Search results for: sensors networks

Authors: Yin Yuanling

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A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

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Authors: Mustafa Sahin, İlkay Yavrucuk

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This paper presents a wind turbine envelope protection control algorithm that protects Variable Speed Variable Pitch (VSVP) wind turbines from damage during operation throughout their below and above rated regions, i.e. from cut-in to cut-out wind speed. The proposed approach uses a neural network that can adapt to turbines and their operating points. An algorithm monitors instantaneous wind and turbine states, predicts a wind speed that would push the turbine to a pre-defined envelope limit and, when necessary, realizes an avoidance action. Simulations are realized using the MS Bladed Wind Turbine Simulation Model for the NREL 5 MW wind turbine equipped with baseline controllers. In all simulations, through the proposed algorithm, it is observed that the turbine operates safely within the allowable limit throughout the below and above rated regions. Two example cases, adaptations to turbine operating points for the below and above rated regions and protections are investigated in simulations to show the capability of the proposed envelope protection system (EPS) algorithm, which reduces excessive wind turbine loads and expectedly increases the turbine service life.

Keywords: adaptive envelope protection control, limit detection and avoidance, neural networks, ultimate load reduction, wind turbine power control

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Authors: Adithya Jaikumar, Sudarsan Jayasingh

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The dramatic technology led changes that continue to take place at the market place has led to the emergence and implication of online brand pages on social media networks. The Facebook brand page has become extremely popular among different brands. The primary aim of this study was to identify the impact of post formats and content type on customer engagement in Facebook brand pages. Methodology used for this study was to analyze and categorize 9037 content messages posted by 20 automobile brands in India during April 2014 to March 2015 and the customer activity it generated in return. The data was obtained from Fanpage karma- an online tool used for social media analytics. The statistical technique used to analyze the count data was negative binomial regression. The study indicates that there is a statistically significant relationship between the type of post and the customer engagement. The study shows that photos are the most posted format and highest engagement is found to be related to videos. The finding also reveals that social events and entertainment related content increases engagement with the message.

Keywords: content analysis, customer engagement, digital engagement, facebook brand pages, social media

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Burak Köken, Hatice D. Arslan, Bilgehan Y. Çakmak

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Analyzing the relation networks between the hospital buildings which have complex structure and distinctive spatial relationships is quite difficult. The hospital buildings which require specialty in spatial relationship solutions during design and self-innovation through the developing technology should survive and keep giving service even after the disasters such as earthquakes. In this study, a hospital building where the load-bearing system was strengthened because of the insufficient earthquake performance and the construction of an additional building was required to meet the increasing need for space was discussed and a comparative spatial evaluation of the hospital building was made with regard to its status before the change and after the change. For this reason, spatial organizations of the building before change and after the change were analyzed by means of Space Syntax method and the effects of the change on space organization parameters were searched by applying an analytical procedure. Using Depthmap UCL software, connectivity, visual mean depth, beta and visual integration analyses were conducted. Based on the data obtained after the analyses, it was seen that the relationships between spaces of the building increased after the change and the building has become more explicit and understandable for the occupants. Furthermore, it was determined according to findings of the analysis that the increase in depth causes difficulty in perceiving the spaces and the changes considering this problem generally ease spatial use.

Keywords: architecture, hospital building, space syntax, strengthening

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Authors: Jung-Hun Cho, Tae-Heon Moon, Jin-Hak Lee

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Land price contains the comprehensive characteristics of urban space, representing the social and economic features of the city. Accordingly, land price can be utilized as an indicator, which can identify the changes of spatial structure and socioeconomic variations caused by urban development. This study attempted to explore the changes in land price by a new road construction. Methodologically, it adopted Space Syntax, which can interpret urban spatial structure comprehensively, to identify the relationship between the forms of road networks and land price. The result of the regression analysis showed the ‘integration index’ of Space Syntax is statistically significant and has a strong correlation with land price. If the integration value is high, land price increases proportionally. Subsequently, using regression equation, it tried to predict the land price changes of each of the lots surrounding the roads that are newly opened. The research methods or study results have the advantage of predicting the changes in land price in an easy way. In addition, it will contribute to planners and project managers to establish relevant polices and smoothing urban regeneration projects through enhancing residents’ understanding by providing possible results and advantages in their land price before the execution of urban regeneration and development projects.

Keywords: space syntax, urban regeneration, spatial structure, official land price

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Authors: Nishank Raisinghani

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Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

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Authors: Colette Faucher

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In modern asymmetric conflicts, the Armed Forces generally have to intervene in countries where the internal peace is in danger. They must make the local population an ally in order to be able to deploy the necessary military actions with its support. For this purpose, psychological operations (PSYOPs) are used to shape people’s behaviors and emotions by the modification of their attitudes in acting on their perceptions. PSYOPs aim at elaborating and spreading a message that must be read, listened to and/or looked at, then understood by the info-targets in order to get from them the desired behavior. A message can generate in the info-targets, reasoned thoughts, spontaneous emotions or reflex behaviors, this effect partly depending on the means of conveyance used to spread this message. In this paper, we focus on psychological operations that generate emotions. We present a method based on the Intergroup Emotion Theory, that determines, from the characteristics of the conveyed message and of the people from the population directly reached by the means of conveyance (direct info-targets), the emotion likely to be triggered in them and we simulate the propagation of the effects of such a message on indirect info-targets that are connected to them through the social networks that structure the population.

Keywords: military psychological operations, social identity, social network, emotion propagation

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Authors: Jean Berger, Mohamed Barkaoui

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Discrete search path planning in time-constrained uncertain environment relying upon imperfect sensors is known to be hard, and current problem-solving techniques proposed so far to compute near real-time efficient path plans are mainly bounded to provide a few move solutions. A new information-theoretic –based open-loop decision model explicitly incorporating false alarm sensor readings, to solve a single agent military logistics search-and-delivery path planning problem with anticipated feedback is presented. The decision model consists in minimizing expected entropy considering anticipated possible observation outcomes over a given time horizon. The model captures uncertainty associated with observation events for all possible scenarios. Entropy represents a measure of uncertainty about the searched target location. Feedback information resulting from possible sensor observations outcomes along the projected path plan is exploited to update anticipated unit target occupancy beliefs. For the first time, a compact belief update formulation is generalized to explicitly include false positive observation events that may occur during plan execution. A novel genetic algorithm is then proposed to efficiently solve search path planning, providing near-optimal solutions for practical realistic problem instances. Given the run-time performance of the algorithm, natural extension to a closed-loop environment to progressively integrate real visit outcomes on a rolling time horizon can be easily envisioned. Computational results show the value of the approach in comparison to alternate heuristics.

Keywords: search path planning, false alarm, search-and-delivery, entropy, genetic algorithm

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Authors: Sheikh, R. Author, Bhardwaj S. Author, Jr.

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The emphasis of this paper is to investigate the identity shifts experienced within the Indian community and among Muslim refugee women in Sydney. Using Goffman’s paradigm of everyday interactions, attention is paid to how migrants navigate and perform their multiple identities in their daily life. By focusing on narratives of the migrant- migration is understood as processual instead of a one time decision of re-location. The paper aims to highlight how individuals choose and re-adapt their cultural and social practices within the context of Australia. Migrant narratives are rooted in specific socio-cultural settings of one’s own community as well as the nature of migration to a specific country. Differences and similarities will be observed within the Indian community, and among Muslim refugee women in terms of how identity is negotiated, social networks are re-established in Australia. Some attention will also be paid to difficulties that are being faced by migrants-especially in terms of Muslim identity for Refugee women, particularly in terms of assimilation, building on Ghassan Hage’s use of appraisal theory and how a diversity of language and religion is accommodated within the Indian community. By using two diverse groups, it would be able to identify and contrast migrant experiences.

Keywords: identity, migrant, refugee, women, assimilation, narratives

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Authors: Borhan Marzougui

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Road and Transport Authority (RTA) is moving ahead with the implementation of the leader’s vision in exploring all avenues that may bring better security and safety services to the community. Smart transport means using smart technologies such as IoT (Internet of Things). This technology continues to affirm its important role in the context of Information and Transportation Systems. In fact, IoT is a network of Internet-connected objects able to collect and exchange different data using embedded sensors. With the growth of IoT, Distributed Denial of Service (DDoS) attacks is also growing exponentially. DDoS attacks are the major and a real threat to various transportation services. Currently, the defense mechanisms are mainly passive in nature, and there is a need to develop a smart technique to handle them. In fact, new IoT devices are being used into a botnet for DDoS attackers to accumulate for attacker purposes. The aim of this paper is to provide a relevant understanding of dangerous types of DDoS attack related to IoT and to provide valuable guidance for the future IoT security method. Our methodology is based on development of the distributed algorithm. This algorithm manipulates dedicated intelligent and cooperative agents to prevent and to mitigate DDOS attacks. The proposed technique ensure a preventive action when a malicious packets start to be distributed through the connected node (Network of IoT devices). In addition, the devices such as camera and radio frequency identification (RFID) are connected within the secured network, and the data generated by it are analyzed in real time by intelligent and cooperative agents. The proposed security system is based on a multi-agent system. The obtained result has shown a significant reduction of a number of infected devices and enhanced the capabilities of different security dispositives.

Keywords: IoT, DDoS, attacks, botnet, security, agents

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Authors: Paula Lopez-Arce, Hector Altamirano, Dimitrios Rovas, James Berry, Bryan Hindle, Steven Hodgson

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Efficient mould remediation and accurate moisture diagnostics leading to condensation and mould growth in dwellings are largely untapped. Number of factors are contributing to the rising trend of excessive moisture in homes mainly linked with modern living, increased levels of occupation and rising fuel costs, as well as making homes more energy efficient. Environmental monitoring by means of data collection though loggers sensors and survey forms has been performed in a range of buildings from different UK regions. Air and surface temperature and relative humidity values of residential areas affected by condensation and/or mould issues were recorded. Additional measurements were taken through different trials changing type, location, and position of loggers. In some instances, IR thermal images and ventilation rates have also been acquired. Results have been interpreted together with environmental key parameters by processing and connecting data from loggers and survey questionnaires, both in buildings with and without moisture issues. Monitoring exercises carried out during Winter and Spring time show the importance of developing and following accurate protocols for guidance to obtain consistent, repeatable and comparable results and to improve the performance of environmental monitoring. A model and a protocol are being developed to build a diagnostic tool with the goal of performing a simple but precise residential atmospheric moisture diagnostics to distinguish the cause entailing condensation and mould generation, i.e., ventilation, insulation or heating systems issue. This research shows the relevance of monitoring and processing environmental data to assign moisture risk levels and determine the origin of condensation or mould when dealing with a building atmospheric moisture excess.

Keywords: environmental monitoring, atmospheric moisture, protocols, mould

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Authors: Giselda Cristina Ferreira, Carlos Alberto Kamienski, Ana Lígia Scott

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The COVID-19 pandemic has enhanced the anti-vaccination movement in Brazil, supported by unscientific theories and false news and the possibility of wide communication through social networks such as Twitter, Facebook, and YouTube. The World Health Organization (WHO) classified the large volume of information on the subject against COVID-19 as an Infodemic. In this paper, we present a protocol to identify polarizing users (called polarizers) and study the profiles of Brazilian polarizers on Twitter (renamed to X some weeks ago). We analyzed polarizing interactions on Twitter (in Portuguese) to identify the main polarizers and how the conflicts they caused influenced the COVID-19 vaccination rate throughout the pandemic. This protocol uses data from this social network, graph theory, Java, and R-studio scripts to model and analyze the data. The information about the vaccination rate was obtained in a public database for the government called OpenDataSus. The results present the profiles of Twitter’s Polarizer (political position, gender, professional activity, immunization opinions). We observed that social and political events influenced the participation of these different profiles in conflicts and the vaccination rate.

Keywords: Twitter, polarization, vaccine, Brazil

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Authors: Tsu-Hsu Yen

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The interfacial gas adsorption presents a frequent challenge and opportunity for micro-/nano-fluidic operation. In this study, we investigate the wettability, gas accumulation, and nanobubble formation on various homogeneous surface conditions by using MD simulation, including a series of 3D and quasi-2D argon-water-solid systems simulation. To precisely determine the wettability on various substrates, several indicators were calculated. Among these wettability indicators, the water PMF (potential of mean force) has the most correlation tendency with interfacial water molecular orientation than depletion layer width and droplet contact angle. The results reveal that the aggregation of argon molecules on substrates not only depending on the level of hydrophobicity but also determined by the competition between gas-solid and water-solid interaction as well as water molecular structure near the surface. In addition, the surface nanobubble is always observed coexisted with the gas enrichment layer. The water structure adjacent to water-gas and water-solid interfaces also plays an important factor in gas out-flux and gas aggregation, respectively. The quasi-2D simulation shows that only a slight difference in the curved argon-water interface from the plane interface which suggests no noticeable obstructing effect on gas outflux from the gas-water interfacial water networks.

Keywords: gas aggregation, interfacial nanobubble, molecular dynamics simulation, wettability

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Authors: Yassamina Berkane, Leila Kloul, Yoann Demoli

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Quality of life, environmental impact, congestion of mobility means, and infrastructures remain significant challenges for urban mobility. Solutions like car sharing, spatial redesign, eCommerce, and autonomous vehicles will likely increase the unit veh-km and the density of cars in urban traffic, thus reducing congestion. However, the impact of such solutions is not clear for researchers. Congestion arises from growing populations that must travel greater distances to arrive at similar locations (e.g., workplaces, schools) during the same time frame (e.g., rush hours). This paper first reviews the research and application cases of urban congestion methods through recent years. Rethinking the question of time, it then investigates people’s willingness and flexibility to adapt their arrival and departure times from workplaces. We use neural networks and methods of supervised learning to apply a new methodology for predicting peoples' intentions from their responses in a questionnaire. We created and distributed a questionnaire to more than 50 companies in the Paris suburb. Obtained results illustrate that our methodology can predict peoples' intentions to reschedule their activities (work, study, commerce, etc.).

Keywords: urban mobility, decongestion, machine learning, neural network

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Authors: Harshit Mahandra, Rashmi Singh, Bina Gupta

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Molybdenum is widely used in thermocouples, anticathode of X-ray tubes and in the production of alloys of steels. Molybdenum compounds are extensively used as a catalyst in petroleum-refining industries for hydrodesulphurization. Activity of the catalysts decreases gradually with time and are dumped as hazardous waste due to contamination with toxic materials during the process. These spent catalysts can serve as a secondary source for metal recovery and help to sort out environmental and economical issues. In present study, extraction and separation of molybdenum from a Mo-Co spent catalyst leach liquor containing 0.870 g L⁻¹ Mo, 0.341 g L⁻¹ Co, 0.422 ×10⁻¹ g L⁻¹ Fe and 0.508 g L⁻¹ Al in 3 mol L⁻¹ HCl has been investigated using solvent extraction technique. The extracted molybdenum has been finally recovered as molybdenum trioxide. Leaching conditions used were- 3 mol L⁻¹ HCl, 90°C temperature, solid to liquid ratio (w/v) of 1.25% and reaction time of 60 minutes. 96.45% molybdenum was leached under these conditions. For the extraction of molybdenum from leach liquor, Cyphos IL 104 [trihexyl(tetradecyl)phosphonium bis(2,4,4-trimethylpentyl)phosphinate] in toluene was used as an extractant. Around 91% molybdenum was extracted with 0.02 mol L⁻¹ Cyphos IL 104, and 75% of molybdenum was stripped from the loaded organic phase with 2 mol L⁻¹ HNO₃ at A/O=1/1. McCabe Thiele diagrams were drawn to determine the number of stages required for the extraction and stripping of molybdenum. According to McCabe Thiele plots, two stages are required for both extraction and stripping of molybdenum at A/O=1/1 which were also confirmed by countercurrent simulation studies. Around 98% molybdenum was extracted in two countercurrent extraction stages with no co-extraction of cobalt and aluminum. Iron was removed from the loaded organic phase by scrubbing with 0.01 mol L⁻¹ HCl. Quantitative recovery of molybdenum is achieved in three countercurrent stripping stages at A/O=1/1. Trioxide of molybdenum was obtained from strip solution and was characterized by XRD, FE-SEM and EDX techniques. Molybdenum trioxide due to its distinctive electrochromic, thermochromic and photochromic properties is used as a smart material for sensors, lubricants, and Li-ion batteries. Molybdenum trioxide finds application in various processes such as methanol oxidation, metathesis, propane oxidation and in hydrodesulphurization. It can also be used as a precursor for the synthesis of MoS₂ and MoSe₂.

Keywords: Cyphos IL 104, molybdenum, spent Mo-Co catalyst, recovery

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Authors: E. Farg, M. M. El-Sharkawy, M. S. Mostafa, S. M. Arafat

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Mapping water stress provides important baseline data for sustainable agriculture. Recent developments in the new Sentinel-1 data which allow the acquisition of high resolution images and varied polarization capabilities. This study was conducted to detect and quantify vegetation water content from canopy backscatter for extracting spatial information to encourage drought mapping activities throughout new reclaimed sandy soils in western Nile delta, Egypt. The performance of radar imagery in agriculture strongly depends on the sensor polarization capability. The dual mode capabilities of Sentinel-1 improve the ability to detect water stress and the backscatter from the structure components improves the identification and separation of vegetation types with various canopy structures from other features. The fieldwork data allowed identifying of water stress zones based on land cover structure; those classes were used for producing harmonious water stress map. The used analysis techniques and results show high capability of active sensors data in water stress mapping and monitoring especially when integrated with multi-spectral medium resolution images. Also sub soil drip irrigation systems cropped areas have lower drought and water stress than center pivot sprinkler irrigation systems. That refers to high level of evaporation from soil surface in initial growth stages. Results show that high relationship between vegetation indices such as Normalized Difference Vegetation Index NDVI the observed radar backscattering. In addition to observational evidence showed that the radar backscatter is highly sensitive to vegetation water stress, and essentially potential to monitor and detect vegetative cover drought.

Keywords: canopy backscatter, drought, polarization, NDVI

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Authors: P. S. Jagadeesh Kumar, Yang Yung, Mingmin Pan, Xianpei Li, Wenli Hu

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Glaucoma is impelled by optic nerve mutilation habitually represented as cupping and visual field injury frequently with an arcuate pattern of mid-peripheral loss, subordinate to retinal ganglion cell damage and death. Glaucoma is the second foremost cause of blindness and the chief cause of permanent blindness worldwide. Consequently, all-embracing study into the analysis and empathy of glaucoma is happening to escort deep learning based neural network intrusions to deliberate this substantial optic neuropathy. This paper advances an ophthalmic hashing based supervision of glaucoma and corneal disorders preeminent on deep graphical model. Ophthalmic hashing is a newly proposed method extending the efficacy of visual hash-coding to predict glaucoma corneal disorder matching, which is the faster than the existing methods. Deep graphical model is proficient of learning interior explications of corneal disorders in satisfactory time to solve hard combinatoric incongruities using deep Boltzmann machines.

Keywords: corneal disorders, deep Boltzmann machines, deep graphical model, glaucoma, neural networks, ophthalmic hashing

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, HGG

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Authors: Najib Al-Fadhali, Huda Majid

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In this article, a substrate integrated waveguide cavity slot antenna was designed using a computer simulation technology software tool to address the specific design challenges for millimeter-wave communications posed by future satellite communications. Due to the symmetrical structure, a high-order mode is generated in SIW, which yields high gain and high efficiency with a compact feed structure. The antenna has dimensions of 20 mm x 20 mm x 1.34 mm. The proposed antenna bandwidth ranges from 45 GHz to 50 GHz, covering a Q-band application such as satellite communication. Antenna efficiency is above 80% over the operational frequency range. The gain of the antenna is above 9 dB with a peak value of 9.4 dB at 47.5 GHz. The proposed antenna is suitable for various millimeter-wave applications such as sensing, body imaging, indoor scenarios, new generations of wireless networks, and future satellite communications. The simulated results show that the SIW antenna resonates throughout the bands of 45 to 50 GHz, making this new antenna cover all applications within this range. The reflection coefficients are below 10 dB in most ranges from 45 to 50 GHz. The compactness, integrity, reliability, and performance at various operating frequencies make the proposed antenna a good candidate for future satellite communications.

Keywords: ultra-wideband, Q-band, SIW, mm-wave, satellite communications

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Authors: Albert Alexander Stonier

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Due to the fossil fuel exhaustion and environmental pollution, renewable energy sources especially solar photovoltaic system plays a predominant role in providing energy to the consumers. It has been estimated that by 2050 the renewable energy sources will satisfy 50% of the total energy requirement of the world. In this context, the faults in the conversion process require a special attention which is considered as a major problem. A fault which remains even for a few seconds will cause undesirable effects to the system. The presentation comprises of the analysis, causes, effects and mitigation methods of various faults occurring in the entire solar photovoltaic energy conversion process. In order to overcome the faults in the system, an intelligent based artificial neural networks and fuzzy logic are proposed which can significantly mitigate the faults. Hence the presentation intends to find the problem in renewable energy and provides the possible solution to overcome it with simulation and experimental results. The work performed in a 3kWp solar photovoltaic plant whose results cites the improvement in reliability, availability, power quality and fault tolerant ability.

Keywords: solar photovoltaic, power electronics, power quality, PWM

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Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

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Authors: Ali Kadivar, Kaveh Niayesh

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The integration of green energy resources is a major focus, and the role of Medium Voltage Direct Current (MVDC) systems is exponentially expanding. However, the protection of MVDC systems against DC faults is a challenge that can have consequences on reliable and safe grid operation. This challenge reveals the need for MVDC circuit breakers (MVDC CB), which are in infancies of their improvement. Therefore will be a lack of MVDC CBs standards, including thresholds for acceptable power losses and operation speed. To establish a baseline for comparison purposes, a benchmark system for testing future MVDC CBs is vital. The literatures just give the timing sequence of each switch and the emphasis is on the topology, without in-depth study on the control algorithm of DCCB, as the circuit breaker control system is not yet systematic. A digital testing benchmark is designed for the Proof-of-concept of simulation studies using software models. It can validate studies based on real-time digital simulators and Transient Network Analyzer (TNA) models. The proposed experimental setup utilizes data accusation from the accurate sensors installed on the tested MVDC CB and through general purpose input/outputs (GPIO) from the microcontroller and PC Prototype studies in the laboratory-based models utilizing Hardware-in-the-Loop (HIL) equipment connected to real-time digital simulators is achieved. The improved control algorithm of the circuit breaker can reduce the peak fault current and avoid arc resignation, helping the coordination of DCCB in relay protection. Moreover, several research gaps are identified regarding case studies and evaluation approaches.

Keywords: DC circuit breaker, hardware-in-the-loop, real time digital simulation, testing benchmark

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Authors: Muhammad Zainuddin Arriafdi, Hamudah Hakimah Abdullah, Mohd Helmi Sani, Wan Azlina Ahmad, Muhd Nazrul Hisham Zainal Alam

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The biotechnology process development demands numerous experimental works. In laboratory environment, this is typically carried out using a shake flask platform. This paper presents the design and fabrication of a miniature bioreactor system as an alternative research tool for bioprocessing. The working volume of the reactor is 100 ml, and it is made of plastic. The main features of the reactor included stirring control, temperature control via the electrical heater, aeration strategy through a miniature air compressor, and online optical cell density (OD) sensing. All sensors and actuators integrated into the reactor was controlled using an Arduino microcontroller platform. In order to demonstrate the functionality of such miniature bioreactor concept, series of batch Saccharomyces cerevisiae fermentation experiments were performed under various glucose concentrations. Results attained from the fermentation experiments were utilized to solve the Monod equation constants, namely the saturation constant, Ks, and cells maximum growth rate, μmax as to further highlight the usefulness of the device. The mixing capacity of the reactor was also evaluated. It was found that the results attained from the miniature bioreactor prototype were comparable to results achieved using a shake flask. The unique features of the device as compared to shake flask platform is that the reactor mixing condition is much more comparable to a lab-scale bioreactor setup. The prototype is also integrated with an online OD sensor, and as such, no sampling was needed to monitor the progress of the reaction performed. Operating cost and medium consumption are also low and thus, making it much more economical to be utilized for biotechnology process development compared to lab-scale bioreactors.

Keywords: biotechnology, miniature bioreactor, research tools, Saccharomyces cerevisiae

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Authors: Min Jin Kwon, Seung Won Kim, Eui Yeon Kim, Haeyoung Lee

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Numerous anonymous chat apps that help people to connect with random strangers have been released in South Korea. However, they become a serious problem for young people since young people often use them for channels of prostitution or sexual violence. Although ISPs in South Korea are responsible for making inappropriate content inaccessible on their networks, they do not block traffic of random chat apps since 1) the use of random chat apps is entirely legal. 2) it is reported that they use HTTP proxy blocking so that non-HTTP traffic cannot be blocked. In this paper, we propose a service model that can block random chat apps at home routers. A service provider manages a blacklist that contains blocked apps’ information. Home routers that subscribe the service filter the traffic of the apps out using deep packet inspection. We have implemented a prototype of the proposed model, including a centralized server providing the blacklist, a Raspberry Pi-based home router that can filter traffic of the apps out, and an Android app used by the router’s administrator to locally customize the blacklist.

Keywords: deep packet inspection, internet filtering, juvenile protection, technical blocking

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Authors: Joseph Wolff, Jeffrey Eilbott

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Autism Spectrum Disorders (ASD) are a spectrum of social disorders characterized by deficits in social communication, verbal ability, and interaction that can vary in severity. In recent years, researchers have used magnetic resonance imaging (MRI) to help detect how neural patterns in individuals with ASD differ from those of neurotypical (NT) controls for classification purposes. This study analyzed the classification of ASD within males and females using functional MRI data. Functional connectivity (FC) correlations among brain regions were used as feature inputs for machine learning algorithms. Analysis was performed on 558 cases from the Autism Brain Imaging Data Exchange (ABIDE) I dataset. When trained specifically on females, the algorithm underperformed in classifying the ASD subset of our testing population. Although the subject size was relatively smaller in the female group, the manual matching of both male and female training groups helps explain the algorithm’s bias, indicating the altered sex abnormalities in functional brain networks compared to typically developing peers. These results highlight the importance of taking sex into account when considering how generalizations of findings on males with ASD apply to females.

Keywords: autism spectrum disorder, machine learning, neuroimaging, sex differences

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Authors: Yichao Ma, Chengsiong Chin, Wailok Woo

Abstract:

Since the last decade, there has been a rapid growth in digital multimedia, such as high-resolution media files and three-dimentional movies. Hence, there is a need for large digital storage such as Hard Disk Drive (HDD). As such, users expect to have a quieter HDD in their laptop. In this paper, a jury test has been conducted on a group of 34 people where 17 of them are students who is the potential consumer, and the remaining are engineers who know the HDD. A total 13 HDD sound samples have been selected from over hundred HDD noise recordings. These samples are selected based on an agreed subjective feeling. The samples are played to the participants using head acoustic playback system which enabled them to experience as similar as possible the same environment as have been recorded. Analysis has been conducted and the obtained results have indicated different group has different perception over the noises. Two neural network-based acoustic annoyance models are established based on back propagation neural network. Four psychoacoustic metrics, loudness, sharpness, roughness and fluctuation strength, are used as the input of the model, and the subjective evaluation results are taken as the output. The developed models are reasonably accurate in simulating both training and test samples.

Keywords: hdd noise, jury test, neural network model, psychoacoustic annoyance

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Authors: A. Shebani, C. Pislaru

Abstract:

Wear of materials is an everyday experience and has been observed and studied for long time. The prediction of wear is a fundamental problem in the industrial field, mainly correlated to the planning of maintenance interventions and economy. Pin-on-disc test is the most common test which is used to study the wear behaviour. In this paper, the pin-on-disc (AEROTECH UNIDEX 11) is used for the investigation of the effects of normal load and hardness of material on the wear under dry and sliding conditions. In the pin-on-disc rig, two specimens were used; one, a pin which is made of steel with a tip, is positioned perpendicular to the disc, where the disc is made of aluminium. The pin wear and disc wear were measured by using the following instruments: The Talysurf instrument, a digital microscope, and the alicona instrument; where the Talysurf profilometer was used to measure the pin/disc wear scar depth, and the alicona was used to measure the volume loss for pin and disc. After that, the Archard model, American Society for Testing and Materials model (ASTM), and neural network model were used for pin/disc wear modelling and the simulation results are implemented by using the Matlab program. This paper focuses on how the alicona can be considered as a powerful tool for wear measurements and how the neural network is an effective algorithm for wear estimation.

Keywords: wear modelling, Archard Model, ASTM Model, Neural Networks Model, Pin-on-disc Test, Talysurf, digital microscope, Alicona

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Authors: Abrar Al-Enzi

Abstract:

This study investigates the role of the popular utilization of Wasta within Arab societies. Wasta, by definition, is a set of personal networks based on family or kinship ties in which power and influence are utilized to get things done. As Wasta evolved, it became intensely rooted in Arab cultures, which is considered as an intrinsic tool of the culture, a method of doing business transactions and as a family obligation. However, the consequences related to Wasta in business are substantial as it impacts organizational performance, employee’s perception of the organization and the atmosphere between employees. To date, there has been little in-depth organizational research on the impact of Wasta. Hence, the question that will be addressed is: Does Wasta influence human resource management, knowledge sharing and innovation in Kuwait, which in turn affects employees’ commitment within organizations? As a result, a mixed method sequential exploratory research design will be used to examine the mentioned subject, which consists of three phases: (1) Doing some initial exploratory interviews; (2) Developing a paper-based and online survey (Quantitative method) based on the findings; (3) Lastly, following up with semi-structured interviews (Qualitative method). The rationale behind this approach is that both qualitative and quantitative methods complement each other by providing a more complete picture of the subject matter.

Keywords: commitment, HRM practices, social capital, Wasta

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Authors: Ofoegbu Ositadinma Edward

Abstract:

This research paper discusses a web based monitoring system that enables effective monitoring of fuel pump output and sales volume from distributed fuel stations under the domain of a single company/organization. The traditional method of operation by these organizations in Nigeria is non-automated and accounting for dispensed product is usually approximated and manual as there is little or no technology implemented to presently provide information relating to the state of affairs in the station both to on-ground staff and to supervisory staff that are not physically present in the station. This results in unaccountable losses in product and revenue as well as slow decision making. Remote monitoring technology as a vast research field with numerous application areas incorporating various data collation techniques and sensor networks can be applied to provide information relating to fuel pump status in distributed fuel stations reliably. Thus, the proposed system relies upon a microcontroller, keypad and pump to demonstrate the traditional fuel dispenser. A web-enabled PC with an accompanying graphic user interface (GUI) was designed using virtual basic which is connected to the microcontroller via the serial port which is to provide the web implementation.

Keywords: fuel pump, microcontroller, GUI, web

Procedia PDF Downloads 417