Disorders of the heart and blood vessels are the leading cause of health problems and death. Early detection of them is extremely valuable as it can prevent serious incidents (e.g. heart attack, stroke) and associated complications. This requires extending the typical mobile monitoring methods (e.g. Holter ECG, tele-ECG) by introduction of integrated, multiparametric solutions for continuous monitoring of the cardiovascular system.
In this paper we propose the wearable system that integrates measurements of cardiac data with actual estimation of the cardiovascular risk level. It consists of two wirelessly connected devices, one designed in the form of a necklace, the another one in the form of a bracelet (wrist watch). These devices enable continuous measurement of electrocardiographic, plethysmographic (impedance-based and optical-based) and accelerometric signals. Collected signals and calculated parameters indicate the electrical and mechanical state of the heart and are processed to estimate a risk level. Depending on the risk level an appropriate alert is triggered and transmitted to predefined users (e.g. emergency departments, the family doctor, etc.).
The In this paper stabilisation problem of LC ladder network is established. We studied the following cases: stabilisation by inner
resistance, by velocity feedback and stabilisation by dynamic linear feedback, in particularly stabilisation by first range dynamic feedback. The global asymptotic stability of the respectively system is proved by LaSalle’s theorem. In the proof the observability of the dynamic system plays an essential role. Numerical calculations were made using the Matlab/Simulink program.
Leak detection in transmission pipelines is important for safe operation of pipelines. The probability of leaks may be occurred at any time and location, therefore pipeline leak detection systems play a key role in minimization of the occurrence of leaks probability and their impacts. During the operation of the network there are various accidents or intentional actions that lead to leaks of gas pipelines. For each network failure, a quick reaction is needed before it causes more damage. Methods that are used to detect such network failures are three-staged-: early identification of leakage, an accurate indication of its location and determine the amount of lost fluid. Methods for leak detection can be divided into two main groups: external methods (hardware) and internal methods (software). External leak detection methods require additional, often expensive equipment mounted on the network, or use systems that could display only local damage on the pipeline. The alternative are the internal methods which use available network measurements and signalling gas leakage signal based on the mathematical models of the gas flow. In this paper, a new method of leak detection based on a mathematical model of gas flow in a transient state has been proposed.
This paper deals with two control algorithms which utilize learning of their models’ parameters. An adaptive and artificial neural network control techniques are described and compared. Both control algorithms are implemented in MATLAB and Simulink environment, and they are used in the simulation of a postion control of the LWR 4+ manipulator subjected to unknown disturbances. The results, showing the better performance of the artificial neural network controller, are shown. Advantages and disadvantages of both controllers are discussed. The usefulness of the learning algorithms for the control of LWR 4+ robots is discussed. Preliminary experiments dealing with dynamic properties of the two LWR 4+ robots are reported.
The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag
networks is one of the important issues in the field of RFID, which affects the reading performance of
RFID multi-tag networks. In this paper, a novel method for 3D coordinate measurement of RFID multitag
networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of
RFID multi-tag networks from different angles. The iterative threshold segmentation and the morphological
filtering method are used to process the images. The template matching method is respectively used to
determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag. After that, the
3D coordinate of each tag is obtained. Finally, a back-propagation (BP) neural network is used to model
the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance.
The BP neural network can predict the reading distances of unknown tag groups and find out the optimal
distribution structure of the tag groups corresponding to the maximum reading distance. In the future work,
the corresponding in-depth research on the neural network to adjust the distribution of tags will be done.
The active distribution network (ADN) represents the future development of distribution networks, whether the islanding phenomenon occurs or not determines the control strategy adopted by the ADN. The best wavelet packet has a better time-frequency characteristic than traditional wavelet analysis in the different signal processing, because it can extract better and more information from the signal effectively. Based on wavelet packet energy and the neural network, the islanding phenomenon of the ADN can be detected. Firstly, the wavelet packet is used to decompose current and voltage signals of the public coupling point between the distributed photovoltaic (PV) system and power grid, and calculate the energy value of each decomposed frequency band. Secondly, the network is trained using the constructed energy characteristic matrix as a neural network learning sample. At last, in order to achieve the function of identification for islanding detection, lots of samples are trained in the neural network. Based on the actual circumstance of PV operation in the ADN, the MATLAB/SIMULINK simulation model of the ADN is established. After the simulation, there are good output results, which show that the method has the characteristics of high identification accuracy and strong generalization ability.
Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.
The number of publications inspired by Bruno Latour’s social thought has significantly grown in Poland over the last decade. Among them there are theoretical analyses, research programms as well as projects of social engineering. This situation makes it urgent to examine the credibility of Latour’s vision of science and society. The present article claims that the premises as well as arguments of the French thinker are not only fallacious but also dangerous. A number of absurdities following from the actor-network theory become evident in the works of the Polish followers of Latour. Thus the article focuses on selected examples of them. In the conclusion the author indicates certain advantages for Latour’s readers and formulates several hypotheses about the possible reasons for Latour’s growing popularity.
Noise control has gained a lot of attention recently. However, presence of nonlinearities in signal paths for some applications can cause significant difficulties in the operation of control algorithms. In particular, this problem is common in structural noise control, which uses a piezoelectric shunt circuit. Not only vibrating structures may exhibit nonlinear characteristics, but also piezoelectric actuators. In this paper, active device casing is addressed. The objective is to minimize the noise coming out of the casing, by controlling vibration of its walls. The shunt technology is applied. The proposed control algorithm is based on algorithms from a group of soft computing. It is verified by means of simulations using data acquired from a real object.
This study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.
An available bandwidth at a link is an unused capacity. Its measuring and/or estimation is not simple in practice. On the other hand, we know that its continuous knowledge is crucial for the operation of almost all networks. Therefore, there is a continuous effort in improving the existing and developing new methods of available bandwidth measurement and/or estimation. This paper deals with these problems. Network calculus terminology allows to express an available bandwidth in terms of a service curve. The service curve is a function representing a service available for a traffic flow which can be measured/estimated in a node as well as at an endto- end connection of a network. An Internet traffic is highly unpredictable what hinders to a large extent an execution of the tasks mentioned above. This paper draws attention to pitfalls and difficulties with application of the existing network calculus methods of an available bandwidth estimation in a real Internet Service Provider (ISP) network. The results achieved in measurements have been also confirmed in simulations performed as well as by mathematical considerations presented here. They give a new perspective on the outcomes obtained by other authors and on their interpretations.
The paper puts forward a method of designing and creating a complete computer system for monitoring and diagnosis of business and industrial facilities, as well as for control purposes. The proposed solution represents a computer-network system being a practical tool for communication, control and management of modern plants and enterprises. The applied concept of communication, based on the Service Oriented Architecture (SOA), makes a new attempt to solve certain performance problems met when using a (previously developed) Networked Object Monitor (NOM). The principal idea of increasing the performance of NOM lies in employing a common data bus, refereed to as a Diagnostic Service Bus (DSB), in the NOM monitor. The paper also describes a preliminary concept of a network description language (SMOL), which is designed to describe the functions, mechanisms, and network devices and to be a basis for simulation and verification of the NOMmonitor function.
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