Constant Weights Research Articles

LDA Lattices Without Dithering Achieve Capacity on the Gaussian Channel

This paper deals with Low-Density Construction-A (LDA) lattices, which are obtained via Construction A from non-binary low-density parity-check codes. More precisely, a proof is provided that Voronoi constellations of LDA lattices achieve capacity of the AWGN channel under lattice encoding and decoding for every signal-to-noise ratio greater than 1. This is obtained after showing the same result for more general Construction-A lattice constellations. The theoretical analysis is carried out in a way that allows to describe how the prime number underlying Construction A behaves as a function of the lattice dimension. Moreover, no dithering is required in the transmission scheme, simplifying some previous solutions of the problem. Remarkably, capacity is achievable with LDA lattice codes whose parity-check matrices have constant row and column Hamming weights. Some expansion properties of random bipartite graphs constitute an extremely important tool for dealing with sparse matrices and allow to find a lower bound for the minimum Euclidean distance of LDA lattices in our ensemble.

Proton-induced X-ray emission (PIXE) analysis of trace elements of total atmospheric deposit (TAD) around a smelting industry: Aerial pollution monitor

ABSTRACTThis study monitored total atmospheric deposit (TAD) around a smelting plant in Ile-Ife, Nigeria to assess the contributions of the industry to Nigeria's air-shed pollution. Samples were collected for 12 consecutive months using an Australian model gauge, harvested after 30 d of exposure, filtered and dried to constant weights using oven. Monthly deposition rates were determined and elemental characterizations of the TAD samples were done using proton-induced X-ray emission technique. Results revealed that the monthly deposition rates ranged from 0.69 to 4.62 gm−3 and was more pronounced in Harmattan. Twenty-three elements were detected; their total elemental concentrations ranged from 1.23 to 13.63 mg m−3 with Fe having the highest concentrations, while Na has the least. Pearson correlation indicated that some elements have common sources and/or similar chemical properties; enrichment factor showed that Fe, Zn, Pb, Mn were extremely enriched in the study area, modified degree of contamination (3.75) and pollution load intensity (1.21) showed that the neighborhoods could be exposed to high air pollution related impacts. Positive matrix factorization model revealed three distinct sources: Smelting emission (63%), biomass burning (17%), and suspended input material emissions (21%). The study concluded that the smelting activities posed a great hazard to receptors around the smelting industry.

Effect of Drying Temperature on the Chemical Qualities of Breadfruit

Drying is described as the reduction of material moisture to the required dryness values as a definite process and it is of major interest in food processing. This study aimed at studying the influence of drying temperature on the functional and pasting properties of breadfruit flour using cabinet dryer with a view of establishing potential industrial utilization of breadfruit flour as food and food ingredients. Freshly harvested breadfruits (matured) (Artocarpus atilis) were obtained from International Institute for Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria. They were washed in clean water to remove adhering latex and dirt and were peeled, rewashed, drained and sliced manually to 3mm thickness for uniformity in drying, and then dried with a cabinet dryer at 50, 60 and 70°C respectively until constant weights were achieved, after which they were milled into flour. The flour samples were screened through a 0.20mm British standard sieve. Pasting and functional properties were determined using standard methods. The variations in the functional and pasting properties were significant at p<0.05 for temperature of 50, 60 and 70°C respectively. The functional properties of the breadfruit flour ranged from 279.17% - 374.73% for oil absorption capacity, 0.34% - 0.46% for bulk density, 93.63% - 94.70% for dispersibility, 200%–800% for foaming capacity, 2% for gelation, 390.32% - 467.04% for water absorption capacity, 137.07% - 230.67% for swelling capacity at 55°C, 217.29% -333.32% for swelling capacity at 65°C, 142.73% - 323.60% for swelling capacity at 75°C, and 4.58% - 6.27% for solubility at 55°C, 3.24% - 6.40% for solubility at 65°C, 1.97% - 6.66% for solubility at 75°C. The pasting properties for the breadfruit flour ranged from 2309.00RVU - 3142.50RVU, 2145.5RVU-2955.5RVU, 159.5VU - 187.5RVU, 4879.00RVU - 7192.50RVU, 2729.50RVU -4237.00RVU, 6.10 - 6.27 and 50.40°C–53.98°C for peak viscosity, trough viscosity, breakdown viscosity, final viscosity, setback viscosity, peak time and pasting temperature. Respectively. There was an increase in functional and pasting properties as the temperature increases. This result showed that the breadfruit flour can be used as food ingredients.

Heterogeneous Network Access Network Selection Algorithm Based on Analytic Hierarchy Process and Improved Variable Weight

Aiming at the problem that the weights of constant weights cannot be changed once they are determined, this paper proposes a network access selection algorithm based on analytic hierarchy process and improved variable weight. The algorithm calculates the weights of indexes based on the analytic hierarchy process, and then introduces the improved punishment variable weight theory. According to user and traffic preferences, the indexes are prioritized. When indexes are flawed, different levels of indexes are subject to different levels of punishment. The algorithm can change the constant weight based on actual parameter values and user preferences, which makes the final decision-making result more scientific and reasonable.

Finite-horizon differential games for missile–target interception system using adaptive dynamic programming with input constraints

ABSTRACTIn this paper, the problem of intercepting a manoeuvring target within a fixed final time is posed in a non-linear constrained zero-sum differential game framework. The Nash equilibrium solution is found by solving the finite-horizon constrained differential game problem via adaptive dynamic programming technique. Besides, a suitable non-quadratic functional is utilised to encode the control constraints into a differential game problem. The single critic network with constant weights and time-varying activation functions is constructed to approximate the solution of associated time-varying Hamilton–Jacobi–Isaacs equation online. To properly satisfy the terminal constraint, an additional error term is incorporated in a novel weight-updating law such that the terminal constraint error is also minimised over time. By utilising Lyapunov's direct method, the closed-loop differential game system and the estimation weight error of the critic network are proved to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is demonstrated by using a simple non-linear system and a non-linear missile–target interception system, assuming first-order dynamics for the interceptor and target.

Dynamic Learning From Adaptive Neural Control of Robot Manipulators With Prescribed Performance

This paper presents dynamic learning from adaptive neural control (ANC) with prescribed tracking error performance for an $ {n}$ -link robot manipulator subjected to unknown system dynamics and external disturbances. To achieve the prescribed performance, a performance function is introduced to describe the performance restrictions on tracking errors, and then specific performance requirements are served as a priori condition of tracking control design. By an error transformation method, the constrained tracking control problem of the original robot manipulator is transformed into the stabilization problem of an unconstrained augmented system. Then, a novel ANC scheme is proposed for the unconstrained system by combining a filter tracking error with radial basis function (RBF) neural network (NN) approximator, and all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. The external disturbances might make it difficult to achieve the accurate convergence of NN weight estimates. To overcome this difficulty, an appropriate state transformation is introduced to transform the closed-loop system into a linear time-varying system with small perturbed terms. Under partial persistent excitation condition of RBF NNs, the convergence of NN weight estimates is guaranteed, and then the experienced knowledge on the unknown robot manipulator dynamics can be stored with NN constant weights. Using the experienced knowledge, a static neural learning control is proposed to improve the system performances without time-consuming online parameter adjustment process, and the proposed learning control can also guarantee the prescribed transient and steady-state tracking control performance. Simulation results demonstrate the effectiveness of the proposed method.

Harvesting Excess Volatility

I investigate the volatility pumping intuition in a simple discrete time version of Luenberger[1998]. The literature argues that investors can benefit from volatility by rebalancing positions to constant weights, thereby trading against past returns. I analyze the pay-off of the trading policy induced by rebalancing to understand its return and risk benefits. When returns are i.i.d., the rebalanced portfolio underperforms in expectation its buy-and-hold counterpart so that the trading benefits have to be risk based. I demonstrate analytically that to improve the expected relative return, prices have to exhibit excess volatility. Mean reversion is needed to make volatility pumping a reality for investors.

Clutter covariance matrix estimation using weight vectors in knowledge‐aided STAP

A knowledge-aided space-time adaptive processing (STAP) is a quite useful technique to suppress non-stationary and heterogeneous clutter. It estimates a covariance matrix by combining a conventional covariance matrix based on secondary data with a synthesised one by prior information. A new combining method is presented, where weight vectors, rather than constant weights, are used to combine two covariance matrices. In this method, the weight vectors are derived in a way to maximise clutter-to-noise ratio of the combined covariance matrix. A numerical simulation is conducted for a bistatic radar scenario where clutter non-stationarity and heterogeneity can be assumed and the performance of the proposed method is demonstrated in terms of clutter suppression and target detection.

A Relation of Dominance for the Bicriterion Bus Routing Problem

Abstract A bicriterion bus routing (BBR) problem is described and analysed. The objective is to find a route from the start stop to the final stop minimizing the time and the cost of travel simultaneously. Additionally, the time of starting travel at the start stop is given. The BBR problem can be resolved using methods of graph theory. It comes down to resolving a bicriterion shortest path (BSP) problem in a multigraph with variable weights. In the paper, differences between the problem with constant weights and that with variable weights are described and analysed, with particular emphasis on properties satisfied only for the problem with variable weights and the description of the influence of dominated partial solutions on non-dominated final solutions. This paper proposes methods of estimation a dominated partial solution for the possibility of obtaining a non-dominated final solution from it. An algorithm for solving the BBR problem implementing these estimation methods is proposed and the results of experimental tests are presented.

The Schramm-Loewner equation for multiple slits

We prove that any disjoint union of finitely many simple curves in the upper half-plane can be generated in a unique way from the chordal multiple-slit Loewner equation with constant weights.

Finite-Horizon $H_{\infty }$ Tracking Control for Unknown Nonlinear Systems With Saturating Actuators.

In this paper, a neural network (NN)-based online model-free integral reinforcement learning algorithm is developed to solve the finite-horizon optimal tracking control problem for completely unknown nonlinear continuous-time systems with disturbance and saturating actuators (constrained control input). An augmented system is constructed with the tracking error system and the command generator system. A time-varying Hamilton-Jacobi-Isaacs (HJI) equation is formulated for the augmented problem, which is extremely difficult or impossible to solve due to its time-dependent property and nonlinearity. Then, an actor-critic-disturbance NN structure-based scheme is proposed to learn the time-varying solution to the HJI equation in real time without using the knowledge of system dynamics. Since the solution to the HJI equation is time-dependent, the form of NNs representation with constant weights and time-dependent activation functions is considered. Furthermore, an extra error is incorporated in order to satisfy the terminal constraints in the weight update law. Convergence and stability proofs are given based on the Lyapunov theory for nonautonomous systems. Two simulation examples are provided to demonstrate the effectiveness of the designed algorithm.

Backward Shifts on Double Sequence Spaces

In this paper, we consider the (weighted) backward shift operators on the double sequence spaces. We establish necessary and sufficient conditions for the pair of backward shifts with constant weights as well as variable weights to be hypercyclic, weakly mixing and mixing.

AHP‐IFM Target: An Innovative Method to Define Reliability Target in an Aerospace Prototype Based on Analytic Hierarchy Process

AbstractReliability target definition is a crucial aspect of any reliability analysis. In literature, there are two types of analysis. The first one, called ‘bottom‐up’, goes back to the system's target using data of units through a fault tree analysis. Reliability data of components could be only partially available, particularly in the case of innovative systems. In the second type of analysis, called ‘top‐down’, starting from similar systems, the target of each unit is defined, by applying allocation techniques. Also, in this case, reliability data of similar systems might not be available, and the choice of the most appropriate technique could be tricky. The purpose of the present research is to combine the advantages of both usual approaches. The newly developed approach is based on the integrated factors method, whose values are adjusted trough a multicriteria method, the analytic hierarchy process, depending on the importance of each factor and each unit. The innovation of the proposed model consists in its dynamism, as most of the literature methods use constant weights for the factors involved in reliability allocation. No method takes into account the assignment of a different level of significance (weight) to different units of the system, simultaneously with the considered factors. The developed approach has been applied on an aerospace prototype system. The results show the goodness of the new method and its ability to overcome the problems noted in literature. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Estimation of fundamental period of reinforced concrete shear wall buildings using self organization feature map

The Self-Organization Feature Map as an unsupervised network is very widely used these days in engineering science. The applied network in this paper is the Self Organization Feature Map with constant weights which includes Kohonen Network. In this research, Reinforced Concrete Shear Wall buildings with different stories and heights are analyzed and a database consisting of measured fundamental periods and characteristics of 78 RC SW buildings is created. The input parameters of these buildings include number of stories, height, length, width, whereas the output parameter is the fundamental period. In addition, using Genetic Algorithm, the structure of the Self-Organization Feature Map algorithm is optimized with respect to the numbers of layers, numbers of nodes in hidden layers, type of transfer function and learning. Evaluation of the SOFM model was performed by comparing the obtained values to the measured values and values calculated by expressions given in building codes. Results show that the Self-Organization Feature Map, which is optimized by using Genetic Algorithm, has a higher capacity, flexibility and accuracy in predicting the fundamental period.

Eigenvalues of Random Matrices with Isotropic Gaussian Noise and the Design of Diffusion Tensor Imaging Experiments.

Tensor-valued and matrix-valued measurements of different physical properties are increasingly available in material sciences and medical imaging applications. The eigenvalues and eigenvectors of such multivariate data provide novel and unique information, but at the cost of requiring a more complex statistical analysis. In this work we derive the distributions of eigenvalues and eigenvectors in the special but important case of m×m symmetric random matrices, D, observed with isotropic matrix-variate Gaussian noise. The properties of these distributions depend strongly on the symmetries of the mean tensor/matrix, D̄. When D̄ has repeated eigenvalues, the eigenvalues of D are not asymptotically Gaussian, and repulsion is observed between the eigenvalues corresponding to the same D̄ eigenspaces. We apply these results to diffusion tensor imaging (DTI), with m = 3, addressing an important problem of detecting the symmetries of the diffusion tensor, and seeking an experimental design that could potentially yield an isotropic Gaussian distribution. In the 3-dimensional case, when the mean tensor is spherically symmetric and the noise is Gaussian and isotropic, the asymptotic distribution of the first three eigenvalue central moment statistics is simple and can be used to test for isotropy. In order to apply such tests, we use quadrature rules of order t ≥ 4 with constant weights on the unit sphere to design a DTI-experiment with the property that isotropy of the underlying true tensor implies isotropy of the Fisher information. We also explain the potential implications of the methods using simulated DTI data with a Rician noise model.

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

Development of feedstocks based on steel matrix composites for metal injection moulding

Feedstock behaviour during processing via powder injection moulding (PIM) has a critical influence on the properties of the final parts. The objective of the present study is to analyze the effect of vanadium carbide (VC) addition (1, 3, 6, 10 and 14wt.% VC) on the rheological response and stability of AISI M2 high speed steel PIM feedstocks. A binder system consisting of high density polyethylene (HDPE) and paraffin wax (PW) is proposed. The effects of VC concentration, powder loading, apparent viscosity and temperature have been investigated via capillary rheometer. The feedstocks with a 14wt.% of VC, which exhibited a variable flow behaviour was discarded. The rest of the feedstocks showed a pseudoplastic behaviour which is suitable for injection moulding and in particular, those reinforced with 6 and 10wt.% of VC exhibited the best rheological responses. In order to maximize the powder loading and ensure safety conditions, the feedstocks reinforced with 6wt.% (5.9vol.%) and 10wt.% (9.8vol.%) of VC with 68vol.% of powder loading and 3wt.% (2.8vol.%) of VC with 62vol.% of powder loading were selected to continue the rheological analysis. Rheological parameters such as flow behaviour index (n), yield stress (ơy), flow activation energy (Ea) and the general mouldability index (αstv) have been analyzed for the above mentioned feedstocks and showed suitable values for the injection moulding. The green parts produced using the three feedstocks have constant weights and dimensions, good mouldability, density, shape retention and stiffness. Therefore, the developed feedstocks are suitable for PIM.

Forecasting the realized range-based volatility using dynamic model averaging approach

In this study, we forecast the realized range-based volatility (RRV) using the heterogeneous autoregressive realized range-based volatility (HAR-RRV) model and its various extensions, which are called HAR-RRV-type models. We first consider the time-varying property of those models’ parameters using the dynamic model averaging (DMA) approach and evaluate the forecasting performance of three types: individual HAR-RRV-type models, combined models with constant weights, and combined models with time-varying weights. Our out-of-sample empirical results show that combined models with time-varying weights can not only generate more accurate forecasts, but also beat individual models and combined models with constant weights.

The effect of material attributes and process parameters on the powder bed uniformity during a low-dose dosator capsule filling process

The objective of this work was to assess the effect of process parameters of a dosator nozzle machine on the powder bed uniformity of inhalation powders with various characteristics during a low-dose dosator capsule filling process. Three grades of lactose excipients were extensively characterized and filled into size 3 capsules using different dosing chamber lengths (2.5, 5mm), nozzle diameters (1.9, 3.4mm), powder bed heights (5, 10mm) and filling speeds (500, 3000capsules/h).The fill weight and the weight variability of Lactohale 100 (large particles, good flowability, low cohesion) remained almost the same, regardless of the process parameters throughout the capsule filling run time. Moreover, for this powder an increase in the fill weight at a higher filling speed was observed in all cases. Fill weight variability was significantly higher for lower dosing chamber volumes at a filling speed of 3000 capsules per hour. Lactohale 220 (small particles, poor flowability, high cohesion) delivered entirely different results. After a certain run time, depending on instrumental settings, a ‘steady-state’ with constant fill weights and low weight variability was achieved. For this highly cohesive powder, a high dosing chamber volume requires a low filling speed in order for the powder to completely fill the dosator nozzle. Moreover, it was established that a dosing chamber length of 2.5mm and a powder bed height of 10mm were required due to the powder’s high fill weight variability over time, while the dosator size had no effect on it.In summary, the layer uniformity, the fill weight and the weight variability strongly depend on the powder characteristics and the instrumental settings. The results indicate that Lactohale 220 requires special attention during low-dose capsule filling. The study presents excellent insights into the effect of material attributes and process parameters on the layer uniformity and the quality of end product.

A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO’s parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate.