Search results for: Wireless Sensor Networks

Authors: V. K. Banga

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Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.

Keywords: artificial intelligence, kinematics, robotic arm, neural networks, fuzzy logic

Procedia PDF Downloads 293

Authors: Paul Lam, Chi Him Chan, Alan Tse

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Language laboratories have been widely used in language learning, starting in the middle of the last century as one of the earliest forms of educational technology. They are designed to assist students’ language learning with technological innovations. Traditional language laboratories provide individual workstations that allow students to access multimedia language resources. In this type of facility, students can train their listening and speaking abilities, and teachers can also assess the performance of an individual student. Although such a setting promotes a student-centered pedagogy by encouraging students to work at their own pace and according to their own needs, it still favours a traditional, behaviourist language learning pedagogy which focuses on repetitive drilling. The change of pedagogies poses challenges to both the teachers and the facilities. The peer-learning pedagogy advocates that language learning should focus on the social aspect, which emphasizes the importance of everyday communication in language learning. The self-access, individual workstation language laboratories may not be able to provide the flexibility for interaction in the new pedagogies. Modern advancement in technology is another factor that drove our language laboratory renovation. In particular, mobile and wireless technology enabled the use of smaller and more flexible devices, making possible much clever use of space. The Chinese University of Hong Kong (CUHK) renovated nine existing language laboratories to provide lighter and more advanced equipment, movable tables, and round desks. These facilities allow more flexibility and encourage students’ interaction. It is believed that the renovated language laboratories can serve different peer learning activities and thus support peer-learning pedagogies in language teaching and learning. A survey has been conducted to collect comments from the teachers who have used the renovated language laboratories and received forty-four response. The teachers’ comments reveal that they experienced different challenges in using the renovated language laboratories, and there is a need to provide guidance to teachers during the technological and pedagogical transition. For example, teachers need instruction on using the newly installed devices such as touch-monitor and visualizer. They also need advice on planning new teaching and learning activities. Nevertheless, teachers appreciated that the renovated language laboratories are flexible and provide more spaces for different learning activities.

Keywords: language laboratories, language learning, peer-learning, student interaction

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Authors: Majid Farsadrouh Rashti, Parisa Jahani, Amir Shafiee, Mehrdad Mofidi

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The dihydroxybenzene isomers, hydroquinone (HQ), catechol (CC) and resorcinol (RS) have been widely recognized as important environmental pollutants due to their toxicity and low degradability in the ecological environment. Speciation of HQ, CC and RS is very important for environmental analysis because they co-exist of these isomers in environmental samples and are too difficult to degrade as an environmental contaminant with high toxicity. There are many analytical methods have been reported for detecting these isomers, such as spectrophotometry, fluorescence, High-performance liquid chromatography (HPLC) and electrochemical methods. These methods have attractive advantages such as simple and fast response, low maintenance costs, wide linear analysis range, high efficiency, excellent selectivity and high sensitivity. A novel modified glassy carbon electrode (GCE) with Pd@ Cu/CNTs core−shell nanowires for the simultaneous determination of hydroquinone (HQ), catechol (CC) and resorcinol (RS) is described. A detailed investigation by field emission scanning electron microscopy and electrochemistry was performed in order to elucidate the preparation process and properties of the GCE/ Pd/CuNWs-CNTs. The electrochemical response characteristic of the modified GPE/LFOR toward HQ, CC and RS were investigated by cyclic voltammetry, differential pulse voltammetry (DPV) and Chronoamperometry. Under optimum conditions, the calibrations curves were linear up to 228 µM for each with detection limits of 0.4, 0.6 and 0.8 µM for HQ, CC and RS, respectively. The diffusion coefficient for the oxidation of HQ, CC and RS at the modified electrode was calculated as 6.5×10⁻⁵, 1.6 ×10⁻⁵ and 8.5 ×10⁻⁵ cm² s⁻¹, respectively. DPV was used for the simultaneous determination of HQ, CC and RS at the modified electrode and the relative standard deviations were 2.1%, 1.9% and 1.7% for HQ, CC and RS, respectively. Moreover, GCE/Pd/CuNWs-CNTs was successfully used for determination of HQ, CC and RS in real samples.

Keywords: dihydroxybenzene isomers, galvanized copper nanowires, electrochemical sensor, Palladium, speciation

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Authors: Jens Ehm

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Manufacturing in supply chains requires an efficient organisation of production and transport processes in order to guarantee the supply of all partners within the chain with the material that is needed for the reliable fulfilment of tasks. If one partner is not able to supply products for a certain period, these products might be missing as the working material for the customer to perform the next manufacturing step, potentially as supply for further manufacturing steps. This way, local disruptions can influence the whole supply chain. In order to avoid material shortages, an efficient scheduling of tasks is necessary. However, the occurrence of unexpected disruptions cannot be eliminated, so that a modification of the schedule should be arranged as fast as possible. This paper discusses the challenges for the implementation of real-time scheduling and control methods and presents a graph-based approach that enables the integrated scheduling of production and transport processes for multiple supply chain partners and offers the potential for quick adaptations to parts of the initial schedule.

Keywords: production, logistics, integrated scheduling, real-time scheduling

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Authors: Zhaoxin Luo, Michael Zhu

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In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem.

Keywords: nature language processing, recurrent neural network, hierarchical structure, document classification, Chinese

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Authors: Proud Arunrangsiwed, Sarinya Kongtieng

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Teachers and university lecturers face an unsolved problem, which is students’ multitasking behaviors during class time, such as texting or playing a game. It is important to examine the most powerful predictor that can result in students’ educational performances. Meta-analysis was used to analyze the research articles, which were published with the keywords, multitasking, class performance, and texting. We selected 14 research articles published during 2008-2013 from online databases, and four articles met the predetermined inclusion criteria. Effect size of each pair of variables was used as the dependent variable. The findings revealed that the students’ expectancy and value on SNSs usages is the best significant predictor of their educational performances, followed by their motivation and ability in using SNSs, prior educational performances, usage behaviors of SNSs in class, and their personal characteristics, respectively. Future study should conduct a longitudinal design to better understand the effect of multitasking in the classroom.

Keywords: meta-regression analysis, social networking sites, academic Performances, multitasking, motivation

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Authors: Mohamed Farhat O. Hameed, Yasamin K. A. Alrayk, A. A Shaalan, S. S. A. Obayya

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The surface plasmon resonance (SPR) sensors are widely used due to its high sensitivity with molecular labels free. The commercial SPR sensors depend on the conventional prism-coupled configuration. However, this type of configuration suffers from miniaturization and integration. Therefore, the search for compact, portable and highly sensitive SPR sensors becomes mandatory.In this paper, sensitivity enhancement of a novel photonic crystal fiber biosensoris introduced and studied. The suggested design has microstructure of air holes in the core region surrounded by two large semicircular metallized channels filled with the analyte. The inner surfaces of the two channels are coated by a silver layer followed by a gold layer.The simulation results are obtained using full vectorial finite element methodwith perfect matched layer (PML) boundary conditions. The proposed design depends on bimetallic configuration to enhance the biosensor sensitivity. Additionally, the suggested biosensor can be used for multi-channel/multi-analyte sensing. In this study, the sensor geometrical parameters are studied to maximize the sensitivity for the two polarized modes. The numerical results show that high refractive index sensitivity of 4750 nm/RIU (refractive index unit) and 4300 nm/RIU can be achieved for the quasi (transverse magnetic) TM and quasi (transverse electric) TE modes of the proposed biosensor, respectively. The reportedbiosensor has advantages of integration of microfluidics setup, waveguide and metallic layers into a single structure. As a result, compact biosensor with better integration compared to conventional optical fiber SPR biosensors can be obtained.

Keywords: photonic crystal fibers, gold, silver, surface plasmon, biosensor

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Authors: Wullapa Wongsinlatam

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Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples.

Keywords: artificial neural networks, back propagation algorithm, time series, local minima problem, metaheuristic optimization

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Authors: Yaghoubi N., Moore Z., Van Der Veen S. M., Pidcoe P. E., Thomas J. S., Dexheimer B.

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Optoelectronic 3D motion capture systems, such as the Vicon kinematic system, are widely utilized in biomedical research to track joint motion. These systems are considered powerful and accurate measurement tools with <2 mm average error. However, these systems are costly and may be difficult to implement and utilize in a clinical setting. 3D virtual reality (VR) is gaining popularity as an affordable and accessible tool to investigate motor control and perception in a controlled, immersive environment. The HTC Vive VR system includes puck-style trackers that seamlessly integrate into its VR environments. These affordable, wireless, lightweight trackers may be more feasible for clinical kinematic data collection. However, the accuracy of HTC Vive Trackers (3.0), when compared to optoelectronic 3D motion capture systems, remains unclear. In this preliminary study, we compared the HTC Vive Tracker system to a Vicon kinematic system in a simulated lumbar flexion task. A 6-DOF robot arm (SCORBOT ER VII, Eshed Robotec/RoboGroup, Rosh Ha’Ayin, Israel) completed various reaching movements to mimic increasing levels of hip flexion (15°, 30°, 45°). Light reflective markers, along with one HTC Vive Tracker (3.0), were placed on the rigid segment separating the elbow and shoulder of the robot. We compared position measures simultaneously collected from both systems. Our preliminary analysis shows no significant differences between the Vicon motion capture system and the HTC Vive tracker in the Z axis, regardless of hip flexion. In the X axis, we found no significant differences between the two systems at 15 degrees of hip flexion but minimal differences at 30 and 45 degrees, ranging from .047 cm ± .02 SE (p = .03) at 30 degrees hip flexion to .194 cm ± .024 SE (p < .0001) at 45 degrees of hip flexion. In the Y axis, we found a minimal difference for 15 degrees of hip flexion only (.743 cm ± .275 SE; p = .007). This preliminary analysis shows that the HTC Vive Tracker may be an appropriate, affordable option for gross motor motion capture when the Vicon system is not available, such as in clinical settings. Further research is needed to compare these two motion capture systems in different body poses and for different body segments.

Keywords: lumbar, vivetracker, viconsystem, 3dmotion, ROM

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Authors: Juan Bojórquez, Henry E. Reyes, Edén Bojórquez, Alfredo Reyes-Salazar

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This paper addressed the use of Artificial Intelligence to obtain the structural reliability of reinforced concrete buildings. For this purpose, artificial neuronal networks (ANN) are developed to predict seismic demand hazard curves. In order to have enough input-output data to train the ANN, a set of reinforced concrete buildings (low, mid, and high rise) are designed, then a probabilistic seismic hazard analysis is made to obtain the seismic demand hazard curves. The results are then used as input-output data to train the ANN in a feedforward backpropagation model. The predicted values of the seismic demand hazard curves found by the ANN are then compared. Finally, it is concluded that the computer time analysis is significantly lower and the predictions obtained from the ANN were accurate in comparison to the values obtained from the conventional methods.

Keywords: structural reliability, seismic design, machine learning, artificial neural network, probabilistic seismic hazard analysis, seismic demand hazard curves

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Authors: Huseyin A. Yavasoglu, Yusuf Ziya Tengiz, Ali Göksenli

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In this study, an automatic feeding machine for different type and class of bovine animals is designed. Daily nutrition of a bovine consists of grass, corn, straw, silage, oat, wheat and different vitamins and minerals. The amount and mixture amount of each of the nutrition depends on different parameters of the bovine. These parameters are; age, sex, weight and maternity of the bovine, also outside temperature. The problem in a farm is to constitute the correct mixture and amount of nutrition for each animal. Faulty nutrition will cause an insufficient feeding of the animal concluding in an unhealthy bovine. To solve this problem, a new automatic feeding machine is designed. Travelling of the machine is performed by four tires, which is pulled by a tractor. The carrier consists of eight bins, which each of them carries a nutrition type. Capacity of each unit is 250 kg. At the bottom of each chamber is a sensor measuring the weight of the food inside. A funnel is at the bottom of each chamber by which open/close function is controlled by a valve. Each animal will carry a RFID tag including ID on its ear. A receiver on the feeding machine will read this ID and by given previous information by the operator (veterinarian), the system will detect the amount of each nutrition unit which will be given to the selected animal for feeding. In the system, each bin will open its exit gate by the help of the valve under the control of PLC (Programmable Logic Controller). The amount of each nutrition type will be controlled by measuring the open/close time. The exit canals of the bins are collected in a reservoir. To achieve a homogenous nitration, the collected feed will be mixed by a worm gear. Further the mixture will be transported by a help of a funnel to the feeding unit of the animal. The feeding process can be performed in 100 seconds. After feeding of the animal, the tractor pulls the travelling machine to the next animal. By the help of this system animals can be feeded by right amount and mixture of nutrition

Keywords: bovine, feeding, nutrition, transportation, automatic

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Authors: Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu

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Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounders. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, with an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make any assumption about the data generating process and the type of the variables. Experiments using both synthetic and real-world datasets show the effectiveness of the method.

Keywords: average causal effect, hidden confounding, bound estimation, generative adversarial learning

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Authors: S. M. Abdullah Al Mamun

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Human Computer Interaction (HCI) is an excellent area for the researchers to make daily life more simple and fast. Necessary hardware equipments for any BCI are generally expensive and not affordable for most of the people. Emotiv is one of the solutions for this problem, which can provide electroencephalograph (EEG) signal and explain the brain activities. BCI virtual speller was one of the important applications for the people who have lost their hand or speaking ability because of diseases or unexpected accident. In this paper, a matrix speller has been designed for the first time for Bengali speaking people around the world. Bengali is one of the most commonly spoken languages. Among them, a lot of disabled person will be able to express their desire in their mother tongue. This application is also usable for the social networks and daily life communications. For this virtual keyboard, the well-known matrix speller method with column flashing is applied and controlled by single Emokey only. Emokey is a great feature which translates emotional state for application inputs. In this paper, it is presented that the ITR (Information Transfer Rate) were 29.4 bits/min and typing speed achieved up to 7.43 char/per min.

Keywords: brain computer interface, Emotiv EPOC, EEG, virtual keyboard, matrix speller

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Authors: Bruno R. A. Oliveira, Italo V. V. Braga, Jonas P. Reges, Luiz P. O. Santos, Sidney C. Duarte, Emilson R. R. Melo, Auzuir R. Alexandria

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This project aims to building a controlled greenhouse, or for better understanding, a structure where one can maintain a given range of temperature values (°C) coming from radiation emitted by an incandescent light, as previously defined, characterizing as a kind of on-off control and a differential, which is the plotting of temperature versus time graphs assisted by MATLAB software via serial communication. That way it is possible to connect the stove with a computer and monitor parameters. In the control, it was performed using a PIC 16F877A microprocessor which enabled convert analog signals to digital, perform serial communication with the IC MAX232 and enable signal transistors. The language used in the PIC's management is Basic. There are also a cooling system realized by two coolers 12V distributed in lateral structure, being used for venting and the other for exhaust air. To find out existing temperature inside is used LM35DZ sensor. Other mechanism used in the greenhouse construction was comprised of a reed switch and a magnet; their function is in recognition of the door position where a signal is sent to a buzzer when the door is open. Beyond it exist LEDs that help to identify the operation which the stove is located. To facilitate human-machine communication is employed an LCD display that tells real-time temperature and other information. The average range of design operating without any major problems, taking into account the limitations of the construction material and structure of electrical current conduction, is approximately 65 to 70 ° C. The project is efficient in these conditions, that is, when you wish to get information from a given material to be tested at temperatures not as high. With the implementation of the greenhouse automation, facilitating the temperature control and the development of a structure that encourages correct environment for the most diverse applications.

Keywords: greenhouse, microcontroller, temperature, control, MATLAB

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Authors: Montaceur Zaghdoud, Mohamed Moussaoui, Jalel Akaichi

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Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks.

Keywords: approximate graph matching, hybrid frequent subgraph mining, graph mining, possibility theory

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Akshaya P., Priyanjali Prabhkaran

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The paper examines the formulation of strategies to develop a comprehensive model of city level water utility management to addressing urban sanitation issues. The water is prime life sustaining natural resources and nature’s gifts to all living beings on the earth multiple urban sanitation issues are addressed in the supply of water in a city. Many of these urban sanitation issues are linked to population expansion and economic inequity. Increased usage of water and the development caused water scarcity. The lack of water supply results increases the chance of unhygienic situations in the cities. In this study, the urban sanitation issues are identified with respect to water supply and utility management. The study compared based on their best practices and initiatives. From this, best practices and initiatives identify suitable sustainable measures to address water supply issues in the city level. The paper concludes with the listed provision that should be considered suitable measures for water supply and utility management in city level to address the urban sanitation issues.

Keywords: water, benchmarking water supply, water supply networks, water supply management

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Authors: Rasha A.Ibrahim El-Ghazawy, Ashraf M. El-Saeed, Heusin El-Shafey, M. Abdel-Raheim, Maher A. El-Sockary

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Two thermotropic liquid crystalline curing agents based on abietic acid with different mesogens (LCC1 and LCC2) were synthesized for producing thermally stable liquid crystal networks suitable for high performance epoxy coatings. Differential scanning calorimetry (DSC) and polarized optical microscope (POM) was used to identify the liquid crystal phase transformation temperatures and texture, respectively. POM micro graphs for both LCCs revealing cholesteric texture. A multifunctional epoxy resin with two abietic acid moieties was also synthesized. Dynamic mechanical (DMA) and thermogravimetric (TGA) analyses show that the fully bio-based cured epoxies by either LCCs possess high glass transition temperature (Tg), high modulus (G`) and improved thermal stability. The chemical structure of the synthesized LCCs and epoxy resin was investigated through FTIR and 1HNMR spectroscopic techniques.

Keywords: abietic acid, dynamic mechanical analysis, epoxy resin, liquid crystal, thermo gravimetric analysis

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Authors: Francesco Buccafurri, Celeste Romolo

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Nowadays phishing is the most frequent starting point of cyber-attack vectors. Phishing is implemented both via email and social network messages. While a wide scientific literature exists which addresses the problem of contrasting email spam-phishing, no specific countermeasure has been so far proposed for phishing included into private messages of social network platforms. Unfortunately, the problem is severe. This paper proposes an approach against social network phishing, based on a non invasive collaborative information-sharing approach which leverages blockchain. The detection method works by filtering candidate messages, by distilling them by means of a distance-preserving hash function, and by publishing hashes over a public blockchain through a trusted smart contract (thus avoiding denial of service attacks). Phishing detection exploits social information embedded into social network profiles to identify similar messages belonging to disjoint contexts. The main contribution of the paper is to introduce a new approach to contrasting the problem of social network phishing, which, despite its severity, received little attention by both research and industry.

Keywords: phishing, social networks, information sharing, blockchain

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Authors: Yu Guan, Song Lu, Wei Yuan, Heping Zhang

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Gas fire extinguishing system is widely used due to its cleanliness and efficiency, and since its spray will be affected by many factors such as convection and obstacles in jetting region, so in order to evaluate its effectiveness, detecting concentration distribution in the jetting area is indispensable, which is commonly achieved by aspirating concentration detection technique. During the concentration measurement, the response time of detector is a very important parameter, especially for those fire-extinguishing systems with rapid gas dispersion. Long response time will not only underestimate its concentration but also prolong the change of concentration with time. Therefore it is necessary to analyze the factors influencing the response time. In the paper, an aspirating concentration detection method was introduced, which is achieved by using a small critical nozzle and a laminar flowmeter, and because of the response time is mainly related to the gas transport process from sampling site to the sensor, the effects of exhaust pipe size, gas flow rate, and gas concentration on its response time were analyzed. During the research, Bromotrifluoromethane (CBrF₃) was used. The effect of the sampling tube was investigated with different length of 1, 2, 3, 4 and 5 m (5mm in pipe diameter) and different pipe diameter of 3, 4, 5, 6 and 8 mm (3m in length). The effect of gas flow rate was analyzed by changing the throat diameter of the critical nozzle with 0.5, 0.682, 0.75, 0.8, 0.84 and 0.88 mm. The effect of gas concentration on response time was studied with the concentration range of 0-25%. The result showed that the response time increased with the increase of both the length and diameter of the sampling pipe, and the effect of length on response time was linear, but for the effect of diameter, it was exponential. It was also found that as the throat diameter of critical nozzle increased, the response time reduced a lot, in other words, gas flow rate has a great influence on response time. For the effect of gas concentration, the response time increased with the increase of the CBrF₃ concentration, and the slope of the curve was reduced.

Keywords: aspirating concentration detection, fire extinguishing, gaseous agent, response time

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Authors: Yana Snegireva

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Carbonate reservoirs are known for their heterogeneity, resulting from various geological processes such as diagenesis and fracturing. These complexities may cause great challenges in understanding fluid flow behavior and predicting the production performance of naturally fractured reservoirs. The investigation of carbonate reservoirs is crucial, as many petroleum reservoirs are naturally fractured, which can be difficult due to the complexity of their fracture networks. This can lead to geological uncertainties, which are important for global petroleum reserves. The problem outlines the key challenges in carbonate reservoir modeling, including the accurate representation of fractures and their connectivity, as well as capturing the impact of fractures on fluid flow and production. Traditional reservoir modeling techniques often oversimplify fracture networks, leading to inaccurate predictions. Therefore, there is a need for a modern approach that can capture the complexities of carbonate reservoirs and provide reliable predictions for effective reservoir management and production optimization. The modern approach to carbonate reservoir modeling involves the utilization of the hybrid fracture modeling approach, including the discrete fracture network (DFN) method and implicit fracture network, which offer enhanced accuracy and reliability in characterizing complex fracture systems within these reservoirs. This study focuses on the application of the hybrid method in the Nepsko-Botuobinskaya anticline of the Eastern Siberia field, aiming to prove the appropriateness of this method in these geological conditions. The DFN method is adopted to model the fracture network within the carbonate reservoir. This method considers fractures as discrete entities, capturing their geometry, orientation, and connectivity. But the method has significant disadvantages since the number of fractures in the field can be very high. Due to limitations in the amount of main memory, it is very difficult to represent these fractures explicitly. By integrating data from image logs (formation micro imager), core data, and fracture density logs, a discrete fracture network (DFN) model can be constructed to represent fracture characteristics for hydraulically relevant fractures. The results obtained from the DFN modeling approaches provide valuable insights into the East Siberia field's carbonate reservoir behavior. The DFN model accurately captures the fracture system, allowing for a better understanding of fluid flow pathways, connectivity, and potential production zones. The analysis of simulation results enables the identification of zones of increased fracturing and optimization opportunities for reservoir development with the potential application of enhanced oil recovery techniques, which were considered in further simulations on the dual porosity and dual permeability models. This approach considers fractures as separate, interconnected flow paths within the reservoir matrix, allowing for the characterization of dual-porosity media. The case study of the East Siberia field demonstrates the effectiveness of the hybrid model method in accurately representing fracture systems and predicting reservoir behavior. The findings from this study contribute to improved reservoir management and production optimization in carbonate reservoirs with the use of enhanced and improved oil recovery methods.

Keywords: carbonate reservoir, discrete fracture network, fracture modeling, dual porosity, enhanced oil recovery, implicit fracture model, hybrid fracture model

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Authors: Umar Albalawi, Sang C. Suh, Jinoh Kim

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As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%).

Keywords: intrusion detection, supervised learning, traffic classification, computer networks

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Authors: Hen Friman

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Various categories of aqueous solutions are discharged within residential, institutional, commercial, and industrial structures. To safeguard public health and preserve the environment, it is imperative to subject wastewater to treatment processes that eliminate pathogens (such as bacteria and viruses), nutrients (such as nitrogen and phosphorus), and other compounds. Failure to address untreated sewage accumulation can result in an array of adverse consequences. Israel exemplifies a special case in wastewater management. Appropriate wastewater treatment significantly benefits sectors such as agriculture, tourism, horticulture, and industry. Nevertheless, untreated sewage in settlements lacking proper sewage collection or transportation networks remains an ongoing and substantial threat. Notably, the process of wastewater treatment entails substantial energy consumption. Consequently, this study explores the integration of solar energy as a renewable power source within the wastewater treatment framework. By incorporating renewable energy sources into the process, costs can be minimized, and decentralized facilities can be established even in areas lacking adequate infrastructure for traditional treatment methods.

Keywords: renewable energy, solar energy, innovative, wastewater treatment

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Authors: Shunsuke Aoki, Taku Ri, Tatsuya Yamazaki

Abstract:

These days, along with the development of e-sports, video games as a competitive sport is attracting attention. But, in many cases, action in the screen does not match the real motion of operation. Inclusiveness of player motion is needed to increase reality and excitement for sports games. Therefore, in this study, the authors propose a method to recognize player motion by using the motion capture technology and develop a virtual basketball game. The virtual basketball game consists of a screen with nine targets, players, depth sensors, and no ball. The players pretend a two-handed basketball shot without a ball aiming at one of the nine targets on the screen. Time-series data of three-dimensional coordinates of player joints are captured by the depth sensor. 20 joints data are measured for each player to estimate the shooting motion in real-time. The trajectory of the thrown virtual ball is calculated based on the time-series data and hitting on the target is judged as success or failure. The virtual basketball game can be played by 2 to 4 players as a competitive game among the players. The developed game was exhibited to the public for evaluation on the authors' university open campus days. 339 visitors participated in the exhibition and enjoyed the virtual basketball game over the two days. A questionnaire survey on the developed game was conducted for the visitors who experienced the game. As a result of the survey, about 97.3% of the players found the game interesting regardless of whether they had experienced actual basketball before or not. In addition, it is found that women are easy to comfort for shooting motion. The virtual game with motion capture technology has the potential to become a universal entertainment between e-sports and actual sports.

Keywords: basketball, motion capture, questionnaire survey, video ga

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Authors: Prajamitra Bhuyan

Abstract:

Transport operators have a range of intervention options available to improve or enhance their networks. But often such interventions are made in the absence of sound evidence on what outcomes may result. Cycling superhighways were promoted as a sustainable and healthy travel mode which aims to cut traffic congestion. The estimation of the impacts of the cycle superhighways on congestion is complicated due to the non-random assignment of such intervention over the transport network. In this paper, we analyse the causal effect of cycle superhighways utilising pre-innervation and post-intervention information on traffic and road characteristics along with socio-economic factors. We propose a modeling framework based on the propensity score and outcome regression model. The method is also extended to doubly robust set-up. Simulation results show the superiority of the performance of the proposed method over existing competitors. The method is applied to analyse a real dataset on the London transport network, and the result would help effective decision making to improve network performance.

Keywords: average treatment effect, confounder, difference-in-difference, intelligent transportation system, potential outcome

Procedia PDF Downloads 238

Authors: Stefanie Siebenhütter

Abstract:

This paper aims to reveal criterions involved in the process of identity-forming among multilingual minority language speakers in Northeastern Thailand and in the capital Bangkok. Using sociolinguistic interviews and questionnaires, it is asked which factors are important for speakers and how they define their identity by their interactions socially as well as linguistically. One key question to answer is how sociolinguistic factors may force or diminish the process of forming social identity of multilingual minority speakers. However, the motivation for specific language use is rarely overt to the speaker’s themselves as well as to others. Therefore, identifying the intentions included in the process of identity construction is to approach by scrutinizing speaker’s behavior and attitudes. Combining methods used in sociolinguistics and social psychology allows uncovering the tools for identity construction that ethnic Kui uses to range themselves within a multilingual setting. By giving an overview of minority speaker’s language use in context of the specific border near multilingual situation and asking how speakers construe identity within this spatial context, the results exhibit some of the subtle and mostly unconscious criterions involved in the ongoing process of identity construction.

Keywords: social identity, identity construction, minority language, multilingualism, social networks, social boundaries

Procedia PDF Downloads 264

Authors: Stefano Zappacosta, Cristiano Bove, Maria Carmela Marinelli, Paola di Lauro, Katarina Spasenovic, Lorenzo Ostano, Giuseppe Aiello, Marco Pietrosanto

Abstract:

This paper proposes a novel neural network approach for assessing flood hazard mapping. The core of the model is a machine learning component fed by frequency ratios, namely statistical correlations between flood event occurrences and a selected number of topographic properties. The proposed hybrid model can be used to classify four different increasing levels of hazard. The classification capability was compared with the flood hazard mapping River Basin Plans (PAI) designed by the Italian Institute for Environmental Research and Defence, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale). The study area of Piemonte, an Italian region, has been considered without loss of generality. The frequency ratios may be used as a standalone block to model the flood hazard mapping. Nevertheless, the mixture with a neural network improves the classification power of several percentage points, and may be proposed as a basic tool to model the flood hazard map in a wider scope.

Keywords: flood modeling, hazard map, neural networks, hydrogeological risk, flood risk assessment

Procedia PDF Downloads 171

Authors: Abe D. Desta

Abstract:

This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Stephanie Nihan, Jayson M. Fadrigalan, Pyay P. San, Steven M. Santos, Weihui Li

Abstract:

People of all ages who use wheelchairs are left with the inconvenience of not having an easy way to take their vital signs. Typically, patients are required to visit the hospital in order to take the vital signs. VitalGO is a wheel chair system that equipped with medical devices to take vital signs and then transmit data to a mobile application for convenient, long term health monitoring. The vital signs include oxygen saturation, heart rate, and blood pressure, breathing rate and body temperature. Oxygen saturation and heart rate are monitored through pulse oximeter. Blood pressure is taken through a radar sensor. Breathing rate is derived through thoracic impedance while body temperature is measured through an infrared thermometer. The application receives data through bluetooth and stores in a database for review in a simple graphical interface. The application will have the ability to display this data over various time intervals such as a day, week, month, 3 months, 6 months and a year. The final system for the mobile app can also provide an interface for both the user and their physician(s) to record notes or keep record of daily symptoms that a patient might be having. The user’s doctor will be granted access by the user to view the patient information for assistance with a more accurate diagnosis. Also, this wheelchair accessory conveniently includes a foldable table/desk as somewhere to place an electronic device that may be used to access the app. The foldable table will overall contribute to the wheelchair user’s increased comfort and will give them somewhere to place food, a book, or any other form of entertainment that would normally be hard to juggle on their lap.

Keywords: wheel chair, vital sign, mobile application, telemedicine

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Authors: Musab Isah

Abstract:

This paper presents a longitudinal analysis of Internet speed data in the Gulf Cooperation Council (GCC) region, focusing on the most populous cities of each of the six countries – Riyadh, Saudi Arabia; Dubai, UAE; Kuwait City, Kuwait; Doha, Qatar; Manama, Bahrain; and Muscat, Oman. The study utilizes data collected from the Measurement Lab (M-Lab) infrastructure over a five-year period from January 1, 2019, to December 31, 2023. The analysis includes downstream and upstream throughput data for the cities, covering significant events such as the launch of 5G networks in 2019, COVID-19-induced lockdowns in 2020 and 2021, and the subsequent recovery period and return to normalcy. The results showcase substantial increases in Internet speeds across the cities, highlighting improvements in both download and upload throughput over the years. All the GCC countries have achieved above-average Internet speeds that can conveniently support various online activities and applications with excellent user experience.

Keywords: internet data science, internet performance measurement, throughput analysis, internet speed, measurement lab, network diagnostic tool

Procedia PDF Downloads 55