Search results for: real time clock

Authors: S. H. Bae, D. W. Cho, W. B. Jeong, J. R. Cho

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Fatigue fracture of an aluminum welded structure is a phenomenon frequently occurring from pores in a weld. In order to grasp the state of the welded structure in operation in real time, the acceleration signal of the structure is measured. At this time, the vibration characteristic of the signal according to the fatigue load is an important parameter of the state diagnosis. This paper was an experimental study on the variation of vibration characteristics of welded beams with vibration aging (especially bending vibration). First simple beams were produced according to welding conditions. Each beam was vibrated and measured beam's PSD (power spectral density) according to the degree of aging. Also, modal testing was conducted to compare the transfer functions of welded beams. Testing result shows that the natural frequencies of the beam changed with the vibration aging due to the change of stiffness in welding part and its stiffness was estimated by the finite element method.

Keywords: modal testing, natural frequency, vibration aging, welded structure

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Authors: Lukasz Grabowski, Tytus Tulwin, Michal Geca, P. Karpinski

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This paper presents the radial aircraft engine model which has been created in AVL Boost software. This model is a one-dimensional physical model of the engine, which enables us to investigate the impact of an ignition system design on engine performance (power, torque, fuel consumption). In addition, this model allows research under variable environmental conditions to reflect varied flight conditions (altitude, humidity, cruising speed). Before the simulation research the identifying parameters and validating of model were studied. In order to verify the feasibility to take off power of gasoline radial aircraft engine model, some validation study was carried out. The first stage of the identification was completed with reference to the technical documentation provided by manufacturer of engine and the experiments on the test stand of the real engine. The second stage involved a comparison of simulation results with the results of the engine stand tests performed on a WSK ’PZL-Kalisz’. The engine was loaded by a propeller in a special test bench. Identifying the model parameters referred to a comparison of the test results to the simulation in terms of: pressure behind the throttles, pressure in the inlet pipe, and time course for pressure in the first inlet pipe, power, and specific fuel consumption. Accordingly, the required coefficients and error of simulation calculation relative to the real-object experiments were determined. Obtained the time course for pressure and its value is compatible with the experimental results. Additionally the engine power and specific fuel consumption tends to be significantly compatible with the bench tests. The mapping error does not exceed 1.5%, which verifies positively the model of combustion and allows us to predict engine performance if the process of combustion will be modified. The next conducted tests verified completely model. The maximum mapping error for the pressure behind the throttles and the inlet pipe pressure is 4 %, which proves the model of the inlet duct in the engine with the charging compressor to be correct.

Keywords: 1D-model, aircraft engine, performance, validation

Procedia PDF Downloads 335

Authors: N. F. Hanna, A. M. Haridy

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One of the main drawbacks of the Modal Pushover Analysis (MPA) is the need to perform nonlinear time-history analysis, which complicates the analysis method and time. A simplified version of the MPA has been proposed based on the concept of the inelastic deformation ratio. Furthermore, the effect of the higher modes of vibration is considered by assuming linearly-elastic responses, which enables the use of standard elastic response spectrum analysis. In this thesis, the simplified MPA (SMPA) method is applied to determine the target global drift and the inter-story drifts of steel frame building. The effect of the higher vibration modes is considered within the framework of the SMPA. A comprehensive survey about the inelastic deformation ratio is presented. After that, a suitable expression from literature is selected for the inelastic deformation ratio and then implemented in the SMPA. The estimated seismic demands using the SMPA, such as target drift, base shear, and the inter-story drifts, are compared with the seismic responses determined by applying the standard MPA. The accuracy of the estimated seismic demands is validated by comparing with the results obtained by the nonlinear time-history analysis using real earthquake records.

Keywords: modal analysis, pushover analysis, seismic performance, target displacement

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Authors: Nhat-To Huynh, Chen-Fu Chien

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Textile batch dyeing scheduling problem is complicated which includes batch formation, batch assignment on machines, batch sequencing with sequence-dependent setup time. Most manufacturers schedule their orders manually that are time consuming and inefficient. More power methods are needed to improve the solution. Motivated by the real needs, this study aims to propose approaches in which genetic algorithm is developed with multi-subpopulation and hybridised with estimation of distribution algorithm to solve the constructed problem for minimising the makespan. A heuristic algorithm is designed and embedded into the proposed algorithms to improve the ability to get out of the local optima. In addition, an empirical study is conducted in a textile company in Taiwan to validate the proposed approaches. The results have showed that proposed approaches are more efficient than simulated annealing algorithm.

Keywords: estimation of distribution algorithm, genetic algorithm, multi-subpopulation, scheduling, textile dyeing

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Authors: Agata Twardoch

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The processes of commodification of real estate and changes in housing markets have led to a situation where the prices of free market housing in European capitals are significantly higher than the purchasing value of average wages. This phenomenon has many negative social and spatial consequences. At the same time, the attractiveness of real estate as an asset makes these processes progress. Out of concern for sustainable social development, city authorities apply solutions to balance the burdensome effects of codification of housing. One of them is a social provision for housing investments. The article presents a comparative study of solutions applied in selected European capitals, on the example of Warsaw, Paris, London, Berlin, Copenhagen, and Vienna. The study was conducted along with works on expert report for the master plan for Warsaw. The forms of commissions applied in Local Planning Acts were compared, with particular reference to spatial solutions. The results of the analysis made it possible to determine common features of the solutions applied and to establish recommendations for further practice. Major findings of the study indicate that requirement of social provision is achievable in spatial planning documents. Study shows that application of social provision in private housing investments is a useful tool in housing policy against commodification.

Keywords: affordable housing, housing provision, spatial planning, sustainable social development

Procedia PDF Downloads 177

Authors: Prem K. Mony, Bharadwaj Amrutur, Prashanth Thankachan, Swarnarekha Bhat, Suman Rao, Maryann Washington, Annamma Thomas, N. Sheela, Hiteshwar Rao, Sumi Antony

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The disease burden among mothers and newborns is caused mostly by a handful of avoidable conditions occurring around the time of childbirth and within the first month following delivery. Real-time monitoring of vital parameters of mothers and neonates offers a potential opportunity to impact access as well as the quality of care in vulnerable populations. We describe the design, development and testing of an innovative wearable device for remote biomonitoring (RBM) of body temperatures in mothers and neonates in a hospital in southern India. The architecture consists of: [1] a low-cost, wearable sensor tag; [2] a gateway device for ‘real-time’ communication link; [3] piggy-backing on a commercial GSM communication network; and [4] an algorithm-based data analytics system. Requirements for the device were: long battery-life upto 28 days (with sampling frequency 5/hr); robustness; IP 68 hermetic sealing; and human-centric design. We undertook pre-clinical laboratory testing followed by clinical trial phases I & IIa for evaluation of safety and efficacy in the following sequence: seven healthy adult volunteers; 18 healthy mothers; and three sets of babies – 3 healthy babies; 10 stable babies in the Neonatal Intensive Care Unit (NICU) and 1 baby with hypoxic ischaemic encephalopathy (HIE). The 3-coin thickness, pebble-design sensor weighing about 8 gms was secured onto the abdomen for the baby and over the upper arm for adults. In the laboratory setting, the response-time of the sensor device to attain thermal equilibrium with the surroundings was 4 minutes vis-a-vis 3 minutes observed with a precision-grade digital thermometer used as a reference standard. The accuracy was ±0.1°C of the reference standard within the temperature range of 25-40°C. The adult volunteers, aged 20 to 45 years, contributed a total of 345 hours of readings over a 7-day period and the postnatal mothers provided a total of 403 paired readings. The mean skin temperatures measured in the adults by the sensor were about 2°C lower than the axillary temperature readings (sensor =34.1 vs digital = 36.1); this difference was statistically significant (t-test=13.8; p<0.001). The healthy neonates provided a total of 39 paired readings; the mean difference in temperature was 0.13°C (sensor =36.9 vs digital = 36.7; p=0.2). The neonates in the NICU provided a total of 130 paired readings. Their mean skin temperature measured by the sensor was 0.6°C lower than that measured by the radiant warmer probe (sensor =35.9 vs warmer probe = 36.5; p < 0.001). The neonate with HIE provided a total of 25 paired readings with the mean sensor reading being not different from the radian warmer probe reading (sensor =33.5 vs warmer probe = 33.5; p=0.8). No major adverse events were noted in both the adults and neonates; four adult volunteers reported mild sweating under the device/arm band and one volunteer developed mild skin allergy. This proof-of-concept study shows that real-time monitoring of temperatures is technically feasible and that this innovation appears to be promising in terms of both safety and accuracy (with appropriate calibration) for improved maternal and neonatal health.

Keywords: public health, remote biomonitoring, temperature surveillance, wearable sensors, mothers and newborns

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Authors: Won-Ho Kim

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In this paper, a design of H.263 based wireless video transceiver is presented for wireless camera system. It uses standard WIFI transceiver and the covering area is up to 100m. Furthermore the standard H.263 video encoding technique is used for video compression since wireless video transmitter is unable to transmit high capacity raw data in real time and the implemented system is capable of streaming at speed of less than 1Mbps using NTSC 720x480 video.

Keywords: wireless video transceiver, video surveillance camera, H.263 video encoding digital signal processing

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Authors: Pyung Soo Kim, Eung Hyuk Lee, Mun Suck Jang

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In the current paper, an indoor positioning system is proposed with consideration of measurement delay. Firstly, an estimation filter with a measurement delay is designed for the indoor positioning mechanism under a weighted least square criterion, which utilizes only finite measurements on the most recent window. The proposed estimation filtering based scheme gives the filtered estimates for position, velocity and acceleration of moving target in real-time, while removing undesired noisy effects and preserving desired moving positions. Secondly, the proposed scheme is shown to have good inherent properties such as unbiasedness, efficiency, time-invariance, deadbeat, and robustness due to the finite memory structure. Finally, computer simulations shows that the performance of the proposed estimation filtering based scheme can outperform to the existing infinite memory filtering based mechanism.

Keywords: indoor positioning system, wireless sensor networks, measurement delay

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Authors: Darren A. Whitaker, Dan Palmer, Jens Wesholowski, James Flaherty, John Mack, Ahmad B. Albadarin, Gavin Walker

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Traditional mechanosynthesis has been performed by either ball milling or manual grinding. However, neither of these techniques allow the easy application of process control. The temperature may change unpredictably due to friction in the process. Hence the amount of energy transferred to the reactants is intrinsically non-uniform. Recently, it has been shown that the use of Twin-Screw extrusion (TSE) can overcome these limitations. Additionally, TSE enables a platform for continuous synthesis or manufacturing as it is an open-ended process, with feedstocks at one end and product at the other. Several materials including metal-organic frameworks (MOFs), co-crystals and small organic molecules have been produced mechanochemically using TSE. The described advantages of TSE are offset by drawbacks such as increased process complexity (a large number of process parameters) and variation in feedstock flow impacting on product quality. To handle the above-mentioned drawbacks, this study utilizes UV-Vis spectrophotometry (InSpectroX, ColVisTec) as an online tool to gain real-time information about the quality of the product. Additionally, this is combined with real-time process information in an Advanced Process Control system (PharmaMV, Perceptive Engineering) allowing full supervision and control of the TSE process. Further, by characterizing the dynamic behavior of the TSE, a model predictive controller (MPC) can be employed to ensure the process remains under control when perturbed by external disturbances. Two reactions were studied; a Knoevenagel condensation reaction of barbituric acid and vanillin and, the direct amidation of hydroquinone by ammonium acetate to form N-Acetyl-para-aminophenol (APAP) commonly known as paracetamol. Both reactions could be carried out continuously using TSE, nuclear magnetic resonance (NMR) spectroscopy was used to confirm the percentage conversion of starting materials to product. This information was used to construct partial least squares (PLS) calibration models within the PharmaMV development system, which relates the percent conversion to product to the acquired UV-Vis spectrum. Once this was complete, the model was deployed within the PharmaMV Real-Time System to carry out automated optimization experiments to maximize the percentage conversion based on a set of process parameters in a design of experiments (DoE) style methodology. With the optimum set of process parameters established, a series of PRBS process response tests (i.e. Pseudo-Random Binary Sequences) around the optimum were conducted. The resultant dataset was used to build a statistical model and associated MPC. The controller maximizes product quality whilst ensuring the process remains at the optimum even as disturbances such as raw material variability are introduced into the system. To summarize, a combination of online spectral monitoring and advanced process control was used to develop a robust system for optimization and control of two TSE based mechanosynthetic processes.

Keywords: continuous synthesis, pharmaceutical, spectroscopy, advanced process control

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Authors: Kung-Jen Tu, Danny Vernatha

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To assist individual departments within universities in their energy management tasks, this study explores the application of Building Information Modeling in establishing the ‘BIM based Energy Management Support System’ (BIM-EMSS). The BIM-EMSS consists of six components: (1) sensors installed for each occupant and each equipment, (2) electricity sub-meters (constantly logging lighting, HVAC, and socket electricity consumptions of each room), (3) BIM models of all rooms within individual departments’ facilities, (4) data warehouse (for storing occupancy status and logged electricity consumption data), (5) building energy management system that provides energy managers with various energy management functions, and (6) energy simulation tool (such as eQuest) that generates real time 'standard energy consumptions' data against which 'actual energy consumptions' data are compared and energy efficiency evaluated. Through the building energy management system, the energy manager is able to (a) have 3D visualization (BIM model) of each room, in which the occupancy and equipment status detected by the sensors and the electricity consumptions data logged are displayed constantly; (b) perform real time energy consumption analysis to compare the actual and standard energy consumption profiles of a space; (c) obtain energy consumption anomaly detection warnings on certain rooms so that energy management corrective actions can be further taken (data mining technique is employed to analyze the relation between space occupancy pattern with current space equipment setting to indicate an anomaly, such as when appliances turn on without occupancy); and (d) perform historical energy consumption analysis to review monthly and annually energy consumption profiles and compare them against historical energy profiles. The BIM-EMSS was further implemented in a research lab in the Department of Architecture of NTUST in Taiwan and implementation results presented to illustrate how it can be used to assist individual departments within universities in their energy management tasks.

Keywords: database, electricity sub-meters, energy anomaly detection, sensor

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Authors: Eyup Dogan

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This empirical aims to analyze the impacts of real income and biomass energy consumption on the level of emissions in the EKC model for the panel of biomass-consuming countries over the period 1980-2011. Because we detect the presence of cross-sectional dependence and heterogeneity across countries for the analyzed data, we use panel estimation methods robust to cross-sectional dependence and heterogeneity. The CADF and the CIPS panel unit root tests indicate that carbon emissions, real income and biomass energy consumption are stationary at the first-differences. The LM bootstrap panel cointegration test shows that the analyzed variables are cointegrated. Results from the panel group-mean DOLS and the panel group-mean FMOLS estimators show that increase in biomass energy consumption decreases CO2 emissions and the EKC hypothesis is validated. Therefore, countries are advised to boost their production and increase the use of biomass energy for lower level of emissions.

Keywords: biomass energy, CO2 emissions, EKC model, heterogeneity, cross-sectional dependence

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Authors: Ziaullah, Shah Wahab Ali

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Micro-grid equipped with heterogeneous energy resources present the idea of small scale distributed energy management (DEM). DEM helps in minimizing the transmission and operation costs, power management and peak load demands. Micro-grids are collections of small, independent controllable power-generating units and renewable energy resources. Micro-grids also motivate to enable active customer participation by giving accessibility of real-time information and control to the customer. The capability of fast restoration against faulty situation, integration of renewable energy resources and Information and Communication Technologies (ICT) make micro-grid as an ideal system for distributed power systems. Micro-grids can have a bank of energy storage devices. The energy management system of micro-grid can perform real-time energy forecasting of renewable resources, energy storage elements and controllable loads in making proper short-term scheduling to minimize total operating costs. We present a review of existing micro-grids optimization objectives/goals, constraints, solution approaches and tools used in micro-grids for energy management. Cost-benefit analysis of micro-grid reveals that cooperation among different micro-grids can play a vital role in the reduction of import energy cost and system stability. Cooperative micro-grids energy trading is an approach to electrical distribution energy resources that allows local energy demands more control over the optimization of power resources and uses. Cooperation among different micro-grids brings the interconnectivity and power trading issues. According to the literature, it shows that open area of research is available for cooperative micro-grids energy trading. In this paper, we proposed and formulated the efficient energy management/trading module for interconnected micro-grids. It is believed that this research will open new directions in future for energy trading in cooperative micro-grids/interconnected micro-grids.

Keywords: distributed energy management, information and communication technologies, microgrid, energy management

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Authors: David S. Rosenstein

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The author believes that modern spoken languages should be equally difficult (or easy) to learn, since all normal children learning their native languages do so at approximately the same rate and with the same competence, progressing from easy to more complex grammar and syntax in the same way. Why then, do some languages seem more difficult than others? Perhaps people are referring to the written language, where it may be true that mastering Chinese requires more time than French, which in turn requires more time than Spanish. But this may be marginal, since Chinese and French children quickly catch up to their Spanish peers in reading comprehension. Rather, the real differences in difficulty derive from two sources: hardened L1 language habits trying to cope with contrasting L2 habits; and unfamiliarity with unique L2 characteristics causing faulty expectations. It would seem that effective L2 teaching and learning must take these two sources of difficulty into consideration. The author feels that the latter (faulty expectations) causes the greatest difficulty, making effective teaching and learning somewhat different for each given foreign language. Examples from Chinese and other languages are presented.

Keywords: learning different languages, language learning difficulties, faulty language expectations

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Authors: Rahul Paul, Peter Mctaggart, Luke Skinner

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Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.

Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry

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Authors: L. Fridrichova, R. Knížek, V. Bajzík

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Drape is one of the important visual characteristics of the fabric. This paper is introducing an innovative method of measurement and evaluation of the drape shape of the fabric. The measuring principle is based on the possibility of multiple vertical strain of the fabric. This method more accurately simulates the real behavior of the fabric in the process of draping. The method is fully automated, so the sample can be measured by using any number of cycles in any time horizon. Using the present method of measurement, we are able to describe the viscoelastic behavior of the fabric.

Keywords: drape, drape shape, automated drapemeter, fabric

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Authors: Rohita Gupta, G. S. Brah, R. Verma, C. S. Mukhopadhayay

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Although chicken strains show differences in susceptibility to a number of diseases, the underlying immunological basis is yet to be elucidated. In the present study, heterophils were subjected to LPS stimulation and total RNA extraction, further differential gene expression was studied in broiler, layer and indigenous Aseel strain by Real Time RT-PCR at different time periods before and after induction. The expression of the 14 AvBDs and chTLR 1, 2, 3, 4, 5, 7, 15 and 21 was detectable in heterophils. The expression level of most of the AvBDs significantly increased (P<0.05) 3 hours post in vitro lipopolysaccharide challenge. Higher expression level and stronger activation of most AvBDs, NFkB-1 and IRF-3 in heterophils was observed, with the stimulation of LPS in layer compared to broiler, and in Aseel compared to both layer and broiler. This investigation will allow more refined interpretation of immuno-genetic basis of the variable disease resistance/susceptibility in divergent stock of chicken including indigenous breed. Moreover this study will be helpful in formulation of strategy for isolation of antimicrobial peptides from heterophils.

Keywords: differential expression, heterophils, cytokines, defensin, TLR

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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

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Authors: Sa-Kil Kim, Jang-Soo Lee

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On March 31, 1993, a severe fire accident took place in a nuclear power plant located in Narora in North India. The event involved a major fire in the turbine building of NAPS unit-1 and resulted in a total loss of power to the unit for 17 hours. In addition, there was a heavy ingress of smoke in the control room, mainly through the intake of the ventilation system, forcing the operators to vacate the control room. The Narora fire accident provides us lessons indicating that operators could lose their mind and predictable behaviors during a fire. After the Fukushima accident, which resulted from a natural disaster, unanticipated external events are also required to be prepared and controlled for the ultimate safety of nuclear power plants. From last year, our research team has developed a test and evaluation facility that can simulate external events such as an earthquake and fire based on the operators’ real-sense. As one of the results of the project, we proposed a unit real-sense-based facility that can simulate fire events in a control room for utilizing a test-bed of human factor validation. The test-bed has the operator’s workstation shape and functions to simulate fire conditions such as smoke, heat, and auditory alarms in accordance with the prepared fire scenarios. Furthermore, the test-bed can be used for the operators’ training and experience.

Keywords: human behavior in fire, human factors validation, nuclear power plants, real-sense-based fire simulation

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Authors: Amir Seyfoori, Ehsan Samiei, Mohsen Akbari

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The use of microtechnology for detection and high yield isolation of circulating tumor cells (CTCs) has shown enormous promise as an indication of clinical metastasis prognosis and cancer treatment monitoring. The Immunomagnetic assay has been also coupled to microtechnology to improve the selectivity and efficiency of the current methods of cancer biomarker isolation. In this way, generation and configuration of the local high gradient magnetic field play essential roles in such assay. Additionally, considering the intrinsic heterogeneity of cancer cells, real-time analysis of isolated cells is necessary to characterize their responses to therapy. Totally, on-chip isolation and monitoring of the specific tumor cells is considered as a pressing need in the way of modified cancer therapy. To address these challenges, we have developed a bi-layer magnetic-based microfluidic chip for enhanced CTC detection and capturing. Micromagnet arrays at the bottom layer of the chip were fabricated using a new method of magnetic nanoparticle paste deposition so that they were arranged at the center of the chain microchannel with the lowest fluid velocity zone. Breast cancer cells labelled with EPCAM-conjugated smart microgels were immobilized on the tip of the micromagnets with greater localized magnetic field and stronger cell-micromagnet interaction. Considering different magnetic nano-powder usage (MnFe2O4 & gamma-Fe2O3) and micromagnet shapes (ellipsoidal & arrow), the capture efficiency of the systems was adjusted while the higher CTC capture efficiency was acquired for MnFe2O4 arrow micromagnet as around 95.5%. As a proof of concept of on-chip tumor cell monitoring, magnetic smart microgels made of thermo-responsive poly N-isopropylacrylamide-co-acrylic acid (PNIPAM-AA) composition were used for both purposes of targeted cell capturing as well as cell monitoring using antibody conjugation and fluorescent dye loading at the same time. In this regard, magnetic microgels were successfully used as cell tracker after isolation process so that by raising the temperature up to 37⁰ C, they released the contained dye and stained the targeted cell just after capturing. This microfluidic device was able to provide a platform for detection, isolation and efficient real-time analysis of specific CTCs in the liquid biopsy of breast cancer patients.

Keywords: circulating tumor cells, microfluidic, immunomagnetic, cell isolation

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Authors: Rachel Goh, Alexander McNab, Brent Alsop, David O'Hare

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In a busy airport, the decision whether to take passengers aside and search their luggage for dangerous items can have important consequences. If an officer fails to search and stop a bag containing a dangerous object, a life-threatening incident might occur. But stopping a bag unnecessarily means that the officer might lose time searching the bag and face an angry passenger. Passengers’ bags, however, are often cluttered with personal belongings of varying shapes and sizes. It can be difficult to determine what is dangerous or not, especially if the decisions must be made quickly in cases of busy flight schedules. Additionally, the decision to search bags is often made with input from the surrounding officers on duty. This scenario raises several questions: 1) Past findings suggest that humans are more reliant on an automated aid when under time pressure in a visual search task, but does this translate to human-human reliance? 2) Are humans more likely to agree with another person if the person is assumed to be an expert or a novice in these ambiguous situations? In the present study, forty-one participants performed a simulated luggage-screening task. They were partnered with an advisor of two different statuses (expert vs. novice), but of equal accuracy (90% correct). Participants made two choices each trial: their first choice with no advisor input, and their second choice after advisor input. The second choice was made within either 2 seconds or 8 seconds; failure to do so resulted in a long time-out period. Under the 2-second time pressure, participants were more likely to disagree with their own first choice and agree with the expert advisor, regardless of whether the expert was right or wrong, but especially when the expert suggested that the bag was safe. The findings indicate a tendency for people to assume less responsibility for their decisions and defer to their partner, especially when a quick decision is required. This over-reliance on others’ opinions might have negative consequences in real life, particularly when relying on fallible human judgments. More awareness is needed regarding how a stressful environment may influence reliance on other’s opinions, and how better techniques are needed to make the best decisions under high stress and time pressure.

Keywords: advisors, decision-making, time pressure, trust

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Authors: Xuewei Li, Jingfeng Yuan, Jianliang Zhou

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The 21st century has been called the century of human exploitation of underground space. Due to the characteristics of large quantity, tight schedule, low safety reserve and high uncertainty of deep foundation pit engineering, accidents frequently occur in deep foundation pit engineering, causing huge economic losses and casualties. With the successful application of information technology in the construction industry, building information modeling has become a research hotspot in the field of architectural engineering. Therefore, the application of building information modeling (BIM) and other information communication technologies (ICTs) in construction safety management is of great significance to improve the level of safety management. This research summed up the mechanism of the deep foundation pit engineering accident through the fault tree analysis to find the control factors of deep foundation pit engineering safety management, the deficiency existing in the traditional deep foundation pit construction site safety management. According to the accident cause mechanism and the specific process of deep foundation pit construction, the hazard information of deep foundation pit engineering construction site was identified, and the hazard list was obtained, including early warning information. After that, the system framework was constructed by analyzing the early warning information demand and early warning function demand of the safety management system of deep foundation pit. Finally, the safety management system of deep foundation pit construction site based on BIM through combing the database and Web-BIM technology was developed, so as to realize the three functions of real-time positioning of construction site personnel, automatic warning of entering a dangerous area, real-time monitoring of deep foundation pit structure deformation and automatic warning. This study can initially improve the current situation of safety management in the construction site of deep foundation pit. Additionally, the active control before the occurrence of deep foundation pit accidents and the whole process dynamic control in the construction process can be realized so as to prevent and control the occurrence of safety accidents in the construction of deep foundation pit engineering.

Keywords: Web-BIM, safety management, deep foundation pit, construction

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Shreya Asthana

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Testers know that the feature they tested on stage is working perfectly in production only after release went live. Sometimes something breaks in production and testers get to know through the end user’s bug raised. The panic mode starts when your staging test results do not reflect current production behavior. And you started doubting your testing skills when finally the user reported a bug to you. Testers can deploy their confidence on release day by testing on production. Once you start doing testing in production, you will see test result accuracy because it will be running on real time data and execution will be a little faster as compared to staging one due to elimination of bad data. Feature flagging, canary releases, and data cleanup can help to achieve this technique of testing. By this paper it will be easier to understand the steps to achieve production testing before making your feature live, and to modify IT company’s testing procedure, so testers can provide the bug free experience to the end users. This study is beneficial because too many people think that testing should be done in staging but not in production and now this is high time to pull out people from their old mindset of testing into a new testing world. At the end of the day, it all just matters if the features are working in production or not.

Keywords: bug free production, new testing mindset, testing strategy, testing approach

Procedia PDF Downloads 75

Authors: Minas Balyan

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In the third-order nonlinear Takagi’s equations for monochromatic waves and in the third-order nonlinear time-dependent dynamical diffraction equations for X-ray pulses for forbidden reflections the Fourier-coefficients of the linear and the third order nonlinear susceptibilities are zero. The dynamical diffraction in the nonlinear case is related to the presence in the nonlinear equations the terms proportional to the zero order and the second order nonzero Fourier coefficients of the third order nonlinear susceptibility. Thus in the third order nonlinear Bragg diffraction case a nonlinear analogue of the well known Renninger effect takes place. In this work, the ‘third order nonlinear Renninger effect’ is considered theoretically and numerically. If the reflection exactly is forbidden the diffracted wave’s amplitude is zero both in Laue and Bragg cases since the boundary conditions and dynamical diffraction equations are compatible with zero solution. But in real crystals due to some percent of dislocations and other localized defects, the atoms are displaced with respect to their equilibrium positions. Thus in real crystals susceptibilities of forbidden reflection are by some order small than for usual not forbidden reflections but are not exactly equal to zero. The numerical calculations for susceptibilities two order less than for not forbidden reflection show that in Bragg geometry case the nonlinear reflection curve’s behavior is the same as for not forbidden reflection, but for forbidden reflection the rocking curves’ width, center and boundaries are two order sensitive on the input intensity value. This gives an opportunity to investigate third order nonlinear X-ray dynamical diffraction for not intense beams – 0.001 in the units of critical intensity.

Keywords: third order nonlinearity, Bragg diffraction, nonlinear Renninger effect, rocking curves

Procedia PDF Downloads 405

Authors: Evgenia Iakovleva, Juha Koivisto, Pasi Karppinen, J. Inkinen, Mikko Alava

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Preventing the spread of infectious diseases throughout the worldwide is one of the most important tasks of modern health care. Infectious diseases not only account for one fifth of the deaths in the world, but also cause many pathological complications for the human health. Touch surfaces pose an important vector for the spread of infections by varying microorganisms, including antimicrobial resistant organisms. Further, antimicrobial resistance is reply of bacteria to the overused or inappropriate used of antibiotics everywhere. The biggest challenges in bacterial detection by existing methods are non-direct determination, long time of analysis, the sample preparation, use of chemicals and expensive equipment, and availability of qualified specialists. Therefore, a high-performance, rapid, real-time detection is demanded in rapid practical bacterial detection and to control the epidemiological hazard. Among the known methods for determining bacteria on the surfaces, Hyperspectral methods can be used as direct and rapid methods for microorganism detection on different kind of surfaces based on fluorescence without sampling, sample preparation and chemicals. The aim of this study was to assess the relevance of such systems to remote sensing of surfaces for microorganisms detection to prevent a global spread of infectious diseases. Bacillus subtilis and Escherichia coli with different concentrations (from 0 to 10x8 cell/100µL) were detected with hyperspectral camera using different filters as visible visualization of bacteria and background spots on the steel plate. A method of internal standards was applied for monitoring the correctness of the analysis results. Distances from sample to hyperspectral camera and light source are 25 cm and 40 cm, respectively. Each sample is optically imaged from the surface by hyperspectral imaging system, utilizing a JAI CM-140GE-UV camera. Light source is BeamZ FLATPAR DMX Tri-light, 3W tri-colour LEDs (red, blue and green). Light colors are changed through DMX USB Pro interface. The developed system was calibrated following a standard procedure of setting exposure and focused for light with λ=525 nm. The filter is ThorLabs KuriousTM hyperspectral filter controller with wavelengths from 420 to 720 nm. All data collection, pro-processing and multivariate analysis was performed using LabVIEW and Python software. The studied human eye visible and invisible bacterial stains clustered apart from a reference steel material by clustering analysis using different light sources and filter wavelengths. The calculation of random and systematic errors of the analysis results proved the applicability of the method in real conditions. Validation experiments have been carried out with photometry and ATP swab-test. The lower detection limit of developed method is several orders of magnitude lower than for both validation methods. All parameters of the experiments were the same, except for the light. Hyperspectral imaging method allows to separate not only bacteria and surfaces, but also different types of bacteria, such as Gram-negative Escherichia coli and Gram-positive Bacillus subtilis. Developed method allows skipping the sample preparation and the use of chemicals, unlike all other microbiological methods. The time of analysis with novel hyperspectral system is a few seconds, which is innovative in the field of microbiological tests.

Keywords: Escherichia coli, Bacillus subtilis, hyperspectral imaging, microorganisms detection

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Authors: Wedad Albalawi

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The mathematical inequalities have been the core of mathematical study and used in almost all branches of mathematics as well in various areas of science and engineering. The inequalities by Hardy, Littlewood and Polya were the first significant composition of several science. This work presents fundamental ideas, results and techniques, and it has had much influence on research in various branches of analysis. Since 1934, various inequalities have been produced and studied in the literature. Furthermore, some inequalities have been formulated by some operators; in 1989, weighted Hardy inequalities have been obtained for integration operators. Then, they obtained weighted estimates for Steklov operators that were used in the solution of the Cauchy problem for the wave equation. They were improved upon in 2011 to include the boundedness of integral operators from the weighted Sobolev space to the weighted Lebesgue space. Some inequalities have been demonstrated and improved using the Hardy–Steklov operator. Recently, a lot of integral inequalities have been improved by differential operators. Hardy inequality has been one of the tools that is used to consider integrity solutions of differential equations. Then, dynamic inequalities of Hardy and Coposon have been extended and improved by various integral operators. These inequalities would be interesting to apply in different fields of mathematics (functional spaces, partial differential equations, mathematical modeling). Some inequalities have been appeared involving Copson and Hardy inequalities on time scales to obtain new special version of them. A time scale is an arbitrary nonempty closed subset of the real numbers. Then, the dynamic inequalities on time scales have received a lot of attention in the literature and has become a major field in pure and applied mathematics. There are many applications of dynamic equations on time scales to quantum mechanics, electrical engineering, neural networks, heat transfer, combinatorics, and population dynamics. This study focuses on Hardy and Coposon inequalities, using Steklov operator on time scale in double integrals to obtain special cases of time-scale inequalities of Hardy and Copson on high dimensions. The advantage of this study is that it uses the one-dimensional classical Hardy inequality to obtain higher dimensional on time scale versions that will be applied in the solution of the Cauchy problem for the wave equation. In addition, the obtained inequalities have various applications involving discontinuous domains such as bug populations, phytoremediation of metals, wound healing, maximization problems. The proof can be done by introducing restriction on the operator in several cases. The concepts in time scale version such as time scales calculus will be used that allows to unify and extend many problems from the theories of differential and of difference equations. In addition, using chain rule, and some properties of multiple integrals on time scales, some theorems of Fubini and the inequality of H¨older.

Keywords: time scales, inequality of hardy, inequality of coposon, steklov operator

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Authors: Lorena Caires Moreira, Marcos Kauffman

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This paper outlines the results of experimental research on deploying an emerging augmented reality (AR) system for real-time task assistance of highly customized and high-risk manual operations. The focus is on operators’ training capabilities and the aim is to test if such technologies can support achieving higher levels of knowledge retention and accuracy of task execution to improve health and safety (H and S) levels. The proposed solution is tested and validated using a real-world case study of electric vehicles’ battery module assembly. The experimental results revealed that the proposed AR method improved the training practices by increasing the knowledge retention levels from 40% to 84% and improved the accuracy of task execution from 20% to 71%, compared to the traditional paper-based method. The results of this research can be used as a demonstration of how emerging technologies are advancing the choice of manual, hybrid, or fully automated processes by promoting the connected worker (Industry 5.0) and supporting manufacturing in becoming more resilient in times of constant market changes.

Keywords: augmented reality, extended reality, connected worker, XR-assisted operator, manual assembly, industry 5.0, smart training, battery assembly

Procedia PDF Downloads 126

Authors: Hyun-Woo Cho

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It is quite important to utilize right time and intelligent production monitoring and diagnosis of industrial processes in terms of quality and safety issues. When compared with monitoring task, fault diagnosis represents the task of finding process variables responsible causing a specific fault in the process. It can be helpful to process operators who should investigate and eliminate root causes more effectively and efficiently. This work focused on the active use of combining a nonlinear statistical technique with a preprocessing method in order to implement practical real-time fault identification schemes for data-rich cases. To compare its performance to existing identification schemes, a case study on a benchmark process was performed in several scenarios. The results showed that the proposed fault identification scheme produced more reliable diagnosis results than linear methods. In addition, the use of the filtering step improved the identification results for the complicated processes with massive data sets.

Keywords: diagnosis, filtering, nonlinear statistical techniques, process monitoring

Procedia PDF Downloads 241

Authors: Shahira Mahmoud Yacout

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Writing is an essential skill in many vocational, academic environments, and notably workplaces, yet many students perceive writing as being something tiring and boring or maybe a “waste of time”. Studies in the field of foreign languages related this fact might be due to the lack of connection between what is learned in the university and what students come to encounter in real life situations”. Arabic learners felt they needed more language exposure to the context of their future professions. With this idea in mind, Scenario based learning (SBL) is reported to be an educational approach to motivate, engage and stimulate students’ interest and to achieve the desired writing learning outcomes. In addition, researchers suggested Scenario based learning (SBL)as an instructional approach that develops and enhances students skills through developing higher order thinking skills and active learning. It is a subset of problem-based learning and case-based learning. The approach focuses on authentic rhetorical framing reflecting writing tasks in real life situations. It works successfully when used to simulate real-world practices, providing context that reflects the types of situations professionals respond to in writing. It was claimed that using realistic scenarios customized to the course’s learning objectives as it bridged the gap for students between theory and application. Within this context, it is thought that scenario-based learning is an important approach to enhance the learners’ writing skills and to reflect meaningful learning within authentic contexts. As an Arabicforeign language instructor, it was noticed that students find difficulties in adapting writing styles to authentic writing contexts and addressing different audiences and purposes. This idea is supported by studieswho claimed that AFL students faced difficulties with transferring writing skills to situations outside of the classroom context. In addition, it was observed that some of the Arabic textbooks for teaching Arabic as a foreign language lacked topics that initiated higher order thinking skills and stimulated the learners to understand the setting, and created messages appropriate to different audiences, context, and purposes. The goals of this study are to 1)provide a rational for using scenario-based learning approach to improveAFL learners in writing skills, 2) demonstrate how to design/ implement a scenario-based learning technique aligned with the writing course objectives,3) demonstrate samples of scenario-based approach implemented in AFL writing class, and 4)emphasis the role of peer-review along with the instructor’s feedback, in the process of developing the writing skill. Finally, this presentation highlighted and emphasized the importance of using the scenario-based learning approach in writing as a means to mirror students’ real-life situations and engage them in planning, monitoring, and problem solving. This approach helped in making writing an enjoyable experience and clearly useful to students’ future professional careers.

Keywords: meaningful learning, real life contexts, scenario based learning, writing skill

Procedia PDF Downloads 98

Authors: K. Shiba, T. Kaburagi, Y. Kurihara

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With global aging, people who require care, such as people with dementia (PwD), are increasing within many developed countries. And PwDs may wander and unconsciously set foot outdoors, it may lead serious accidents, such as, traffic accidents. Here, round-the-clock monitoring by caregivers is necessary, which can be a burden for the caregivers. Therefore, an automatic wandering detection system is required when an elderly person wanders outdoors, in which case the detection system transmits a ‘moving’ followed by an ‘absence’ state. In this paper, we focus on the transition from the ‘resting’ to the ‘absence’ state, via the ‘moving’ state as one of the wandering transitions. To capture the transition of the three states, our method based on the hidden Markov model (HMM) is built. Using our method, the restraint where the ‘resting’ state and ‘absence’ state cannot be transmitted to each other is applied. To validate our method, we conducted the experiment with 10 subjects. Our results show that the method can classify three states with 0.92 accuracy.

Keywords: wander, microwave Doppler sensor, respiratory frequency band, the state transition, hidden Markov model (HMM).

Procedia PDF Downloads 183