Search results for: network intrusion prevention

Authors: Jeff Chak-Fu Wong, Tony Chun Yin Yip

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The transition from secondary school to university seems exciting for many first-year students but can be more challenging than expected. Enabling instructors to know students’ learning habits and styles enhances their understanding of the students’ learning backgrounds, allows teachers to provide better support for their students, and has therefore high potential to improve teaching quality and learning, especially in any mathematics-related courses. The aim of this research is to collect students’ data using online surveys, to analyze students’ factors using learning analytics and educational data mining and to discover the characteristics of the students at risk of falling behind in their studies based on students’ previous academic backgrounds and collected data. In this paper, we use correlation-based distance methods and mutual information for measuring student factor relationships. We then develop a factor network using the Minimum Spanning Tree method and consider further study for analyzing the topological properties of these networks using social network analysis tools. Under the framework of mutual information, two graph-based feature filtering methods, i.e., unsupervised and supervised infinite feature selection algorithms, are used to analyze the results for students’ data to rank and select the appropriate subsets of features and yield effective results in identifying the factors affecting students at risk of failing. This discovered knowledge may help students as well as instructors enhance educational quality by finding out possible under-performers at the beginning of the first semester and applying more special attention to them in order to help in their learning process and improve their learning outcomes.

Keywords: students' academic performance, correlation-based distance method, social network analysis, feature selection, graph-based feature filtering method

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Authors: Keyi Wang

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Similar to the touchscreen, hand gesture based human-computer interaction (HCI) is a technology that could allow people to perform a variety of tasks faster and more conveniently. This paper proposes a training method of an image-based hand gesture image and video clip recognition system using a CNN (Convolutional Neural Network) with a dataset. A dataset containing 6 hand gesture images is used to train a 2D CNN model. ~98% accuracy is achieved. Furthermore, a 3D CNN model is trained on a dataset containing 4 hand gesture video clips resulting in ~83% accuracy. It is demonstrated that a Cozmo robot loaded with pre-trained models is able to recognize static and dynamic hand gestures.

Keywords: deep learning, hand gesture recognition, computer vision, image processing

Procedia PDF Downloads 127

Authors: S. Dhivya, M. Abirami, M. Farjana Parveen, M. Keerthiga

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The development of high-speed railway makes people’s lives more and more convenient; meanwhile, handover is the major problem on high-speed railway communication services. In order to overcome that drawback the architecture of Long-Term Evolution (LTE) femtocell networks is used to improve network performance, and the deployment of a femtocell is a key for bandwidth limitation and coverage issues in conventional mobile network system. To increase the handover performance this paper proposed a multiple input multiple output (MIMO) assisted handoff (MAHO) algorithm. It is a technique used in mobile telecom to transfer a mobile phone to a new radio channel with stronger signal strength and improved channel quality.

Keywords: flawless handover, high-speed train, home evolved Node B, LTE, mobile femtocell, RSS

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Authors: Henry Hexmoor, Kailash Yelasani

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We present vehicular platooning as a special case of crowd-sensing framework where sharing sensory information among a crowd is used for their collective benefit. After offering an abstract policy that governs processes involving a vehicular platoon, we review several common scenarios and components surrounding vehicular platooning. We then present a simulated prototype that illustrates efficiency of road usage and vehicle travel time derived from platooning. We have argued that one of the paramount benefits of platooning that is overlooked elsewhere, is the substantial computational savings (i.e., economizing benefits) in acquisition and processing of sensory data among vehicles sharing the road. The most capable vehicle can share data gathered from its sensors with nearby vehicles grouped into a platoon.

Keywords: cloud network, collaboration, internet of things, social network

Procedia PDF Downloads 183

Authors: Daniel Afolayan, Christina Olawepo, Francis Olowookanga, Nguhemen Tingir, Olawale Fadare, John Oko

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In Nigeria, traditional birth attendants (TBAs) can play an important role in the prevention of mother-to-child transmission of HIV. However, their role in improving access to antiretroviral therapy (ART) is unclear. Catholic Caritas Foundation of Nigeria (Caritas Nigeria) is an implementing agency supporting increased access to HIV testing and treatment services in Lagos state through health facilities including Mother Theresa Hospital. Despite intra-facility testing and community outreaches, ART uptake at Mother Theresa Hospital, Lagos was low with 6 individuals on antiretroviral drugs 3 months post-activation. This study explored improving access to ART through linkages with TBAs for ART uptake at the facility. Plan-Do-Study-Act model was used. The goal was to improve uptake of ART from 6 to 80 in 5 months (end of project year). Scanning revealed a network of 15 TBAs with potential as satellites for HIV testing. Caritas Nigeria linked the facility with 15 TBAs who were provided with HIV test kits and trained on HIV testing services for provider-initiated testing and outreaches. Weekly reports and referrals of positives were received, tracked and feedback given on testing yield. These TBAs serve individuals of various age and gender at their trado-medical centres. At the end of 5 months, HIV testing increased by 10,575 (78% from TBAs) and HIV positives obtained improved by 77 (44.2% from TBAs). 55 new individuals were enrolled and commenced on ART (61.8% from TBAs). There was a successful linkage of all clients with escort services due to incentives. Total uptake of ART was 61 (76.3% of target). Structured partnerships between TBAs and HIV care and treatment centers should be strengthened to improve access to ART.

Keywords: access improvement, antiretroviral therapy, traditional birth attendants, uptake

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Authors: X. L. Nguyen, T. Y. Chou, F. Y. Min, F. C. Lin, T. V. Hoang, Y. M. Huang

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In Vietnam, land use planning is utilized as an efficient tool for the local government to adjust land use. However, planned locations are facing disapproval from people who live near these planned sites because of environmental problems. The selection of these locations is normally based on the subjective opinion of decision-makers and is not supported by any scientific methods. Many researchers have applied Multi-Criteria Analysis (MCA) methods in which Analytic Hierarchy Process (AHP) is the most popular techniques in combination with Fuzzy set theory for the subject of rationality assessment of land use planning locations. In this research, the Fuzzy set theory and Analytic Network Process (ANP) multi-criteria-based technique were used for the assessment process. The Fuzzy Analytic Hierarchy Process was also utilized, and the output results from two methods were compared to extract the differences. The 20 planned landfills in Hung Ha district, Thai Binh province, Vietnam was selected as a case study. The comparison results indicate that there are different between weights computed by AHP and ANP methods and the assessment outputs produced from these two methods also slight differences. After evaluation of existing planned sites, some potential locations were suggested to the local government for possibility of land use planning adjusts.

Keywords: Analytic Hierarchy Process, Analytic Network Process, Fuzzy set theory, land use planning

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Authors: Guanghua Zhang, Fubao Wang, Weijun Duan

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Convolution neural network (CNN) has attracted more and more attention on recent years. Especially in the field of computer vision and image classification. However, unsupervised learning with CNN has received less attention than supervised learning. In this work, we use a new powerful tool which is deep convolutional generative adversarial networks (DCGANs) to generate images from Sloan Digital Sky Survey. Training by various star and galaxy images, it shows that both the generator and the discriminator are good for unsupervised learning. In this paper, we also took several experiments to choose the best value for hyper-parameters and which could help to stabilize the training process and promise a good quality of the output.

Keywords: convolution neural network, discriminator, generator, unsupervised learning

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Authors: Nagaratna Rajur, J. D. Mallapur, Y. B. Kirankumar

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The security in mobile communication is very different from the internet or telecommunication, because of its poor user interface and limited processing capacity, as well as combination of complex network protocols. Hence, it poses a challenge for less memory usage and low computation speed based security system. Security involves all the activities that are undertaken to protect the value and on-going usability of assets and the integrity and continuity of operations. An effective network security strategies requires identifying threats and then choosing the most effective set of tools to combat them. Cryptography is a simple and efficient way to provide security in communication. RSA is an asymmetric key approach that is highly reliable and widely used in internet communication. However, it has not been efficiently implemented in mobile communication due its computational complexity and large memory utilization. The proposed algorithm modifies the current RSA to be useful in mobile communication by reducing its computational complexity and memory utilization.

Keywords: M-RSA, sensor networks, sensor applications, security

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Authors: José Anderson Rodrigues de Souza, Teles de Sales Bezerra, Saulo Aislan da Silva Eleuterio, Jeronimo Silva Rocha

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Mobile devices are increasingly occupying sectors of society and one of its most important features is mobility. However, the use of mobile devices is subject to the lifetime of the batteries. Thus, the use of energy batteries has become an important issue in the study of wireless network technologies. In this context, new solutions that enable aggregate energy efficiency not only through energy saving, and principally they are evaluated from a more realistic model of energy discharge, if easy adaptation to existing protocols. This paper presents a study on the energy needed and the lifetime for Wireless Sensor Networks (WSN) using a heterogeneous network and applying the LEACH protocol.

Keywords: wireless sensor networks, energy efficiency, heterogeneous, LEACH protocol

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Authors: Pi Hsia Liang

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The rise of Facebook, Twitter, and other social networks significantly changes in interconnections between people, enhancing the process of information dissemination and amplify the influence of that information. Therefore, to develop informational efficiency or signaling equilibrium type of information environment among social networks, without adverse selection effects, becomes an important issue. Thus, someone may post a piece of intentional information in relation to personal interest for trying to create marginal influence. Therefore, economists are seeking to establish theories of informational efficiency under social network environment in order to resolve adverse selection issues. Reputation could be one of the important factors in the process of creating informational efficiency. Additionally, investors how to process green information, or information of corporate social responsibility is a very important study. This study essentially employs experimental study for examining how investors use stock relevant green information in Facebook and various Taiwan local networks. Facebook, and blogs of Money DJ, Technews and cnYES, respectively, are the primary sites for this examination that also allow to differentiate effects between Facebook and other local social networks. Questionnaire is developed for such an experimental testing. Note that questionnaire allows this study to group, for example, decision frequency and length of time duration focusing on social networks that are used for discriminating investor type and competence of informed investor. This study selects 500 investors that can be separated into two respective 250 samples as the control group and 250 samples in such an experimental. The quantity of sample investor sufficiently results in statistic significance of this experimental study. The empirical results of this study can be used for explaining how financial information in relation to corporate social responsibility would be disseminated in social websites. Therefore, we can lead to better interpretation of price/earnings relationship type of study and empirical studies of green information usefulness or informational efficiency Note that the above mentioned empirical studies did not exist any social network and annual report of corporate social responsibility. This study expects to find the results that both network degree and network cluster significantly affected green information dissemination frequency. In other words, investors with more connections and with high clustered connections might exert a greater influence on their green information dissemination process. The preferred users of financial social networks could make better stock decision that could amplify effects of green information. In addition, Facebook would be more influential than other local Taiwan financial social networks, although Facebook is not a specialized financial social network. In other words, the popularity and reputation effects of Facebook significantly contribute to usefulness of green information and influence of green information. Third, it has a better chance to find rumor or cheating information in local Taiwan financial social networks than Facebook. In other words, Facebook possesses reputation effect, or a better informational efficiency. Or, even though Taiwan local financial social networks have marginal informational effects on stock price, because of shortage of informational efficiency or monitoring system, information could be a tool for those whom owning superior information.

Keywords: network effect on financial services, informational efficiency theory, social networks, social websites

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Authors: A. Majeed, P. Ali

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Before designing an electrical system, the estimation of load is necessary for unit sizing and demand-generation balancing. The system could be a stand-alone system for a village or grid connected or integrated renewable energy to grid connection, especially as there are non–electrified villages in developing countries. In the classical model, the energy demand was found by estimating the household appliances multiplied with the amount of their rating and the duration of their operation, but in this paper, information exists for electrified villages could be used to predict the demand, as villages almost have the same life style. This paper describes a method used to predict the average energy consumed in each two months for every consumer living in a village by Artificial Neural Network (ANN). The input data are collected using a regional survey for samples of consumers representing typical types of different living, household appliances and energy consumption by a list of information, and the output data are collected from administration office of Piramagrun for each corresponding consumer. The result of this study shows that the average demand for different consumers from four villages in different months throughout the year is approximately 12 kWh/day, this model estimates the average demand/day for every consumer with a mean absolute percent error of 11.8%, and MathWorks software package MATLAB version 7.6.0 that contains and facilitate Neural Network Toolbox was used.

Keywords: artificial neural network, load estimation, regional survey, rural electrification

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Authors: Zahzouh Zoubir, Bouzaouit Azzeddine, Gahgah Mounir

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The use of more and more electronic power switches with a nonlinear behavior generates non-sinusoidal currents in distribution networks, which causes damage to domestic and industrial equipment. The multi-level shunt power active filter is subsequently shown to be an adequate solution to the problem raised. Nevertheless, the difficulty of adjusting the active filter DC supply voltage requires another technology to ensure it. In this article, a photovoltaic generator is associated with the DC bus power terminals of the active filter. The proposed system consists of a field of solar panels, three multi-level voltage inverters connected to the power grid and a non-linear load consisting of a six-diode rectifier bridge supplying a resistive-inductive load. Current control techniques of active and reactive power are used to compensate for both harmonic currents and reactive power as well as to inject active solar power into the distribution network. An algorithm of the search method of the maximum power point of type Perturb and observe is applied. Simulation results of the system proposed under the MATLAB/Simulink environment shows that the performance of control commands that reassure the solar power injection in the network, harmonic current compensation and power factor correction.

Keywords: Actif power filter, MPPT, pertub&observe algorithm, PV array, PWM-control

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Authors: A. Basu, A. A. Purohit, M. M. Vaidya, M. D. Kudale

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Mumbai, being traditionally the epicenter of India's trade and commerce, the existing major ports such as Mumbai and Jawaharlal Nehru Ports (JN) situated in Thane estuary are also developing its waterfront facilities. Various developments over the passage of decades in this region have changed the tidal flux entering/leaving the estuary. The intake at Pir-Pau is facing the problem of shortage of water in view of advancement of shoreline, while jetty near Ulwe faces the problem of ship scheduling due to existence of shallower depths between JN Port and Ulwe Bunder. In order to solve these problems, it is inevitable to have information about tide levels over a long duration by field measurements. However, field measurement is a tedious and costly affair; application of artificial intelligence was used to predict water levels by training the network for the measured tide data for one lunar tidal cycle. The application of two layered feed forward Artificial Neural Network (ANN) with back-propagation training algorithms such as Gradient Descent (GD) and Levenberg-Marquardt (LM) was used to predict the yearly tide levels at waterfront structures namely at Ulwe Bunder and Pir-Pau. The tide data collected at Apollo Bunder, Ulwe, and Vashi for a period of lunar tidal cycle (2013) was used to train, validate and test the neural networks. These trained networks having high co-relation coefficients (R= 0.998) were used to predict the tide at Ulwe, and Vashi for its verification with the measured tide for the year 2000 & 2013. The results indicate that the predicted tide levels by ANN give reasonably accurate estimation of tide. Hence, the trained network is used to predict the yearly tide data (2015) for Ulwe. Subsequently, the yearly tide data (2015) at Pir-Pau was predicted by using the neural network which was trained with the help of measured tide data (2000) of Apollo and Pir-Pau. The analysis of measured data and study reveals that: The measured tidal data at Pir-Pau, Vashi and Ulwe indicate that there is maximum amplification of tide by about 10-20 cm with a phase lag of 10-20 minutes with reference to the tide at Apollo Bunder (Mumbai). LM training algorithm is faster than GD and with increase in number of neurons in hidden layer and the performance of the network increases. The predicted tide levels by ANN at Pir-Pau and Ulwe provides valuable information about the occurrence of high and low water levels to plan the operation of pumping at Pir-Pau and improve ship schedule at Ulwe.

Keywords: artificial neural network, back-propagation, tide data, training algorithm

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Authors: Yun-Chung Yu, Yan-Wen Wang, Min-Sheng Liao, Joe-Air Jiang, Yuen-Chung Lee

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This paper proposes to use a Long Range Wide Area Network (LoRaWAN) for a smart pest monitoring system which aims at the oriental fruit fly (Bactrocera dorsalis) to improve the communication efficiency of the system. The oriental fruit fly is one of the main pests in Southeast Asia and the Pacific Rim. Different smart pest monitoring systems based on the Internet of Things (IoT) architecture have been developed to solve problems of employing manual measurement. These systems often use Octopus II, a communication module following the 2.4GHz IEEE 802.15.4 ZigBee specification, as sensor nodes. The Octopus II is commonly used in low-power and short-distance communication. However, the energy consumption increase as the logical topology becomes more complicate to have enough coverage in the large area. By comparison, LoRaWAN follows the Low Power Wide Area Network (LPWAN) specification, which targets the key requirements of the IoT technology, such as secure bi-directional communication, mobility, and localization services. The LoRaWAN network has advantages of long range communication, high stability, and low energy consumption. The 433MHz LoRaWAN model has two superiorities over the 2.4GHz ZigBee model: greater diffraction and less interference. In this paper, The Octopus II module is replaced by a LoRa model to increase the coverage of the monitoring system, improve the communication performance, and prolong the network lifetime. The performance of the LoRa-based system is compared with a ZigBee-based system using three indexes: the packet receiving rate, delay time, and energy consumption, and the experiments are done in different settings (e.g. distances and environmental conditions). In the distance experiment, a pest monitoring system using the two communication specifications is deployed in an area with various obstacles, such as buildings and living creatures, and the performance of employing the two communication specifications is examined. The experiment results show that the packet receiving the rate of the LoRa-based system is 96% , which is much higher than that of the ZigBee system when the distance between any two modules is about 500m. These results indicate the capability of a LoRaWAN-based monitoring system in long range transmission and ensure the stability of the system.

Keywords: LoRaWan, oriental fruit fly, IoT, Octopus II

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Authors: Lina Abou Haibeh, Nadir Hakem, Ousama Abu Safia

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In this paper, the performance and quality of Voice over IP (VoIP) calls carried over a Mobile Ad Hoc Network (MANET) which has a number of SIP nodes registered on a SIP Proxy are analyzed. The testing campaigns are carried out in an indoor corridor structure having a well-defined channel’s characteristics and model for the different voice codecs, G.711, G.727 and G.723.1. These voice codecs are commonly used in VoIP technology. The calls’ quality are evaluated using four Quality of Service (QoS) metrics, namely, mean opinion score (MOS), jitter, delay, and packet loss. The relationship between the wireless channel’s parameters and the optimum codec is well-established. According to the experimental results, the voice codec G.711 has the best performance for the proposed MANET topology

Keywords: wireless channel modelling, Voip, MANET, session initiation protocol (SIP), QoS

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

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The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Mohd Salmi Md Noorani, Ghada Al-Mahbashi, Sakhinah Abu Bakar

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This paper investigates projective lag synchronization (PLS) behavior in drive response dynamical networks (DRDNs) model with identical nodes. A hybrid feedback control method is designed to achieve the PLS with mismatch and without mismatch terms. The stability of the error dynamics is proven theoretically using the Lyapunov stability theory. Finally, analytical results show that the states of the dynamical network with non-delayed coupling can be asymptotically synchronized onto a desired scaling factor under the designed controller. Moreover, the numerical simulations results demonstrate the validity of the proposed method.

Keywords: drive-response dynamical network, projective lag synchronization, hybrid feedback control, stability theory

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Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: deep learning network, smart metering, water end use, water-energy data

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Authors: Mawa Abdelbagi Mohamed Mohamed, Eslam Alaa Elbahlawan

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This study examines the complex social fabric woven within the Abu-Shouk internally displaced persons camp, shaped by the chaotic waves of armed conflict and displacement in the early 2000s. The impact of war, economic constraints, and altered living conditions have fundamentally reshaped families and traditions, presenting both challenges and opportunities. In this exploration, we navigate the resilience of a community thrust into adversity, spotlighting how it emerged as essential in rebuilding social bonds. Exchanges of culture, intertribal marriages, and communal gathering spots have become essential tools in fostering cohesion and understanding amidst diversity. However, this resilience has been tested by the intrusion of politics, leading to shifts in governance structures and community dynamics. As we conclude, it is evident that while the scars of displacement and political disruptions are present, the community's enduring spirit and adaptability shine through. Empowering the community to rebuild their social tapestry, bridging the gaps, and restoring unity amid evolving political realities remain the beacon guiding their journey toward a shared, purposeful future.

Keywords: darfur, internally displaced person, social fabric, conflict

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Authors: Igor Astrov, Natalya Berezovski

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This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for vectored thrust aerial vehicle (VTAV). With the SA strategy, we proposed a neural network motion control procedure to address the dynamics variation and performance requirement difference of flight trajectory for a VTAV. This control strategy with using of NARMA-L2 neurocontroller for chosen model of VTAV has been verified by simulation of take-off and forward maneuvers using software package Simulink and demonstrated good performance for fast stabilization of motors, consequently, fast SA with economy in energy can be asserted during search-and-rescue operations.

Keywords: NARMA-L2 neurocontroller, situational awareness, vectored thrust aerial vehicle, aviation

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Authors: Mehrdad Nouri Khajavi, Gollamhassan Payganeh, Mohsen Fallah Tafti

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Fault diagnosis is an important aspect of maintaining rotating machinery health and increasing productivity. Many researches has been done in this regards. Many faults such as unbalance, misalignment, looseness, bearing faults, etc. have been considered and diagnosed with different techniques. Most of the researches in fault diagnosis of rotating machinery deal with single fault. Where as in reality faults usually occur simultaneously and it is, therefore, necessary to recognize them at the same time. In this research, two of the most common faults namely unbalance and misalignment have been considered simultaneously with different intensity and then identified and classified with the use of Multi-Layer Perception Neural Network (MLPNN). Processed Vibration signals are used as the input to the MLPNN, and the class of mixed unbalancy, and misalignment is the output of the NN.

Keywords: unbalance, parallel misalignment, combined faults, vibration signals

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Authors: Bilal Haider, Farhan Aadil

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A Vehicular Ad hoc Network (VANET) provides various services to end-users traveling on the road at high speeds. However, this high-speed mobility of mobile nodes can cause frequent service disruptions. Various mobility management protocols exist for managing node mobility, but due to their centralized nature, they tend to suffer in the VANET environment. In this research, we proposed a distributed mobility management protocol using software-defined networks (SDN) for VANETs. Instead of relying on a centralized mobility anchor, the mobility functionality is distributed at multiple infrastructural nodes. The protocol is based on the classical Proxy Mobile IP version 6 (PMIPv6). It is evident from simulation results that this work has improved the network performance with respect to nodes throughput, delay, and packet loss.

Keywords: SDN, VANET, mobility management, optimization

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Authors: Dalia Rimawi, Walid Salameh, Amal Al-Omari, Hadeel AbdelKhaleq

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Predication of Survival time of patients with cancer, is a core factor that influences oncologist decisions in different aspects; such as offered treatment plans, patients’ quality of life and medications development. For a long time proportional hazards Cox regression (ph. Cox) was and still the most well-known statistical method to predict survival outcome. But due to the revolution of data sciences; new predication models were employed and proved to be more flexible and provided higher accuracy in that type of studies. Artificial neural network is one of those models that is suitable to handle time to event predication. In this study we aim to compare ph Cox regression with artificial neural network method according to data handling and Accuracy of each model.

Keywords: Cox regression, neural networks, survival, cancer.

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Authors: Tzu-Jung Huang, Hsun-Jung Cho, Chien-Chia Liäm Huang

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To improve the level of service (LoS) of urban arterial systems containing a series of signalized intersections, a proper design of offsets for all intersections associated is of great importance. The MAXBAND model has been the most common approach for this purpose. In this paper, we propose a MAXBAND model with phase constraints so that the lengths of the phases in a cycle are variable. In other words, the length of a cycle is also variable in our setting. We conduct experiments on a real-world traffic network, having several major paths, in Taiwan for numerical evaluations. Actual traffic data were collected through on-site experiments. Numerical evidences suggest that the improvements are around 32%, on average, in terms of total delay of the entire network.

Keywords: arterial progression, MAXBAND, signal control, offset

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Authors: Slimane Ouhmad, Abdellah Halimi

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In this work, we have applied neural networks method MLP type to a database from an array of six sensors for the detection of three toxic gases. As the choice of the number of hidden layers and the weight values has a great influence on the convergence of the learning algorithm, we proposed, in this article, a mathematical formulation to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases on the one hand, and optimize the computation time on the other hand, the comparison to other results achieved in this case.

Keywords: MLP Neural Network, back-propagation, number of neurons in the hidden layer, identification, computing time

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Authors: James Beauchemin

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Research indicates that college students are experiencing mental health challenges of greater severity, and an increased number of students are seeking help. Contributing to the compromised wellness of the college student population are the prevalence of unhealthy lifestyle habits and behaviors such as alcohol consumption, tobacco use, dietary concerns, risky sexual behaviors, and lack of physical activity. Alternative approaches are needed for this population that emphasize prevention and holistic lifestyle change that mitigate mental health and wellness challenges and alleviate strain on campus resources. This presentation will introduce a Solution-Focused Wellness (SFW) intervention model and examine wellness domains solution-focused strategies to promote personal well-being, and provide supporting research from multiple studies that illustrate intervention effectiveness with a collegiate population. Given the subjective and personal nature of wellness, a therapeutic approach that provides the opportunity for individuals to conceptualize and operationalize wellness themselves is critical to facilitating lasting wellness-based change. Solution-Focused Brief Therapy (SFBT) is a strength-based modality defined by its emphasis on constructing solutions rather than focusing on problems and the assumption that clients have the resources and capacity to change. SFBT has demonstrated effectiveness as a brief therapeutic intervention with the college population in groups and related to health and wellness. By integrating SFBT strategies with personal wellness, a brief intervention was developed to support college students in establishing lifestyles trends consistent with their conceptualizations of wellness. Research supports the effectiveness of a SFW model in improving college student wellness in both face-to-face and web-based formats. Outcomes of controlled and longitudinal studies will be presented, demonstrating significant improvements in perceptions of stress, life satisfaction, happiness, mental health, well-being, and resilience. Overall, there is compelling evidence that utilization of a Solution-Focused Brief Therapy approach with college students can help to improve personal wellness and establish healthy lifestyle trends, providing an effective prevention-focused strategy for college counseling centers and wellness centers to employ. Primary research objectives include: 1)establish an evidence-based approach to facilitating wellness pro motion among the college student population, 2) examine the effectiveness of a Solution-Focused Wellness (SFW) intervention model in decreasing stress, improving personal wellness, mental health, life satisfaction, and resiliency,3) investigate intervention impacts over time (e.g. 6-week post-intervention), and 4) demonstrate SFW intervention utility in wellness promotion and associated outcomes when compared with no-treatment control, and alternative intervention approaches.

Keywords: wellness, college students, solution-focused, prevention

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Authors: Guo Rui, Cao Xinhu

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In recent decades, the world has endured severe challenges in light of climate change, epidemics, geopolitics, terrorism, economic uncertainties, as well as regional conflicts and rivalries. The appropriate use of critical infrastructures (Cis) is confronted. Ports, as typical Cis cover more than 80% of the global freight movement. Within this context, even the minimal disruption of port operations could cause malfunction of the holistic supply chain network and substantial economic losses. Hence, it is crucial to evaluate port performance from the perspective of resilience. Research on resilience and risk/safety management has been increasing, however, it needs more attention, as it could prevent potential socio-economic losses and inspire decision-makers to make resilience-based decisions to answer the challenges, such as COVID-19. To facilitate better moves from decision-makers, ports need to identify proper factors influencing port resilience. Inappropriately influenced factor selection could have a cascading effect on undesirable port performances. Thus, a systematic evaluation of factors is essential to stimulate the improvement process of port resilience investigation. This study zooms into container ports considering their critical role in international trade and global supply chains. 440 articles are selected after relevance ranking, and consequently, 62 articles are scrutinized after the title and abstract screening. Forty-one articles are included for bibliographic analysis in the end. It is found that there is no standardized index system to measure port resilience. And most studies evaluate port resilience merely in the recovery phase. Only two articles cover absorption, adaption and recovery state. However, no literature involves the prevention state. Hence, a uniform resilience index system is expected with a clear resilience definition. And port safety and security should also be considered while evaluating port resilience.

Keywords: port resilience, port safety and security, literature review, index system, port performance

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Authors: Wided Abidi, Tahar Ezzedine

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Low-Energy Adaptive Clustering Hierarchy (LEACH) has been considered as one of the effective hierarchical routing algorithms that optimize energy and prolong the lifetime of network. Since the selection of Cluster Head (CH) in LEACH is carried out randomly, in this paper, we propose an approach of electing CH based on LEACH protocol. In other words, we present a formula for calculating the threshold responsible for CH election. In fact, we adopt three principle criteria: the remaining energy of node, the number of neighbors within cluster range and the distance between node and CH. Simulation results show that our proposed approach beats LEACH protocol in regards of prolonging the lifetime of network and saving residual energy.

Keywords: wireless sensors networks, LEACH protocol, cluster head election, energy efficiency

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Authors: Dhvanidevi N. Jadeja, Daya S. Kaul, Anurag A. Kandya

Abstract:

Municipal Solid Waste (MSW) collection in a city is performed in less effective manner which results in the poor management of the environment and natural resources. Municipal corporation does not possess efficient waste management and recycling programs because of the complex task involving many factors. Solid waste collection system depends upon various factors such as manpower, number and size of vehicles, transfer station size, dustbin size and weight, on-road traffic, and many others. These factors affect the collection cost, energy and overall municipal tax for the city. Generally, different types of waste are scattered throughout the city in a heterogeneous way that poses changes for efficient collection of solid waste. Efficient waste collection and transportation strategy must be effectively undertaken which will include optimization of routes, volume of waste, and manpower. Being these optimized, the overall cost can be reduced as the fuel and energy requirements would be less and also the municipal waste taxes levied will be less. To carry out the optimization study of collection system various data needs to be collected from the Ahmedabad municipal corporation such as amount of waste generated per day, number of workers, collection schedule, road maps, number of transfer station, location of transfer station, number of equipment (tractors, machineries), number of zones, route of collection etc. The ArcGis Network Analyst is introduced for the best routing identification applied in municipal waste collection. The simulation consists of scenarios of visiting loading spots in the municipality of Ahmedabad, considering dynamic factors like network traffic changes, closed roads due to natural or technical causes. Different routes were selected in a particular area of Ahmedabad city, and present routes were optimized to reduce the length of the routes, by using ArcGis Network Analyst. The result indicates up to 35% length minimization in the routes.

Keywords: collection routes, efficiency, municipal solid waste, optimization

Procedia PDF Downloads 124

Authors: Mostafa Mjahed

Abstract:

Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

Procedia PDF Downloads 107