Search results for: threshold detecting

Authors: Sanjay Adhikesaven

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Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open-source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.

Keywords: computer vision, deep learning, workplace safety, automation

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Authors: Dimitar Karastoyanov, Todor Penchev

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The paper presents the research of a kind of autonomous mobile robots, intended for work and adaptive perception in unknown and unstructured environment. The objective are robots, dedicated for multi-sensory environment perception and exploration, like measurements and samples taking, discovering and putting a mark on the objects as well as environment interactions–transportation, carrying in and out of equipment and objects. At that ground classification of the different types mobile robots in accordance with the way of locomotion (wheel- or chain-driven, walking, etc.), used drive mechanisms, kind of sensors, end effectors, area of application, etc. is made. Modular system for the mechanical construction of the mobile robots is proposed. Special PLC on the base of AtMega128 processor for robot control is developed. Electronic modules for the wireless communication on the base of Jennic processor as well as the specific software are developed. The methods, means and algorithms for adaptive environment behaviour and tasks realization are examined. The methods of group control of mobile robots and for suspicious objects detecting and handling are discussed too.

Keywords: mobile robots, wireless communications, environment investigations, group control, suspicious objects

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Authors: Samira Taleb, Sakina Aoun, Slimane Ziani, Zoheir Mentouri, Adel Boudiaf

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Rolling is a pressure treatment technique that modifies the shape of steel ingots or billets between rotating rollers. During this process, defects may form on the surface of the rolled sheets and are likely to affect the performance and quality of the finished product. In our study, we developed a method for detecting surface hole defects using a pixel classification approach. This work includes several steps. First, we performed image preprocessing to delimit areas with and without hole defects on the sheet image. Then, we developed the histograms of each area to generate the gray level membership intervals of the pixels that characterize each area. As we noticed an intersection between the characteristics of the gray level intervals of the images of the two areas, we finally performed a learning step based on a series of detection tests to refine the membership intervals of each area, and to choose the defect detection criterion in order to optimize the recognition of the surface hole.

Keywords: classification, defect, surface, detection, hole

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Authors: Koichi Kurita

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In this study, an effective non-contact technique for the detection of human physical activity is proposed. The technique is based on detecting the electrostatic induction current generated by the walking motion under non-contact and non-attached conditions. A theoretical model for the electrostatic induction current generated because of a change in the electric potential of the human body is proposed. By comparing the obtained electrostatic induction current with the theoretical model, it becomes obvious that this model effectively explains the behavior of the waveform of the electrostatic induction current. The normal walking motions are recorded using a portable sensor measurement located in a passageway　of office building. The obtained results show that detailed information regarding physical activity such as a walking cycle can be estimated using our proposed technique. This suggests that the proposed technique which is based on the detection of the walking signal, can be successfully applied to the detection of human walking motion in a secured building.

Keywords: human walking motion, access control, electrostatic induction, alarm monitoring

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Authors: Yu-Lin Shen, Ming-Kuen Chang

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In addition to a considerable amount of machinery and equipment, intricacies of the transmission pipeline exist in Petrochemical plants. Long term corrosion may lead to pipeline thinning and rupture, causing serious safety concerns. With the advances in non-destructive testing technology, more rapid and long-range ultrasonic detection techniques are often used for pipeline inspection, EMAT without coupling to detect, it is a non-contact ultrasonic, suitable for detecting elevated temperature or roughened e surface of line. In this study, we prepared artificial defects in pipeline for Electromagnetic Acoustic Transducer testing (EMAT) to survey the relationship between the defect location, sizing and the EMAT signal. It was found that the signal amplitude of EMAT exhibited greater signal attenuation with larger defect depth and length. In addition, with bigger flat hole diameter, greater amplitude attenuation was obtained. In summary, signal amplitude attenuation of EMAT was affected by the defect depth, defect length and the hole diameter and size.

Keywords: EMAT, artificial defect, NDT, ultrasonic testing

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Authors: A. Preetha Priyadharshini, S. B. M. Priya

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In this paper, we consider the MU-MISO downlink scenario, under imperfect channel state information (CSI). The main issue in imperfect CSI is to keep the probability of each user achievable outage rate below the given threshold level. Such a rate outage constraints present significant and analytical challenges. There are many probabilistic methods are used to minimize the transmit optimization problem under imperfect CSI. Here, decomposition based large deviation inequality and Bernstein type inequality convex restriction methods are used to perform the optimization problem under imperfect CSI. These methods are used for achieving improved output quality and lower complexity. They provide a safe tractable approximation of the original rate outage constraints. Based on these method implementations, performance has been evaluated in the terms of feasible rate and average transmission power. The simulation results are shown that all the two methods offer significantly improved outage quality and lower computational complexity.

Keywords: imperfect channel state information, outage probability, multiuser- multi input single output, channel state information

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Authors: Azza Habibullah

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The principal objective of this paper is to show the use of participatory culture in the parliamentarian campaign. The use of this method has always been non-popular amongst the parliamentarian candidates due to the amount of times and energy that they need to spent with the constituents. However, due to many parliamentarian corruption cases in the last five years period, some political party have been losing peoples trust. That political party trust lost had also affecting the parliamentarian candidates electability, so they invent some creative campaign method that involving their constituent with more intimates and friendly environment. In this paper, an observation is done to a parliamentarian candidate from Partai Keadilan Sejahtera (Prosperous Justice Party) in Bandung and Cimahi City area, West Java. This Parliamentraian candidate is known for her personal-approach campaign method such as a puppet show, hanging out with group of ex-bike gang leaders, and going fishing with the constituent. This paper will compare her method with other parliamentarian candidates from the same party as her that mostly use mainstream campaign method such as open speech, print media, an other one way campaign method. While the other parliamentarian candidates failed to reach the parliamentarian threshold, the participatory method had proven as an effective method.

Keywords: participatory culture, Indonesian parliamentary election, Prosperous Justice Party, electability

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Authors: Seonggil Kim, Jeonghun Nam, Chae Seung Lim

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Rapid diagnosis of fungal infections is critical for rapid antifungal therapy. However, it is difficult to detect extremely low concentration fungi in blood sample. To address the limitation, separation and concentration of fungi in blood sample are required to enhance the sensitivity of PCR analysis. In this study, we demonstrated a sheathless separation and concentration of fungi, candida cells using a viscoelastic fluid. To validate the performance of the device, microparticle mixture (2 and 13 μm) was used, and those particles were successfully separated based on the size difference at high flow rate of 100 μl/min. For the final application, successful separation of the Candida cells from the white blood cells (WBCs) was achieved. Based on the viscoelastic lateral migration toward the equilibrium position, Candida cells were separated and concentrated by center focusing, while WBCs were removed by patterning into two streams between the channel center and the sidewalls. By flow cytometric analysis, the separation efficiency and the purity were evaluated as ~99% and ~ 97%, respectively. From the results, the device can be the powerful tool for detecting extremely rare disease-related cells.

Keywords: candida cells, concentration, separation, viscoelastic fluid

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Authors: Krystian Chachula, Robert Nowak

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Manufacturing home-made explosives and synthetic drugs is an increasing problem in Europe. To combat that, a data fusion system is proposed for the detection and localization of production sites in urban environments. The data consists of measurements of properties of wastewater performed by various sensors installed in a sewage network. A four-stage fusion strategy allows detecting sources of waste products from known chemical reactions. First, suspicious measurements are used to compute the amount and position of discharged compounds. Then, this information is propagated through the sewage network to account for missing sensors. The next step is clustering and the formation of tracks. Eventually, tracks are used to reconstruct discharge events. Sensor measurements are simulated by a subsystem based on real-world data. In this paper, different discharge scenarios are considered to show how the parameters of used algorithms affect the effectiveness of the proposed system. This research is a part of the SYSTEM project (SYnergy of integrated Sensors and Technologies for urban sEcured environMent).

Keywords: continuous monitoring, information fusion and sensors, internet of things, multisensor fusion

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Authors: Zakheleni P. Dube, Phatu W. Mashela

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Minimum inhibition concentration (MIC) is the lowest concentration of a chemical that brings about significant inhibition of target organism. The conventional method for establishing the MIC for phytonematicides is tedious. The objective of this study was to use the Curve-fitting Allelochemical Response Data (CARD) to determine the MIC for pure cucurbitacin A on Meloidogyne incognita second-stage juveniles (J2) hatch, immobility and mortality. Meloidogyne incognita eggs and freshly hatched J2 were separately exposed to a series of pure cucurbitacin A concentrations of 0.00, 0.25, 0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 2.00, 2.25 and 2.50 μg.mL⁻¹for 12, 24, 48 and 72 h in an incubator set at 25 ± 2°C. Meloidogyne incognita J2 hatch, immobility and mortality counts were determined using a stereomicroscope and the significant means were subjected to the CARD model. The model exhibited density-dependent growth (DDG) patterns of J2 hatch, immobility and mortality to increasing concentrations of cucurbitacin A. The average MIC for cucurbitacin A on M. incognita J2 hatch, immobility and mortality were 2.2, 0.58 and 0.63 µg.mL⁻¹, respectively. Meloidogyne incognita J2 hatch had the highest average MIC value followed by mortality and immobility had the least. In conclusion, the CARD model was able to generate MIC for cucurbitacin A, hence it could serve as a valuable tool in the chemical-nematode bioassay studies.

Keywords: inhibition concentration, phytonematicide, sensitivity index, threshold stimulation, triterpenoids.

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Authors: T. Prabakaran

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Gas Insulated Substations (GIS) are widely used as important switchgear equipment because of its high reliability, low space requirement, low risk factor and easy maintenance, yet some failures have been reported. Some of the failures are due to presence of metallic particles inside the GIS compartment. The defect can be generated in GIS during production, maintenance, installation and can be due to ageing of the component. The Ultra-High Frequency (UHF) method is used to diagnose the insulation condition of GIS by detecting the PD signals in GIS. This paper identifies PD patterns for free moving particle defect and particle fixed on cone using UHF method. As insulation failure usually starts with PD activity, this paper investigates the differences in PD characteristics in SF6 gas with different types of defects. Experimental results show that correct identification of defects can be achieved based on considered PD characteristics. The method can be applied to prove the quality of assembly work at commissioning, also on a regular basis after many years in service to detect aged and conducting particles as a part of the condition based maintenance.

Keywords: gas insulated substation, partial discharge, free moving particle defect, particle fixed on cone defect, ultra high frequency method

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Authors: Takuya Sumi, Syoko Nukaya, Takashi Kaburagi, Hiroshi Tanaka, Kajiro Watanabe, Yosuke Kurihara

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We propose an unconstrained measurement system for scratching motion based on mathematical model of unconstrained bed sensing method which could measure the bed vibrations due to the motion of the person on the bed. In this paper, we construct mathematical model of the unconstrained bed monitoring system, and we apply the unconstrained bed sensing method to the system for detecting scratching motion. The proposed sensors are placed under the three bed feet. When the person is lying on the bed, the output signals from the sensors are proportional to the magnitude of the vibration due to the scratching motion. Hence, we could detect the subject’s scratching motion from the output signals from ceramic sensors. We evaluated two scratching motions using the proposed system in the validity experiment as follows: First experiment is the subject’s scratching the right side cheek with his right hand, and; second experiment is the subject’s scratching the shin with another foot. As the results of the experiment, we recognized the scratching signals that enable the determination when the scratching occurred. Furthermore, the difference among the amplitudes of the output signals enabled us to estimate where the subject scratched.

Keywords: unconstrained bed sensing method, scratching, body movement, itchy, piezoceramics

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Authors: Ishraq M. Anjum

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Distributed Bragg reflectors are used in vertical-cavity surface-emitting lasers (VCSELs) in order to achieve very high reflectivity. Use of metamaterial in place of distributed Bragg reflector can reduce the device size significantly. A silicon-based metamaterial near perfect reflector is designed to be used in place of distributed Bragg reflectors in VCSELs. Mie resonance in dielectric microparticles is exploited in order to design the metamaterial. A reflectivity of 98.31% is achieved using finite-difference time-domain method. An 808nm double intra-cavity contacted VCSEL structure with 1.5 λ cavity is proposed using this metamaterial near perfect reflector. The active region is designed to be composed of seven GaAs/AlGaAs quantum wells. Upon numerical investigation of the designed VCSEL structure, the threshold current is found to be 2.96 mA at an aperture of 40 square micrometers and the maximum output power is found to be 71 mW at a current of 141 mA. Miniaturization of conventional VCSELs is possible using this design.

Keywords: GaAs, LASER, metamaterial, VCSEL, vertical cavity surface emitting laser

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Authors: Jesmin Akter, Chang Hyuk Ahn, Ilho Kim, Fumitake Nishimura, Jaiyeop Lee

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This study aimed to compare two pre-treatment and two RNA extraction methods, namely PEG, and Nano bubble, Viral RNA Soil, and Mini Kit, in terms of their efficiency in detecting SARS-CoV-2 and PMMoV in influent and 1st sedimentation samples from a wastewater treatment plant. The extracted RNA samples were quantified and evaluated for purity, yield, and integrity. The results indicated that the nanobubble PEG method provided the highest yield of RNA, while the QIAamp Viral RNA Mini Kit produced the purest RNA samples. In terms of sensitivity and specificity, all these methods were able to detect SARS-CoV-2 and PMMoV in both influent and 1st sedimentation samples. However, the nanobubble PEG method showed slightly higher sensitivity compared to the other methods. These findings suggest that the choice of RNA extraction method should depend on the downstream application and the quality of the RNA required. The study also highlights the potential of wastewater-based epidemiology as an effective and non-invasive method for monitoring the spread of infectious diseases in a community.

Keywords: influent, PMMoV, SARS-CoV-2, wastewater based epidemiology

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Authors: Tejeet Singh, Ishvneet Singh, Vinay Gupta

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The present study focussed on carrying out the creep analysis in an isotropic thick-walled composite cylindrical pressure vessel composed of aluminium matrix reinforced with silicon-carbide in particulate form. The creep behaviour of the composite material has been described by the threshold stress based creep law. The value of stress exponent appearing in the creep law was selected as 3, 5 and 8. The constitutive equations were developed using well known von-Mises yield criteria. Models were developed to find out the distributions of creep stresses and strain rate in thick-walled composite cylindrical pressure vessels under internal pressure. In order to obtain the stress distributions in the cylinder, the equilibrium equation of the continuum mechanics and the constitutive equations are solved together. It was observed that the radial stress, tangential stress and axial stress increases along with the radial distance. The cross-over was also obtained almost at the middle region of cylindrical vessel for tangential and axial stress for different values of stress exponent. The strain rates were also decreasing in nature along the entire radius.

Keywords: creep, composite, cylindrical vessel, internal pressure

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Authors: Yassa Nacera, Badji Abderrezak, Saidoune Abdelmalek, Houassine Hamza

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Several kinds of faults can occur in a permanent magnet synchronous machine (PMSM) systems: bearing faults, electrically short/open faults, eccentricity faults, and demagnetization faults. Demagnetization fault means that the strengths of permanent magnets (PM) in PMSM decrease, and it causes low output torque, which is undesirable for EVs. The fault is caused by physical damage, high-temperature stress, inverse magnetic field, and aging. Motor current signature analysis (MCSA) is a conventional motor fault detection method based on the extraction of signal features from stator current. a simulation model of the PMSM under partial demagnetization and uniform demagnetization fault was established, and different degrees of demagnetization fault were simulated. The harmonic analyses using the Fast Fourier Transform (FFT) show that the fault diagnosis method based on the harmonic wave analysis is only suitable for partial demagnetization fault of the PMSM and does not apply to uniform demagnetization fault of the PMSM.

Keywords: permanent magnet, diagnosis, demagnetization, modelling

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Authors: Oladapo Tolulope Ibitoye

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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

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Authors: Mallikarjuna Rao Gundavarapu, Ch. Mallikarjuna Rao, K. Anuradha Bai

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Real Time Video Tracking is a challenging task for computing professionals. The performance of video tracking techniques is greatly affected by background detection and elimination process. Local regions of the image frame contain vital information of background and foreground. However, pixel-level processing of local regions consumes a good amount of computational time and memory space by traditional approaches. In our approach we have explored the concurrent computational ability of General Purpose Graphic Processing Units (GPGPU) to address this problem. The Gaussian Mixture Model (GMM) with adaptive weighted kernels is used for detecting the background. The weights of the kernel are influenced by local regions and are updated by inter-frame variations of these corresponding regions. The proposed system has been tested with GPU devices such as GeForce GTX 280, GeForce GTX 280 and Quadro K2000. The results are encouraging with maximum speed up 10X compared to sequential approach.

Keywords: connected components, embrace threads, local weighted kernel, structuring elements

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Authors: R. Z. R. R. Rosdin, N. M. Ali, S. W. Harun, H. Arof

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We have experimentally demonstrated bright-dark pulses in a nonlinear polarization rotation (NPR) based mode-locked Erbium-doped fiber laser (EDFL) with a long cavity configuration. Bright–dark pulses could be achieved when the laser works in the passively mode-locking regime and the net group velocity dispersion is quite anomalous. The EDFL starts to generate a bright pulse train with degenerated dark pulse at the mode-locking threshold pump power of 35.09 mW by manipulating the polarization states of the laser oscillation modes using a polarization controller (PC). A split bright–dark pulse is generated when further increasing the pump power up to 37.95 mW. Stable bright pulses with no obvious evidence of a dark pulse can also be generated when further adjusting PC and increasing the pump power up to 52.19 mW. At higher pump power of 54.96 mW, a new form of bright-dark pulse emission was successfully identified with the repetition rate of 29 kHz. The bright and dark pulses have a duration of 795.5 ns and 640 ns, respectively.

Keywords: Erbium-doped fiber laser, nonlinear polarization rotation, bright-dark pulse, photonic

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Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

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It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

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Authors: Arūnas Burinskas

Abstract:

Although Europe is on the threshold of a new industrial revolution called Industry 4.0, many believe that this will increase the flexibility of production, the mass adaptation of products to consumers and the speed of their service; it will also improve product quality and dramatically increase productivity. However, as expected, all the benefits of Industry 4.0 face many of the inevitable changes and challenges they pose. One of them is the inevitable transformation of current competition and business models. This article examines the possible results of competitive conversion from the classic Bertrand and Cournot models to qualitatively new competition based on innovation. Ability to deliver a new product quickly and the possibility to produce the individual design (through flexible and quickly configurable factories) by reducing equipment failures and increasing process automation and control is highly important. This study shows that the ongoing transformation of the competition model is changing the game. This, together with the creation of complex value networks, means huge investments that make it particularly difficult for small and medium-sized enterprises. In addition, the ongoing digitalization of data raises new concerns regarding legal obligations, intellectual property, and security.

Keywords: Bertrand and Cournot Competition, competition model, industry 4.0, industrial organisation, monopolistic competition

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Authors: Elnaz Lashgari, Emel Demircan

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Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

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Authors: Ismail Bin Mohd Saaid, Abubakar Abubakar Umar

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Despite the successes recorded by Alkaline/Surfactant/Polymer (ASP) flooding as an effective chemical EOR technique, the combination CEOR is not unassociated with stern glitches, one of which is the scaling of downhole equipment. One of the major issues inside the oil industry is how to control scale formation, regardless of whether it is in the wellhead equipment, down-hole pipelines or even the actual field formation. The best approach to handle the challenge associated with oilfield scale formation is the application of scale inhibitors to avert the scale formation. Chemical inhibitors have been employed in doing such. But due to environmental regulations, the industry have focused on using green scale inhibitors to mitigate the formation of scales. This paper compares the scale inhibition performance of Polyaspartic acid and sodium polyacrylic acid, both commercial green scale inhibitors, in mitigating silicate scales formed during Alkaline/Surfactant/polymer flooding under static conditions. Both PASP and TH5000 are non-threshold inhibitors, therefore their efficiency was only seeing in delaying the deposition of the silicate scales.

Keywords: alkaline/surfactant/polymer flooding (ASP), polyaspartic acid (PASP), sodium polyacrylate (SPA)

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Authors: A. Casale, J. Alessi, C. N. Bianchi, G. Bozzini, M. Brunoldi, V. Cappanera, P. Corvisiero, G. Fanciulli, D. Grosso, N. Magnoli, A. Mandich, C. Melchiorre, C. Morri, P. Povero, N. Stasi, M. Taiuti, G. Viano, M. Wurtz

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This work describes a real-time algorithm for detecting, tracking and classifying single motorboats, developed using the acoustic data recorded by a hydrophone array within the framework of EU LIFE + project ARION (LIFE09NAT/IT/000190). The project aims to improve the conservation status of bottlenose dolphins through a real-time simultaneous monitoring of their population and surface ship traffic. A Passive Acoustic Monitoring (PAM) system is installed on two autonomous permanent marine buoys, located close to the boundaries of the Marine Protected Area (MPA) of Portofino (Ligurian Sea- Italy). Detecting surface ships is also a necessity in many other sensible areas, such as wind farms, oil platforms, and harbours. A PAM system could be an effective alternative to the usual monitoring systems, as radar or active sonar, for localizing unauthorized ship presence or illegal activities, with the advantage of not revealing its presence. Each ARION buoy consists of a particular type of structure, named meda elastica (elastic beacon) composed of a main pole, about 30-meter length, emerging for 7 meters, anchored to a mooring of 30 tons at 90 m depth by an anti-twist steel wire. Each buoy is equipped with a floating element and a hydrophone tetrahedron array, whose raw data are send via a Wi-Fi bridge to a ground station where real-time analysis is performed. Bottlenose dolphin detection algorithm and ship monitoring algorithm are operating in parallel and in real time. Three modules were developed and commissioned for ship monitoring. The first is the detection algorithm, based on Time Difference Of Arrival (TDOA) measurements, i.e., the evaluation of angular direction of the target respect to each buoy and the triangulation for obtaining the target position. The second is the tracking algorithm, based on a Kalman filter, i.e., the estimate of the real course and speed of the target through a predictor filter. At last, the classification algorithm is based on the DEMON method, i.e., the extraction of the acoustic signature of single vessels. The following results were obtained; the detection algorithm succeeded in evaluating the bearing angle with respect to each buoy and the position of the target, with an uncertainty of 2 degrees and a maximum range of 2.5 km. The tracking algorithm succeeded in reconstructing the real vessel courses and estimating the speed with an accuracy of 20% respect to the Automatic Identification System (AIS) signals. The classification algorithm succeeded in isolating the acoustic signature of single vessels, demonstrating its temporal stability and the consistency of both buoys results. As reference, the results were compared with the Hilbert transform of single channel signals. The algorithm for tracking multiple targets is ready to be developed, thanks to the modularity of the single ship algorithm: the classification module will enumerate and identify all targets present in the study area; for each of them, the detection module and the tracking module will be applied to monitor their course.

Keywords: acoustic-noise, bottlenose-dolphin, hydrophone, motorboat

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Authors: Ashlesha Khanapure, Harsh Kashyap, Abhinav Anand, Sanjana Habib, Anupama Bidargaddi

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Technologies that are developing quickly are being developed daily in a variety of disciplines, particularly the medical field. For the purpose of detecting bone fractures in X-ray pictures of different body segments, our work compares the ResNet-50 and MobileNetV3 architectures. It evaluates accuracy and computing efficiency with X-rays of the elbow, hand, and shoulder from the MURA dataset. Through training and validation, the models are evaluated on normal and fractured images. While ResNet-50 showcases superior accuracy in fracture identification, MobileNetV3 showcases superior speed and resource optimization. Despite ResNet-50’s accuracy, MobileNetV3’s swifter inference makes it a viable choice for real-time clinical applications, emphasizing the importance of balancing computational efficiency and accuracy in medical imaging. We created a graphical user interface (GUI) for MobileNet V3 model bone fracture detection. This research underscores MobileNetV3’s potential to streamline bone fracture diagnoses, potentially revolutionizing orthopedic medical procedures and enhancing patient care.

Keywords: CNN, MobileNet V3, ResNet-50, healthcare, MURA, X-ray, fracture detection

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Authors: Cristian Viespe, Dana Miu

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Surface acoustic wave (SAW) sensors with nanoparticles (NPs) of various dimensions and concentrations embedded in different types of polymer sensing films for detecting volatile organic compounds (VOCs) were studied. The sensors were ‘delay line’ type with a center frequency of 69.4 MHz on ST-X quartz substrates. NPs with different diameters of 7 nm or 13 nm were obtained by laser ablation with lasers having 5 ns or 10 ps pulse durations, respectively. The influence of NPs dimensions and concentrations on sensor properties such as frequency shift, sensitivity, noise and response time were investigated. To the best of our knowledge, the influence of NP dimensions on SAW sensor properties with has not been investigated. The frequency shift and sensitivity increased with increasing NP concentration in the polymer for a given NP dimension and with decreasing NP diameter for a given concentration. The best performances were obtained for the smallest NPs used. The SAW sensor with NPs of 7 nm had a limit of detection (LOD) of 65 ppm (almost five times better than the sensor with polymer alone), and a response time of about 9 s for ethanol.

Keywords: surface acoustic wave sensor, nanoparticles, volatile organic compounds, laser ablation

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Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

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Authors: Abdessalem Abbassi, Ahlem Dakhlaoui, Lota D. Tamini

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This article analyses management of hydropower dams within two different industrial structures: monopolistic and oligopolistic; when hydroelectricity producers are risk averse and face demand uncertainty. In each type of market structure we determine the water release path in closed-loop equilibrium. We show how a monopoly can manage its hydropower dams by additional pumping or storage depending on the relative abundance of water between different regions to smooth the effect of uncertainty on electricity prices. In the oligopolistic case with symmetric rates of risk aversion, we determine the conditions under which the relative scarcity (abundance) of water in the dam of a hydroelectric operator can favor additional strategic pumping (storage) in its competitor’s dams. When there is asymmetry of the risk aversion coefficient, the firm’s hydroelectricity production increases as its competitor’s risk aversion increases, if and only if the average recharge speed of the competitor’s dam exceeds a certain threshold, which is an increasing function of its average water inflows.

Keywords: asymmetric risk aversion, closed-loop Cournot competition, electricity wholesale market, hydropower dams

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Authors: Alireza Ansariyar, Mansoureh Jeihani

Abstract:

Light Detection and Ranging (LiDAR) sensors are capable of recording traffic data including the number of passing vehicles and bicyclists, the speed of vehicles and bicyclists, and the number of conflicts among both road users. In order to collect real-time traffic data and investigate the safety of different road users, a LiDAR sensor was installed at Cold Spring Ln – Hillen Rd intersection in Baltimore City. The frequency and severity of collected real-time conflicts were analyzed and the results highlighted that 122 conflicts were recorded over a 10-month time interval from May 2022 to February 2023. By using an innovative image-processing algorithm, a new safety Measure of Effectiveness (MOE) was proposed to recognize the critical zones for bicyclists entering each zone. Considering the trajectory of conflicts, the results of the analysis demonstrated that conflicts in the northern approach (zone N) are more frequent and severe. Additionally, sunny weather is more likely to cause severe vehicle-bike conflicts.

Keywords: LiDAR sensor, post encroachment time threshold (PET), vehicle-bike conflicts, a measure of effectiveness (MOE), weather condition

Procedia PDF Downloads 236

Authors: Riham Barbar

Abstract:

This paper studies the dynamic effects of charitable giving in a Ramsey model à la Becker and Foias (1994), such that heterogeneity is reduced to two types of agents: rich and poor. It is assumed that rich show a great concern for poor and enjoy giving. The introduction of charitable giving in this paper is inspired from the notion of Zakat (borrowed from the Islamic Economics) and is defined according to the warm-glow of Andreoni (1990). In this framework, we prove the existence of a steady state where only the patient agent holds capital. Furthermore, we show that local indetermincay appears. While moderate values of charitable-giving elasticity makes the appearance of endogenous fluctuations due to self-fulfilling expectations more likely, high values of this elasticity stabilizes endogenous fluctuations, by narrowing down the range of parameter values compatible with local indeterminacy and may rule out expectations-driven fluctuations if it exceeds certain threshold. Finally, cycles of period two emerge. However, charitable-giving makes it less likely for these cycles to emerge.

Keywords: charitable giving, warm-glow, bifurcations, heterogeneous agents, indeterminacy, self-fulfilling expectations, endogenous fluctuations

Procedia PDF Downloads 316