Search results for: energy anomaly detection

Authors: Vrince Vimal, Madhav J. Nigam

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Wireless sensor networks have enticed much of the spotlight from researchers all around the world, owing to its extensive applicability in agricultural, industrial and military fields. Energy conservation node deployment stratagems play a notable role for active implementation of Wireless Sensor Networks. Clustering is the approach in wireless sensor networks which improves energy efficiency in the network. The clustering algorithm needs to have an optimum size and number of clusters, as clustering, if not implemented properly, cannot effectively increase the life of the network. In this paper, an algorithm has been proposed to address connectivity issues with the aim of ensuring the uniform energy consumption of nodes in every part of the network. The results obtained after simulation showed that the proposed algorithm has an edge over existing algorithms in terms of throughput and networks lifetime.

Keywords: Wireless Sensor network (WSN), Random Deployment, Clustering, Isolated Nodes, Networks Lifetime

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Authors: Reza Sedaghati

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The use of Flexible AC Transmission System (FACTS) devices in a power system can potentially overcome limitations of the present mechanically controlled transmission system. Also, the advance of technology makes possible to include new energy storage devices in the electrical power system. The integration of Superconducting Magnetic Energy Storage (SMES) into Static Synchronous Compensator (STATCOM) can lead to increase their flexibility in improvement of power system dynamic behaviour by exchanging both active and reactive powers with power grids. This paper describes structure and behaviour of SMES, specifications and performance principles of the STATCOM/SMES compensator. Moreover, the benefits and effectiveness of integrated SMES with STATCOM in power systems is presented. Also, the performance of the STATCOM/SMES compensator is evaluated using an IEEE 3-bus system through the dynamic simulation by PSCAD/EMTDC software.

Keywords: STATCOM/SMES compensator, chopper, converter, energy storage system, power systems

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Authors: Mojtaba Ghodsi, Morteza Mohammadzaheri, Payam Soltani

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This study provides a detailed analysis of the LTC3588 as a low-power energy storage model, focusing on its internal circuitry and energy harvesting capabilities. The study highlights the relationship between the input and output capacitors and the behavior of the output voltage, particularly its rise time. It was found that increasing the input capacitance (Cᵢₙ) from 1 µF to 220 µF reduces oscillations in the output voltage (Vₒᵤₜ) and slows the rate of increase in the input voltage, demonstrating the impact of input capacitance on voltage dynamics. Furthermore, the study revealed that smaller output capacitors (Cₒᵤₜ) result in fewer voltage jumps required to reach the target output voltage of 3.2 V, suggesting that a smaller Cₒᵤₜ improves voltage regulation speed and stability. The study concludes that both input and output capacitors play a critical role in the LTC3588's performance. Optimizing these capacitors is crucial for efficient energy storage and harvesting in applications requiring minimal power consumption.

Keywords: LTC3588, modeling, Zener diode, LED

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Authors: Hadis Pouyafar, D. Matin Alaghmandan

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Gallium arsenide (GaAs) solar cells are widely used in applications like spacecraft and satellites because they have a high absorption coefficient and efficiency and can withstand high-energy particles such as electrons and protons. With the energy crisis, there's a growing need for efficiency and cost-effective solar cells. GaAs cells, with their 46% efficiency compared to silicon cells 23% can be utilized in buildings to achieve nearly zero emissions. This way, we can use irradiation and convert more solar energy into electricity. III V semiconductors used in these cells offer performance compared to other technologies available. However, despite these advantages, Si cells dominate the market due to their prices. In our study, we took an approach by using software from the start to gather all information. By doing so, we aimed to design the optimal building that harnesses the full potential of solar energy. Our modeling results reveal a future; for GaAs cells, we utilized the Grasshopper plugin for modeling and optimization purposes. To assess radiation, weather data, solar energy levels and other factors, we relied on the Ladybug and Honeybee plugins. We have shown that silicon solar cells may not always be the choice for meeting electricity demands, particularly when higher power output is required. Therefore, when it comes to power consumption and the available surface area for photovoltaic (PV) installation, it may be necessary to consider efficient solar cell options, like GaAs solar cells. By considering the building requirements and utilizing GaAs technology, we were able to optimize the PV surface area.

Keywords: gallium arsenide (GaAs), optimization, sustainable building, GaAs solar cells

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Authors: Michel Moliere

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In the next decades, the thermal power generation segment will survive only if it achieves deep mutations, including drastical abatements of CO2 emissions and strong efficiency gains. In this challenging perspective, stationary gas turbines appear as serious candidates to lead the energy transition. Indeed, during the past decades, these turbomachines have made brisk technological advances in terms of efficiency, reliability, fuel flex (including the combustion of hydrogen), and the ability to hybridize with regenrables. It is, therefore, timely to summarize the progresses achieved by gas turbines in the recent past and to examine what are their assets to face the challenges of the energy transition.

Keywords: energy transition, gas turbines, decarbonization, power generation

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Authors: Hiroshi Nakayama, Yuji Ito

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Synthetic cannabinoids have attracted much public attention recently in Japan. 1-pentyl-3-(1-naphthoyl)-indole (JWH-018), 1-pentyl-2-methyl-3-(1-naphthoyl) indole (JWH-015), 1-(5-fluoropentyl)-3- (1-(2,2,3,3- tetramethylcyclopropyl)) indole (XLR-11) and 1-methyl-3- (1-admantyl) indole (JWH-018 adamantyl analog) are known as synthetic cannabinoids and are also considered dangerous illegal drugs in Japan. It has become necessary to develop sensitive and useful methods for detection of synthetic cannabinoids. We produced two monoclonal antibodies (MAb) against synthetic cannabinoids, named NT1 (IgG1) and NT2 (IgG1), using Hybridoma technology. The cross-reactivity of these produced MAbs was evaluated using a competitive enzyme-linked immunosorbent assay (ELISA). In the results, we found both of these antibodies recognize many kinds of synthetic cannabinoids analog. However, neither of these antibodies recognizes naphtoic acid, 1-methyl-indole and indole known as a raw material of synthetic cannabinoid. Thus, the MAbs produced in this study could be a useful tool for the detection of synthetic cannabinoids.

Keywords: ELISA, monoclonal antibody, sensor, synthetic cannabinoid

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Authors: Silvia Di Turi, Nicolandrea Calabrese, Francesca Caffari, Giulia Centi, Francesca Margiotta, Giovanni Murano, Laura Ronchetti, Paolo Signoretti, Lisa Volpe, Domenico Palladino

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Recent European policies have been set ambitious targets aimed at significantly reducing CO2 emissions by 2030, with a long-term vision of transforming existing buildings into Zero-Emissions Buildings (ZEmB) by 2050. This vision represents a key point for the energy transition as the whole building stock currently accounts for 36% of total energy consumption across the Europe, mainly due to their poor energy performance. The challenge towards Zero-Emissions Buildings is particularly felt in Italy, where a significant number of buildings with historical significance or situated within protected/constrained areas can be found. Furthermore, an estimated 70% of the national building stock are built before 1976, indicating a widespread issue of poor energy performance. Addressing the energy ineƯiciency of these buildings is crucial to refining a comprehensive energy renovation approach aimed at facilitating their energy transition. In this framework the current study focuses on analysing a challenging complex of buildings to be totally restored through significant energy renovation interventions. The goal is to recover these disused buildings situated in a significant archaeological zone of Rome, contributing to the restoration and reintegration of this historically valuable site, while also oƯering insights useful for achieving zeroemission requirements for buildings within such contexts. In pursuit of meeting the stringent zero-emission requirements, a comprehensive study was carried out to assess the complex of buildings, envisioning substantial renovation measures on building envelope and plant systems and incorporating renewable energy system solutions, always respecting and preserving the historic site. An energy audit of the complex of buildings was performed to define the actual energy consumption for each energy service by adopting the hourly calculation methods. Subsequently, significant energy renovation interventions on both building envelope and mechanical systems have been examined respecting the historical value and preservation of site. These retrofit strategies have been investigated with threefold aims: 1) to recover the existing buildings ensuring the energy eƯiciency of the whole complex of buildings, 2) to explore which solutions have allowed achieving and facilitating the ZEmB status, 3) to balance the energy transition requirements with the sustainable aspect in order to preserve the historic value of the buildings and site. This study has pointed out the potentiality and the technical challenges associated with implementing renovation solutions for such buildings, representing one of the first attempt towards realizing this ambitious target for this type of building.

Keywords: energy conservation and transition, complex of buildings in historic site, zero-emission buildings, energy efficiency recovery

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Authors: Xinglu Nie, Feifei Tang, Chuntao Wei, Zhimin Ruan, Qianhong Zhu

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Current method of manual-style inspection can not fully meet the requirement of the urban rail transit security in China. In this paper, an intelligent inspection method using unmanned aerial vehicle (UAV) is utilized. A series of orthophoto of rail transit monitored area was collected by UAV, image correction and registration were operated among multi-phase images, then the change detection was used to detect the changes, judging the engineering activities and human activities that may become potential threats to the security of urban rail. Not only qualitative judgment, but also quantitative judgment of changes in the security control area can be provided by this method, which improves the objectives and efficiency of the patrol results. The No.6 line of Chongqing Municipality was taken as an example to verify the validation of this method.

Keywords: rail transit, control of protected areas, intelligent inspection, UAV, change detection

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Authors: Luis González-Cavieres, Mario Perez-Won, Anais Palma-Acevedo, Gipsy Tabilo-Munizaga, Erick Jara-Quijada, Roberto Lemus-Mondaca

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In this study, pulsed electric field (PEF: 2.0 kV/cm) was used as pretreatment in drying methods, vacuum microwave (VMD); freeze-drying (FD); and hot air (HAD), in Chilean abalone mollusk. Drying parameters, quality, energy consumption, and Sustainability parameters were evaluated. PEF+VMD showed better values than the other drying systems, with drying times 67% and 83% lower than PEF+FD and FD. In the quality parameters, PEF+FD showed a significantly lower value for hardness (250 N), and a lower change of color value (ΔE = 12). In the case of HAD, the PEF application did not significantly influence its processing. In energy parameters, VMD and PEF+VMD reduced energy consumption and CO2 emissions.

Keywords: PEF technology, vacuum microwave drying, energy consumption, CO2 emissions

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Authors: Srinivasan Chandrasekaran, Thailammai Chithambaram, Shihas A. Khader

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The early-stage damage detection in offshore structures requires continuous structural health monitoring and for the large area the position of sensors will also plays an important role in the efficient damage detection. Determining the dynamic behavior of offshore structures requires dense deployment of sensors. The wired Structural Health Monitoring (SHM) systems are highly expensive and always needs larger installation space to deploy. Wireless sensor networks can enhance the SHM system by deployment of scalable sensor network, which consumes lesser space. This paper presents the results of wireless sensor network based Structural Health Monitoring method applied to a scaled experimental model of offshore structure that underwent wave loading. This method determines the serviceability of the offshore structure which is subjected to various environment loads. Wired and wireless sensors were installed in the model and the response of the scaled BLSRP model under wave loading was recorded. The wireless system discussed in this study is the Raspberry pi board with Arm V6 processor which is programmed to transmit the data acquired by the sensor to the server using Wi-Fi adapter, the data is then hosted in the webpage. The data acquired from the wireless and wired SHM systems were compared and the design of the wireless system is verified.

Keywords: condition assessment, damage detection, structural health monitoring, structural response, wireless sensor network

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Authors: Tai-Hsing Lee, Zhe-Wei Lin

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This paper demonstrates an ultra-wide tuning VCO implemented by CMOS 0.18μm process technology. By employing the proposed technique of magnetic energy adjustment in the oscillator tank, our proposed VCO achieves a wide frequency tuning range of 69.46% from 0.9 GHz to 1.86 GHz. The phase noise at an operating frequency of 1.86 GHz is -110 dBc/Hz (Offset frequency=1MHz). Furthermore, it achieves an excellent FOMT of 190.03 dBc/Hz.

Keywords: VCO, Ultra-wide tuning, Frequency tuning range, phase noise, Magnetic energy adjustment

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Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

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This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

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Authors: Parvin Samadi Pakchin, Reza Saber, Hossein Ghanbari, Yadollah Omidi

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Ovarian cancer is one of the leading cause of mortality among the gynecological malignancies, and it remains the one of the most prevalent cancer in females worldwide. Tumor markers are biochemical molecules in blood or tissues which can indicates cancers occurrence in the human body. So, the sensitive and specific detection of cancer markers typically recruited for diagnosing and evaluating cancers. Recently extensive research efforts are underway to achieve a simple, inexpensive and accurate device for detection of cancer biomarkers. Compared with conventional immunoassay techniques, electrochemical immunosensors are of great interest, because they are specific, simple, inexpensive, easy to handling and miniaturization. Moreover, in the past decade nanotechnology has played a crucial role in the development of biosensors. In this study, a signal-off electrochemical immunosensor for the detection of CA125 antigen has been developed using chitosan-gold nanoparticles (CS-AuNP) and multi-wall carbon nanotubes (MWCNT) composites. Toluidine blue (TB) is used as redox probe which is immobilized on the electrode surface. CS-AuNP is synthesized by a simple one step method that HAuCl4 is reduced by NH2 groups of chitosan. The CS-AuNP-MWCNT modified electrode has shown excellent electrochemical performance compared with bare Au electrode. MWCNTs and AuNPs increased electrochemical conductivity and accelerate electrons transfer between solution and electrode surface while excessive amine groups on chitosan lead to the effective loading of the biological material (CA125 antibody) and TB on the electrode surface. The electrochemical, immobilization and sensing properties CS-AuNP-MWCNT-TB modified electrodes are characterized by cyclic voltammetry, electrochemical impedance spectroscopy, differential pulse voltammetry and square wave voltammetry with Fe(CN)63−/4−as an electrochemical redox indicator.

Keywords: signal-off electrochemical biosensor, CA125, ovarian cancer, chitosan-gold nanoparticles

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Authors: Tugba Gurler, Irfan Kurtbas

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Roof influences considerably energy demand of buildings. In order to reduce this energy demand, various solutions have been proposed, such as roofs with variable thermal insulation, cool roofs, green roofs, heat exchangers and ventilated roofs, and phase change material (PCM) layered roofs. PCMs suffer from relatively low thermal conductivity despite of their promise of the energy-efficiency initiatives for thermal energy storage (TES). This study not only presents the thermal performance of the concrete roof with PCM layers but also evaluates the products with different design configurations and thicknesses under Central Anatolia Region, Turkey and Nottinghamshire, UK weather conditions. System design limitations and proposed prediction models are discussed in this study. A two-dimensional numerical model has been developed, and governing equations have been solved at each time step. Upper surfaces of the roofs have been modelled with heat flux conditions, while lower surfaces of the roofs with boundary conditions. In addition, suitable roofs have been modeled under symmetry boundary conditions. The results of the designed concrete roofs with PCM layers have been compared with common concrete roofs in Turkey. The UK and the numerical modeling results have been validated with the data given in the literature.

Keywords: phase change material, regional energy demand, roof layers, thermal energy storage

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Authors: Chia-Ying Yen, Chin-Yuan Hsu

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The lifespans of queen honeybees (Apis mellifera) are much longer than those of worker bees. The expression, concentration, and activity of mitochondrial energy-utilized molecules decreased with age in the trophocytes and oenocytes of worker bees, but they are unknown in queen bees. In this study, the expression, concentration, and activity of mitochondrial energy-utilized molecules were evaluated in the trophocytes and oenocytes of young and old queen bees by biochemical techniques. The results showed that mitochondrial density and mitochondrial membrane potential; nicotinamide adenine dinucleotide (NAD+), nicotinamide adenine dinucleotide reduced form (NADH), and adenosine triphosphate (ATP) levels; the NAD+/NADH ratio; and relative expression of NADH dehydrogenase 1 and ATP synthase normalized against mitochondrial density were not significantly different between young and old queen bees. These findings reveal that mitochondrial energy utilization maintains a young status in the trophocytes and oenocytes of old queen bees and that trophocytes and oenocytes have aging-delaying mechanisms and can be used to study cellular longevity.

Keywords: aging, longevity, mitochondrial energy, queen bees

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Authors: Muhammad SAFDAR, Mehmet Ozaslan, Musarrat Abbas Khan

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Babesiosis is a haemoparasitic disease due to the multiplication of protozoan’s parasite, Babesia ovis in the red blood cells of the host, and contributes numerous economical losses, including sheep and goat ruminants. The early identification and successful treatment of Babesia Ovis spp. belong to the key steps of control and health management of livestock resources. The objective of this study was to construct a polymerase chain reaction (PCR) based method for the detection of Babesia spp. in small ruminants and to determine the risk factors involved in the spreading of babesiosis infections. A total of 100 blood samples were collected from 50 sheep and 50 goats along with different areas of Muzaffargarh, Pakistan, from randomly selected herds. Data on the characteristics of sheep and goats were collected through questionnaires. Of 100 blood samples examined, 18 were positive for Babesia ovis upon microscopic studies, whereas 11 were positive for the presence of Babesia spp. by PCR assay. For the recognition of parasitic DNA, a set of 500bp oligonucleotide was designed by PCR amplification with sequence 18S rRNA gene for B. ovis. The prevalence of babesiosis in small ruminant’s sheep and goat detected by PCR was significantly higher in female animals (28%) than male herds (08%). PCR analysis of the reference samples showed that the detection limit of the PCR assay was 0.01%. Taken together, all data indicated that this PCR assay was a simple, fast, specific detection method for Babesia ovis species in small ruminants compared to other available methods.

Keywords: Babesia ovis, PCR amplification, 18S rRNA, sheep and goat

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Authors: Md. Afroz Ansari

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The Bengal basin is a spectacular example of a peripheral foreland basin formed by the convergence of the Indian plate beneath the Eurasian and Burmese plates. The basin is embraced on three sides; north, west and east by different fault-controlled tectonic features whereas released in the south where the rivers are drained into the Bay of Bengal. The Bengal basin in the eastern part of the Indian subcontinent constitutes the largest fluvio-deltaic to shallow marine sedimentary basin in the world today. This continental basin coupled with the offshore Bengal Fan under the Bay of Bengal forms the biggest sediment dispersal system. The continental basin is continuously receiving the sediments by the two major rivers Ganga and Brahmaputra (known as Jamuna in Bengal), and Meghna (emerging from the point of conflux of the Ganga and Brahmaputra) and large number of rain-fed, small tributaries originating from the eastern Indian Shield. The drained sediments are ultimately delivered into the Bengal fan. The significance of the present study is to delineate the variations in thicknesses of the sediments, different crustal structures, and the mantle lithosphere throughout the onshore-offshore Bengal basin. In the present study, the different crustal/geological units and the shallower mantle lithosphere were delineated by analyzing the Bouguer Gravity Anomaly (BGA) data along two long traverses South-North (running from Bengal fan cutting across the transition offshore-onshore of the Bengal basin and intersecting the Main Frontal Thrust of India-Himalaya collision zone in Sikkim-Bhutan Himalaya) and West-East (running from the Peninsular Indian Shield across the Bengal basin to the Chittagong–Tripura Fold Belt). The BGA map was derived from the analysis of topex data after incorporating Bouguer correction and all terrain corrections. The anomaly map was compared with the available ground gravity data in the western Bengal basin and the sub-continents of India for consistency of the data used. Initially, the anisotropy associated with the thicknesses of the different crustal units, crustal interfaces and moho boundary was estimated through spectral analysis of the gravity data with varying window size over the study area. The 2D density sections along the traverses were finalized after a number of iterations with the acceptable root mean square (RMS) errors. The estimated thicknesses of the different crustal units and dips of the Moho boundary along both the profiles are consistent with the earlier results. Further the results were encouraged by examining the earthquake database and focal mechanism solutions for better understanding the geodynamics. The earthquake data were taken from the catalogue of US Geological Survey, and the focal mechanism solutions were compiled from the Harvard Centroid Moment Tensor Catalogue. The concentrations of seismic events at different depth levels are not uncommon. The occurrences of earthquakes may be due to stress accumulation as a result of resistance from three sides.

Keywords: anisotropy, interfaces, seismicity, spectrum analysis

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Authors: Mohammadmehdi Torfehnejad, Serhan Sensoy

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Many structures sustain damage during a mainshock earthquake but undergo severe damage under aftershocks following the mainshock. Past researches have studied aftershock effects through different methodologies, but few structural systems have been evaluated for these effects. Collapse capacity and energy absorption mechanism of the Special Steel Moment Frame (SSMF) system is evaluated in this study, under aftershock earthquakes when prior damage is caused by the mainshock. A twenty-story building is considered in assessing the residual collapse capacity and energy absorption mechanism under aftershock excitation. In addition, various levels of mainshock damage are considered and reflected through two different response parameters. Aftershock collapse capacity is estimated using incremental dynamic analysis (IDA) applied following the mainshock. The study results reveal that the collapse capacity of high-rise structures undergoes a remarkable reduction for high level of mainshock damage. The energy absorption in the columns is decreased by increasing the level of mainshock damage.

Keywords: seismic collapse, mainshock-aftershock effect, incremental dynamic analysis, energy absorption

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Authors: A. Kavya Sri, K. Sai Vineela, R. Vanitha, S. Rohith

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Large pieces of ice that break from the glaciers are known as icebergs. The threat that icebergs pose to navigation, production of offshore oil and gas services, and underwater pipelines makes their detection crucial. In this project, an automated iceberg tracking method using deep learning techniques and satellite images of icebergs is to be developed. With a temporal resolution of 12 days and a spatial resolution of 20 m, Sentinel-1 (SAR) images can be used to track iceberg drift over the Southern Ocean. In contrast to multispectral images, SAR images are used for analysis in meteorological conditions. This project develops a web-based graphical user interface to detect and track icebergs using sentinel-1 images. To track the movement of the icebergs by using temporal images based on their latitude and longitude values and by comparing the center and area of all detected icebergs. Testing the accuracy is done by precision and recall measures.

Keywords: synthetic aperture radar (SAR), icebergs, deep learning, spatial resolution, temporal resolution

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Authors: Maman Ali M. Moustapha, Qian Yu, Benjamin Adjei Danquah

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This paper investigates the dynamic interaction between renewable energy consumption (REC) and economic growth using dataset from the Economic Community of West African States (ECOWAS) from 2002 to 2016. For this study the Autoregressive Distributed Lag- Bounds test approach (ARDL) was used to examine the long run relationship between real gross domestic product and REC, while VECM based on Granger causality has been used to examine the direction of Granger causality. Our empirical findings indicate that REC has significant and positive impact on real gross domestic product. In addition, we found that REC and the percentage of access to electricity had unidirectional Granger causality to economic growth while carbon dioxide emission has bidirectional Granger causality to economic growth. Our findings indicate also that 1 per cent increase in the REC leads to an increase in Real GDP by 0.009 in long run. Thus, REC can be a means to ensure sustainable economic growth in the ECOWAS sub-region. However, it is necessary to increase further support and investments on renewable energy production in order to speed up sustainable economic development throughout the region

Keywords: Economic Growth, Renewable Energy, Sustainable Development, Sustainable Energy

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Authors: Raunak Munjal, Mohammad Akif Ali, Prithiv Raj

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This study investigates the relative effectiveness of various flight controllers for drone obstacle avoidance. To assess ultrasonic sensors' performance in real-time obstacle detection, they are integrated with ESP32 and Arduino Nano controllers. The study determines which controller is most effective for this particular application by analyzing important parameters such as accuracy (mean absolute error), standard deviation, and mean distance range. Furthermore, the study explores the possibility of incorporating state-driven algorithms into the Arduino Nano configuration to potentially improve obstacle detection performance. The results offer significant perspectives for enhancing sensor integration, choosing the best flight controller for obstacle avoidance, and maybe enhancing drones' general environmental navigation ability.

Keywords: ultrasonic distance measurement, accuracy and consistency, flight controller comparisons, ESP32 vs arduino nano

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Authors: M. Graus, K. Westhoff, X. Xu

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The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies.

Keywords: data analytics, green production, industrial energy management, optimization, renewable energies, simulation

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Authors: Sarita Sindhu, Vinay Kumar

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The growing demand for efficient energy storage in applications such as portable electronics, electric vehicles, and renewable energy systems has emphasized the need for advanced energy storage materials. This study addresses the pressing need for efficient energy storage materials by exploring the synthesis and application of a composite of activated carbon (AC) and nickel sulfide (NiS) for supercapacitors. Activated carbon, possessing high surface area and excellent electrochemical stability, was combined with nickel sulfide, a transition metal sulfide with high theoretical capacitance, to enhance the electrochemical performance of the composite material. Characterization techniques, including scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR), were employed to analyze the morphology, crystalline structure, and bonding characteristics, confirming the successful formation of a uniformly distributed AC/NiS composite. Electrochemical evaluations revealed that the AC/NiS composite exhibited superior capacitance, excellent rate capability, and enhanced cycling stability compared to pure AC and NiS. The synergistic effect of the large surface area from activated carbon and redox-active sites of nickel sulfide provided an improved energy storage capacity, making this composite a promising electrode material for high-performance supercapacitors.

Keywords: activated carbon, energy storage, sulfide, surface area

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Authors: Yannick Verbelen, Sam De Winne, Niek Blondeel, Ann Peeters, An Braeken, Abdellah Touhafi

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The growing popularity of solid state thermoelectric devices in cooling applications has sparked an increasing diversity of thermoelectric coolers (TECs) on the market, commonly known as “Peltier modules”. They can also be used as generators, converting a temperature difference into electric power, and opportunities are plentiful to make use of these devices as thermoelectric generators (TEGs) to supply energy to low power, autonomous embedded electronic applications. Their adoption as energy harvesters in this new domain of usage is obstructed by the complex thermoelectric models commonly associated with TEGs. Low cost TECs for the consumer market lack the required parameters to use the models because they are not intended for this mode of operation, thereby urging an alternative method to obtain electric power estimations in specific operating conditions. The design of the test setup implemented in this paper is specifically targeted at benchmarking commercial, off-the-shelf TECs for use as energy harvesters in domestic environments: applications with limited temperature differences and space available. The usefulness is demonstrated by testing and comparing single and multi stage TECs with different sizes. The effect of a boost converter stage on the thermoelectric end-to-end efficiency is also discussed.

Keywords: thermoelectric cooler, TEC, complementary balanced energy harvesting, step-up converter, DC/DC converter, energy harvesting, thermal harvesting

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Authors: Muhammad Afzal, Muhammad Sajjad

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Climate change has become a global environmental challenge and it has affected the world’s economy. Its impact is widespread across all major sectors of the economy i.e. agriculture, industry, and services sectors. This study attempts to measure the long run as well as the short-run dynamic between energy; environment and economic growth by using Autoregressive Distributed Lag (ARDL) bound testing approach at aggregate as well as sectoral level. We measured the causal relationship between electricity consumption, fuel consumption, CO₂ emission, and real Gross Domestic Product (GDP) for the period of 1980 to 2016 for Pakistan. Our co-integration results reveal that all the variables are co-integrated at aggregate as well as at sectoral level. Electricity consumption shows two-way casual relation at for industry, services and aggregate level. The inverted U-Curve hypothesis tested the relationship between greenhouse gas emissions and per capita GDP and results supported the Environment Kuznet Curve (EKC) hypothesis. This study cannot ignore the importance of energy for economic growth but prefers to focus on renewable and green energy to pave on the trajectory of development.

Keywords: climate change, economic growth, energy, environment

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Authors: William Sidharta, Chin-Tu Lu

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Fluorescent ultraviolet lamp generates ultraviolet light which is commonly used in industrial field with certain purposes especially for curing process. Due to the value of inefficiency, there are changes in energy from electrical energy to the heat energy and this would make a defect on the industrial product caused by high temperature of lamp tube during ultraviolet light emission. The condition of industrial scale is further worsening, since commonly using dozens of fluorescent ultraviolet lamps to support huge production process and then it will generates much more heat energy. The maximum temperature of fluorescent ultraviolet lamp will get affected by arranging the lamp tube reflector and this study presents CFX simulation results of the maximum lamp tube temperature with some different reflector arrangements on purely natural convection phenomena. There exists certain spaces value of the reflector and the lamp tube to obtaining lower maximum temperature of the fluorescent ultraviolet lamp.

Keywords: CFX simulation, fluorescent UV lamp, lamp tube reflector, UV light

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Authors: Dayo Samuel Banjo, Connice Trimmingham, Niloofar Yousefi, Nitin Agarwal

Abstract:

Technological advancement and its omnipresent connection have pushed humans past the boundaries and limitations of a computer screen, physical state, or geographical location. It has provided a depth of avenues that facilitate human-computer interaction that was once inconceivable such as audio and body language detection. Given the complex modularities of emotions, it becomes vital to study human-computer interaction, as it is the commencement of a thorough understanding of the emotional state of users and, in the context of social networks, the producers of multimodal information. This study first acknowledges the accuracy of classification found within multimodal emotion detection systems compared to unimodal solutions. Second, it explores the characterization of multimedia content produced based on their emotions and the coherence of emotion in different modalities by utilizing deep learning models to classify emotion across different modalities.

Keywords: affective computing, deep learning, emotion recognition, multimodal

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Authors: Anuta Mukherjee, Saswati Mukherjee

Abstract:

Data is growing everyday. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing a large increase in the growth of data. It is a rich source especially for sentiment detection or mining since people often express honest opinion through tweets. However, although sentiment analysis is a well-researched topic in text, this analysis using Twitter data poses additional challenges since these are unstructured data with abbreviations and without a strict grammatical correctness. We have employed collision theory to achieve sentiment analysis in Twitter data. We have also incorporated discourse analysis in the collision theory based model to detect accurate sentiment from tweets. We have also used the retweet field to assign weights to certain tweets and obtained the overall weightage of a topic provided in the form of a query. Hadoop has been exploited for speed. Our experiments show effective results.

Keywords: sentiment analysis, twitter, collision theory, discourse analysis

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Authors: Y. Kourd, D. Lefebvre

Abstract:

The monitoring of industrial processes is required to ensure operating conditions of industrial systems through automatic detection and isolation of faults. This paper proposes a method of fault diagnosis based on a neuro-fuzzy hybrid structure. This hybrid structure combines the selection of threshold and decision tree. The validation of this method is obtained with the DAMADICS benchmark. In the first phase of the method, a model will be constructed that represents the normal state of the system to fault detection. Signatures of the faults are obtained with residuals analysis and selection of appropriate thresholds. These signatures provide groups of non-separable faults. In the second phase, we build faulty models to see the flaws in the system that cannot be isolated in the first phase. In the latest phase we construct the tree that isolates these faults.

Keywords: decision tree, residuals analysis, ANFIS, fault diagnosis

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Authors: Geeta Kumari, Prabu Vairakannu

Abstract:

Underground coal gasification (UCG) technology is an efficient and an economic in-situ clean coal technology, which converts unmineable coals into calorific valuable gases. This technology avoids ash disposal, coal mining, and storage problems. CO₂ gas can be a potential gasifying medium for UCG. CO₂ is a greenhouse gas and, the liberation of this gas to the atmosphere from thermal power plant industries leads to global warming. Hence, the capture and reutilization of CO₂ gas are crucial for clean energy production. However, the reactivity of high ash Indian coals with CO₂ needs to be assessed. In the present study, two varieties of Indian coals (low ash and high ash) are used for thermogravimetric analyses (TGA). Two low ash north east Indian coals (LAC) and a typical high ash Indian coal (HAC) are procured from the coal mines of India. Low ash coal with 9% ash (LAC-1) and 4% ash (LAC-2) and high ash coal (HAC) with 42% ash are used for the study. TGA studies are carried out to evaluate the activation energy for pyrolysis and gasification of coal under N₂ and CO₂ atmosphere. Coats and Redfern method is used to estimate the activation energy of coal under different temperature regimes. Volumetric model is assumed for the estimation of the activation energy. The activation energy estimated under different temperature range. The inherent properties of coals play a major role in their reactivity. The results show that the activation energy decreases with the decrease in the inherent percentage of coal ash due to the ash layer hindrance. A reverse trend was observed with volatile matter. High volatile matter of coal leads to the estimation of low activation energy. It was observed that the activation energy under CO₂ atmosphere at 400-600°C is less as compared to N₂ inert atmosphere. At this temperature range, it is estimated that 15-23% reduction in the activation energy under CO₂ atmosphere. This shows the reactivity of CO₂ gas with higher hydrocarbons of the coal volatile matters. The reactivity of CO₂ with the volatile matter of coal might occur through dry reforming reaction in which CO₂ reacts with higher hydrocarbon, aromatics of the tar content. The observed trend of Ea in the temperature range of 150-200˚C and 400-600˚C is HAC > LAC-1 >LAC-2 in both N₂ and CO₂ atmosphere. At the temperature range of 850-1000˚C, higher activation energy is estimated when compared to those values in the temperature range of 400-600°C. Above 800°C, char gasification through Boudouard reaction progressed under CO₂ atmosphere. It was observed that 8-20 kJ/mol of activation energy is increased during char gasification above 800°C compared to volatile matter pyrolysis between the temperature ranges of 400-600°C. The overall activation energy of the coals in the temperature range of 30-1000˚C is higher in N₂ atmosphere than CO₂ atmosphere. It can be concluded that higher hydrocarbons such as tar effectively undergoes cracking and reforming reactions in presence of CO₂. Thus, CO₂ gas is beneficial for the production of high calorific value syngas using high ash Indian coals.

Keywords: clean coal technology, CO₂ gasification, activation energy, underground coal gasification

Procedia PDF Downloads 175