Search results for: feed-forward neural network

Authors: Nazma Sultana

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Social media has become an important part of our daily life. It has a significance influences on the people who use them in their daily life frequently. The number of people using social network sites has been increasing continuously. For this frequent utilization has started to affect our social life. This study examine whether the use of social network sites affects the psychosocial interaction between children and their parents. At first parents introduce their children to the internet and different type of device in their early childhood. Many parents use device for feeding their children by watching rhyme or cartoon. As a result children are habituate with it. In Bangladesh 70% people are heavy internet users. About 23 percent of them spend more than five hours on the social networking sites a day. Media are increasing pervasive in the lives of children-roughly the average child today spends nearly about 45 hours per week with media, compared with 17 hours with parents and 30 hours in school. According to a social learning theory, children & adolescents learn by observing & imitating what they see on screen particularly when these behaviors are realistic or are rewarded. The influence of the media on the psychosocial development of children is profound. Thus it is important for parents to provide guidance on age-appropriate use of all media, including television, radio, music, video games and the internet.

Keywords: social media, psychosocial, Technology, Parent, Social Relationship, Adolescents, Teenage, Youth

Procedia PDF Downloads 93

Authors: Faruk Ortes, Derya Karabulut, Yunus Ziya Arslan

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Myoelectric features gathering from musculature environment are considered on a preferential basis to perceive muscle activation and control human arm prostheses according to recent experimental researches. EMG (electromyography) signal based human arm prostheses have shown a promising performance in terms of providing basic functional requirements of motions for the amputated people in recent years. However, these assistive devices for neurorehabilitation still have important limitations in enabling amputated people to perform rather sophisticated or functional movements. Surface electromyogram (EMG) is used as the control signal to command such devices. This kind of control consists of activating a motion in prosthetic arm using muscle activation for the same particular motion. Extraction of clear and certain neural information from EMG signals plays a major role especially in fine control of hand prosthesis movements. Many signal processing methods have been utilized for feature extraction from EMG signals. The specific objective of this study was to compare widely used time domain features of EMG signal including integrated EMG(IEMG), root mean square (RMS) and waveform length(WL) for prediction of externally applied forces to human hands. Obtained features were classified using artificial neural networks (ANN) to predict the forces. EMG signals supplied to process were recorded during only type of muscle contraction which is isometric and isotonic one. Experiments were performed by three healthy subjects who are right-handed and in a range of 25-35 year-old aging. EMG signals were collected from muscles of the proximal part of the upper body consisting of: biceps brachii, triceps brachii, pectorialis major and trapezius. The force prediction results obtained from the ANN were statistically analyzed and merits and pitfalls of the extracted features were discussed with detail. The obtained results are anticipated to contribute classification process of EMG signal and motion control of powered human arm prosthetics control.

Keywords: assistive devices for neurorehabilitation, electromyography, feature extraction, force estimation, human arm prosthesis

Procedia PDF Downloads 348

Authors: C. Iraklis, G. Evmiridis, A. Iraklis

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Renewable energy sources and distributed power generation units already have an important role in electrical power generation. A mixture of different technologies penetrating the electrical grid, adds complexity in the management of distribution networks. High penetration of distributed power generation units creates node over-voltages, huge power losses, unreliable power management, reverse power flow and congestion. This paper presents an optimization algorithm capable of reducing congestion and power losses, both described as a function of weighted sum. Two factors that describe congestion are being proposed. An upgraded selective particle swarm optimization algorithm (SPSO) is used as a solution tool focusing on the technique of network reconfiguration. The upgraded SPSO algorithm is achieved with the addition of a heuristic algorithm specializing in reduction of power losses, with several scenarios being tested. Results show significant improvement in minimization of losses and congestion while achieving very small calculation times.

Keywords: congestion, distribution networks, loss reduction, particle swarm optimization, smart grid

Procedia PDF Downloads 427

Authors: Elmira Ghanbari, Jan Van Esch, Stephen J. Picken, Sahil Aggarwal

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Organogels are formed by the assembly of low molecular weight gelators (LMWG) into fibrous structures. The assembly of these molecules into crystalline fibrous structures occurs as a result of reversible interactions such as π-stacking, hydrogen-bonding, and van der Waals interactions. Bisamide organogelators with two amide groups have been used as one of LMWGs which show efficient assembly behavior via hydrogen bonding for network formation, the formation of a crystalline network for solvent entrapment. In this study, different bisamide gelators with different lengths of alkyl chains have been added to the bisamide parent gels. The effect of the addition of bisamide additives on the gelation of bisamide gels is described. Investigation of the thermal properties of the gels by differential scanning calorimetry and dropping ball techniques indicated that the bisamide gels can be formed by the addition of a high concentration of the second bisamide components. The microstructure of the gels with different gelator components has been visualized with scanning electron microscopy (SEM) which has shown systematic woven, platelet-like, and a combination of those morphologies for different gels. Examining the addition of a range of bisamide additives with different structural characteristics than the parent bisamide gels has confirmed the effect of the molecular structure on the morphology of the bisamide gels and their final properties.

Keywords: bisamide organogelator additives, gel morphology, gel properties, self-assembly

Procedia PDF Downloads 180

Authors: Babesse Saad, Ameddah Djemeleddine

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In this paper, a learning algorithm using neuronal networks to improve the roll stability and prevent the rollover in a single unit heavy vehicle is proposed. First, LQR control to keep balanced normalized rollovers, between front and rear axles, below the unity, then a data collected from this controller is used as a training basis of a neuronal regulator. The ANN controller is thereafter applied for the nonlinear side force model, and gives satisfactory results than the LQR one.

Keywords: rollover, single unit heavy vehicle, neural networks, nonlinear side force

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Authors: Sekkal Nawel, Mahammed Nadir

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The proliferation of online activities on Online Social Networks (OSNs) has captured significant user attention. However, this growth has been hindered by the emergence of fraudulent accounts that do not represent real individuals and violate privacy regulations within social network communities. Consequently, it is imperative to identify and remove these profiles to enhance the security of OSN users. In recent years, researchers have turned to machine learning (ML) to develop strategies and methods to tackle this issue. Numerous studies have been conducted in this field to compare various ML-based techniques. However, the existing literature still lacks a comprehensive examination, especially considering different OSN platforms. Additionally, the utilization of bio-inspired algorithms has been largely overlooked. Our study conducts an extensive comparison analysis of various fake profile detection techniques in online social networks. The results of our study indicate that supervised models, along with other machine learning techniques, as well as unsupervised models, are effective for detecting false profiles in social media. To achieve optimal results, we have incorporated six bio-inspired algorithms to enhance the performance of fake profile identification results.

Keywords: machine learning, bio-inspired algorithm, detection, fake profile, system, social network

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Authors: Yo-Sheng Lin, Chien-Chin Wang, Yun-Wen Lin, Chien-Yo Lee

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A wide band power amplifier (PA) for 60 GHz and 77 GHz direct-conversion transceiver using standard 90 nm CMOS technology is reported. The PA comprises a cascode input stage with a wide band T-type input-matching network and inductive interconnection and load, followed by a common-source (CS) gain stage and a CS output stage. To increase the saturated output power (PSAT) and power-added efficiency (PAE), the output stage adopts a two-way power dividing and combining architecture. Instead of the area-consumed Wilkinson power divider and combiner, miniature low-loss transmission-line inductors are used at the input and output terminals of each of the output stages for wide band input and output impedance matching to 100 ohm. This in turn results in further PSAT and PAE enhancement. The PA consumes 92.2 mW and achieves maximum power gain (S21) of 23.3 dB at 56 GHz, and S21 of 21.7 dB and 14 dB, respectively, at 60 GHz and 77 GHz. In addition, the PA achieves excellent saturated output power (PSAT) of 10.6 dB and maximum power added efficiency (PAE) of 12.3% at 60 GHz. At 77 GHz, the PA achieves excellent PSAT of 10.4 dB and maximum PAE of 6%. These results demonstrate the proposed wide band PA architecture is very promising for 60 GHz wireless personal local network (WPAN) and 77 GHz automobile radar systems.

Keywords: 60 GHz, 77 GHz, PA, WPAN, automotive radar

Procedia PDF Downloads 563

Authors: Jan-Peter Mund, Christian Kind

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In view of the ongoing climate change and global warming, fires along railways in Germany are occurring more frequently, with sometimes massive consequences for railway operations and affected railroad infrastructure. In the absence of systematic studies within the infrastructure network of German Rail, little is known about the causes of such embankment fires. Since a further increase in these hazards is to be expected in the near future, there is a need for a sound knowledge of triggers and drivers for embankment fires as well as methodical knowledge of prediction tools. Two predictable future trends speak for the increasing relevance of the topic: through the intensification of the use of rail for passenger and freight transport (e.g..: doubling of annual passenger numbers by 2030, compared to 2019), there will be more rail traffic and also more maintenance and construction work on the railways. This research project approach uses satellite data to identify historical embankment fires along rail network infrastructure. The team links data from these fires with infrastructure and weather data and trains a machine-learning model with the aim of predicting fire hazards on sections of the track. Companies reflect on the results and use them on a pilot basis in precautionary measures.

Keywords: embankment fires, railway maintenance, machine learning, remote sensing, VIIRS data

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Authors: Atefeh Pourshafie, Homayoun Bakhtiari

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The main task of power distribution companies is to supply the power required by customers in an acceptable level of quality and reliability. Some key performance indicators for electric power distribution companies are those evaluating the continuity of supply within the network. More than other problems, power outages (due to lightning, flood, fire, earthquake, etc.) challenge economy and business. In addition, end users expect a reliable power supply. Reliability indices are evaluated on an annual basis by the specialized holding company of Tavanir (Power Produce, Transmission& distribution company of Iran) . Evaluation of reliability indices is essential for distribution companies, and with regard to the privatization of distribution companies, it will be of particular importance to evaluate these indices and to plan for their improvement in a not too distant future. According to IEEE-1366 standard, there are too many indices; however, the most common reliability indices include SAIFI, SAIDI and CAIDI. These indices describe the period and frequency of blackouts in the reporting period (annual or any desired timeframe). This paper calculates reliability indices for three sample feeders in Lorestan Electric Power Distribution Company and defines the threshold values in a ten-month period. At the end, strategies are introduced to reach the threshold values in order to increase customers' satisfaction.

Keywords: power, distribution network, reliability, outage

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Authors: Mengmeng Liu, J. David Frost

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Interstate highway system within the Atlanta Perimeter plays an important role in residents’ daily life. The serious influence of Atlanta I-85 Collapses implies that transportation system in the region lacks a cohesive and comprehensive transportation plan. Therefore, disaster resilience analysis of the transportation system is necessary. Resilience is the system’s capability to persist or to maintain transportation services when exposed to changes or shocks. This paper analyzed the resilience of the whole transportation system within the Perimeter and see how removing interstates within the Perimeter will affect the resilience of the transportation system. The data used in the paper are Atlanta transportation networks and LEHD Origin-Destination Employment Statistics data. First, we calculate the traffic flow on each road section based on LEHD data assuming each trip travel along the shortest travel time paths. Second, we calculate the measure of resilience, which is flow-based connectivity and centrality of the transportation network, and see how they will change if we remove each section of interstates from the current transportation system. Finally, we get the resilience function curve of the interstates and identify the most resilient interstates section. The resilience analysis results show that the framework of calculation resilience is effective and can provide some useful information for the transportation planning and sustainability analysis of the transportation infrastructures.

Keywords: connectivity, interstate highway system, network analysis, resilience analysis

Procedia PDF Downloads 235

Authors: Marcos Valdez Alexander Junior, Igor Aguilar-Alonso

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Malnutrition is the root of different diseases that universally affect everyone, diseases such as obesity and malnutrition. The objective of this research is to predict the calories of the food to be eaten, developing a smart mobile application to show the user if a meal is balanced. Due to the large percentage of obesity and malnutrition in Peru, the present work is carried out. The development of the intelligent application is proposed with a three-layer architecture, and for the prediction of the nutritional value of the food, the use of pre-trained models based on convolutional neural networks is proposed.

Keywords: volume estimation, calorie estimation, artificial vision, food nutrition

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Authors: Ananya Gangavarapu

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Convolutional Neural Networks (CNN) based models are providing diagnostic capabilities on par with the medical specialists in many specialty areas. However, collecting the medical data for training purposes is very challenging because of the increased regulations around data collections and privacy concerns around personal health data. The gathering of the data becomes even more difficult if the capture devices are edge-based mobile devices (like smartphones) with feeble wireless connectivity in rural/remote areas. In this paper, I would like to highlight Federated Learning approach to mitigate data privacy and security issues.

Keywords: deep learning in healthcare, data privacy, federated learning, training in distributed environment

Procedia PDF Downloads 122

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves.

Keywords: artificial neural networks, classification, hydraulics, predictive quality, deep learning

Procedia PDF Downloads 216

Authors: Natalie Riley, Anita Quigley, Robert M. I. Kapsa, George W. Greene

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As the fields of medicine and bionics grow rapidly in technological advancement, the future and success of it depends on the ability to effectively interface between the artificial and the biological worlds. The biggest obstacle when it comes to implantable, electronic medical devices, is maintaining a ‘clean’, low noise electrical connection that allows for efficient sharing of electrical information between the artificial and biological systems. Implant fouling occurs with the adhesion and accumulation of proteins and various cell types as a result of the immune response to protect itself from the foreign object, essentially forming an electrical insulation barrier that often leads to implant failure over time. Lubricin (LUB) functions as a major boundary lubricant in articular joints, a unique glycoprotein with impressive anti-adhesive properties that self-assembles to virtually any substrate to form a highly ordered, ‘telechelic’ polymer brush. LUB does not passivate electroactive surfaces which makes it ideal, along with its innate biocompatibility, as a coating for implantable bionic electrodes. It is the aim of the study to investigate LUB’s anti-fouling properties and its potential as a safe, bioinspired material for coating applications to enhance the performance and longevity of implantable medical devices as well as reducing the frequency of implant replacement surgeries. Native, bovine-derived LUB (N-LUB) and recombinant LUB (R-LUB) were applied to gold-coated mylar surfaces. Fibroblast, chondrocyte and neural cell types were cultured and grown on the coatings under both passive and electrically stimulated conditions to test the stability and anti-adhesive property of the LUB coating in the presence of an electric field. Lactate dehydrogenase (LDH) assays were conducted as a directly proportional cell population count on each surface along with immunofluorescent microscopy to visualize cells. One-way analysis of variance (ANOVA) with post-hoc Tukey’s test was used to test for statistical significance. Under both passive and electrically stimulated conditions, LUB significantly reduced cell attachment compared to bare gold. Comparing the two coating types, R-LUB reduced cell attachment significantly compared to its native counterpart. Immunofluorescent micrographs visually confirmed LUB’s antiadhesive property, R-LUB consistently demonstrating significantly less attached cells for both fibroblasts and chondrocytes. Preliminary results investigating neural cells have so far demonstrated that R-LUB has little effect on reducing neural cell attachment; the study is ongoing. Recombinant LUB coatings demonstrated impressive anti-adhesive properties, reducing cell attachment in fibroblasts and chondrocytes. These findings and the availability of recombinant LUB brings into question the results of previous experiments conducted using native-derived LUB, its potential not adequately represented nor realized due to unknown factors and impurities that warrant further study. R-LUB is stable and maintains its anti-fouling property under electrical stimulation, making it suitable for electroactive surfaces.

Keywords: anti-fouling, bioinspired, cell attachment, lubricin

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Authors: Natalia Strobel, Jan Kratzer

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In this paper, we test whether different types of research and development (R&D) alliances positively impact the radical innovation performance of firms. We differentiate between the R&D alliances without extern R&D orders and embeddedness in knowledge source network. We test the differences between the domestically diversified R&D alliances and R&D alliances diversified abroad. Moreover, we test how non-price competition influences the impact of domestically diversified R&D alliances, and R&D alliance diversified abroad on radical innovation performance. Our empirical analysis is based on the comprehensive Swiss innovation panel, which allowed us to study 3520 firms between the years between 1996 and 2011 in 3 years intervals. We analyzed the data with a linear estimation with Swamy-Aurora transformation using plm package in R software. Our results show as hypothesized a positive impact of R&D alliances diversity abroad as well as domestically on radical innovation performance. The effect of non-price interaction is in contrast to our hypothesis, not significant. This suggests that diversity of R&D alliances is highly advantageous independent of non-price competition.

Keywords: R&D alliances, partner diversity, knowledge source diversity, non-price competition, absorptive capacity

Procedia PDF Downloads 347

Authors: Pramod Somvanshi, Manu Tomar, K. V. Venkatesh

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Insulin and glucagon are responsible for homeostasis of key plasma metabolites like glucose, amino acids and fatty acids in the blood plasma. These hormones act antagonistically to each other during the secretion and signaling stages. In the present work, we analyze the effect of macronutrients on the response from integrated insulin and glucagon signaling pathways. The insulin and glucagon pathways are connected by DAG (a calcium signaling component which is part of the glucagon signaling module) which activates PKC and inhibits IRS (insulin signaling component) constituting a crosstalk. AKT (insulin signaling component) inhibits cAMP (glucagon signaling component) through PDE3 forming the other crosstalk between the two signaling pathways. Physiological level of anabolism and catabolism is captured through a metric quantified by the activity levels of AKT and PKA in their phosphorylated states, which represent the insulin and glucagon signaling endpoints, respectively. Under resting and starving conditions, the phosphorylation metric represents homeostasis indicating a balance between the anabolic and catabolic activities in the tissues. The steady state analysis of the integrated network demonstrates the presence of a bistable response in the phosphorylation metric with respect to input plasma glucose levels. This indicates that two steady state conditions (one in the homeostatic zone and other in the anabolic zone) are possible for a given glucose concentration depending on the ON or OFF path. When glucose levels rise above normal, during post-meal conditions, the bistability is observed in the anabolic space denoting the dominance of the glycogenesis in liver. For glucose concentrations lower than the physiological levels, while exercising, metabolic response lies in the catabolic space denoting the prevalence of glycogenolysis in liver. The non-linear positive feedback of AKT on IRS in insulin signaling module of the network is the main cause of the bistable response. The span of bistability in the phosphorylation metric increases as plasma fatty acid and amino acid levels rise and eventually the response turns monostable and catabolic representing diabetic conditions. In the case of high fat or protein diet, fatty acids and amino acids have an inhibitory effect on the insulin signaling pathway by increasing the serine phosphorylation of IRS protein via the activation of PKC and S6K, respectively. Similar analysis was also performed with respect to input amino acid and fatty acid levels. This emergent property of bistability in the integrated network helps us understand why it becomes extremely difficult to treat obesity and diabetes when blood glucose level rises beyond a certain value.

Keywords: bistability, diabetes, feedback and crosstalk, obesity

Procedia PDF Downloads 254

Authors: John Narh

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There is high rate of deforestation in Ghana due to agricultural expansion, illegal mining and illegal logging. While it is attempting to address the illegalities, Ghana has also initiated a reforestation program known as the Modified Taungya System (MTS). Within the MTS framework, farmers are allocated degraded forestland and provided with tree seedlings to practice agroforestry until the trees form canopy. Yet, the political, ecological and economic models that inform the selection of tree species, the motivations of participating farmers as well as the factors that accounts for differential access to the land and performance of farmers engaged in the program lie underexplored. Using a sequential explanatory mixed methods approach in five forest-fringe communities in the Eastern Region of Ghana, the study reveals that economic factors and Ghana’s commitment to international conventions on the environment underpin the selection of tree species for the MTS program. Social network and access to remittances play critical roles in having access to, and enhances poor farmers’ chances in the program respectively. Farmers are more motivated by the access to degraded forestland to cultivate food crops than having a share in the trees that they plant. As such, in communities where participating farmers are not informed about their benefit in the tree that they plant, the program is largely unsuccessful.

Keywords: translocality, deforestation, forest management, social network

Procedia PDF Downloads 75

Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, Lionel L. J. Ang, Algernon C. S. Hong, Danette S. E. Tan, Grace H. B. Foo, K. Q. Hong, L. M. Cheng, M. L. Leong

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This paper presents a novel rapid soil classification technique that combines computer vision with four-probe soil electrical resistivity method and cone penetration test (CPT), to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from local construction projects are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labour-intensive. Thus, a rapid classification method is needed at the SGs. Computer vision, four-probe soil electrical resistivity and CPT were combined into an innovative non-destructive and instantaneous classification method for this purpose. The computer vision technique comprises soil image acquisition using industrial grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). Complementing the computer vision technique, the apparent electrical resistivity of soil (ρ) is measured using a set of four probes arranged in Wenner’s array. It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the soil strength is measured using a modified mini cone penetrometer, and w is measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay” and an even mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay”. It is also found that these parameters can be integrated with the computer vision technique on-site to complete the rapid soil classification in less than three minutes.

Keywords: Computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

Procedia PDF Downloads 191

Authors: Michael Ray Brunt

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China has been the site of some of the most significant earthquakes in history; however, earthquake monitoring has long been the provenance of universities and research institutions. The China Digital Seismographic Network was initiated in 1983 and improved significantly during 1992-1993. Data from the CDSN is widely used by government and research institutions, and, generally, this data is not readily accessible to middle and high school students. An educational seismic network in China is needed to provide collaboration and research opportunities for students and engaging students around the country in scientific understanding of earthquake hazards and risks while promoting community awareness. In 2022, the Tsinghua International School (THIS) Seismology Team, made up of enthusiastic students and facilitated by two experienced teachers, was established. As a group, the team’s objective is to install seismographs in schools throughout China, thus creating an educational seismic network that shares data from the THIS Educational Seismic Network (THIS-ESN) and facilitates collaboration. The THIS-ESN initiative will enhance education and outreach in China about earthquake risks and hazards, introduce seismology to a wider audience, stimulate interest in research among students, and develop students’ programming, data collection and analysis skills. It will also encourage and inspire young minds to pursue science, technology, engineering, the arts, and math (STEAM) career fields. The THIS-ESN utilizes small, low-cost RaspberryShake seismographs as a powerful tool linked into a global network, giving schools and the public access to real-time seismic data from across China, increasing earthquake monitoring capabilities in the perspective areas and adding to the available data sets regionally and worldwide helping create a denser seismic network. The RaspberryShake seismograph is compatible with free seismic data viewing platforms such as SWARM, RaspberryShake web programs and mobile apps are designed specifically towards teaching seismology and seismic data interpretation, providing opportunities to enhance understanding. The RaspberryShake is powered by an operating system embedded in the Raspberry Pi, which makes it an easy platform to teach students basic computer communication concepts by utilizing processing tools to investigate, plot, and manipulate data. THIS Seismology Team believes strongly in creating opportunities for committed students to become part of the seismological community by engaging in analysis of real-time scientific data with tangible outcomes. Students will feel proud of the important work they are doing to understand the world around them and become advocates spreading their knowledge back into their homes and communities, helping to improve overall community resilience. We trust that, in studying the results seismograph stations yield, students will not only grasp how subjects like physics and computer science apply in real life, and by spreading information, we hope students across the country can appreciate how and why earthquakes bear on their lives, develop practical skills in STEAM, and engage in the global seismic monitoring effort. By providing such an opportunity to schools across the country, we are confident that we will be an agent of change for society.

Keywords: collaboration, outreach, education, seismology, earthquakes, public awareness, research opportunities

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Authors: G. Arockiasamy, B. Sujeevan Kumar, Surendheran

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Human progress and development are highly influenced by the power of information access and technology. A global and multi-national transformation all over the word is possible due to digitalization. In the process of exchanging information, experience, and resources, there is a radical shift in who controls them. Mass media has turned the world into a global village by strengthening communication network. As a result, a new digital culture has emerged as a social network commonly known as new media. Today the advancement of technology is at the doorstep of everyone linking to anywhere. The traditional social restrictions are broken down by the new type of virtual communication modality that transcends people beyond boundaries At the same time media empire has invaded every nook and corner of the world through great expansion. Media activism is growing stronger and stronger but the truth and true meaning lost in the process. This paper explores the peoples’ attitude to media activism and tracing its direction. The methodology employed is random sampling survey and content analysis method. Both qualitatively and quantitatively measured. The findings tend to show 60 percent indicate media activism as positive and others indicate as negative. As a conclusion, media activism has danger within but depends on nature of the development of human orientation.

Keywords: media activism, media industry, program, truth information, orientation and nature

Procedia PDF Downloads 188

Authors: Robson C. Santos, David D. Oliveira, Matheus M. Reis, Gerson G. Cunha, Marcos A. C. Moreira

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The project demonstrates the construction of a solar power generation, integrated inverter equipment to a "Grid-Tie" by converting direct current generated by solar panels, into alternating current, the same parameters of frequency and voltage concessionaire distribution network. The energy generated is quantified by smart metering module that transmits the information in specified periods of time to a microcontroller via GSM modem. The modem provides the measured data on the internet, using networks and cellular antennas. The monitoring, fault detection and maintenance are performed by a supervisory station. Employed board types, best inverter selection and studies about control equipment and devices have been described. The article covers and explores the global trend of implementing smart distribution electrical energy networks and the incentive to use solar renewable energy. There is the possibility of the excess energy produced by the system be purchased by the local power utility. This project was implemented in residences in the rural community of the municipality of Cabo Frio/RJ. Data could be seen through daily measurements during the month of November 2013.

Keywords: rural residence, supervisory, smart grid, solar energy

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Authors: Jose Flores, Nadia Gamboa

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A proper water quality management requires the establishment of a monitoring network. Therefore, evaluation of the efficiency of water quality monitoring networks is needed to ensure high-quality data collection of critical quality chemical parameters. Unfortunately, in some Latin American countries water quality monitoring programs are not sustainable in terms of recording historical data or environmentally representative sites wasting time, money and valuable information. In this study, multivariate statistical techniques, such as principal components analysis (PCA) and hierarchical cluster analysis (HCA), are applied for identifying the most significant monitoring sites as well as critical water quality parameters in the monitoring network of the Jequetepeque River basin, in northern Peru. The Jequetepeque River basin, like others in Peru, shows socio-environmental conflicts due to economical activities developed in this area. Water pollution by trace elements in the upper part of the basin is mainly related with mining activity, and agricultural land lost due to salinization is caused by the extensive use of groundwater in the lower part of the basin. Since the 1980s, the water quality in the basin has been non-continuously assessed by public and private organizations, and recently the National Water Authority had established permanent water quality networks in 45 basins in Peru. Despite many countries use multivariate statistical techniques for assessing water quality monitoring networks, those instruments have never been applied for that purpose in Peru. For this reason, the main contribution of this study is to demonstrate that application of the multivariate statistical techniques could serve as an instrument that allows the optimization of monitoring networks using least number of monitoring sites as well as the most significant water quality parameters, which would reduce costs concerns and improve the water quality management in Peru. Main socio-economical activities developed and the principal stakeholders related to the water management in the basin are also identified. Finally, water quality management programs will also be discussed in terms of their efficiency and sustainability.

Keywords: PCA, HCA, Jequetepeque, multivariate statistical

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Authors: Ren-Jieh Kuo, Chun-Shou Huang

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The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance.

Keywords: flavor forecasting, artificial neural networks, Support Vector Regression, China

Procedia PDF Downloads 468

Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed

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Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).

Keywords: Bayesian network, classification, expert knowledge, structure learning, surface water analysis

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Authors: Boonrat Plangsorn

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Using ebooks can make not only a meaningful learning and achievement for students, but also can help teacher effectively enhance and improve their teaching. Nowadays, teachers try to develop ebooks for their class but it does not success in some cases because they do not have clear understanding on how to design, develop, and using ebooks that align with their teaching and learning objectives. Thus, the processes of using, designing, and producing ebooks have become one of important competency for teacher because it will enhance teacher’s knowledge for ebooks production. The purposes of this research were: (1) to develop the competency of producing and using ebooks of teachers in Chachengsao and (2) to promote the using ebooks of teachers in Chachengsao. The research procedures were divided into four phases. Phase I (study components and process of the designing and development of ebooks) was an interview in which the qualitative data were collected from five experts in instructional media. Phase II (develop teachers’ competency of producing ebooks) was a workshop for 28 teachers in Chachengsao. Phase III (study teachers’ using ebooks) was an interview in which the qualitative data were collected from seven teachers. Phase IV (study teachers’ using ebooks) was an interview in which the qualitative data were collected from six teachers. The research findings were as follows: 1. The components of ebooks comprised three components: ebooks structure, multimedia, and hyperlink. The eleven processes of design ebooks for education included (1) analyze the ebooks objective, (2) analyze learner characteristics, (3) set objective, (4) set learning content, (5) learner’s motivation, (6) design and construct activity, (7) design hyperlink, (8) produce script and storyboard, (9) confirm storyboard by expert, (10) develop ebooks, and (11) evaluate ebooks. 2. The evaluation of designing and development of ebooks for teacher workshop revealed the participants’ highest satisfaction (M = 4.65). 3. The teachers’ application of ebooks were found that obstacles of producing an ebooks: Time period of producing ebooks, a readiness of school resources, and small teacher network of producing and using ebooks. The result of using ebooks was students’ motivation. 4. The teachers’ ebooks utilization through educational research network of teacher in Chachengsao revealed that the characteristic of ebooks consist of picture, multimedia, voice, music, video, and hyperlink. The application of ebooks caused students interested in the contents; enjoy learning, and enthusiastic learning.

Keywords: ebooks, producing ebooks competency, using ebooks competency, educational research network

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Authors: R. N. Begum, Ambalika Sharma, G. K. Singh

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Identity theft has serious ramifications beyond data and personal information loss. This necessitates the implementation of robust and efficient user identification systems. Therefore, automatic biometric recognition systems are the need of the hour, and ECG-based systems are unquestionably the best choice due to their appealing inherent characteristics. The CNNs are the recent state-of-the-art techniques for ECG-based user identification systems. However, the results obtained are significantly below standards, and the situation worsens as the number of users and types of heartbeats in the dataset grows. As a result, this study proposes a highly accurate and resilient ECG-based person identification system using CNN's dense learning framework. The proposed research explores explicitly the calibre of dense CNNs in the field of ECG-based human recognition. The study tests four different configurations of dense CNN which are trained on a dataset of recordings collected from eight popular ECG databases. With the highest FAR of 0.04 percent and the highest FRR of 5%, the best performing network achieved an identification accuracy of 99.94 percent. The best network is also tested with various train/test split ratios. The findings show that DenseNets are not only extremely reliable but also highly efficient. Thus, they might also be implemented in real-time ECG-based human recognition systems.

Keywords: Biometrics, Dense Networks, Identification Rate, Train/Test split ratio

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Authors: Christo Nicholls

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The National Electricity Grid (NEG) in South Africa has been under strain for the last five years. This overburden of the NEG led Eskom (the State-Owned Entity responsible for the NEG) to implement a blunt methodology to assist them in reducing the maximum demand (MD) on the NEG, when required, called Loadshedding. The challenge of this methodology is that not only does it lead to immense technical issues with the distribution network equipment, e.g., transformers, due to the frequent abrupt off and on switching, it also has a broader negative fiscal impact on the distributors, as their key consumers (commercial & industrial) are now grid defecting due to the lack of Electricity Security Provision (ESP). This paper provides a pragmatic alternative methodology utilizing specific functionalities embedded within direct-connect single and three-phase Advanced Meter Infrastructure (AMI) Solutions deployed within the distribution network, in conjunction with a Multi-Agent Systems Based AI implementation focused on Automated Negotiation Peer-2-Peer trading. The results of this research clearly illustrate, not only does methodology provide a factual percentage contribution towards the NEG MD at the point of consideration, it also allows the distributor to leverage the real-time MD data from key consumers to activate complex, yet impact-measurable Demand Response (DR) programs.

Keywords: AI, AMI, demand response, multi-agent

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Authors: Raquel Camprubí

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Cultural tourism has been postulated as relevant motivation for tourist over the world during the last decades. In this context, museums are the main attraction for cultural tourists who are seeking to connect with the history and culture of the visited place. From the point of view of an urban destination, museums and other cultural resources are essential to have a strong tourist supply at the destination, in order to be capable of catching attention and interest of cultural tourists. In particular, museums’ challenge is to be prepared to offer the best experience to their visitors without to forget their mission-based mainly on protection of its collection and other social goals. Thus, museums individually want to be competitive and have good positioning to achieve their strategic goals. The life cycle of the destination and the level of maturity of its tourism product influence the need of tourism agents to cooperate and collaborate among them, in order to rejuvenate their product and become more competitive as a destination. Additionally, prior studies have considered an approach of different models of a public and private partnership, and collaborative and cooperative relations developed among the agents of a tourism destination. However, there are no studies that pay special attention to museums and the strategic alliances developed to obtain mutual benefits. Considering this background, the purpose of this study is to analyze in what extent museums of a given urban destination have established strategic links and relations among them, in order to improve their competitive position at both individual and destination level. In order to achieve the aim of this study, the city of Girona (Spain) and the museums located in this city are taken as a case study. Data collection was conducted using in-depth interviews, in order to collect all the qualitative data related to nature, strengthen and purpose of the relational ties established among the museums of the city or other relevant tourism agents of the city. To conduct data analysis, a Social Network Analysis (SNA) approach was taken using UCINET software. Position of the agents in the network and structure of the network was analyzed, and qualitative data from interviews were used to interpret SNA results. Finding reveals the existence of strong ties among some of the museums of the city, particularly to create and promote joint products. Nevertheless, there were detected outsiders who have an individual strategy, without collaboration and cooperation with other museums or agents of the city. Results also show that some relational ties have an institutional origin, while others are the result of a long process of cooperation with common projects. Conclusions put in evidence that collaboration and cooperation of museums had been positive to increase the attractiveness of the museum and the city as a cultural destination. Future research and managerial implications are also mentioned.

Keywords: cultural tourism, competitiveness, museums, Social Network analysis

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Authors: Meng Xu, Alexis K. H. Lau, Ming Xu, Bill Barron, Narges Shahraki

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As an emerging unconventional energy, shale gas has been an economically viable step towards a cleaner energy future in U.S. China also has shale resources that are estimated to be potentially the largest in the world. In addition, China has enormous unmet for a clean alternative to substitute coal. Nonetheless, the geological complexity of China’s shale basins and issues of water scarcity potentially impose serious constraints on shale gas development in China. Further, even if China could replicate to a significant degree the U.S. shale gas boom, China faces the problem of transporting the gas efficiently overland with its limited pipeline network throughput capacity and coverage. The aim of this study is to identify the potential bottlenecks in China’s gas transmission network, as well as to examine the shale gas development affecting particular supply locations and demand centers. We examine this through application of three scenarios with projecting domestic shale gas supply by 2020: optimistic, medium and conservative shale gas supply, taking references from the International Energy Agency’s (IEA’s) projections and China’s shale gas development plans. Separately we project the gas demand at provincial level, since shale gas will have more significant impact regionally than nationally. To quantitatively assess each shale gas development scenario, we formulated a gas pipeline optimization model. We used ArcGIS to generate the connectivity parameters and pipeline segment length. Other parameters are collected from provincial “twelfth-five year” plans and “China Oil and Gas Pipeline Atlas”. The multi-objective optimization model uses GAMs and Matlab. It aims to minimize the demands that are unable to be met, while simultaneously seeking to minimize total gas supply and transmission costs. The results indicate that, even if the primary objective is to meet the projected gas demand rather than cost minimization, there’s a shortfall of 9% in meeting total demand under the medium scenario. Comparing the results between the optimistic and medium supply of shale gas scenarios, almost half of the shale gas produced in Sichuan province and Chongqing won’t be able to be transmitted out by pipeline. On the demand side, the Henan province and Shanghai gas demand gap could be filled as much as 82% and 39% respectively, with increased shale gas supply. To conclude, the pipeline network in China is currently not sufficient in meeting the projected natural gas demand in 2020 under medium and optimistic scenarios, indicating the need for substantial pipeline capacity expansion for some of the existing network, and the importance of constructing new pipelines from particular supply to demand sites. If the pipeline constraint is overcame, Beijing, Shanghai, Jiangsu and Henan’s gas demand gap could potentially be filled, and China could thereby reduce almost 25% its dependency on LNG imports under the optimistic scenario.

Keywords: energy policy, energy systematic analysis, scenario analysis, shale gas in China

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Authors: Hosein Hamisheh Bahar, Mohammad Hossein Fazel Zarandi, Akbar Esfahanipour

Abstract:

In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.

Keywords: fuzzy genetic network programming, hybrid expert system, technical trading signal, Tehran stock exchange

Procedia PDF Downloads 317