Application In Real Systems Research Articles

Conflict evidence management in fault diagnosis

Dempster–Shafer (D–S) theory of evidence is widely used in many real application systems. It can not only deal with imprecise and uncertain information but also combine evidences of different sensors. Therefore it plays an important role in multi-sensor reports’ combination in fault diagnosis. However, when the evidences highly conflict with others, Dempster’s combination rule may lead to a counter-intuitive result and come to a wrong conclusion. It is inevitable to handle conflict in fault diagnosis. This paper proposes a new method to address the issue. Deng entropy function is adopted to measure the information volume of evidences. Evidence distance is introduced to represent the compatibility of evidences. An improved combination method considering both the uncertainty of evidences and the conflict degree of the system is proposed. The proposed method can deal with conflicting evidences efficiently. An application in fault diagnosis is illustrated to show the efficiency of the new method and the result is compared with that of other methods. Besides, and example in IRIS based on information fusion is given to validate the accuracy of the proposed method.

Interaction Study Between Humin and Phosphate: Possible Environmental Remediation for Domestic Wastewater

The increase in demand for food due to the rapid population growth in recent years has raised the use of fertilizers, particularly phosphate salts. This fact has contributed to the excess amount of phosphorus species in aquatic systems. This is due to the leaching of these species present in the fertilizers applied to the soil to aquatic environments and may lead to eutrophication in these environments. Substances capable of interacting with the phosphate in the aquatic environment are promising for the reduction on the environmental impact. The humin, an insoluble fraction of humic material, has potential for phosphate retention, behaving like a chelating resin. Thus, the purpose of this research was to study the interaction between humin and phosphate. The equilibrium time between humin and phosphate was 15 min, where hydrogenionic potential (pH) 4.0 was the most effective in the interaction process. In this pH, the humin retained 33% of phosphate added. The complexing capacity of the humin-phosphate system was 11.53 mg g−1. The adsorption studies indicated that the system follows a kinetic pseudo-second-order model. The Freundlich model was the most suitable to describe the phosphate adsorption process in humin. To evaluate the humin application in real systems, humin was added to the domestic wastewater. Sixteen percent of the total phosphate was adsorbed by the humin. Based on these results, humin has the potential to phosphate retention in domestic wastewater and could be used as a chelating resin minimizing environmental impact.

A fuzzy approach to reliability analysis

Most of the existing approaches for fuzzy reliability analysis are based on fuzzy probability. The aim of this paper is to describe fuzzy reliability using fuzzy differential equation. The reliability of a system in real world applications is affected by some uncertain parameters. Fuzzy reliability is a way to present the reliability function uncertainly using fuzzy parameters. In the proposed fuzzy differential equation for reliability, two types of fuzzy derivative: Hukuhara derivative and generalized differentiability are used. It is proved that the Hukuhara differentiability is not adequate for fuzzy reliability analysis. Finally, using the fuzzy integration, the concept of fuzzy mean time to failure (FMTTF) will be introduced. Some numerical simulations are presented to show the applicability and validity of generalized differentiability, in comparison with the Hukuhara differentiability results for fuzzy reliability analysis.

Graphics in Combination Handguns Preparation Classification (Gaps)

The goal of the article is to present a novel Multimedia Firearms Training Classification. The system was developed in order to compensate for major problems of existing shooting training systems. Designed and implemented solution can be characterized by five major advantages: algorithm for automatic geometric calibration, algorithm of photometric recalibration, firearms hit point detection using thermal imaging camera, IR laser spot tracking algorithm for after action review analysis, implementation of ballistics equations. Combination of the abovementioned advantages in a single multimedia firearms training system creates a comprehensive solution for detecting and tracking of the target point usable for shooting training systems and improving intervention tactics of uniformed services. Introduced algorithms of geometric and photometric recalibration allows using of the economically viable commercially available projectors for systems that require long and intensive use without most of negative impacts on color mapping of existing multi-projector multimedia shooting range systems. The article presents the results of the developed algorithms and their application in real training systems.

An Approach on Non-Discriminatory Losses Charge Allocation for Deregulated Power Market Using Meta-Heuristic-Optimization-Based-Electricity-Tracing (MOET)

This paper presents an alternative technique for losses charge allocation using electricity tracing theory. The meta-heuristic optimization technique was incorporated to perform the tracing process of powers and losses contributed by individual generator and load. In producing a better optimization engine, hybridization between the well known Genetic Algorithm (GA) and continuous domain Ant Colony Optimization (ACOR) was performed for fast optimization process with optimal solution. The proposed method gives reliable perspective when allocating the losses charge on consumers as it is free from assumption and also applicable in any conditions of power system. Comparative studies and analysis have justified the capability of the proposed method for non-discriminatory losses charge allocation in deregulated power market. Furthermore, comparison between various meta-heuristic algorithms and analysis on their performance has reduced the doubt for real system application.

Feasibility of Locating Leakages in Sewage Pressure Pipes Using the Distributed Temperature Sensing Technology

The cost effective maintenance of underwater pressure pipes for sewage disposal in Austria requires the detection and localization of leakages. Extrusion of wastewater in lakes can heavily influence the water and bathing quality of surrounding waters. The Distributed Temperature Sensing (DTS) technology is a widely used technique for oil and gas pipeline leakage detection. While in pipeline leakage detection, fiber optic cables are installed permanently at the outside or within the protective sheathing of the pipe; this paper aims at testing the feasibility of detecting leakages with temporary introduced fiber optic cable inside the pipe. The detection and localization were tested in a laboratory experiment. The intrusion of water from leakages into the pipe, producing a local temperature drop, served as indicator for leakages. Measurements were taken under varying measurement conditions, including the number of leakages as well as the positioning of the fiber optic cable. Experiments showed that leakages could be detected accurately with the proposed methodology, when measuring resolution, temperature gradient and measurement time were properly selected. Despite the successful application of DTS for leakage detection in this lab environment, challenges in real system applications may arise from temperature gradients within the pipe system over longer distances and the placement of the cable into the real pipe system.

Energetically self-sufficient robot group study kit

The article considers the project of educational and research software and hardware kit designed for use in case-based educational project activities based on the theme of energetically autonomous robots. An important feature of the complex is its interdisciplinarity since robotics combines many fields of science and technology — mechanical design, electronics, programming, elements of artificial intelligence, energy science and others. Students can receive basic knowledge in these areas as well as gain practical skills in solving real problems that require a convergent approach. The kit serves as an experimental basis that allows to study intricacies of robot control and its hardware and software design while making structural changes, including using different power units: solar, fuel, thermoelectric and others. Power units (converters and the various sources of renewable energy) may be considered both from the theoretical point of view, i.e. from the principles of their functioning and from the practical aspect — studying their practical application in real systems. Another important aspect of the system is the development of software architecture for a robot and for a team of robots as well as studying the interactions between them. In addition to performing the target task, ancillary tasks such as maintaining the battery level, communication with other team members in a multi-agent system, should be considered in the control algorithm of the robot, which allows to study the distribution of priorities between these objectives, as shown in the example of multicriteria optimization. The prototype of the kit and its usage according to the described approach have been partially validated by conducting computational experiments with algorithms based on different search methods (random search, closest source and multi-criteria optimization methods), multi-agent paradigm and adaptive control which show a variety of possible approaches and illustrate the process of students' work. The hardware base has been validated by testing various energy modules and assembling robot using the modules of the proposed kit. The results showed the possibility and potential of studying a variety of interdisciplinary themes using the developed hardware and software complex.

Improved Control Strategy of Variable Speed Pumps in Complex Chilled Water Systems Involving Plate Heat Exchangers

The low delta-T syndrome is one of the major faults that affect the operation and energy performance of the chilled water systems in real applications, particularly for the complex chilled water systems. Low delta-T syndrome refers to the situation where the measured mean temperature difference of the overall terminal air-handling units is much lower than the expected value. This paper presents an online robust control strategy for practical applications to avoid the low delta-T syndrome for chilled water systems including complex systems. In this robust control strategy, a temperature set-point reset scheme is developed aiming at providing the reliable temperature set-point for enhancing the operation reliability of chilled water pumps. In addition, a flow-limiting control scheme is employed to perform the function of actively eliminating the deficit flow in the bypass line by a feedback mechanism. This robust pump speed control strategy has been implemented and evaluated on a real com-plex chilled water system in a high-rise building. The site test results show that the temperature set-point given by the proposed strategy is reliable and the system temperature difference is significantly raised by eliminating the deficit flow problem. When compared to the conventional control strategies, 77.8% of the total chilled water pump energy was saved in the test period. The actual total pump energy saving was 140,776 kWh (39.4%) in the test of a year after implementing the robust control strategy in the studied system.

Reversible and irreversible aggregation of magnetic liposomes.

Understanding stabilization and aggregation in magnetic nanoparticle systems is crucial to optimizing the functionality of these systems in real physiological applications. Here we address this problem for a specific, yet representative, system. We present an experimental and analytical study on the aggregation of superparamagnetic liposomes in suspension in the presence of a controllable external magnetic field. We study the aggregation kinetics and report an intermediate time power law evolution and a long time stationary value for the average aggregate diffusion coefficient, both depending on the magnetic field intensity. We then show that the long time aggregate structure is fractal with a fractal dimension that decreases upon increasing the magnetic field intensity. By scaling arguments we also establish an analytical relation between the aggregate fractal dimension and the power law exponent controlling the aggregation kinetics. This relation is indeed independent on the magnetic field intensity. Despite the superparamagnetic character of our particles, we further prove the existence of a population of surviving aggregates able to maintain their integrity after switching off the external magnetic field. Finally, we suggest a schematic interaction scenario to rationalize the observed coexistence between reversible and irreversible aggregation.

High Precision Traffic Monitoring Alert System using Image Background Subtraction

Objective: Monitoring the road traffic is helpful to know about the traffic density of a particular location, which helps in redirecting the traffic when crowded. In this paper, traffic density is monitored and vehicle count is estimated using background subtraction algorithm. Methods: The proposed technique utilizes a novel Motion Detection and Running Gaussian Average (MDRGA) recursive technique. The frame is captured from the input video sequence and is transformed to 0 to 255 gray levels from RGB color mode. This conversion considers the RGB values for each pixel and yield the corresponding reflectance value proportional to the brightness percentage of that pixel. The edge detection is then performed on the gray converted by locating the pixels of the image that correspond to the edges. Findings: The proposed system detects motion by comparing sequential frames crossing an imaginary line marked in the reference frame. Thus, the traffic density is detected and the proposed method is found to be efficient in terms of cost, quality and accuracy. This algorithm fetches the input video quickly from camera or storage device, processes immediately and gives the count of vehicles. This proposed system avoids occlusion problems and gives better results. Applications/ Improvements: This algorithm is helpful to avoid traffic collisions at peak areas, to avoid accidents and to reach the destination on time. Thus, the proposed system finds its application in real time systems and provides risk free environment.

Synchronization in slowly switching networks of coupled oscillators.

Networks whose structure of connections evolves in time constitute a big challenge in the study of synchronization, in particular when the time scales for the evolution of the graph topology are comparable with (or even longer than) those pertinent to the units’ dynamics. We here focus on networks with a slow-switching structure, and show that the necessary conditions for synchronization, i.e. the conditions for which synchronization is locally stable, are determined by the time average of the largest Lyapunov exponents of transverse modes of the switching topologies. Comparison between fast- and slow-switching networks allows elucidating that slow-switching processes prompt synchronization in the cases where the Master Stability Function is concave, whereas fast-switching schemes facilitate synchronization for convex curves. Moreover, the condition of slow-switching enables the introduction of a control strategy for inducing synchronization in networks with arbitrary structure and coupling strength, which is of evident relevance for broad applications in real world systems.

Continuous Force Decoding from Local Field Potentials of the Primary Motor Cortex in Freely Moving Rats.

Local field potential (LFP) signals recorded by intracortical microelectrodes implanted in primary motor cortex can be used as a high informative input for decoding of motor functions. Recent studies show that different kinematic parameters such as position and velocity can be inferred from multiple LFP signals as precisely as spiking activities, however, continuous decoding of the force magnitude from the LFP signals in freely moving animals has remained an open problem. Here, we trained three rats to press a force sensor for getting a drop of water as a reward. A 16-channel micro-wire array was implanted in the primary motor cortex of each trained rat, and obtained LFP signals were used for decoding of the continuous values recorded by the force sensor. Average coefficient of correlation and the coefficient of determination between decoded and actual force signals were r = 0.66 and R2 = 0.42, respectively. We found that LFP signal on gamma frequency bands (30–120 Hz) had the most contribution in the trained decoding model. This study suggests the feasibility of using low number of LFP channels for the continuous force decoding in freely moving animals resembling BMI systems in real life applications.

An iterative learning approach for anticipatory traffic signal control on urban networks

ABSTRACTTraffic signal control on urban road networks offers high opportunity to improve traffic operations. Among others, anticipatory control determines signal timings to optimize network performance, taking into account explicitly the route choice responses and resulting (equilibrium) network flow patterns. However, the optimal control decisions are usually calculated using equilibrium flow models that are in general only a coarse representation of reality. As a result, model–reality mismatch often leads to suboptimal conditions characterized by unexpected congestion effects. This paper presents a novel method to support control decisions for practical applications in real traffic systems that operate repeatedly, for instance from week to week, month to month, and in the presence of flow measurements. The proposed method generates a sequence of control settings to track the real flow response, by observing errors between modeled flows and the measurements. Improvement in the control performance is achieved by learning from this error information. A theoretical analysis on convergence of the control sequence is provided and the impact of different weighting parameters on the convergence performance is discussed. Numerical examples on a test network confirm the effectiveness of the proposed iterative learning approach in tackling modeling error, as well as a main role of model bias correction in tracking reality. The impact of weighting parameters on its convergence is also illustrated.

Effect of relative humidity on the peeling behavior of a thin film on a rigid substrate.

Inspired by gecko adhesion in humid environments, a modified Kendall's model is established in order to investigate the effect of relative humidity on the interfacial peeling behavior of a thin film adhering on a rigid substrate. When the humidity is less than 90%, a monolayer of water molecules adsorbed on the substrate surface induces a strong disjoining pressure at the interface. As a result, the steady-state peel-off force between the thin film and substrate is significantly enhanced. When the humidity is greater than 90%, water molecules condense into water droplets. Four different peeling models are established on this occasion, depending on the surface wettability of the film and substrate. It is found that the steady-state peel-off force is influenced by the water meniscus in a complicated manner, which is either enhanced or reduced by the water capillarity comparing to that predicted by the classical Kendall's model, i.e., a dry peeling model. It should be noted that, at the vicinity of the wetting transition, the peel-off force of the four models can be reduced to an identical one, which means the four peeling models can transit from one to another continuously. The present model, as an extension of the classical Kendall's one, should be useful not only for understanding gecko adhesion in humid environments, but also for analyzing interface behaviors of a film-substrate system in real applications.

A Sensor-Fusing System for Spatial Circle and Trunk Parameter Estimation

This paper proposes a fusing system consisting of a camera, a line laser, and an inertial measurement unit (IMU) for spatial circle and trunk parameter estimation. The line laser provides a stripe on the object surface that is captured by the camera. When using the system to scan an object, the 3D coordinate points on the surface of the scanned object are calculated with respect to a world coordinate frame by fusing the system movement and laser point coordinates, where the system movement is estimated from the IMU data by solving a smoothing optimization problem. Based on the information from the scanning process, algorithms are derived for detecting the spatial circle’s diameter and the trunk’s parameters. We also performed experiments to verify the feasibility of the system in real applications.

Post-denitrification using alginate beads containing organic carbon and activated sludge microorganisms.

Nitrate concentration in the final effluent is a key issue in pre-denitrification biological treatment systems. This study investigated post-denitrification with alginate beads containing immobilized activated sludge microorganisms and organic carbon source. A batch study was first performed to identify suitable carbon sources among acetate, glucose, calcium tartrate, starch and canola oil on the basis of nitrate removal and bead stability. Canola oil and starch beads exhibited significantly higher denitrification rates, greater bead stability and lower nitrite accumulation (6 mg/L and 10 mg/L, respectively). Glucose and acetate beads showed longer acclimation phases and degraded faster whereas tartrate beads had higher nitrite build-up (39 mg/L) and degraded due to brittleness. Post-denitrification with canola oil and starch beads was investigated in the final clarifier of a coupled upflow bioreactor and aerobic system treating synthetic dairy farm wastewater, and showed a denitrification efficiency of >90%. Beads faded in 12 days due to alginate degradation. Therefore, enhancement in bead strength or use of more stable nontoxic gel would be required to further prolong the treatment. Moreover, this study was conducted at laboratory scale and further research is needed for application in real systems.

Toward growing modular deep neural networks for continuous speech recognition

The performance drop of typical automatic speech recognition systems in real applications is related to their not properly designed structure and training procedure. In this article, a growing modular deep neural network (MDNN) for speech recognition is introduced. According to its structure, this network is pre-trained in a special manner. The ability of the MDNN to grow enables it to implement spatiotemporal information of the frame sequences at the input and their labels at the output layer at the same time. The trained network with such a double spatiotemporal (DST) structure has learned valid phonetic sequences subspace. Therefore, it can filter out invalid output sequences in its own structure. In order to improve the proposed network performance in speaker variations, two speaker adaptation methods are also presented in this work. In these adaptation methods, the network trains how to move distorted input representations nonlinearly to their optimal positions or to adapt itself based on the input information. To evaluate the proposed MDNN structure and its modified versions, two Persian speech datasets are used: FARSDAT and Large FARSDAT. As there is no frame-level transcription for large vocabulary speech datasets, a semi-supervised learning algorithm is explored to train MDNN on Large FARSDAT. Experimental results on FARSDAT verify that implementing the DST structure besides speaker adaptation methods achieves up to 7.3 and 10.6 % absolute phone accuracy rate improvement over the MDNN and typical hidden Markov model, respectively. Likewise, semi-supervised training of the grown MDNN on Large FARSDAT improves its recognition performance up to 5 %.

Photoinduced Synthesis of Dual-Emissive Tetraphenylethene-Based Dendrimers with Tunable Aggregates and Solution States Emissions.

Photoactive materials are actively researched, piloting breakthroughs that have enriched fundamental understanding of science, and have led to real applications. Tetraphenylethene, a photoactive molecule that is of interest from fundamental and applied perspectives, features photochemical properties that are not exploited in the design of photoactive, dual-emissive materials. Here, tetraphenylethene-based, dual-emissive dendrimers are constructed via a synthetic approach that involves a photochemical reaction that exploits the photochemistry of tetraphenylethene. These dendrimers are emissive in solution and in the aggregate state with tunable dual emissions at 368 and 469 nm. The photochemical reaction also tunes the size of the aggregates, increasing the size after UV irradiation. The reported synthetic strategy is a direct and facile approach to accessing dual-emissive macromolecules, especially tetraphenylethene-based systems for real applications.

All-optical graphene modulator based on optical Kerr phase shift

Graphene-based optical modulators have recently attracted much attention because of their characteristic ultrafast and broadband response. Their modulation depth (MD) and overall transmittance (OT), however, are often limited by optical loss arising from interband transitions. We report here an all-optical, all-fiber optical modulator with a Mach–Zehnder interferometer structure that has significantly higher MD and OT than graphene-based loss modulators. It is based on the idea of converting optically induced phase modulation in the graphene-cladded arm of the interferometer to intensity modulation at the output of the interferometer. The device has the potential to be integrable into a photonic system in real applications.

A new three-dimensional chaotic system, its dynamical analysis and electronic circuit applications

In this paper, a new three-dimensional chaotic system is introduced, which contains the quadratic, cubic and quartic nonlinearities. Basic dynamical characteristics of this new system are studied such as equilibria, eigenvalue structures, Lyapunov exponent spectrum, fractal dimension and chaotic behaviors. Moreover, this paper investigates bifurcation analysis of the proposed chaotic system by means of a selected parameter. The chaotic system has been investigated by detailed numerical as well as theoretical analysis. Amplitude values are important in chaotic systems for real environment applications because of electronic components and materials limitations. Thus, the new chaotic system is rescaled and executed an electronic circuit implementation for real environment application. The obtained results show that this system has complex dynamics with some interesting characteristics and deserves a further detailed investigation. The new chaotic system can be useful in many engineering and scientific applications such as physics, control, cryptology and random number generator (RNG).