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- Volume 14, Issue 1, 2019
Current Signal Transduction Therapy - Volume 14, Issue 1, 2019
Volume 14, Issue 1, 2019
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Optimal Placement of FACTS Devices for Voltage Stability Improvement with Economic Consideration Using Firefly Algorithm
Authors: S. Rajasekaran and S. MuralidharanBackground: Increasing power demand forces the power systems to operate at their maximum operating conditions. This leads the power system into voltage instability and causes voltage collapse. To avoid this problem, FACTS devices have been used in power systems to increase system stability with much reduced economical ratings. To achieve this, the FACTS devices must be placed in exact location. This paper presents Firefly Algorithm (FA) based optimization method to locate these devices of exact rating and least cost in the transmission system. Methods: Thyristor Controlled Series Capacitor (TCSC) and Static Var Compensator (SVC) are the FACTS devices used in the proposed methodology to enhance the voltage stability of power systems. Considering two objectives of enhancing the voltage stability of the transmission system and minimizing the cost of the FACTS devices, the optimal ratings and cost were identified for the devices under consideration using Firefly algorithm as an optimization tool. Also, a model study had been done with four different cases such as normal case, line outage case, generator outage case and overloading case (140%) for IEEE 14,30,57 and 118 bus systems. Results: The optimal locations to install SVC and TCSC in IEEE 14, 30, 57 and 118 bus systems were evaluated with minimal L-indices and cost using the proposed Firefly algorithm. From the results, it could be inferred that the cost of installing TCSC in IEEE bus system is slightly higher than SVC.For showing the superiority of Firefly algorithm, the results were compared with the already published research finding where this problem was solved using Genetic algorithm and Particle Swarm Optimization. It was revealed that the proposed firefly algorithm gives better optimum solution in minimizing the L-index values for IEEE 30 Bus system. Conclusion: The optimal placement, rating and cost of installation of TCSC and SVC in standard IEEE bus systems which enhanced the voltage stability were evaluated in this work. The need of the FACTS devices was also tested during the abnormal cases such as line outage case, generator outage case and overloading case (140%) with the proposed Firefly algorithm. Outputs reveal that the recognized placement of SVC and TCSC reduces the probability of voltage collapse and cost of the devices in the transmission lines. The capability of Firefly algorithm was also ensured by comparing its results with the results of other algorithms.
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Application of Evolutionary Algorithms for Harmonic Profile Optimization in Symmetric Multilevel Inverter used in Medical Electronic Equipments
Authors: S. Menaka and S. MuralidharanBackground: Inverters are finding applications in electrical field where conversion from Direct Current (DC) to Alternate Current (AC) has become an inevitable option. The need of Multi Level Inverter (MLI) is to provide a high output power from medium voltage source. High power quality is the very basic important requirement for MLI used in medical electronic equipment. Harmonic elimination in MLI is a challenging one and is solved by optimum switching of power electronic switches present in the MLI topology. Methods: Several control strategies have been proposed for harmonic elimination in multilevel inverters. Newton-Raphson method is the conventional and iterative based method to obtain optimum switching angle to minimize the Total Harmonic Distortion (THD). But it requires an initial guess of switching angles which is very close to the exact solution. To overcome this, harmonic elimination is converted into an optimization task and is solved by using evolutionary algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). GA and PSO offer optimum switching angles to minimize THD and used to increase the robustness of the system. Results: The performance of proposed symmetric 21 level multilevel inverter with GA and PSO techniques are analysed and the objective of minimum THD is obtained by using MATLABSIMULINK software.In experimental setup, FPGA controller has been used to generate the Pulse Width Modulation (PWM) control signals according to the proposed soft computing based switching strategy. The experimental result is used to verify the ability of the proposed system for the generation of desired output voltage with minimum THD. Conclusion: The proposed symmetric MLI with evolutionary algorithm based switching is a good choice for medical electronic equipment used in hospital to obtain quality power with minimum THD.
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Anfis Classifier Based Moving Object Detection and Segmentation in Indoor and Outdoor Environments
Authors: A. Shyamala, S. Selvaperumal and G. PrabhakarBackground: Moving object detection in dynamic environment video is more complex than the static environment videos. In this paper, moving objects in video sequences are detected and segmented using feature extraction based Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier approach. The proposed moving object detection methodology is tested on different video sequences in both indoor and outdoor environments. Methods: This proposed methodology consists of background subtraction and classification modules. The absolute difference image is constructed in background subtraction module. The features are extracted from this difference image and these extracted features are trained and classified using ANFIS classification module. Results: The proposed moving object detection methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure. The proposed moving object segmentation methodology is executed on different Central Processing Unit (CPU) processor as 1.8 GHz and 2.4 GHz for evaluating the performance during moving object segmentation. At present, some moving object detection systems used 1.8 GHz CPU processor. Recently, many systems for moving object detection are using 2.4 GHz CPU processor. Hence, CPU processors 1.8 GHz and 2.4 GHz are used in this paper for detecting the moving objects in video sequences. Table 1 shows the performance evaluation of proposed moving object detection on CPU processor 1.8 GHz (100 sequence). Table 2 shows the performance evaluation of proposed moving object detection on CPU processor 2.8 GHz (100 sequence). The average moving object detection time on CPU processor 1.8 GHz for fountain sequence is 62.5 seconds, for airport sequence is 64.7 seconds, for meeting room sequence is 71.6 seconds and for Lobby sequence is 73.5 seconds, respectively, as depicted in Table 3. The average elapsed time for moving object detection on 100 sequences is 68.07 seconds. The average moving object detection time on CPU processor 2.4 GHz for fountain sequence is 56.5 seconds, for airport sequence is 54.7 seconds, for meeting room sequence is 65.8 seconds and for Lobby sequence is 67.5 seconds, respectively, as depicted in Table 4. The average elapsed time for moving object detection on 100 sequences is 61.12 seconds. It is very clear from Table 3 and Table 4; the moving object detection time is reduced when the frequency of the CPU processor increases. Conclusion: In this paper, moving object is detected and segmented using ANFIS classifier. The proposed method initially segments the background image and then features are extracted from the threshold image. These features are trained and classified using ANFIS classification method. The proposed moving object detection method is tested on different video sequences which are obtained from different indoor and outdoor environments. The performance of the proposed moving object detection and segmentation methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure.
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Design and Implementation of High Power LED Lighting System for Health Care Applications
Authors: J. Gnanavadivel, N.S. Kumar, S.T.J. Christa and P. YogalakshmiBackground: A conventional front-end rectifier causes line current distortion and reduces the power factor, which result in lowering power quality for Light Emitting Diode (LED) drive system. Hence, this paper projects the design, simulation and comparison of novel PI tuned by the fuzzy logic controller with the conventional PI controller for modified SEPIC rectifier to produce the required load voltage along with supply-side unity power factor and less distorted supply current with limited harmonic content for LED lighting in healthcare applications. A prototype of 100W, 48V LED driver was developed for testing the performance of the controller. Methods: This paper presents the modified SEPIC LED driver with PI integrated fuzzy and classical PI for controlling voltage. For controlling source current, classical PI is chosen. Both are equipped with the modified SEPIC rectifier. Both conventional PI control and novel fuzzy tuned performances were compared. Results: The proposed control topology operated modified SEPIC rectifier was analyzed and the corresponding power factor and THD were measured. The operational evaluation of the proposed LED driver using fuzzy tuned-PI/PI controller combinations for different output power is provided in Table 2. Sustained regulated DC voltage of 48V was achieved even when the load resistance varied within a specific range. Power factor of 0.9995, which is close to unity, was also achieved. The relative analysis was made with conventional PI and trendy PI integrated FLC controller which is provided in Table 3. The usage of PI integrated fuzzy logic controller minimized the peak overshoot to be around 1.3% and rise time of 0.5s which are lower when compared to the conventional PI controller. With reference to Fig. (8a), the source current THD of the conventional PI controller was observed to be around 7.39%. Having PI integrated FLC, THD was further reduced and for rated load, it was found to be 1.39%. The power factor of the conventional PI controller is around 0.9974. PI integrated fuzzy logic controller improved the power factor to 0.9995 with fuzzy tuned PI controller in action as shown in Fig. (8b). A prototype of 100W, 48V LED driver was developed for testing the performance of the controller. A power quality analyser was employed for measuring power factor and THD, shown in Fig. (10a). 3.633% of harmonic distortions at the source current and 0.9980% of input power factor was achieved for rated load power. 4.510% of supply current THD with 0.9931% input power factor was achieved for low load power. Conclusion: This manuscript suggests a modified single switch SEPIC LED driver for 48V output operated healthcare equipment. Simulation study of this driver shows the better performance. In order to analyze the performance, a comparative study was conducted by using the classical PI and the novel PI integrated fuzzy controller. Satisfactory results regarding enhanced quality of power, regulated load voltage, quick rise time and settling time were achieved. The source current THD has been reduced to around 1.39% which is less than 5% as per the IEEE-516 prescribed standard and the power factor has been improved to 0.9995 by implementing the fuzzy tuned PI controller. The above results favor the suggested modified SEPIC LED driver for practical healthcare applications.
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ANN-based Maximum Power Point Tracking for a Large Photovoltaic Farm Through Wireless Sensor Networks
Authors: K. Annaraja, S. S. Sundaram, S. Selvaperumal and G. PrabhakarBackground: A novel system for the usage of Maximum Power Point Tracking of an expansive Solar Photo Voltaic (SPV) farm subjected to conceivable incomplete shading is displayed in this paper. The SPV farm being spread over an expansive territory a remote sensor organize is utilized for checking the sun based protection in the region of each board. The motivation behind the remote sensor organize is to screen the sunlight based protection at various areas near each of the PV board from the tremendous region of the photograph voltaic homestead comprising of countless voltaic boards. The observed protection information is utilized by a prepared. Artificial Neural Network to locate the ideal DC terminal voltage to be kept up over the general DC terminals of the photograph voltaic ranch. All the PV boards are associated in arrangement association with the fundamental bye pass diodes. The DC control accessible at the yield terminals of the SPV cultivate is first DC to DC changed over with a Positive Output Luo Converter (POLC) and bolstered to a heap. A MATLAB Simulink based reproduction was created to approve the proposed system. Methods: Maximum Power Point Tracking based on Artificial Neural Network through wireless sensor networks. Results: As the result of the proposed idea and its implementation in MATLAB we have two sets of results. In either case the input is a vector of 40 elements and the output of the first segment of the work is the estimation of the threshold PV terminal voltage that will guarantees maximum power point operation. In the first case we have the MATLAB SIMULINK implementation of the basic configuration of the forty PV panels arranged in series connection and we have provided a facility to edit the solar insulation levels pertaining to the individual PV panels. In this first configuration we have set a continuously variable PV current for all the panels and the PV current for all the panel are the same. Using this setup, for any combination of solar insulation pattern of the forty panels the overall PV curve and the overall VI curve can be drawn in MATLAB. As the simulation runs the PV current is changed from 0 to the maximum or the short circuit current level in a slowly rising manner implemented using a ramp signal. During this period the total power output and the terminal voltage of the PV farm are sent to the work space and the data is thus collected in the workspace of MATLAB. Using basic MATLAB commands the maximum power output and the PV terminal voltage corresponding to the maximum power output are obtained. The PV current at maximum power output condition, the corresponding PV farm terminal voltage, the maximum power output recorded at this condition all correspond to the present insulation vector condition. This way, by changing the elements of the insulation for all the forty panels in a random manner we obtain for each case the Ipmax[i], Pmax[i], Vpmax[i] and this corresponds to insulation[n,i]. Where n is the number of panels, in this case 40 and i the ith experiment. In each experiment the solar insulation level of all the forty panels can be changed and the parameters Vpmax[i], Ipmax[i] and Pmax[i] can be obtained. The value of the harvested power as found from the characteristics for any given set of insulation is denoted as the estimated power. The value of power as obtained from the proposed ANN SMC POLC combination is denoted as the Actual Power. Conclusion: A wireless network based insulation monitoring has been done. An ANN based MPPT algorithm has been developed that gives the reference MPP voltage. The sliding mode control scheme uses the reference voltage and produces the switching pulses for the POLC. The ANN had been trained with a number of combinations of different insulation values falling on each of the forty panels and the ANN gives the correct reference voltage for any combination of insulation levels that were not used while training. The sliding mode controller uses this reference voltage and gives the switching pulses to the POLC that harvests the maximum power output to the RL load. The proposed system has been implemented in the MATLAB SIMULINK environment and has thus been validated. The obtained results have been compared against the maximum power output values that could be derived from the characteristic curves obtained for the given combination of insulation levels. The proposed system gives results very close to the values obtained from the characteristics. As a future work the proposed idea can be validated using hardware based experimental setup.
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Targeting Negative Regulators of TRIF-dependent TLR Signaling Pathway as a Novel Therapeutic Strategy
Authors: P. Mosaddeghi, N. Nezafat, M. Negahdaripour, M. Eslami and Y. GhasemiBackground: Toll-Like Receptors (TLRs) are a subclass of pathogen-associated molecular patterns (PAMPs). There is a growing interest in the use of TLR agonists for various pathological dysfunctions, including cancer, microbial infections, and inflammatory diseases. TLR3/4 agonists that can induce TIR-domain-containing adapter-inducing interferon-β (TRIF)- dependent pathway have shown fewer toxic immunostimulatory responses in comparison to other small molecules. Furthermore, TLR3 agonists indicate promising anti-tumor potential in cancer immunotherapy either as vaccine adjuvant or monotherapy. Objective: It is logical to assume that the induction of the genes that are involved in TRIF pathway to augment their pleiotropic effects on different cells via TLR agonists, could enhance the treatment process of disease while minimizing the toxicity related to using other small molecules. Methods: An extensive literature search to identify the negative regulators of TRIF-dependent signaling pathway and their biological functions was performed from two databases PubMed and Scopus. Results: Negative regulators of TRIF signaling pathways were identified. In addition, structure and function of sterile α- and armadillo-motif containing protein (SARM), the only TIR domaincontaining adaptor protein that inhibits TRIF-dependent activation, were briefly reviewed. Conclusion: We proposed that the manipulation of TRIF signaling pathway via targeting its negative regulators could be used as an approach to modulate the functions of this pathway without undesired toxic proinflammatory responses.
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Proteome Analysis Revealed Jak/Stat Signaling and Cytoskeleton Rearrangement Proteins in Human Lung Epithelial Cells During Interaction with Aspergillus terreus
Authors: R. Thakur and J. ShankarBackground: Aspergillus terreus is an emerging etiological agent of invasive and allergic aspergillosis in immunocompromised individuals. The main risk groups are individuals having cancer, acute leukemia and those who undergo bone marrow transplantation. The human lung epithelial cells constitute the first line of defense against inhaled conidia of A. terreus. The aim of the study was to understand how human lung epithelial cells respond to A. terreus conidia during the interaction and to decipher proteins/pathways underlying in host defense. Methods: Protein samples were extracted from human lung epithelial cells (A549) infected with and without A. terreus conidia. Proteins were identified using QTOF-LC-MS/MS followed by analysis using Protein Lynx Global Services software (2.2.5) against Homo sapiens UniProt database. Results: A total of 1253 proteins in human lung epithelial cells were identified during the interaction with Aspergillus terreus conidia, whereas 427 proteins were identified in uninfected lung epithelial cells. We have observed 63 proteins in both the conditions. Gene ontology and KEEG pathway analysis of proteins from infected lung epithelial cells showed proteins from cytoskeleton rearrangement, transport, transcription and signal transduction pathways, such as Jak/Stat, NOD like receptor signaling, Toll–like receptor signaling, NF-kβ signaling and TNF signaling pathways. These signaling proteins suggested the strong immune response in lung epithelial cells against A. terreus conidia. Also, cytoskeleton rearrangement proteins depicted the internalization of A. terreus conidia by human lung epithelial cells. Conclusion: Our study has contributed to understand the interaction response of human lung epithelial cells during A. terreus infection. Also, our study may facilitate the identification of inflammatory biomarker against A. terreus.
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IoT based Low Power Wearable ECG Monitoring System
Authors: A.S. Kulkarni, M. Suchetha and N. KumaravelBackground: An electrocardiogram device monitors the cardiac status of a patient by recording the heart’s electrical potential vs time. Such devices play a very important role to save the life of patients who survive a heart attack or suffer from these patients. An early detection of conditions that lead to the onset of cardiac arrest allows doctors to provide proper treatment on time and prevents death or disability from cardiac arrest. Most developing countries have very poor information about these health care issues. Methods: An actual deployment of the system was used to evaluate key aspects of the system architecture, in particular, the possibility to monitor the ECG signal of single patients in a large area and for a long time the possibility to access ECG data through the web interface. The test deployment consisted of ECG sensor AD8232, wi-fi module and IoT server. The IoT server was installed on a Linux/ windows machine. The wifi has been configured to connect to the server, through an ADSL router. Conclusion: We have proposed a wireless wearable ECG monitoring system enabled with an IoT platform that integrates heterogeneous nodes of ECG sensor and applications, has a long battery life and provides a high-quality ECG signal. The system allows monitoring single/multiple patients on a relatively large indoor area (home, building, nursing home, etc). As observed, this result is obtained through a careful set of choices at the level of components, circuit solutions, and algorithms. We would like to stress the fact that a dedicated overall output is not enough to achieve an advantage in terms of overall sensor performance. The latter depends on the optimization of the whole sensor. Indeed, this proposed ECG sensor, based on a high-performance ADC and an arm processor, provides much better performance, in terms of power consumption and noise, than many proposed system based on a purposely designed front-end chip.
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Design and Evaluation of a Modulated TENS Stimulation in Medical Pain Therapy
Authors: L. Bouafif and N. EllouzeBackground: Transcutaneous Electrical Nerve Stimulation (TENS) is a non-invasive pain therapy that uses the sensory effects of an electrical current applied to the skin. Some clinical studies demonstrated that this treatment helps to reduce acute and chronic pains, while others gave sometimes contradictory or uncertain conclusions about the performances of this strategy according to pathology classification. The purpose of this study is the development and evaluation of a new modulated version of transcutaneous electrical nerve stimulation called PWM-TENS. The principle is based on an automatic variation of the stimulation parameters (frequency, amplitude, duration, shape, cyclic ratio) according to the pain evolution. Methods: The study was a controlled clinical trial involving 15 participants, divided into 2 groups. The first experimental group performed modulated PWM-TENS electro-stimulation sessions applied to the painful areas 3 to 4 times a day, for one month. The second control group did parallel treatments by Placebo. The evaluation of the pain intensity is done with the Visual Analog Scale (EVA), the DN4 and SF36 questionnaires. Results: The tests and measurements with our embedded PWM-TENS technique demonstrated that we succeeded to increase the analgesic effect after stopping the stimulation and reduced the pain sensation by about 60%. An improvement in pain intensity scores and questionnaires (EVA, DN4), as well as the quality of life score (SF36), was observed. Also, a reduction of the treatment period from 3 to 1 month was also obtained. Conclusion: The first results clinically observed in the PWM-TENS technique are encouraging. The findings of this study confirm that this noninvasive strategy is suitable and useful for acute pains coming from the nociceptive, neuropathic and musculoskeletal origin. However, its efficiency is moderated and less adapted for low back pain. The experiments make it possible to estimate whether this modulated TENS method could improve existing anti-pain therapies, taking into account objective and subjective evaluation criteria. But this study must be followed by large population samples to answer all the problems of acute and chronic pains.
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Volumes & issues
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Volume 19 (2024)
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Volume 18 (2023)
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Volume 17 (2022)
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Volume 16 (2021)
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Volume 15 (2020)
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Volume 14 (2019)
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Volume 13 (2018)
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Volume 12 (2017)
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Volume 11 (2016)
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Volume 10 (2015)
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Volume 9 (2014)
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Volume 8 (2013)
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Volume 7 (2012)
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Volume 6 (2011)
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Volume 5 (2010)
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Volume 4 (2009)
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Volume 3 (2008)
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Volume 2 (2007)
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Volume 1 (2006)