Correction to: Security analysis of an audio data encryption scheme based on key chaining and DNA encoding

Multimedia Tools and Applications - A Correction to this paper has been published: https://doi.org/10.1007/s11042-021-10592-x     

Forecasting conditional correlations in stock,bond and foreign exchange markets

The paper forecasts conditional correlations between three classes of international financial assets, namely stock, bond and foreign exchange. Two countries are considered, namely Australia and New Zealand. Forecasting will be conducted using three multivariate GARCH models, namely the CCC model [T. Bollerslev, Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model, Rev. Econ. Stat. 72 (1990) 498–505], VARMA-GARCH model [S. Ling, M. McAleer, Asymptotic theory for a vector ARMA-GARCH model, Econometric Theory 19 (2003) 280–310], and VARMA-AGARCH model [M. McAleer, S. Hoti, F. Chan, Structure and asymptotic theory for multivariate asymmetric volatility, Econometric Rev., in press]. A rolling window technique is used to forecast 1-day ahead conditional correlations. To evaluate the impact of model specification on conditional correlations forecasts, this paper calculates and compares the correlations between conditional correlations forecasts resulted from the three models. The paper finds the evidence of volatility spillovers and asymmetric effect of negative and positive shock on the conditional variance in most pairs of series. However, it suggests that incorporating volatility spillovers and asymmetric do not contribute to better conditional correlations forecasts.     

Shape estimation of distorted flexible structures

The present work discusses the optimal placement of sensors in truss structures in order to obtain best possible information regarding the distortions of the structure. The estimation goal is to reconstruct the deformed shape of the structure, at the controlled degrees of freedom, from the sensor readings. A basic assumption is that the structure is subjected to a parametric disturbance field. We distinguish between disturbances which cause uniform or arbitrary distortions of the structure, and disturbances which cause structured distortions. Uniform distortions can be construed as white noise, that is, distortions which have no characteristics. Structured distortions are chromatic, they have some characteristics which can be helpful in estimating the shape. Although the disturbance in either case is random it is assumed that its magnitude is confined to a hyper sphere. The estimator is based on the least squares method, hence the estimated shape is the one with least RMS displacement for the given sensor readings. To evaluate the performance of each set of sensors a measure is derived based on the concept of the worst case distortion. The measure is the largest possible error between the estimated and the actual displacements, at the CDOF. For small number of sensors all possible arrangements can be generated and compared. Larger trusses with a moderate number of sensors generate prohibitively large number of possible configurations, hence heuristic search techniques are employed. The theory has been applied to 2D and 3D flexible trusses. Results show that for reasonable shape estimation a relatively large number of sensors is needed. It is also shown that when using sensors which measure mainly the distortions of the controlled degrees of freedom, significant improvements in the shape estimation can be obtained.     

An augmented beam search-based algorithm for the circular open dimension problem

In this paper, we discuss the circular open dimension problem (CODP); that is a problem of the cutting/packing family. In CODP, we are given an initial strip of fixed width W and unlimited length, as well as a finite set N of n circular pieces C_i of known radius r_i,i ∈ N. The objective is to search for a global optimum corresponding to the minimum length of the initial strip containing the n pieces. We propose an augmented algorithm for solving the CODP which combines a beam search, a binary search and the well-known multi-start strategy. In addition, in order to increase the efficiency of the algorithm, we incorporate a strategy based on the separate beams instead of the pooled ones. The performance of the proposed algorithm is evaluated on a set of benchmark instances composed of a group taken from the literature and another group of randomly generated instances. The results show that the proposed algorithm is able to improve several best known solutions of the literature and it remains competitive for the new generated ones.     

Adaptive beam search lookahead algorithms for the circular packing problem

This paper addresses the circular packing problem (CPP), which consists in packing n circles C_i, each of known radius r_i, i∈N={1, …, n}, into the smallest containing circle C. The objective is to determine the radius r of C as well as the coordinates (x_i, y_i) of the center of C_i, i∈N. CPP is solved using two adaptive algorithms that adopt a binary search to determine r, and a beam search to check the feasibility of packing n circles into C when the radius is fixed at r. A node of level ?, ?=1, …, n, of the beam search tree corresponds to a partial packing of ? circles of N into C. The potential of each node of the tree is assessed using a lookahead strategy that, starting with the partial packing of the current node, assigns each unpacked circle to its maximum hole degree position. The beam search stops either when the lookahead strategy identifies a feasible packing or when it has fathomed all nodes. The computational tests on a set of benchmark instances show the effectiveness of the proposed adaptive algorithms.     

Energy inefficiency diagnosis in industrial process through one-class machine learning techniques

In the era of Industry 4.0, the ease of access to precise measurements in real-time and the existence of machine-learning (ML) techniques will play a vital role in building practical tools to isolate inefficiencies in energy-intensive processes. This paper aims at developing an abnormal event diagnosis (AED) tool based on ML techniques for monitoring the operation of industrial processes. This tool makes it easier for operators to accomplish their tasks and to make quick and accurate decisions to ensure highly efficient processes. One of the most popular ML techniques for AED is the multivariate statistical control (MSC) method; it only requires the dataset of the normal operating conditions (NOC) to detect and identify the variables that contribute to abnormal events (AEs). Despite the popularity of MSC, it is challenging to select the appropriate method for detecting and isolating all possible abnormalities a complex industrial process can experience. To address this limitation and improve efficiency, we have developed a generic methodology that integrates different ML techniques into a unified multiagent based approach, the selected ML techniques are supposed to be built using only the normal operating condition. For the sake of demonstration, we chose a combination of two ML methods: principal component analysis and k-nearest neighbors (k-NN). The k-NN was integrated into the proposed multiagent to take into account the nonlinearity and multimodality that frequently occur in industrial processes. In addition, we modified a k-NN method proposed in the literature to reduce computation time during real-time detection and isolation. Finally, the proposed methodology was successfully validated to monitor the energy efficiency of a reboiler located in a thermomechanical pulp mill.

     

Tackling Complexity in High Performance Computing Applications

We present a software framework that supports the specification of user-definable configuration options in HPC applications independently of the application code itself. Such options include model parameter values, the selection of numerical algorithm, target platform etc. and additional constraints that prevent invalid combinations of options from being made. Such constraints, which are capable of describing complex cross-domain dependencies, are often crucial to the correct functioning of the application and are typically either completely absent from the code or a hard to recover from it. The framework uses a combination of functional workflows and constraint solvers. Application workflows are built from a combination of functional components: higher-order co-ordination forms and first-order data processing components which can be either concrete or abstract, i.e. without a specified implementation at the outset. A repository provides alternative implementations for these abstract components. A constraint solver, written in Prolog, guides a user in making valid choices of parameters, implementations, machines etc. for any given context. Partial designs can be stored and shared providing a systematic means of handling application use and maintenance. We describe our methodology and illustrate its application in two classes of application: a data intensive commercial video transcoding example and a numerically intensive incompressible Navier–Stokes solver.     

Self-generation of reward by logarithmic transformation of multiple sensor evaluations

Artificial Life and Robotics - Although the design of the reward function in reinforcement learning is important, it is difficult to design a system that can adapt to a variety of environments and...     

Discrete kinetic theory for 2D modeling of a moving crowd: Application to the evacuation of a non-connected bounded domain

This paper concerns the mathematical modeling of the motion of a crowd in a non connected bounded domain, based on kinetic and stochastic game theories. The proposed model is a mesoscopic probabilistic approach that retains features obtained from both micro- and macro-scale representations; pedestrian interactions with various obstacles being managed from a probabilistic perspective. A proof of the existence and uniqueness of the proposed mathematical model’s solution is given for large times. A numerical resolution scheme based on the splitting method is implemented and then applied to crowd evacuation in a non connected bounded domain with one rectangular obstacle. The evacuation time of the room is then calculated by our technique, according to the dimensions and position of a square-shaped obstacle, and finally compared to the time obtained by a deterministic approach by means of randomly varying some of its parameters.     

A wideband rectifier array on dual‐polarized differential‐feed fractal slotted ground antenna for RF energy harvesting

This article reports a novel wideband rectenna for RF energy harvesting applications. A wideband fractal slotted ground antenna (SGA) is adopted. The operating frequency bands of the antenna are GSM, UMTS, Wi‐Fi, and LTE2600/4G bands. The antenna is fed by a dual‐polarized and differential‐feed (DP‐DF) microstrip lines disposed with an angle of 90° each relative to the other. The feed lines are etched on the bottom side of the substrate and connected to an array of four wideband RF‐to‐DC rectifiers. A nonuniform transmission lines filter ensures wideband behavior for each rectifier. The rectenna performances are simulated and measured. The experiments show an output DC voltage of 1 V at a power density of 26.6 μW/cm² over the frequency band of operation with a peak efficiency of 50%. The proposed rectenna is suitable for energy harvesting applications in urban environments.