Self-mated tribological systems based on multilayer micro/nanocrystalline CVD diamond coatings

The tribological response of multilayer micro/nanocrystalline diamond coatings grown by the hot filament CVD technique is investigated. These multigrade systems were tailored to comprise a starting microcrystalline diamond (MCD) layer with high adhesion to a silicon nitride (Si3N4) ceramic substrate, and a top nanocrystalline diamond (NCD) layer with reduced surface roughness. Tribological tests were carried out with a reciprocating sliding configuration without lubrication. Such composite coatings exhibit a superior critical load before delamination (130–200 N), when compared to the mono- (60–100 N) and bilayer coatings (110 N), considering ∼10 µm thick films. Regarding the friction behaviour, a short-lived initial high friction coefficient was followed by low friction regimes (friction coefficients between 0.02 and 0.09) as a result of the polished surfaces tailored by the tribological solicitation. Very mild to mild wear regimes (wear coefficient values between 4.1×10−8 and 7.7×10−7 mm3 N−1 m−1) governed the wear performance of the self-mated multilayer coatings when subjected to high-load short-term tests (60–200 N; 2 h; 86 m) and medium-load endurance tests (60 N; 16 h; 691 m).

Self-similarity principle: the reduced description of randomness

A new general fitting method based on the Self-Similar (SS) organization of random sequences is presented. The proposed analytical function helps to fit the response of many complex systems when their recorded data form a self-similar curve. The verified SS principle opens new possibilities for the fitting of economical, meteorological and other complex data when the mathematical model is absent but the reduced description in terms of some universal set of the fitting parameters is necessary. This fitting function is verified on economical (price of a commodity versus time) and weather (the Earth’s mean temperature surface data versus time) and for these nontrivial cases it becomes possible to receive a very good fit of initial data set. The general conditions of application of this fitting method describing the response of many complex systems and the forecast possibilities are discussed.

Self-adaptive combination of global tabu search and local search for nonlinear equations

Solving systems of nonlinear equations is a very important task since the problems emerge mostly through the mathematical modelling of real problems that arise naturally in many branches of engineering and in the physical sciences. The problem can be naturally reformulated as a global optimization problem. In this paper, we show that a self-adaptive combination of a metaheuristic with a classical local search method is able to converge to some difficult problems that are not solved by Newton-type methods.

Scheduling a cutting and treatment stainless steel sheet line with self-management capabilities

With advancement in computer science and information technology, computing systems are becoming increasingly more complex with an increasing number of heterogeneous components. They are thus becoming more difficult to monitor, manage, and maintain. This process has been well known as labor intensive and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an innovative framework for scheduling system management by combining Autonomic Computing (AC) paradigm, Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems

Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined.

On-line self-healing of circuits implemented on reconfigurable FPGAs

To boost logic density and reduce per unit power consumption SRAM-based FPGAs manufacturers adopted nanometric technologies. However, this technology is highly vulnerable to radiation-induced faults, which affect values stored in memory cells, and to manufacturing imperfections. Fault tolerant implementations, based on Triple Modular Redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like module placement, the effects of multi- bit upsets (MBU) or fault accumulation, have also to be addressed. In case of a fault occurrence the correct operation of the affected module must be restored and/or the current state of the circuit coherently re-established. A solution that enables the autonomous restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in real-time, while keeping the normal operation of the circuit, is presented in this paper.

A framework for self-healing radiation-tolerant implementations on reconfigurable FPGAs

To increase the amount of logic available in SRAM-based FPGAs manufacturers are using nanometric technologies to boost logic density and reduce prices. However, nanometric scales are highly vulnerable to radiation-induced faults that affect values stored in memory cells. Since the functional definition of FPGAs relies on memory cells, they become highly prone to this type of faults. Fault tolerant implementations, based on triple modular redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like the effects of multi-bit upsets (MBU) or fault accumulation, have also to be addressed. Furthermore, in case of a fault occurrence the correct operation of the affected module must be restored and the current state of the circuit coherently re-established. A solution that enables the autonomous correct restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in realtime, while keeping the normal operation of the circuit, is presented in this paper.

A self-healing real-time system based on run-time self-reconfiguration

The new generations of SRAM-based FPGA (field programmable gate array) devices are the preferred choice for the implementation of reconfigurable computing platforms intended to accelerate processing in real-time systems. However, FPGA's vulnerability to hard and soft errors is a major weakness to robust configurable system design. In this paper, a novel built-in self-healing (BISH) methodology, based on run-time self-reconfiguration, is proposed. A soft microprocessor core implemented in the FPGA is responsible for the management and execution of all the BISH procedures. Fault detection and diagnosis is followed by repairing actions, taking advantage of the dynamic reconfiguration features offered by new FPGA families. Meanwhile, modular redundancy assures that the system still works correctly

Negotiation mechanism for self-organized scheduling system with collective intelligence

Current Manufacturing Systems challenges due to international economic crisis, market globalization and e-business trends, incites the development of intelligent systems to support decision making, which allows managers to concentrate on high-level tasks management while improving decision response and effectiveness towards manufacturing agility. This paper presents a novel negotiation mechanism for dynamic scheduling based on social and collective intelligence. Under the proposed negotiation mechanism, agents must interact and collaborate in order to improve the global schedule. Swarm Intelligence (SI) is considered a general aggregation term for several computational techniques, which use ideas and inspiration from the social behaviors of insects and other biological systems. This work is primarily concerned with negotiation, where multiple self-interested agents can reach agreement over the exchange of operations on competitive resources. Experimental analysis was performed in order to validate the influence of negotiation mechanism in the system performance and the SI technique. Empirical results and statistical evidence illustrate that the negotiation mechanism influence significantly the overall system performance and the effectiveness of Artificial Bee Colony for makespan minimization and on the machine occupation maximization.

Double power laws, fractals and self-similarity

Power law (PL) distributions have been largely reported in the modeling of distinct real phenomena and have been associated with fractal structures and self-similar systems. In this paper, we analyze real data that follows a PL and a double PL behavior and verify the relation between the PL coefficient and the capacity dimension of known fractals. It is to be proved a method that translates PLs coefficients into capacity dimension of fractals of any real data.

Minimization of open orders using interval graphs

In this paper we address an order processing optimization problem known as the Minimization of Open Stacks Problem (MOSP). This problem consists in finding the best sequence for manufacturing the different products required by costumers, in a setting where only one product can be made at a time. The objective is to minimize the maximum number of incomplete orders from costumers that are being processed simultaneously. We present an integer programming model, based on the existence of a perfect elimination order in interval graphs, which finds an optimal sequence for the costumers orders. Among other economic advantages, manufacturing the products in this optimal sequence reduces the amount of space needed to store incomplete orders.

Sequencing cutting patterns with colored interval graphs

The problem addressed here originates in the industry of flat glass cutting and wood panel sawing, where smaller items are cut from larger items accordingly to predefined cutting patterns. In this type of industry the smaller pieces that are cut from the patterns are piled around the machine in stacks according to the size of the pieces, which are moved to the warehouse only when all items of the same size have been cut. If the cutting machine can process only one pattern at a time, and the workspace is limited, it is desirable to set the sequence in which the cutting patterns are processed in a way to minimize the maximum number of open stacks around the machine. This problem is known in literature as the minimization of open stacks (MOSP). To find the best sequence of the cutting patterns, we propose an integer programming model, based on interval graphs, that searches for an appropriate edge completion of the given graph of the problem, while defining a suitable coloring of its vertices.

Using interval graphs in an order processing optimization problem

In this paper we address an order processing optimization problem known as minimization of open stacks (MOSP). We present an integer pro gramming model, based on the existence of a perfect elimination scheme in interval graphs, which finds an optimal sequence for the costumers orders.

Novel LTCC-potentiometric microfluidic device for biparametric analysis of organic compounds carrying plastic antibodies as ionophores: Application to sulfamethoxazole and trimethoprim

Monitoring organic environmental contaminants is of crucial importance to ensure public health. This requires simple, portable and robust devices to carry out on-site analysis. For this purpose, a low-temperature co-fired ceramics (LTCC) microfluidic potentiometric device (LTCC/μPOT) was developed for the first time for an organic compound: sulfamethoxazole (SMX). Sensory materials relied on newly designed plastic antibodies. Sol–gel, self-assembling monolayer and molecular-imprinting techniques were merged for this purpose. Silica beads were amine-modified and linked to SMX via glutaraldehyde modification. Condensation polymerization was conducted around SMX to fill the vacant spaces. SMX was removed after, leaving behind imprinted sites of complementary shape. The obtained particles were used as ionophores in plasticized PVC membranes. The most suitable membrane composition was selected in steady-state assays. Its suitability to flow analysis was verified in flow-injection studies with regular tubular electrodes. The LTCC/μPOT device integrated a bidimensional mixer, an embedded reference electrode based on Ag/AgCl and an Ag-based contact screen-printed under a micromachined cavity of 600 μm depth. The sensing membranes were deposited over this contact and acted as indicating electrodes. Under optimum conditions, the SMX sensor displayed slopes of about −58.7 mV/decade in a range from 12.7 to 250 μg/mL, providing a detection limit of 3.85 μg/mL and a sampling throughput of 36 samples/h with a reagent consumption of 3.3 mL per sample. The system was adjusted later to multiple analyte detection by including a second potentiometric cell on the LTCC/μPOT device. No additional reference electrode was required. This concept was applied to Trimethoprim (TMP), always administered concomitantly with sulphonamide drugs, and tested in fish-farming waters. The biparametric microanalyzer displayed Nernstian behaviour, with average slopes −54.7 (SMX) and +57.8 (TMP) mV/decade. To demonstrate the microanalyzer capabilities for real applications, it was successfully applied to single and simultaneous determination of SMX and TMP in aquaculture waters.

Analysis of self-interference within DAG tasks

6th Real-Time Scheduling Open Problems Seminar (RTSOPS 2015), Lund, Sweden.