A distributed scheduling for agro-food manufacturing problems

Workshop scheduling problems can be considered as one of the main factors to improve the productivity and efficiency of a manufacturing system. The continuous evolution and the dynamic characteristics of industrial workshops, particularly those of agro-food industries, impose the generation of a real time decision scheduling process. In the agro-food industries, the products to be processed and the used primary products are characterized by their limit validity dates which generate some particularly and especially antagonist criteria. A distributed decision support system for real time scheduling is described in this paper. It is based on an original cooperative approach aimed to elaborate robust decisions that propose to the "best action" to the decision maker. The decision process is distributed all along the production chain. At each decision time, in order to ensure coherency, we have to take into account the neighborhood constraints in order to simultaneously satisfy different criteria which do not have the same importance. An aggregation criteria system is defined to manage the criteria among their importance degrees. We define a decisional model based on the evaluation and the comparison of a set of proposed actions.     

Improving the Performance of Feedforward Neural Networks by Noise Injection into Hidden Neurons

The generalization ability of feedforward neural networks (NNs) depends on the size of training set and the feature of the training patterns. Theoretically the best classification property is obtained if all possible patterns are used to train the network, which is practically impossible. In this paper a new noise injection technique is proposed, that is noise injection into the hidden neurons at the summation level. Assuming that the test patterns are drawn from the same population used to generate the training set, we show that noise injection into hidden neurons is equivalent to training with noisy input patterns (i.e., larger training set). The simulation results indicate that the networks trained with the proposed technique and the networks trained with noisy input patterns have almost the same generalization and fault tolerance abilities. The learning time required by the proposed method is considerably less than that required by the training with noisy input patterns, and it is almost the same as that required by the standard backpropagation using normal input patterns.     

Experimental analysis of the influence of the voltage waveform on ozone generation efficiency

Although the dielectric barrier discharge (DBD) has been widely considered and studied for ozone generation, only a few studies have examined the influence of the alternating voltage waveform on the ozone rate production. This paper analyses the influence of the voltage shape on the ozone concentration and the energy efficiency of a DBD cylindrical ozone generator. Three voltage signals were studied using a high-voltage amplifier: sinusoidal, triangular and rectangular signals with voltage values up to 8 kV and frequencies up to 1.1 kHz. The obtained results showed that the efficiency of the ozone generator depends strongly on the type of the voltage waveform. The maximum values of the energy efficiency and the ozone concentration were obtained with the triangular voltage signal. This wave shape configuration has been successfully used for discolouration of water contaminated by textile dye.     

Optimal Sizing of a DBD Ozone Generator Using Response Surface Modeling

Dielectric barrier discharge (DBD) reactors used as ozone generators are well known today and widely used for water treatment and air disinfection. The purpose of this article is to propose an experimental procedure based on the response surface modeling in order to optimize the geometrical dimensions of the cylindrical shape ozone generator, i.e., the discharge gap and the electrodes length. Because an effective ozone generator is expected to give high ozone concentration with a minimum of power requirements, the applied high voltage was associated with the geometrical parameters to carry out a composite centered faces design. Obtained results indicate that for an efficient ozone generator, length of the electrodes needs to be optimized while the discharge gap should be minimized.     

Engineering ATM networks for congestion avoidance

With the combination of telecommunication, entertainment and computer industries, computer networking is adopting a new method called Asynchronous Transfer Mode (ATM) networking. Congestion control plays an important role in the effective and stable operation of ATM networks. Traffic management concerns with the design of a set of mechanisms which ensure that the network bandwidth, buffer and computational resources are efficiently utilized while meeting the various Quality of Service (QoS) guarantees given to sources as part of a traffic contract. In this paper, the most widely recognized congestion control schemes for ABR service are investigated. Some of these schemes show either lack of scalability or fairness while other well‐behaved schemes may require a highly complex switch algorithm that is unsuitable for implementation in cell‐switching high‐speed ATM networks. A new and improved congestion control scheme is proposed to support the best‐effort ABR traffic. This algorithm provides the congestion avoidance ability with high throughput and low delay, in addition to achieving the max–min fairness allocation. This revised version was published online in June 2006 with corrections to the Cover Date.     

Lensless electron reflection microscopy using a coaxial point-source structure

A lensless image of the surface of a crystal is obtained by the reflection on this surface of a low-energy electron beam originated from a point source integrated in a coaxial structure. The point source is a sharp field emission tip and a free propagation of reflected electrons results from the shielding of the tip voltage provided by the coaxial structure. Images are obtained for an incidence angle between 3 and 45 degrees and for nA incident currents with a kinetic energy down to 40 V. On silicon surfaces a magnification up to a few thousands and a spatial resolution of 100 nm are demonstrated.     

Decision support system for urban transportation networks

This paper deals with the real-time regulation of traffic within a disrupted transportation system. We outline the necessity of a decision support system that detects, analyzes, and resolves the unpredicted disturbances. Due to the distributed aspects of transportation systems, we present a multi-agent approach for the regulation process. Moreover, this approach also includes an evolutionary algorithm that is based on an original genetic coding representing the decisions on a set of vehicles and stops affected by the disturbance. This set constitutes, in fact, the space-time horizon of the regulation process. The evolutionary algorithm then treats the regulation problem as an optimization and provides the regulator with relevant decisions that can result in a partial reconfiguration of the network.     

Comparative Experimental Study between Surface and Volume DBD Ozone Generator

Volume Dielectric Barrier Discharge (DBD) is nowadays considered the most effective way for ozone generation in the industry. Some papers were published only on surface discharge reactors applied for ozone generation. This article describes an experimental investigation for the comparison between these two reactor types of ozone generation. Two surface and volume DBD reactors of cylindrical shape were used in the same experimental conditions. Obtained results showed that although the majority of ozone generators are of volume discharge type, the surface DBD presents significant superiority in terms of ozone generation and energy efficiency.     

Estimating Forest Fire Losses Using Stochastic Approach: Case Study of the Kroumiria Mountains (Northwestern Tunisia)

ABSTRACT

Kroumiria Mountains (northwestern Tunisia) have experienced major fires, making them the main loss reason of Tunisian forested areas. The ability of accurately forecasting or modeling forest fire areas may significantly aid optimizing fire-fighting strategies. However, there are still limitations in the empirical study of forest fire loss estimation because the poor availability and low quality of fire data. In this study, a stochastic approach based on Markov process was developed for the prediction of burned areas, using available meteorological data sets and GIS layers related to the forest under analysis. The Self-organizing map (SOM) was initially used to classify spatiotemporal factors influencing the fire behavior. Subsequently, the SOM clusters were incorporated into a Hidden Markov Model (HMM) framework to model their corresponding burned areas. Results achieved using a database of 829 forest fires records between 1985 and 2016, showed the appropriateness of the HMM approach for the prediction of burned areas compared with a state-of-the art machine learning methods. The transition probability matrix (TPM) and the emission probability matrix (EPM) were also analyzed to further understand the spatiotemporal patterns of fire losses.