were measured on metallic cobalt. Beyond the ^56,57,58,60Co cobalt isotopes, we also identified ⁵⁷Ni, ⁵⁴Mn, ⁵⁶Mn and ⁵⁹Fe in the deuteron experiments. For the above radionuclides, the excitation functions in the measured energy range were determined and compared with the data found in the literature and with the results of model calculations (ALICE-IPPE, EMPIRE-D, EAF, and TALYS (TENDL)). The excitation functions agree with previous measurements; furthermore, we calculated the yield and thin layer activation (TLA) curves that are necessary for practical and industrial applications.     

A closed-form approach for identification of dynamical contact parameters in spindle–holder–tool assemblies

Accurate identification of contact dynamics is very crucial in predicting the dynamic behavior and chatter stability of spindle–tool assemblies in machining centers. It is well known that the stability lobe diagrams used for predicting regenerative chatter vibrations can be obtained from the tool point frequency response function (FRF) of the system. As previously shown by the authors, contact dynamics at the spindle–holder and holder–tool interfaces as well as the dynamics of bearings affect the tool point FRF considerably. Contact stiffness and damping values alter the frequencies and peak values of dominant vibration modes, respectively. Fast and accurate identification of contact dynamics in spindle–tool assemblies has become an important issue in the recent years. In this paper, a new method for identifying contact dynamics in spindle–holder–tool assemblies from experimental measurements is presented. The elastic receptance coupling equations are employed in a simple manner and closed-form expressions are obtained for the stiffness and damping parameters of the joint of interest. Although this study focuses on the contact dynamics at the spindle–holder and holder–tool interfaces of the assembly, the identification approach proposed in this paper might as well be used for identifying the dynamical parameters of bearings, spindle–holder interface and as well as other critical joints. After presenting the mathematical theory, an analytical case study is given for demonstration of the identification approach. Experimental verification is provided for identification of the dynamical contact parameters at the holder–tool interface of a spindle–holder–tool assembly.