Search results for: driving monitoring

Authors: Richard Joseph, Nerey Mvungi

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An electric power system includes a generating, a transmission, a distribution and consumers subsystems. An electrical power network in Tanzania keeps growing larger by the day and become more complex so that, most utilities have long wished for real-time monitoring and remote control of electrical power system elements such as substations, intelligent devices, power lines, capacitor banks, feeder switches, fault analyzers and other physical facilities. In this paper, the concept of automation of management of power systems from generation level to end user levels was determined by using Power System Simulator for Engineering (PSS/E) version 30.3.2.

Keywords: automation, distribution subsystem, generating subsystem, PSS/E, TANESCO, transmission subsystem

Procedia PDF Downloads 674

Authors: Rebecca Angeles

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This case study features the Kuehne + Nagel PharmaChain solution for ‘cold chain’ pharmaceutical and biologic product shipments with IOT-enabled features for shipment temperature and location tracking. Using the case study method and content analysis, this research project investigates the application of the structurational model of technology theory introduced by Orlikowski in order to interpret the firm’s entry and participation in the IOT-impelled marketplace.

Keywords: Internet of Things (IOT), radio frequency identification (RFID), structurational model of technology (Orlikowski), supply chain management

Procedia PDF Downloads 232

Authors: Mehdi Modabberifar, Sanaz Jabary, Mojtaba Ghodsi

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Stiffness sensing is an important issue in medical diagnostic, robotics surgery, safe handling, and safe grasping of objects in production lines. Detecting and obtaining the characteristics in dwelling lumps embedded in a soft tissue and safe removing and handling of detected lumps is needed in surgery. Also in industry, grasping and handling an object without damaging in a place where it is not possible to access a human operator is very important. In this paper, a method for object handling is presented. It is based on the use of an intelligent gripper to detect the object stiffness and then setting a programmable force for grasping the object to move it. The main components of this system includes sensors (sensors for measuring force and displacement), electrical (electrical and electronic circuits, tactile data processing and force control system), mechanical (gripper mechanism and driving system for the gripper) and the display unit. The system uses a rotary potentiometer for measuring gripper displacement. A microcontroller using the feedback received by the load cell, mounted on the finger of the gripper, calculates the amount of stiffness, and then commands the gripper motor to apply a certain force on the object. Results of Experiments on some samples with different stiffness show that the gripper works successfully. The gripper can be used in haptic interfaces or robotic systems used for object handling.

Keywords: gripper, haptic, stiffness, robotic

Procedia PDF Downloads 358

Authors: Younghoon Kim, Seoung Bum Kim

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One-class classification plays an important role in detecting outlier and abnormality from normal observations. In the previous research, several attempts were made to extend the scope of application of the one-class classification techniques to statistical process control problems. For most previous approaches, such as support vector data description (SVDD) control chart, the design of the control limits is commonly based on the assumption that the proportion of abnormal observations is approximately equal to an expected Type I error rate in Phase I process. Because of the limitation of the one-class classification techniques based on convex optimization, we cannot make the proportion of abnormal observations exactly equal to expected Type I error rate: controlling Type I error rate requires to optimize constraints with integer decision variables, but convex optimization cannot satisfy the requirement. This limitation would be undesirable in theoretical and practical perspective to construct effective control charts. In this work, to address the limitation of previous approaches, we propose the one-class classification algorithm based on the mixed integer programming technique, which can solve problems formulated with continuous and integer decision variables. The proposed method minimizes the radius of a spherically shaped boundary subject to the number of normal data to be equal to a constant value specified by users. By modifying this constant value, users can exactly control the proportion of normal data described by the spherically shaped boundary. Thus, the proportion of abnormal observations can be made theoretically equal to an expected Type I error rate in Phase I process. Moreover, analogous to SVDD, the boundary can be made to describe complex structures by using some kernel functions. New multivariate control chart applying the effectiveness of the algorithm is proposed. This chart uses a monitoring statistic to characterize the degree of being an abnormal point as obtained through the proposed one-class classification. The control limit of the proposed chart is established by the radius of the boundary. The usefulness of the proposed method was demonstrated through experiments with simulated and real process data from a thin film transistor-liquid crystal display.

Keywords: control chart, mixed integer programming, one-class classification, support vector data description

Procedia PDF Downloads 174

Authors: Flavio Cordeiro Fontanella, Asla Medeiros e Sá, Moacyr Alvim Horta Barbosa da Silva

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In the present work, we analyse the performance of football results forecasting models. In order to do so, we have performed the data collection from eight different forecasting models during the 2021 Brazilian football season. First, we guide the analysis through visual representations of the data, designed to highlight the most prominent features and enhance the interpretation of differences and similarities between the models. We propose using a 2-simplex triangle to investigate visual patterns from the results forecasting models. Next, we compute the expected points for every team playing in the championship and compare them to the final league standings, revealing interesting contrasts between actual to expected performances. Then, we evaluate forecasts’ accuracy using the Ranked Probability Score (RPS); models comparison accounts for tiny scale differences that may become consistent in time. Finally, we observe that the Wisdom of Crowds principle can be appropriately applied in the context, driving into a discussion of results forecasts usage in practice. This paper’s primary goal is to encourage football forecasts’ performance discussion. We hope to accomplish it by presenting appropriate criteria and easy-to-understand visual representations that can point out the relevant factors of the subject.

Keywords: accuracy evaluation, Brazilian championship, football results forecasts, forecasting models, visual analysis

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Authors: Adeniyi Onasanya, Maher Elshakankiri

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In recent years, the Internet of Things (IoT) has constituted a driving force of modern technological advancement, and it has become increasingly common as its impacts are seen in a variety of application domains, including healthcare. IoT is characterized by the interconnectivity of smart sensors, objects, devices, data, and applications. With the unprecedented use of IoT in industrial, commercial and domestic, it becomes very imperative to harness the benefits and functionalities associated with the IoT technology in (re)assessing the provision and positioning of healthcare to ensure efficient and improved healthcare delivery. In this research, we are focusing on two important services in healthcare systems, which are cancer care services and business analytics/cloud services. These services incorporate the implementation of an IoT that provides solution and framework for analyzing health data gathered from IoT through various sensor networks and other smart devices in order to improve healthcare delivery and to help health care providers in their decision-making process for enhanced and efficient cancer treatment. In addition, we discuss the wireless sensor network (WSN), WSN routing and data transmission in the healthcare environment. Finally, some operational challenges and security issues with IoT-based healthcare system are discussed.

Keywords: IoT, smart health care system, business analytics, (wireless) sensor network, cancer care services, cloud services

Procedia PDF Downloads 177

Authors: S. Jamshidi, E. Feizi Ashtiani, M. Ardestani, A. Feizi Ashtiani

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Waste load allocation (WLA) strategies usually intend to find economical policies for water resource management. Water quality trading (WQT) is an approach that uses discharge permit market to reduce total environmental protection costs. This primarily requires assigning discharge limits known as total maximum daily loads (TMDLs). These are determined by monitoring organizations with respect to the receiving water quality and remediation capabilities. The purpose of this study is to compare two approaches of TMDL assignment for WQT policy in small catchment area of Haraz River, in north of Iran. At first, TMDLs are assigned uniformly for the whole point sources to keep the concentrations of BOD and dissolved oxygen (DO) at the standard level at checkpoint (terminus point). This was simply simulated and controlled by Qual2kw software. In the second scenario, TMDLs are assigned using multi objective particle swarm optimization (MOPSO) method in which the environmental violation at river basin and total treatment costs are minimized simultaneously. In both scenarios, the equity index and the WLA based on trading discharge permits (TDP) are calculated. The comparative results showed that using economically optimized TMDLs (2nd scenario) has slightly more cost savings rather than uniform TMDL approach (1st scenario). The former annually costs about 1 M$ while the latter is 1.15 M$. WQT can decrease these annual costs to 0.9 and 1.1 M$, respectively. In other word, these approaches may save 35 and 45% economically in comparison with command and control policy. It means that using multi objective decision support systems (DSS) may find more economical WLA, however its outcome is not necessarily significant in comparison with uniform TMDLs. This may be due to the similar impact factors of dischargers in small catchments. Conversely, using uniform TMDLs for WQT brings more equity that makes stakeholders not feel that much envious of difference between TMDL and WQT allocation. In addition, for this case, determination of TMDLs uniformly would be much easier for monitoring. Consequently, uniform TMDL for TDP market is recommended as a sustainable approach. However, economical TMDLs can be used for larger watersheds.

Keywords: waste load allocation (WLA), water quality trading (WQT), total maximum daily loads (TMDLs), Haraz River, multi objective particle swarm optimization (MOPSO), equity

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Authors: Doris Lacej, Eni Bushi

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Cleaning and disinfection procedures are essential for maintaining the cleanliness status of the pharmaceutical manufacturing environment particularly of the cleanrooms and sterile unit area. The Good Manufacturing Practice (GMP) Annex 1 recommendation highly requires the implementation of the standard and validated cleaning and disinfection protocols. However, environmental monitoring has shown that even a validated cleaning method with certified agents may result in the presence of atypical microorganisms’ colony that exceeds GMP limits for a specific cleanroom area. In response to this issue, this case study aims to arrive at the root cause of the microbial contamination observed in the sterile production environment in Profarma pharmaceutical industry in Albania through applying a problem-solving practical approach that ensures the appropriate sterility grade. The guidelines and literature emphasize the importance of several factors in the prevention of possible microbial contamination occurring in cleanrooms, grade A and C. These factors are integrated into a practical framework, to identify the root cause of the presence of Aspergillus Niger colony in the sterile production environment in Profarma pharmaceutical industry in Albania. In addition, the application of a semi-automatic disinfecting system such as H2O2 FOG into sterile grade A and grade C cleanrooms has been an effective solution in eliminating the atypical colony of Aspergillus Niger. Selecting the appropriate detergents and disinfectants at the right concentration, frequency, and combination; the presence of updated and standardized guidelines for cleaning and disinfection as well as continuous training of operators on these practices in accordance with the updated GMP guidelines are some of the identified factors that influence the success of achieving sterility grade. However, to ensure environmental sustainability it is important to be prepared for identifying the source of contamination and making the appropriate decision. The proposed case-based practical approach may help pharmaceutical companies to achieve sterile production and cleanliness environmental sustainability in challenging situations. Apart from the integration of valid agents and standardized cleaning and disinfection protocols according to GMP Annex 1, pharmaceutical companies must be careful and investigate the source and all the steps that can influence the results of an abnormal situation. Subsequently apart from identifying the root cause it is important to solve the problem with a successful alternative approach.

Keywords: cleanrooms, disinfectants, environmental monitoring, GMP Annex 1

Procedia PDF Downloads 216

Authors: Salima Bella, Assia Belbachir, Ghalem Belalem

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This paper deals with the problem of monitoring and cleaning dirty zones of oceans using unmanned vehicles. We present a centralized cooperative architecture for unmanned aerial vehicles (UAVs) to monitor ocean regions and clean dirty zones with the help of unmanned surface vehicles (USVs). Due to the rapid deployment of these unmanned vehicles, it is convenient to use them in oceanic regions where the water pollution zones are generally unknown. In order to optimize this process, our solution aims to detect and reduce the pollution level of the ocean zones while taking into account the problem of fault tolerance related to these vehicles.

Keywords: centralized architecture, fault tolerance, UAV, USV

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Authors: Anita Khadka, Gregory Epiphaniou, Carsten Maple

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There is a dramatic surge in the adoption of machine learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. With the application of learning methods in such diverse domains, artificial intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been on developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and three defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt machine learning techniques in security-critical areas such as the nuclear industry without rigorous testing since they may be vulnerable to adversarial attacks. While common defence methods can effectively defend against different attacks, none of the three considered can provide protection against all five adversarial attacks analysed.

Keywords: adversarial machine learning, attacks, defences, nuclear industry, crack detection

Procedia PDF Downloads 158

Authors: P. Prasannakumar, L. Myoung Young, G. Seung Kye, P. Sang Hun, S. Chul Gyu

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In this paper, we discussed the coupling medium in the optoacoustic imaging. The coupling medium is placed between the scanned object and the ultrasound transducers. Water with varying temperature was used as the coupling medium. The water temperature is gradually varied between 25 to 40 degrees. This heating process is taken with care in order to avoid the bubble formation. Rise in the photoacoustic signal is noted through an unfocused transducer with frequency of 2.25 MHz as the temperature increases. The temperature rise is monitored using a NTC thermistor and the values in degrees are calculated using an embedded evaluation kit. Also the temperature is transmitted to PC through a serial communication. All these processes are synchronized using a trigger signal from the laser source.

Keywords: embedded, optoacoustic, ultrasound , unfocused transducer

Procedia PDF Downloads 349

Authors: Housein Deli, Loui Al-Shrouf, Hammoud Al Joumaa, Mohieddine Jelali

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When forming metallic materials, fluctuations in material properties, process conditions, and wear lead to deviations in the component geometry. Several hundred features sometimes need to be measured, especially in the case of functional and safety-relevant components. These can only be measured offline due to the large number of features and the accuracy requirements. The risk of producing components outside the tolerances is minimized but not eliminated by the statistical evaluation of process capability and control measurements. The inspection intervals are based on the acceptable risk and are at the expense of productivity but remain reactive and, in some cases, considerably delayed. Due to the considerable progress made in the field of condition monitoring and measurement technology, permanently installed sensor systems in combination with machine learning and artificial intelligence, in particular, offer the potential to independently derive forecasts for component geometry and thus eliminate the risk of defective products - actively and preventively. The reliability of forecasts depends on the quality, completeness, and timeliness of the data. Measuring all geometric characteristics is neither sensible nor technically possible. This paper, therefore, uses the example of car seat rail production to discuss the necessary first step of feature selection and reduction by correlation analysis, as otherwise, it would not be possible to forecast components in real-time and inline. Four different car seat rails with an average of 130 features were selected and measured using a coordinate measuring machine (CMM). The run of such measuring programs alone takes up to 20 minutes. In practice, this results in the risk of faulty production of at least 2000 components that have to be sorted or scrapped if the measurement results are negative. Over a period of 2 months, all measurement data (> 200 measurements/ variant) was collected and evaluated using correlation analysis. As part of this study, the number of characteristics to be measured for all 6 car seat rail variants was reduced by over 80%. Specifically, direct correlations for almost 100 characteristics were proven for an average of 125 characteristics for 4 different products. A further 10 features correlate via indirect relationships so that the number of features required for a prediction could be reduced to less than 20. A correlation factor >0.8 was assumed for all correlations.

Keywords: long-term SHM, condition monitoring, machine learning, correlation analysis, component prediction, wear prediction, regressions analysis

Procedia PDF Downloads 48

Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

Procedia PDF Downloads 176

Authors: Sihem Kadem, Ratiba Irinislimane, Naima Belhaneche

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The natural resistance to biodegradation of polymeric materials prepared from petroleum-based source and the management of their wastes in the environment are the driving forces to replace them by other biodegradable materials from renewable resources. For that, in this work new biocomposites materials have been synthesis from sunflower oil (Helianthus annuus) and alfa plants (Stipatenacissima) as natural based resources. The sunflower oil (SFO) was chemically modified via epoxidation then acrylation reactions to obtain acrylated epoxidized sunflower oil resin (AESFO). The AESFO resin was then copolymerized with styrene as co-monomer in the presence of boron trifluoride (BF3) as cationic initiator and cobalt octoate (Co) as catalyst. The alfa fibers were treated with alkali treatment (5% NaOH) before been used as bio-reinforcement. Biocomposites were prepared by mixing the resin with untreated and treated alfa fibers at different percentages. A biodegradation study was carried out for the synthesized biocomposites in a solid medium (burial in the soil) by evaluated, first, the loss of mass, the results obtained were reached between 7.8% and 11% during one year. Then an observation under an optical microscope was carried out, after one year of burial in the soil, microcracks, brown and black spots were appeared on the samples surface. This results shows that the synthesized biocomposites have a great aptitude for biodegradation.

Keywords: alfa fiber, biocomposite, biodegradation, soil, sunflower oil

Procedia PDF Downloads 160

Authors: Zhiwen Yang, Jingyuan Li, Zhenkai Xie, Jian Ling, Jiguang Wang, Mengliang Li

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To reflect the effects of different emission control strategies, such as retrofitting after-treatment system and replacing with natural gas-fueled vehicles, on particle number (PN), particle mass (PM) and nitrogen oxides (NOx) emissions emitted by urban bus, a portable emission measurement system (PEMS) was employed herein to conduct real-world driving emission measurements on a diesel oxidation catalytic converter (DOC) and catalyzed diesel particulate filter (CDPF) retrofitting China IV diesel bus, a China IV diesel bus, and a China V natural gas bus. The results show that both tested diesel buses possess markedly advantages in NOx emission control when compared to the lean-burn natural gas bus equipped without any NOx after-treatment system. As to PN and PM, only the DOC+CDPF retrofitting diesel bus exhibits enormous benefits on emission control relate to the natural gas bus, especially the normal diesel bus. Meanwhile, the differences in PM and PN emissions between retrofitted and normal diesel buses generally increase with the increase in vehicle-specific power (VSP). Furthermore, the differences in PM emissions, especially those in the higher VSP ranges, are more significant than those in PN. In addition, the maximum peak PN particle size (32 nm) of the retrofitted diesel bus was significantly lower than that of the normal diesel bus (100 nm). These phenomena indicate that the CDPF retrofitting can effectively reduce diesel bus exhaust particle emissions, especially those with large particle sizes.

Keywords: CDPF, diesel, natural gas, real-world emissions

Procedia PDF Downloads 297

Authors: Nacer Eddine Chelbi, Ayet Bagane, Annie Saleh, Claude Sauvageau, Denis Gingras

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As recently stated by National Highway Traffic Safety Administration (NHTSA), to demonstrate the expected performance of a highly automated vehicles system, test approaches should include a combination of simulation, test track, and on-road testing. In this paper, we propose a new validation method for autonomous vehicles involving on-road tests (Field Operational Tests), test track (Test Matrix) and simulation (Worst Case Scenarios). We concentrate our discussion on the simulation aspects, in particular, we extend recent work based on Importance Sampling by using a Metropolis-Hasting algorithm (MHS) to sample collected data from the Safety Pilot Model Deployment (SPMD) in lane-change scenarios. Our proposed MH sampling method will be compared to the Importance Sampling method, which does not perform well in high-dimensional problems. The importance of this study is to obtain a sampler that could be applied to high dimensional simulation problems in order to reduce and optimize the number of test scenarios that are necessary for validation and certification of autonomous vehicles.

Keywords: automated driving, autonomous emergency braking (AEB), autonomous vehicles, certification, evaluation, importance sampling, metropolis-hastings sampling, tests

Procedia PDF Downloads 289

Authors: Djelloul Benatiallah, Ali Benatiallah, Abdelkader Harouz

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Geographic Information Systems (GIS) include various methods and computer techniques to model, capture digitally, store, manage, view and analyze. Geographic information systems have the characteristic to appeal to many scientific and technical field, and many methods. In this article we will present a complete and operational geographic information system, following the theoretical principles of data management and adapting to spatial data, especially data concerning the monitoring of drinking water supply wells (DWS) Adrar region. The expected results of this system are firstly an offer consulting standard features, updating and editing beneficiaries and geographical data, on the other hand, provides specific functionality contractors entered data, calculations parameterized and statistics.

Keywords: GIS, DWS, drilling, Adrar

Procedia PDF Downloads 309

Authors: Yasser O. Abualnaja1, Alexandra Pavlidou2, Taha Boksmati3, Ahmad Alharbi3, Hammad Alsulmi3, Saleh Omar Maghrabi3, Hassan Mowalad3, Rayan Mutwalli3, James H. Churchill4, Afroditi Androni2, Dionysios Ballas2, Ioannis Hatzianestis2, Harilaos Kontoyiannis2, Angeliki Konstantinopoulou2, Georgios Krokkos1, 5, Georgios Pappas2, Vassilis P. Papadopoulos2, Konstantinos Parinos2, Elvira Plakidi2, Eleni Rousselaki2, Dimitris Velaoras2, Panagiota Zachioti2, Theodore Zoulias2, Ibrahim Hoteit5.

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The coastal areas along the Kingdom of Saudi Arabia on both the Red Sea and Arabian Gulf have been witnessing in the past decades an unprecedented economic growth and a rapid increase in anthropogenic activities. Therefore, the Saudi Arabian government has decided to frame a strategy for sustainable development of the coastal and marine environments, which comes in the context of the Vision 2030, aimed at providing the first comprehensive ‘Status Quo Assessment’ of the Kingdom’s coastal and marine environments. This strategy will serve as a baseline assessment for future monitoring activities; this baseline is relied on scientific evidence of the drivers, pressures, and their impact on the environments of the Red Sea and Arabian Gulf. A key element of the assessment was the cumulative pressures of the hotspots analysis, which was developed following the principles of the Driver-Pressure-State-Impact-Response (DPSIR) framework and using the cumulative pressure and impact assessment methodology. Ten hotspot sites were identified, eight in the Red Sea and two in the Arabian Gulf. Thus, multidisciplinary research cruises were conducted throughout the Red Sea and the Arabian Gulf coastal and marine environments in June/July 2021 and September 2021, respectively, in order to understand the relative impact of hydrography and the various pressures on the quality of seawater and sediments. The main objective was to record the physical and biogeochemical parameters along the coastal waters of the Kingdom, tracing the dispersion of contaminants related to specific pressures. The assessment revealed the effect of hydrography on the trophic status of the southern marine coastal areas of the Red Sea. Jeddah Lagoon system seems to face significant eutrophication and pollution challenges, whereas sediments are enriched in some heavy metals in many areas of the Red Sea and the Arabian Gulf. This multidisciplinary research in the Red Sea and the Arabian Gulf coastal waters will pave the way for future detailed environmental monitoring strategies for the Saudi Arabian marine environment.

Keywords: arabian gulf, contaminants, hotspot, red sea

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Authors: Damian Czubak

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The Białowieża Forest is a very valuable natural area, included in the World Natural Heritage at UNESCO, where, due to infestation by the bark beetle (Ips typographus), norway spruce (Picea abies) have deteriorated. This catastrophic scenario led to an increase in fire danger. This was due to the occurrence of large amounts of dead wood and grass cover, as light penetrated to the bottom of the stands. These factors in a dry state are materials that favour the possibility of fire and the rapid spread of fire. One of the objectives of the study was to monitor the quantitative and qualitative changes of combustible material on the permanent decay plots of spruce stands from 2012-2022. In addition, the size of the area with highly flammable vegetation was monitored and a classification of the stands of the Białowieża Forest by flammability classes was made. The key factor that determines the potential fire hazard of a forest is combustible material. Primarily its type, quantity, moisture content, size and spatial structure. Based on the inventory data on the areas of forest districts in the Białowieża Forest, the average fire load and its changes over the years were calculated. The analysis was carried out taking into account the changes in the health status of the stands and sanitary operations. The quantitative and qualitative assessment of fallen timber and fire load of ground cover used the results of the 2019 and 2021 inventories. Approximately 9,000 circular plots were used for the study. An assessment was made of the amount of potential fuel, understood as ground cover vegetation and dead wood debris. In addition, monitoring of areas with vegetation that poses a high fire risk was conducted using data from 2019 and 2021. All sub-areas were inventoried where vegetation posing a specific fire hazard represented at least 10% of the area with species characteristic of that cover. In addition to the size of the area with fire-prone vegetation, a very important element is the size of the fire load on the indicated plots. On representative plots, the biomass of the land cover was measured on an area of 10 m2 and then the amount of biomass of each component was determined. The resulting element of variability of ground covers in stands was their flammability classification. The classification developed made it possible to track changes in the flammability classes of stands over the period covered by the measurements.

Keywords: classification, combustible material, flammable vegetation, Norway spruce

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Authors: Mohmmad A. Obeidat, Amjed Al Fahoum, Ayman M. Mansour

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The recognition of an abnormal activity of the brain functionality is a vital issue. To determine the type of the abnormal activity either a brain image or brain signal are usually considered. Imaging localizes the defect within the brain area and relates this area with somebody functionalities. However, some functions may be disturbed without affecting the brain as in epilepsy. In this case, imaging may not provide the symptoms of the problem. A cheaper yet efficient approach that can be utilized to detect abnormal activity is the measurement and analysis of the electroencephalogram (EEG) signals. The main goal of this work is to come up with a new method to facilitate the classification of the abnormal and disorder activities within the brain directly using EEG signal processing, which makes it possible to be applied in an on-line monitoring system.

Keywords: EEG, wavelet, epilepsy, detection

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Gaelle Candel, David Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

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Authors: Irmak Karaduman, Tugba Corlu, Sezin Galioglu, Burcu Akata, M. Ali Yildirim, Aytunç Ateş, Selim Acar

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Breath analysis represents a promising non-invasive, fast and cost-effective alternative to well-established diagnostic and monitoring techniques such as blood analysis, endoscopy, ultrasonic and tomographic monitoring. Portable, non-invasive, and low-cost breath analysis devices are becoming increasingly desirable for monitoring different diseases, especially asthma. Beacuse of this, NO gas sensing at low concentrations has attracted progressive attention for clinical analysis in asthma. Recently, nanomaterials based sensors are considered to be a promising clinical and laboratory diagnostic tool, because its large surface–to–volume ratio, controllable structure, easily tailored chemical and physical properties, which bring high sensitivity, fast dynamic processand even the increasing specificity. Among various nanomaterials, semiconducting metal oxides are extensively studied gas-sensing materials and are potential sensing elements for breathanalyzer due to their high sensitivity, simple design, low cost and good stability.The sensitivities of metal oxide semiconductor gas sensors can be enhanced by adding noble metals. Doping contents, distribution, and size of metallic or metal oxide catalysts are key parameters for enhancing gas selectivity as well as sensitivity. By manufacturing doping MOS structures, it is possible to develop more efficient sensor sensing layers. Zeolites are perhaps the most widely employed group of silicon-based nanoporous solids. Their well-defined pores of sub nanometric size have earned them the name of molecular sieves, meaning that operation in the size exclusion regime is possible by selecting, among over 170 structures available, the zeolite whose pores allow the pass of the desired molecule, while keeping larger molecules outside.In fact it is selective adsorption, rather than molecular sieving, the mechanism that explains most of the successful gas separations achieved with zeolite membranes. In view of their molecular sieving and selective adsorption properties, it is not surprising that zeolites have found use in a number of works dealing with gas sensing devices. In this study, the Cu doped ZnO nanostructure film was produced by SILAR method and investigated the NO gas sensing properties. To obtain the selectivity of the sample, the gases including CO,NH3,H2 and CH4 were detected to compare with NO. The maximum response is obtained at 85 C for 20 ppb NO gas. The sensor shows high response to NO gas. However, acceptable responses are calculated for CO and NH3 gases. Therefore, there are no responses obtain for H2 and CH4 gases. Enhanced to selectivity, Cu doped ZnO nanostructure film was coated with zeolite A thin film. It is found that the sample possess an acceptable response towards NO hardly respond to CO, NH3, H2 and CH4 at room temperature. This difference in the response can be expressed in terms of differences in the molecular structure, the dipole moment, strength of the electrostatic interaction and the dielectric constant. The as-synthesized thin film is considered to be one of the extremely promising candidate materials in electronic nose applications. This work is supported by The Scientific and Technological Research Council of Turkey (TUBİTAK) under Project No, 115M658 and Gazi University Scientific Research Fund under project no 05/2016-21.

Keywords: Cu doped ZnO, electrical characterization, gas sensing, zeolite

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Authors: Avelyno D'Costa, S. K. Shyama, M. K. Praveen Kumar

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The coast of Goa, India receives constant anthropogenic stress through its major rivers which carry mining rejects of iron and manganese ores from upstream mining sites and petroleum hydrocarbons from shipping and harbor-related activities which put the aquatic fauna such as bivalves at risk. The present study reports the pollution status of the Goan coast by the above xenobiotics employing genotoxicity studies. This is further supplemented by the quantification of total petroleum hydrocarbons (TPHs) and various trace metals (iron, manganese, copper, cadmium, and lead) in gills of the estuarine clam, Meretrix ovum as well as from the surrounding water and sediment, over a two-year sampling period, from January 2013 to December 2014. Bivalves were collected from a probable unpolluted site at Palolem and a probable polluted site at Vasco, based upon the anthropogenic activities at these sites. Genotoxicity was assessed in the gill cells using the comet assay and micronucleus test. The quantity of TPHs and trace metals present in gill tissue, water and sediments were analyzed using spectrofluorometry and atomic absorption spectrophotometry (AAS), respectively. The statistical significance of data was analyzed employing Student’s t-test. The relationship between DNA damage and pollutant concentrations was evaluated using multiple regression analysis. Significant DNA damage was observed in the bivalves collected from Vasco which is a region of high industrial activity. Concentrations of TPHs and trace metals (iron, manganese, and cadmium) were also found to be significantly high in gills of the bivalves collected from Vasco compared to those collected from Palolem. Further, the concentrations of these pollutants were also found to be significantly high in the water and sediments at Vasco compared to that of Palolem. This may be due to the lack of industrial activity at Palolem. A high positive correlation was observed between the pollutant levels and DNA damage in the bivalves collected from Vasco suggesting the genotoxic nature of these pollutants. Further, M. ovum can be used as a bioindicator species for monitoring the level of pollution of the estuarine/coastal regions by TPHs and trace metals.

Keywords: comet assay, metals, micronucleus test, total petroleum Hydrocarbons

Procedia PDF Downloads 237

Authors: Namrata Hangala

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This paper dives into the powerful intersection between film music and storytelling and how it elevates the visuals while primarily focusing on Mychael Danna’s compositions for the study. Danna, an Academy Award-winning composer, is known for his brilliant ability to mix non-Western and culturally rich instruments with minimalist techniques. This unique approach forms the backbone of the analysis here. We take a close look at key scenes from films like Life of Pi, Moneyball, The Good Dinosaur, and Little Miss Sunshine, where Danna’s music plays a crucial role in shaping the story. By breaking down how these scores impact the scenes emotionally and dramatically, we can see how his music becomes part of the narrative itself. The paper blends different approaches to get to the heart of this scene-by-scene breakdowns, music theory, audience survey, and even insights directly from Danna. It discusses how his scores deepen the emotional connection and give more weight to the visual storytelling. The research also dives into the use of leitmotifs, cultural authenticity, and how his music can psychologically impact the viewer, making the story even more powerful. This study reveals how film music, especially Danna’s, doesn’t just sit in the background. It’s often the driving force behind the emotional and narrative core of the film, anchoring the visuals and shaping the way the viewers experience the story.

Keywords: ethnomusicology, psychological impact, film scores, cultural music, compositional techniques, emotional storytelling

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Authors: I. T. Adebayo, J. R. Aworemi, V. A. Ogundele, O. J. Babalola, J. O. Ajayi

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The behavior of road users is a critical element in road safety. However, despite the proven effectiveness of the seat belt in reducing injury severity, some drivers still do not use the seat belt. The study, therefore, examined seat belt usage among drivers in Ogbomoso, Oyo State, Nigeria. Face-to-face surveys were conducted to elicit data on seat belt usage and road behaviors on 54,038 drivers plying the General-Lautech Road in Ogbomoso over a period of six months. Data collected were analyzed using both descriptive and inferential statistics. Findings revealed that seat belt usage was moderate among private car drivers in the morning (52.75 percent) and much lower in the evening (37.67 percent), while less than 30 percent of the commercial bus/taxi drivers used the seat during both periods. The paired T-test revealed a significant difference in seat belt usage by all the drivers in the morning and in the evening, as indicated with a T value of 113.977 and the level of significance p < 0.05. The study concluded that there is a significant difference in the usage of the seat belt by both private car and commercial bus/taxi drivers in Ogbomoso as most drivers made use of the seat belt, especially in the morning, to avoid being delayed when flagged down and fined for non-compliance with seat belt usage by the road traffic law enforcement agents. However, most of the drivers cited inadequate/lack of enforcement of compulsory use of seat belts by the relevant agencies and discomfort as the major reasons for not using the seat belt while driving.

Keywords: drivers, road , seat belt, traffic psychology

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Authors: Kiamran Radjabli

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State Estimator became an intrinsic part of Energy Management Systems (EMS). The SCADA measurements received from the field are processed by the State Estimator in order to accurately determine the actual operating state of the power systems and provide that information to other real-time network applications. All EMS vendors offer a State Estimator functionality in their baseline products. However, setting up and ensuring that State Estimator consistently produces a reliable solution often consumes a substantial engineering effort. This paper provides generic recommendations and describes a simple practical approach to efficient tuning of State Estimator, based on the working experience with major EMS software platforms and consulting projects in many electrical utilities of the USA.

Keywords: convergence, monitoring, state estimator, performance, troubleshooting, tuning, power systems

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Authors: Talha Ahmed, Ishtiaq Hassan

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Urban areas or metropolitan is portrayed by the very high density of population due to the result of these economic activities. Some critical elements, such as urban expansion and climate change, are driving changes in cities with exposure to the incidence and impacts of pluvial floods. Urban communities are recurrently developed by huge spaces by which water cannot enter impermeable surfaces, such as man-made permanent surfaces and structures, which do not cause the phenomena of infiltration and percolation. Urban sprawl can result in increased run-off volumes, flood stage and flood extents during heavy rainy seasons. The flood risks require a thorough examination of all aspects affecting to severe an event in order to accurately estimate their impacts and other risk factors associated with them. For risk evaluation and its impact due to urbanization, an integrated hydrological modeling approach is used on the study area in Islamabad (Pakistan), focusing on a natural water body that has been adopted in this research. The vulnerability of the physical elements at risk in the research region is analyzed using GIS and SOBEK. The supervised classification of land use containing the images from 1980 to 2020 is used. The modeling of DEM with selected return period is used for modeling a hydrodynamic model for flood event inundation. The selected return periods are 50,75 and 100 years which are used in flood modeling. The findings of this study provided useful information on high-risk places and at-risk properties.

Keywords: urbanization, flood, flood risk, GIS

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Authors: Gae Hee Song

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This paper aims to address the evolution of public-private partnerships for mainstreaming an evaluation approach in the community-based education project. It examines the distinctive features of Save the Children’s School Me project in terms of empowerment evaluation principles introduced by David M. Fetterman, especially community ownership, capacity building, and organizational learning. School Me is a Save the Children Korea funded-project, having been implemented in Cote d’Ivoire and Sierra Leone since 2016. The objective of this project is to reduce gender-based disparities in school completion and learning outcomes by creating an empowering learning environment for girls and boys. Both quasi-experimental and experimental methods for impact evaluation have been used to explore changes in learning outcomes, gender attitudes, and learning environments. To locate School Me in the public-private partnership framework for community empowerment and sustainability, the data have been collected from School Me progress/final reports, baseline, and endline reports, fieldwork observations, inter-rater reliability of baseline and endline data collected from a total of 75 schools in Cote d’Ivoire and Sierra Leone. The findings of this study show that School Me project has a significant evaluation component, including qualitative exploratory research, participatory monitoring, and impact evaluation. It strongly encourages key actors, girls, boys, parents, teachers, community leaders, and local education authorities, to participate in the collection and interpretation of data. For example, 45 community volunteers collected baseline data in Cote d’Ivoire; on the other hand, three local government officers and fourteen enumerators participated in the follow-up data collection of Sierra Leone. Not only does this public-private partnership improve local government and community members’ knowledge and skills of monitoring and evaluation, but the evaluative findings also help them find their own problems and solutions with a strong sense of community ownership. Such community empowerment enables Save the Children country offices and member offices to gain invaluable experiences and lessons learned. As a result, empowerment evaluation leads to community-oriented governance and the sustainability of the School Me project.

Keywords: community empowerment, Cote d’Ivoire, empowerment evaluation, public-private partnership, save the children, school me, Sierra Leone, sustainability

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Authors: Shanshan Guo, Xiaoying Zhu, Dominik Jańczewski, Koon Gee Neoh

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Surface charge and wettability are two prominent physical factors governing cell adhesion and have been extensively studied in the literature. However, a comparison between the two driving forces in terms of their independent and cooperative effects in affecting cell adhesion is rarely explored on a systematic and quantitative level. Herein, we formulate a protocol which allows two-dimensional and independent control over both surface charge and wettability. This protocol enables the unambiguous comparison of the effects of these two properties on cell adhesion. This strategy is implemented by controlling both the relative thickness of polyion layers in the layer-by-layer assembly and the polyion side chain chemical structures. The 2D property matrix spans surface isoelectric point ranging from 5 to 9 and water contact angle from 35º to 70º, with other interferential factors (e.g. roughness) eliminated. The interplay between these two surface variables influences 3T3 fibroblast cell adhesion. The results show that both surface charge and wettability have an effect on its adhesion. The combined effects of positive charge and hydrophilicity led to the highest cell adhesion whereas negative charge and hydrophobicity led to the lowest cell adhesion. Our design strategy can potentially form the basis for studying the distinct behaviors of electrostatic force or wettability driven interfacial phenomena and serving as a reference in future studies assessing cell adhesion to surfaces with known charge and wettability within the property range studied here.

Keywords: cell adhesion, layer-by-layer, surface charge, surface wettability

Procedia PDF Downloads 270