Search results for: disaster detection

Authors: Vinod Ponnagandla, Brenton McLaury, Siamack Shirazi

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Sand production can lead to deposition of particles or erosion. Low production rates resulting in deposition can partially clog systems and cause under deposit corrosion. Commercially available nonintrusive acoustic sand detectors are attractive as they claim to detect sand production. Acoustic sand detectors are used during oil and gas production; however, operators often do not know the threshold detection limits of these devices. It is imperative to know the detection limits to appropriately plan for cleaning of separation equipment or examine risk of erosion. These monitors are based on detecting the acoustic signature of sand as the particles impact the pipe walls. The objective of this work is to determine threshold detection limits for acoustic sand monitors that are commercially available. The minimum threshold sand concentration that can be detected in a pipe are determined as a function of flowing gas and liquid velocities. A large scale flow loop with a 4-inch test section is utilized. Commercially available sand monitors (ClampOn and Roxar) are evaluated for different flow regimes, sand sizes and pipe orientation (vertical and horizontal). The manufacturers’ recommend that the monitors be placed on a bend to maximize the number of particle impacts, so results are shown for monitors placed at 45 and 90 degree positions in a bend. Acoustic sand monitors that clamp to the outside of pipe are passive and listen for solid particle impact noise. The threshold sand rate is calculated by eliminating the background noise created by the flow of gas and liquid in the pipe for various flow regimes that are generated in horizontal and vertical test sections. The average sand sizes examined are 150 and 300 microns. For stratified and bubbly flows the threshold sand rates are much higher than other flow regimes such as slug and annular flow regimes that are investigated. However, the background noise generated by slug flow regime is very high and cause a high uncertainty in detection limits. The threshold sand rates for annular flow and dry gas conditions are the lowest because of high gas velocities. The effects of monitor placement around elbows that are in vertical and horizontal pipes are also examined for 150 micron. The results show that the threshold sand rates that are detected in vertical orientation are generally lower for all various flow regimes that are investigated.

Keywords: acoustic monitor, sand, multiphase flow, threshold

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Authors: Abdul Mannan Akhtar

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In this paper a novel concept of a self learning smart personalized computer assistant (ACPCA) is established which is a context aware system. Based on user habits, moods, and other routines/situational reactions the system will manage various services and suggestions at appropriate times including what schedule to follow, what to watch, what software to be used, what should be deleted etc. This system will utilize a hybrid fuzzyNeural model to predict what the user will do next and support his actions. This will be done by establishing fuzzy sets of user activities, choices, preferences etc. and utilizing their combinations to predict his moods and immediate preferences. Various application of context aware systems exist separately e.g. on certain websites for music or multimedia suggestions but a personalized autonomous system that could adapt to user’s personality does not exist at present. Due to the novelty and massiveness of this concept, this paper will primarily focus on the problem establishment, product features and its functionality; however a small mini case is also implemented on MATLAB to demonstrate some of the aspects of ACPCA. The mini case involves prediction of user moods, activity, routine and food preference using a hybrid fuzzy-Neural soft computing technique.

Keywords: context aware systems, APCPCA, soft computing techniques, artificial intelligence, fuzzy logic, neural network, mood detection, face detection, activity detection

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Authors: N. Das, N. Samanta, L. Pandey, C. Roy Chaudhuri

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Early diagnosis of infection like Hep-B virus in blood is important for low cost medical treatment. For this purpose, it is desirable to develop a point of care device which should be able to detect trace quantities of the target molecule in blood. In this paper, we report a nanoporous silicon oxide sensor which is capable of detecting down to 1fM concentration of Hep-B surface antigen in blood without the requirement of any centrifuge or pre-concentration. This has been made possible by the presence of resonant peak in the sensitivity characteristics. This peak is observed to be dependent only on the concentration of the specific antigen and not on the interfering species in blood serum. The occurrence of opposite impedance change within the pores and at the bottom of the pore is responsible for this effect. An electronic interface has also been designed to provide a display of the virus concentration.

Keywords: impedance spectroscopy, ultrasensitive detection in blood, peak frequency, electronic interface

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Authors: Benazzouz Djamel, Termeche Adel, Touati Youcef, Alem Said, Ouziala Mahdi

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This paper presents an approach which detect and isolate efficiently a fault in a system. This approach avoids false alarms, non-detections and delays in detecting faults. A study case have been proposed to show the importance of taking into consideration the uncertainties in the decision-making procedure and their effect on the degradation diagnostic performance and advantage of using Bond Graph (BG) for such degradation. The use of BG in the Linear Fractional Transformation (LFT) form allows generating robust Analytical Redundancy Relations (ARR’s), where the uncertain part of ARR’s is used to generate the residuals adaptive thresholds. The study case concerns an electromechanical system composed of a motor, a reducer and an external load. The aim of this application is to show the effectiveness of the BG-LFT approach to robust fault detection.

Keywords: bond graph, LFT, uncertainties, detection and faults isolation, ARR

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Authors: Gregory Sullivan

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The evolution of global warming has escalated the number and complexity of forest fires around the world. As an example, the United States and Brazil combined generated more than 30,000 forest fires last year. The impact to our environment, structures and individuals is incalculable. The world has learned to try to take this in stride, trying multiple ways to contain fires. Some countries are trying to use cameras in limited areas. There are discussions of using hundreds of low earth orbit satellites and linking them together, and, interfacing them through ground networks. These are all truly noble attempts to defeat the forest fire phenomenon. But there is a better, simpler answer. A bigger piece of the solutions puzzle is to see the fires while they are small, soon after initiation. The approach is to see the fires while they are very small and report their location (latitude and longitude) to local first responders. This is done by placing a sensor at geostationary orbit (GEO: 26,000 miles above the earth). By placing this small satellite in GEO, we can “stare” at the earth, and sense temperature changes. We do not “see” fires, but “measure” temperature changes. This has already been demonstrated on an experimental scale. Fires were seen at close to initiation, and info forwarded to first responders. it were the first to identify the fires 7 out of 8 times. The goal is to have a small independent satellite at GEO orbit focused only on forest fire initiation. Thus, with one small satellite, focused only on forest fire initiation, we hope to greatly decrease the impact to persons, property and the environment.

Keywords: space detection, wildfire early warning, demonstration wildfire detection and action from space, space detection to first responders

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Authors: Maha Jazouli

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Suicide is one of the most important causes of death in the prison environment, both in Canada and internationally. Rates of attempts of suicide and self-harm have been on the rise in recent years, with hangings being the most frequent method resorted to. The objective of this article is to propose a method to automatically detect in real time suicidal behaviors. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Our proposed system gives us satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.

Keywords: suicide detection, Kinect azure, RGB-D camera, SVM, machine learning, gesture recognition

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: O. Hassoon, M. Tarfoui, A. El Malk

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Fiber Bragg optic sensor embedded in composite material to detect and monitor the damage which is occur in composite structure. In this paper we deal with the mode-Ι delamination to determine the resistance of material to crack propagation, and use the coupling mode theory and T-matrix method to simulating the FBGs spectrum for both uniform and non-uniform strain distribution. The double cantilever beam test which is modeling in FEM to determine the Longitudinal strain, there are two models which are used, the first is the global half model, and the second the sub-model to represent the FBGs with refine mesh. This method can simulate the damage in the composite structure and converting the strain to wavelength shifting of the FBG spectrum.

Keywords: fiber bragg grating, delamination detection, DCB, FBG spectrum, structure health monitoring

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Authors: Chang Ting, Wu Chun Kuan

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Wireless sensing technology is increasingly developed, in order to avoid caregiver neglect children in poor physiological condition, so there are more and more products into the wireless sensor-related technologies, in order to reduce the risk of infants. In view of this, the study will focus on Somatosensory detection wristbands Applied Research of Baby, and to explore through observation and literature, to find design criteria which conform baby products, as well as the advantages and disadvantages of existing products. This study will focus on 0-2 years of infant research and product design, to provide 2-3 new design concepts and products to identify weaknesses through the use of the actual product, further provide future baby wristbands design reference.

Keywords: infants, observation, design criteria, wireless sensing

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Authors: Shahram Jamali, Samira Hamed

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One of the open issues in Random Early Detection (RED) algorithm is how to set its parameters to reach high performance for the dynamic conditions of the network. Although original RED uses fixed values for its parameters, this paper follows a model-based approach to upgrade performance of the RED algorithm. It models the routers queue behavior by using the Markov model and uses this model to predict future conditions of the queue. This prediction helps the proposed algorithm to make some tunings over RED's parameters and provide efficiency and better performance. Widespread packet level simulations confirm that the proposed algorithm, called Markov-RED, outperforms RED and FARED in terms of queue stability, bottleneck utilization and dropped packets count.

Keywords: active queue management, RED, Markov model, random early detection algorithm

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Authors: Sima Ajami, Batool Akbari

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Background: Radio frequency identification (RFID) system has been successfully applied to the areas of manufacturing, supply chain, agriculture, transportation, healthcare, and services. The RFID is already used to track and trace the victims in a disaster situation. Data can be collected in real time and be immediately available to emergency personnel and saves time by the RFID. Objectives: The aim of this study was, first, to identify stakeholders and customers for rescuing earthquake victims, second, to list key internal and external factors to use RFID to track earthquake victims, finally, to assess SWOT for rescuers' viewpoint. Materials and Methods: This study was an applied and analytical study. The study population included scholars, experts, planners, policy makers and rescuers in the "red crescent society of Isfahan province", "disaster management Isfahan province", "maintenance and operation department of Isfahan", "fire and safety services organization of Isfahan municipality", and "medical emergencies and disaster management center of Isfahan". After that, researchers held a workshop to teach participants about RFID and its usages in tracking earthquake victims. In the meanwhile of the workshop, participants identified, listed, and weighed key internal factors (strengths and weaknesses; SW) and external factors (opportunities and threats; OT) to use RFID in tracking earthquake victims. Therefore, participants put weigh strengths, weaknesses, opportunities, and threats (SWOT) and their weighted scales were calculated. Then, participants' opinions about this issue were assessed. Finally, according to the SWOT matrix, strategies to solve the weaknesses, problems, challenges, and threats through opportunities and strengths were proposed by participants. Results: The SWOT analysis showed that the total weighted score for internal and external factors were 3.91 (Internal Factor Evaluation) and 3.31 (External Factor Evaluation) respectively. Therefore, it was in a quadrant SO strategies cell in the SWOT analysis matrix and aggressive strategies were resulted. Organizations, scholars, experts, planners, policy makers and rescue workers should plan to use RFID technology in order to save more victims and manage their life. Conclusions: Researchers suppose to apply SO strategies and use a firm’s internal strength to take advantage of external opportunities. It is suggested, policy maker should plan to use the most developed technologies to save earthquake victims and deliver the easiest service to them. To do this, education, informing, and encouraging rescuers to use these technologies is essential. Originality/ Value: This study was a research paper that showed how RFID can be useful to track victims in earthquake.

Keywords: frequency identification system, strength, weakness, earthquake, victim

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Authors: Byung-Ik Kim, Hong-Koo Kang, Tae-Jin Lee, Hae-Ryong Park

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Cyber terrors against specific enterprises or countries have been increasing recently. Such attacks against specific targets are called advanced persistent threat (APT), and they are giving rise to serious social problems. The malicious behaviors of APT attacks mostly affect websites and penetrate enterprise networks to perform malevolent acts. Although many enterprises invest heavily in security to defend against such APT threats, they recognize the APT attacks only after the latter are already in action. This paper discusses the characteristics of APT attacks at each step as well as the strengths and weaknesses of existing malicious code detection technologies to check their suitability for detecting APT attacks. It then proposes a network-based malicious behavior detection algorithm to protect the enterprise or national networks.

Keywords: Advanced Persistent Threat (APT), malware, network security, network packet, exploit kits

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Authors: R. Burahee, B. Chassinat, T. de Laclos, A. Dépée, A. Sastim

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The purpose of this paper is to propose a solution to detect, predict and reconstitute rain drops in real time – during the night – using an embedded material with an infrared camera. To prevent the system from needing too high hardware resources, simple models are considered in a powerful image treatment algorithm reducing considerably calculation time in OpenCV software. Using a smart model – drops will be matched thanks to a process running through two consecutive pictures for implementing a sophisticated tracking system. With this system drops computed trajectory gives information for predicting their future location. Thanks to this technique, treatment part can be reduced. The hardware system composed by a Raspberry Pi is optimized to host efficiently this code for real time execution.

Keywords: reconstitution, prediction, detection, rain drop, real time, raspberry, infrared

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Authors: Abdurrauf M. Gusbi, Mahmud H. Arhima, Abdurrahim A. Elouzi, Ebtisam A. Benomran, Salsabeela Elmezwghi, Aram Elhatan, Nafesa Elgusbi

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Mass poisonings with methanol are rare but occur regularly both in developed and in non-developing countries. As a result of the tragedy that happened in the city of Tripoli Libya in March during year 2013 a number of patients were admitted to Tripoli Medical Center and Tripoli Central Hospital suffering from poisoning following ingestion of methanol by mistake. Our aims have been formulated to collect Information about those cases as much as we can from the archiving departments from the two hospitals including the number of cases that had been admitted, recovered patients and died victims. This retrospective study was planned to find out the reasons which allow those patients to drink methanol in our Muslim community and also the role of pharmacist to prevent such a disaster that claimed the lives of many people. During this tragedy 291 ospitalized patients their ages between 16-32 years old were admitted to both hospitals, total number of died 189 (121 at Tripoli medical center) and (68 at Tripoli central hospital), demographic data also shows that most of them are male (97%) and (3% female), about 4% of the patients foreigners and 96% were Libyans. There were a lot of obstacles and poor facilities at the time of patient admission as recognized in many cases including lack of first line of treatment. The morbidity was high due to the lack of antidote and availability of dialysis machines at this two main hospitals in Tripoli also according to survey done to the medical staff and also a random number of medical students shows about 28% have no idea about the first aid procedure used for methanol poisoning cases and this due to the absence of continuing education for all medical staff through the establishment of training courses on first aid, rapid diagnosis of poisoning and follow the written procedures to dealing with such cases.

Keywords: ethanol, fomepizole, methanol, poisoning

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Authors: Bidzina Matsaberidze, Anna Sikharulidze, Gia Sirbiladze, Bezhan Ghvaberidze

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In the modern world emergency management decision support systems are actively used by state organizations, which are interested in extreme and abnormal processes and provide optimal and safe management of supply needed for the civil and military facilities in geographical areas, affected by disasters, earthquakes, fires and other accidents, weapons of mass destruction, terrorist attacks, etc. Obviously, these kinds of extreme events cause significant losses and damages to the infrastructure. In such cases, usage of intelligent support technologies is very important for quick and optimal location-transportation of emergency service in order to avoid new losses caused by these events. Timely servicing from emergency service centers to the affected disaster regions (response phase) is a key task of the emergency management system. Scientific research of this field takes the important place in decision-making problems. Our goal was to create an expert knowledge-based intelligent support system, which will serve as an assistant tool to provide optimal solutions for the above-mentioned problem. The inputs to the mathematical model of the system are objective data, as well as expert evaluations. The outputs of the system are solutions for Fuzzy Multi-Objective Emergency Location-Transportation Problem (FMOELTP) for disasters’ regions. The development and testing of the Intelligent Support System were done on the example of an experimental disaster region (for some geographical zone of Georgia) which was generated using a simulation modeling. Four objectives are considered in our model. The first objective is to minimize an expectation of total transportation duration of needed products. The second objective is to minimize the total selection unreliability index of opened humanitarian aid distribution centers (HADCs). The third objective minimizes the number of agents needed to operate the opened HADCs. The fourth objective minimizes the non-covered demand for all demand points. Possibility chance constraints and objective constraints were constructed based on objective-subjective data. The FMOELTP was constructed in a static and fuzzy environment since the decisions to be made are taken immediately after the disaster (during few hours) with the information available at that moment. It is assumed that the requests for products are estimated by homeland security organizations, or their experts, based upon their experience and their evaluation of the disaster’s seriousness. Estimated transportation times are considered to take into account routing access difficulty of the region and the infrastructure conditions. We propose an epsilon-constraint method for finding the exact solutions for the problem. It is proved that this approach generates the exact Pareto front of the multi-objective location-transportation problem addressed. Sometimes for large dimensions of the problem, the exact method requires long computing times. Thus, we propose an approximate method that imposes a number of stopping criteria on the exact method. For large dimensions of the FMOELTP the Estimation of Distribution Algorithm’s (EDA) approach is developed.

Keywords: epsilon-constraint method, estimation of distribution algorithm, fuzzy multi-objective combinatorial programming problem, fuzzy multi-objective emergency location/transportation problem

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Authors: Chenyi Ma

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Background: The second costliest hurricane in U.S. history, Sandy landed in southern New Jersey on October 29, 2012, and struck the entire state with high winds and torrential rains. The disaster killed more than 100 people, left more than 8.5 million households without power, and damaged or destroyed more than 200,000 homes across the state. Immediately after the disaster, public policy support was provided in nine coastal counties that constituted 98% of the major and severely damaged housing units in NJ overall. The programs include Individuals and Households Assistance Program, Small Business Loan Program, National Flood Insurance Program, and the Federal Emergency Management Administration (FEMA) Public Assistance Grant Program. In the most severely affected counties, additional funding was provided through Community Development Block Grant: Reconstruction, Rehabilitation, Elevation, and Mitigation Program, and Homeowner Resettlement Program. How these policies individually and as a whole impacted housing recovery across communities with different socioeconomic and demographic profiles has not yet been studied, particularly in relation to damage levels. The concept of community social vulnerability has been widely used to explain many aspects of natural disasters. Nevertheless, how communities are vulnerable has been less fully examined. Community resilience has been conceptualized as a protective factor against negative impacts from disasters, however, how community resilience buffers the effects of vulnerability is not yet known. Because housing recovery is a dynamic social and economic process that varies according to context, this study examined the path from community vulnerability and resilience to housing recovery looking at both community characteristics and policy interventions. Sample/Methods: This retrospective longitudinal case study compared a literature-identified set of pre-disaster community characteristics, the effects of multiple public policy programs, and a set of time-variant community resilience indicators to changes in housing stock (operationally defined by percent of building permits to total occupied housing units/households) between 2010 and 2014, two years before and after Hurricane Sandy. The sample consisted of 51 municipalities in the nine counties in which between 4% and 58% of housing units suffered either major or severe damage. Structural equation modeling (SEM) was used to determine the path from vulnerability to the housing recovery, via multiple public programs, separately and as a whole, and via the community resilience indicators. The spatial analytical tool ArcGIS 10.2 was used to show the spatial relations between housing recovery patterns and community vulnerability and resilience. Findings: Holding damage levels constant, communities with higher proportions of Hispanic households had significantly lower levels of housing recovery while communities with households with an adult >age 65 had significantly higher levels of the housing recovery. The contrast was partly due to the different levels of total public support the two types of the community received. Further, while the public policy programs individually mediated the negative associations between African American and female-headed households and housing recovery, communities with larger proportions of African American, female-headed and Hispanic households were “vulnerable” to lower levels of housing recovery because they lacked sufficient public program support. Even so, higher employment rates and incomes buffered vulnerability to lower housing recovery. Because housing is the "wobbly pillar" of the welfare state, the housing needs of these particular groups should be more fully addressed by disaster policy.

Keywords: community social vulnerability, community resilience, hurricane, public policy

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Authors: Kapil Pandey, Vishnu Goyal

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Spatial and temporal data analysis is very well known in the field of satellite image processing. When spatial data are correlated with time, series analysis it gives the significant results in change detection studies. In this paper the GIS and Remote sensing techniques has been used to find the change detection using time series satellite imagery of Uttarakhand state during the years of 1990-2010. Natural vegetation, urban area, forest cover etc. were chosen as main landuse classes to study. Landuse/ landcover classes within several years were prepared using satellite images. Maximum likelihood supervised classification technique was adopted in this work and finally landuse change index has been generated and graphical models were used to present the changes.

Keywords: GIS, landuse/landcover, spatial and temporal data, remote sensing

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Authors: Chin-Hsien Yeh, Shao-Wen Chen, Wen-Shu Huang, Chun-Fu Huang, Jong-Rong Wang, Jung-Hua Yang, Yuh-Ming Ferng, Chunkuan Shih

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In this research, a Fukushima-like conditions is simulated with TRACE and RELAP5. Fukushima Daiichi Nuclear Power Plant (NPP) occurred the disaster which caused by the earthquake and tsunami. This disaster caused extended loss of all AC power (ELAP). Hence, loss of ultimate heat sink (LUHS) happened finally. In order to handle Fukushima-like conditions, Taiwan Atomic Energy Council (AEC) commanded that Taiwan Power Company should propose strategies to ensure the nuclear power plant safety. One of the diverse and flexible coping strategies (FLEX) is a different water injection strategy. It can execute core injection at 20 Kg/cm2 without depressurization. In this study, TRACE and RELAP5 were used to simulate Maanshan nuclear power plant, which is a three loops PWR in Taiwan, under Fukushima-like conditions and make sure the success criteria of FLEX. Reducing core cooling ability is due to failure of emergency core cooling system (ECCS) in extended loss of all AC power situation. The core water level continues to decline because of the seal leakage, and then FLEX is used to save the core water level and make fuel rods covered by water. The result shows that this mitigation strategy can cool the reactor pressure vessel (RPV) as soon as possible under Fukushima-like conditions, and keep the core water level higher than Top of Active Fuel (TAF). The FLEX can ensure the peak cladding temperature (PCT) below than the criteria 1088.7 K. Finally, the FLEX can provide protection for nuclear power plant and make plant safety.

Keywords: TRACE, RELAP5/MOD3.3, ELAP, FLEX

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Authors: Oshienemen Albert

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Human’s activities could be pointed as the root cause of almost all environmental damages/ disasters as we contribute to the activities that are currently damaging the ozone layers (global warming), unusual environmental changes and extreme weather conditions (climate change) in recent times. Nigeria just as every other disaster-prone nation is faced with different types of disasters and environmental calamities, starting from terrorist displacement disasters, flood, drought and oil spill hazards. Oil spillage as an environmental disaster has great consequences not just on the environment but on human health, economy and the entire populace that might be involved, which deem necessary to look into the root causes of the incidents and how it can be curtailed. The different incidents of oil spillages and other oil production consequent on the environment is alarming in the Nigerian context and cannot be overemphasized without a critical investigation and synthesis. This paper investigates the root causes of environmental pollution induced by oil spill hazards from petroleum activities within Niger Delta communities of effects and detailed the potential solutions to reduce the causal factors and reoccurrence of the incidents. This study adopts a desk-based approach, interviews with key members of communities which consist of chiefs, youth leaders, and key women within the high environmental damaged communities. Also, Interviews were conducted with environmental expertise representatives from the oil and gas sectors and representatives from oil spill-related agency. Data were analyzed using thematic techniques. The study shows different influencing factors of sabotage and vandalism of oil facilities as such; marginalization, deprivation of resources utility and resource derivation principles were identified as major contributors to vandalism and sabotage act. The study proposed potential strategies to curtail the root causes of sabotage and vandalism as the major causes of environmental devastations in Nigeria.

Keywords: environment, oil spill hazards, Niger delta, Nigeria

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Sylvia Mac

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This ethnographic case study examines disaster capitalism, neoliberal market-based school reforms, and disability through the lens of Disability Studies in Education. More specifically, it explores neoliberalism and special education at a small, urban charter school in a large city in California and the (re)production of social inequality. The study uses Sociology of Special Education to examine the ways in which special education is used to sort and stratify disabled students. At a time when rhetoric surrounding public schools is framed in catastrophic and dismal language in order to justify the privatization of public education, small urban charter schools must be examined to learn if they are living up to their promise or acting as another way to maintain economic and racial segregation. The study concludes that neoliberal contexts threaten successful inclusive education and normalize poor, disabled students’ continued low achievement and poor post-secondary outcomes. This ethnographic case study took place at a small urban charter school in a large city in California. Participants included three special education students, the special education teacher, the special education assistant, a regular education teacher, and the two founders and charter writers. The school claimed to have a push-in model of special education where all special education students were fully included in the general education classroom. Although presented as fully inclusive, some special education students also attended a pull-out class called Study Skills. The study found that inclusion and neoliberalism are differing ideologies that cannot co-exist. Successful inclusive environments cannot thrive while under the influences of neoliberal education policies such as efficiency and cost-cutting. Additionally, the push for students to join the global knowledge economy means that more and more low attainers are further marginalized and kept in poverty. At this school, neoliberal ideology eclipsed the promise of inclusive education for special education students. This case study has shown the need for inclusive education to be interrogated through lenses that consider macro factors, such as neoliberal ideology in public education, as well as the emerging global knowledge economy and increasing income inequality. Barriers to inclusion inside the school, such as teachers’ attitudes, teacher preparedness, and school infrastructure paint only part of the picture. Inclusive education is also threatened by neoliberal ideology that shifts the responsibility from the state to the individual. This ideology is dangerous because it reifies the stereotypes of disabled students as lazy, needs drains on already dwindling budgets. If these stereotypes persist, inclusive education will have a difficult time succeeding. In order to more fully examine the ways in which inclusive education can become truly emancipatory, we need more analysis on the relationship between neoliberalism, disability, and special education.

Keywords: case study, disaster capitalism, inclusive education, neoliberalism

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Authors: Y. T. Gebreslassie, Abrha Tadesse, R. C. Saini, Rishi Pal

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Caffeine, known chemically as 3,7-dihydro-1,3,7-trimethyl-1H-purine-2,6-dione, is a naturally occurring alkaloid classified as an N-methyl derivative of xanthine. Given its widespread use in coffee and other caffeine-containing products, it is the most commonly consumed psychoactive substance in everyday human life. This research aimed to develop a cost-effective, sensitive, and easily manufacturable sensor for the detection of caffeine. Antraquinone-modified carbon paste electrode (AQMCPE) was fabricated, and the electrochemical behavior of caffeine on this electrode was investigated using cyclic voltammetry (CV) and square wave voltammetry (SWV) in a solution of 0.1M perchloric acid at pH 0.56. The modified electrode displayed enhanced electrocatalytic activity towards caffeine oxidation, exhibiting a two-fold increase in peak current and an 82 mV shift of the peak potential in the negative direction compared to an unmodified carbon paste electrode (UMCPE). Exploiting the electrocatalytic properties of the modified electrode, SWV was employed for the quantitative determination of caffeine. Under optimized experimental conditions, a linear relationship between peak current and concentration was observed within the range of 2.0 x 10⁻⁶ to 1.0× 10⁻⁴ M, with a correlation coefficient of 0.998 and a detection limit of 1.47× 10⁻⁷ M (signal-to-noise ratio = 3). Finally, the proposed method was successfully applied to the quantitative analysis of caffeine in pharmaceutical formulations, yielding recovery percentages ranging from 95.27% to 106.75%.

Keywords: antraquinone-modified carbon paste electrode, caffeine, detection, electrochemical sensor, quantitative analysis

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

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This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: vehicle classification, signal processing, road traffic model, magnetic sensing

Procedia PDF Downloads 303

Authors: Jianfang Wang, Xianzhe Chen, Zhuoliang Liu, Cheng-An Tao, Yujiao Li

Abstract:

Organophosphorus (OPs) pesticide used as insecticides are widely used in agricultural pest control, household and storage deworming. The detection of pesticides needs more simple and efficient methods. One of the best ways is to make electrochemical biosensors. In this paper, an electrochemical enzyme biosensor based on acetylcholine esterase (AChE) was constructed, and its sensing properties and sensing mechanisms were studied. Reduced graphene oxide-polydopamine complexes (RGO-PDA), gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) were prepared firstly and composited with AChE and chitosan (CS), then fixed on the glassy carbon electrode (GCE) surface to construct the biosensor GCE/RGO-PDA-AuNPs-AgNPs-AChE-CS by one-pot method. The results show that graphene oxide (GO) can be reduced by dopamine (DA) and dispersed well in RGO-PDA complexes. And the composites have a synergistic catalysis effect and can improve the surface resistance of GCE. The biosensor selectively can detect acetylcholine (ACh) and OPs pesticide with good linear range and high sensitivity. The performance of the biosensor is affected by the ratio and adding ways of AChE and the adding of AuNPs and AChE. And the biosensor can achieve a detection limit of 2.4 ng/L for methyl parathion and a wide linear detection range of 0.02 ng/L ~ 80 ng/L, and has excellent stability, good anti-interference ability, and excellent preservation performance, indicating that the sensor has practical value.

Keywords: acetylcholine esterase, electrochemical biosensor, nanoparticles, organophosphates, reduced graphene oxide

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Authors: Mohammad Javad Seddighi, Avar Almukhtar

Abstract:

The occurrence of natural disasters, such as floods and earthquakes, can cause significant damage to heritage sites and surrounding areas. In Iran, the city of Bam was devastated by an earthquake in 2003, which had a major impact on the rivers and watercourses around the city. This study aims to investigate the environmental design techniques and sustainable hazard mitigation strategies that can be employed to preserve heritage sites in the face of natural disasters, using the Bam experience as a case study. The research employs a mixed-methods approach, combining both qualitative and quantitative data collection and analysis methods. The study begins with a comprehensive literature review of recent publications on environmental design techniques and sustainable hazard mitigation strategies in heritage conservation. This is followed by a field study of the rivers and watercourses around Bam, including the Adoori River (Talangoo) and other watercourses, to assess the current conditions and identify potential hazards. The data collected from the field study is analysed using statistical methods and GIS mapping techniques. The findings of this study reveal the importance of sustainable hazard mitigation strategies and environmental design techniques in preserving heritage sites during natural disasters. The study suggests that these techniques can be used to prevent the outbreak of another natural disaster in Bam and the surrounding areas. Specifically, the study recommends the establishment of a comprehensive early warning system, the creation of flood-resistant landscapes, and the use of eco-friendly building materials in the reconstruction of heritage sites. These findings contribute to the current knowledge of sustainable hazard mitigation and environmental design in heritage conservation.

Keywords: natural disasters, heritage conservation, sustainable hazard mitigation, environmental design, landscape architecture, flood management, disaster resilience

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Authors: Faisal Al Yahmadi, Muhammad R. Ahmed

Abstract:

Electric power is a fundamental necessity in the 21^st century. Consequently, any break in electric power is probably going to affect the general activity. To make the power supply smooth and efficient, a smart grid network is introduced which uses communication technology. In any communication network, security is essential. It has been observed from several recent incidents that adversary causes an interruption to the operation of networks. In order to resolve the issues, it is vital to understand the threats and vulnerabilities associated with the smart grid networks. In this paper, we have investigated the threats and vulnerabilities in Smart Grid Networks (SGN) and the few solutions in the literature. Proposed solutions showed developments in electricity theft countermeasures, Denial of services attacks (DoS) and malicious injection attacks detection model, as well as malicious nodes detection using watchdog like techniques and other solutions.

Keywords: smart grid network, security, threats, vulnerabilities

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Authors: Lukumba Phiri, Simon Tembo, Kumbuso Joshua Nyoni

Abstract:

In Zambia recent initiatives by various power operators like ZESCO, CEC, and consumers like the mines to upgrade power systems into smart grids target an even tighter integration with information technologies to enable the integration of renewable energy sources, local and bulk generation, and demand response. Thus, for the reliable operation of smart grids, its information infrastructure must be secure and reliable in the face of both failures and cyberattacks. Due to the nature of the systems, ICS/SCADA cybersecurity and governance face additional challenges compared to the corporate networks, and critical systems may be left exposed. There exist control frameworks internationally such as the NIST framework, however, there are generic and do not meet the domain-specific needs of the SCADA systems. Zambia is also lagging in cybersecurity awareness and adoption, therefore there is a concern about securing ICS controlling key infrastructure critical to the Zambian economy as there are few known facts about the true posture. In this paper, we introduce a stochastic Edged-based Anomaly Detection for SCADA systems (SEADS) framework for threat modeling and risk assessment. SEADS enables the calculation of steady-steady probabilities that are further applied to establish metrics like system availability, maintainability, and reliability.

Keywords: anomaly, availability, detection, edge, maintainability, reliability, stochastic

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Authors: T. Gangadhararao, K. Krishna Kishore

Abstract:

Motion Estimation (ME) plays a critical role in a video coder, testing such a module is of priority concern. While focusing on the testing of ME in a video coding system, this work presents an error detection and data recovery (EDDR) design, based on the residue-and-quotient (RQ) code, to embed into ME for video coding testing applications. An error in processing Elements (PEs), i.e. key components of a ME, can be detected and recovered effectively by using the proposed EDDR design. The proposed EDDR design for ME testing can detect errors and recover data with an acceptable area overhead and timing penalty.

Keywords: area overhead, data recovery, error detection, motion estimation, reliability, residue-and-quotient (RQ) code

Procedia PDF Downloads 413

Authors: Sukhpreet Singh, Kishan Bhojrath, Vijay, Avinash Kumar, Mandlesh Mishra

Abstract:

The project uses an Arduino and ESP8266 to construct an alcohol detection system with an engine locking mechanism, offering a distinct way to fight drunk driving. An alcohol sensor module is used by the system to determine the amount of alcohol present in the ambient air. When the system detects alcohol levels beyond a certain threshold that is deemed hazardous for driving, it activates a relay module that is linked to the engine of the car, so rendering it inoperable. By preventing people from operating a vehicle while intoxicated, this preventive measure seeks to improve road safety. Adding an ESP8266 module also allows for remote monitoring and notifications, giving users access to real-time status updates on their system. By using an integrated strategy, the initiative provides a workable and efficient way to lessen the dangers related to driving while intoxicated.

Keywords: MQ3 sensor, ESP 8266, arduino, IoT

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Authors: Cheong Hyeon Oh, Dongho Nam, Byungsik Kim

Abstract:

Due to the recent climate change, flood damage caused by local floods and typhoons has frequently occurred, the incidence rate and intensity of wildfires are greatly increased due to increased temperatures and changes in precipitation patterns. Wildfires cause primary damage, such as loss of forest resources, as well as secondary disasters, such as landslides, floods, and debris flow. In many countries around the world, damage and economic losses from secondary damage are occurring as well as the direct effects of forest fires. Therefore, in this study, the Rainfall-Runoff model(S-RAT) was used for the wildfire affected areas in Gangneung and Goseong, which occurred on April 2019, when the stability of vegetation and soil were destroyed by wildfires. Rainfall data from Typhoon Rusa were used in the S-RAT model, and flood discharge was calculated according to changes in land cover before and after wildfire damage. The results of the calculation showed that flood discharge increased significantly due to changes in land cover, as the increase in flood discharge increases the possibility of the occurrence of the debris flow and the extent of the damage, the debris flow height and range were calculated before and after forest fire using RAMMS. The analysis results showed that the height and extent of damage increased after wildfire, but the result value was underestimated due to the characteristics that using DEM and maximum flood discharge of the RAMMS model. This research was supported by a grant(2017-MOIS31-004) from Fundamental Technology Development Program for Extreme Disaster Response funded by Korean Ministry of Interior and Safety (MOIS). This paper work (or document) was financially supported by Ministry of the Interior and Safety as 'Human resoure development Project in Disaster management'.

Keywords: wildfire, debris flow, land cover, rainfall-runoff meodel S-RAT, RAMMS, height

Procedia PDF Downloads 103