Search results for: multivariate time series data

Authors: K. S. Bang, S. H. Yu, J. C. Lee, K. S. Seo, S. H. Lee

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Dual purpose casks are used for storage and transport of spent nuclear fuel assemblies. Therefore, they satisfy the requirements prescribed in the Korea NSSC Act 2013-27, the IAEA Safety Standard Series No. SSR-6, and US 10 CFR Part 71. These regulatory guidelines classify the dual purpose cask as a Type B package, and state that a Type B package must be able to withstand a temperature of 800°C for a period of 30 min. Therefore, a fire test was conducted using a one-sixth slice of a real cask to estimate the thermal integrity of the dual purpose cask at a temperature of 800°C. The neutron shield reached a maximum temperature of 183°C, which indicates that dual purpose cask was properly insulated from the heat of the flames. The temperature rise of the basket during the fire test was 29°C. Therefore, the integrity of a spent nuclear fuel is estimated to be maintained. The temperature was lower when a cooling pin was installed. The neutron shielding was therefore protected adequately by cooling pin. As a result, the thermal integrity of the dual purpose cask was maintained and the cask is judged to be sufficiently safe for temperatures under 800°C.

Keywords: dual purpose cask, spent nuclear fuel, pool fire test, integrity

Procedia PDF Downloads 448

Authors: Abir Derouiche, Abdesslem Layeb

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Association Rule Mining (ARM) is one of the most popular data mining tasks and it is widely used in various areas. The search for association rules is an NP-complete problem that is why metaheuristics have been widely used to solve it. The present paper presents the ARM as an optimization problem and surveys the proposed approaches in the literature based on metaheuristics.

Keywords: Optimization, Metaheuristics, Data Mining, Association rules Mining

Procedia PDF Downloads 144

Authors: Nighat Bakhtiar, Ali Raza Khan, Shahid Khan Khattak, Aamir Ali Syed

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Introduction: Chemoradiation followed by resection has been the standard therapy for resectable (cT1-4aN0-3M0) esophageal carcinoma. The optimal surgical approach remains a matter of debate. Therefore, the purpose of this study was to share our experiences of minimal invasive esophagectomies concerning morbidity, mortality and oncological quality. This study aims to enlighten the world about the surgical outcomes after minimally invasive esophagectomy at Shaukat Khanum Hospital Lahore. Objective: The purpose of this study is to review an institutional experience of Surgical outcomes of Minimal Invasive esophagectomies for esophageal cancer. Methodology: This retrospective study was performed after ethical approval at Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH&RC) Pakistan. Patients who underwent Minimal Invasive esophagectomies for esophageal cancer from March 2018 to March 2023 were selected. Data was collected through the human information system (HIS) electronic database of SKMCH&RC. Data was described using mean and median with minimum and maximum values for quantitative variables. For categorical variables, a number of observations and percentages were reported. Results: A total of 621 patients were included in the study, with the mean age of the patient was 39 years, ranging between 18-58 years. Mean Body Mass Index of patients was 21.2.1±4.1. Neo-adjuvant chemoradiotherapy was given to all patients. The mean operative time was 210.36 ± 64.51 minutes, and the mean blood loss was 121 milliliters. There was one mortality in 90 days, while the mean postoperative hospital stay was 6.58 days with a 4.64 standard deviation. The anastomotic leak rate was 4.2%. Chyle leak was observed in 12 patients. Conclusion: The minimal invasive technique is a safe approach for esophageal cancers, with minimal complications and fast recovery.

Keywords: minimal invasive, esophagectomy, laparscopic, cancer

Procedia PDF Downloads 50

Authors: Mohammad Alibakhshikenari, Ernesto Limiti, Bal S. Virdee

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The paper presents a miniature wideband antenna using composite right/left-handed transmission-line (CRLH-TL) metamaterial. The proposed planar antenna has a fractional bandwidth of 100% and is designed to operate in several frequency bands from 800MHz to 2.40GHz. The antenna is constructed using just two CRLH-TL unit cells comprising of two T-shaped slots that are inverted. The slots contribute towards generating the series left-handed (LH) capacitance CL. The rectangular patch on which the slots are created is grounded with spiral shaped high impedance stubs that contribute towards LH inductance LL. The antenna has a size of 14×6×1.6mm3 (0.037λ0×0.016λ0× 0.004λ0, where λ0 is free space wavelength at 800MHz). The peak gain and efficiency of the antenna are 1.5 dBi and ~75%, respectively, at 1.6GHz. Proposed antenna is suitable for use in wireless systems working at UHF/L/S-bands, in particular, AMPS, GSM, WCDMA, UMTS, PCS, cellular, DCS, IMT-2000, JCDMA, KPCS, GPS, lower band of WiMAX.

Keywords: miniature antenna, composite right/left-handed transmission line (CRLH-TL), wideband antenna, communication transceiver, metamaterials

Procedia PDF Downloads 206

Authors: Gagandeep Singh, Navdeep Singh

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Due to limited battery of sensor nodes, so energy efficiency found to be main constraint in WSN. Therefore the main focus of the present work is to find the ways to minimize the energy consumption problem and will results; enhancement in the network stability period and life time. Many researchers have proposed different kind of the protocols to enhance the network lifetime further. This paper has evaluated the issues which have been neglected in the field of the WSNs. WSNs are composed of multiple unattended ultra-small, limited-power sensor nodes. Sensor nodes are deployed randomly in the area of interest. Sensor nodes have limited processing, wireless communication and power resource capabilities Sensor nodes send sensed data to sink or Base Station (BS). I-LEACH gives adaptive clustering mechanism which very efficiently deals with energy conservations. This paper ends up with the shortcomings of various adaptive clustering based WSNs protocols.

Keywords: WSN, I-Leach, MATLAB, sensor

Procedia PDF Downloads 257

Authors: Seul-Ki Park, Jong-Chun Park, Gyu-Mok Jeon, Dae-Kyung Ock, Seung-Gyu Jeong

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Rogue waves cause frequent accidents of ships and offshore structures, which can result in severe damage to the structures. The Rogue waves, which are also known as big waves, freak waves, extreme waves, monster waves, focused waves, giant waves and abnormal waves, are unexpected and suddenly appearing, and can have a breaking force to destroy the structure even though modern structures are designed to tolerate a breaking wave. In the present study, a series of focused waves are numerically reproduced by concentrating nonlinear multi-directional waves into a target point using a commercial CFD software, Star-CCM+. A flow analysis for investigating the physical characteristics of the focused waves is performed using the Star-CCM+, while it has several difficulties to examine the inner properties of the waves in existing potential theory and experiments. Additionally, the 6-DOF (Degree of Freedom) motion of a floating body interacting with the focused waves are simulated, and the dynamic response of the body are discussed.

Keywords: multidirectional waves, focused waves, rogue waves, wave-structure interaction, numerical wave tank, computational fluid dynamics

Procedia PDF Downloads 231

Authors: Mohadeseh Shojaei Shahrokhabadi, Ding-Geng (Din) Chen

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Positive continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical cost data. To jointly model semi-continuous longitudinal cost data and survival data and to provide marginalized covariate effect estimates, a marginalized two-part joint model (MTJM) has been developed for outcome variables with lognormal distributions. In this paper, we propose MTJM models for outcome variables from a generalized gamma (GG) family of distributions. The GG distribution constitutes a general family that includes approximately all of the most frequently used distributions like the Gamma, Exponential, Weibull, and Log Normal. In the proposed MTJM-GG model, the conditional mean from a conventional two-part model with a three-parameter GG distribution is parameterized to provide the marginal interpretation for regression coefficients. In addition, MTJM-gamma and MTJM-Weibull are developed as special cases of MTJM-GG. To illustrate the applicability of the MTJM-GG, we applied the model to a set of real electronic health record data recently collected in Iran, and we provided SAS code for application. The simulation results showed that when the outcome distribution is unknown or misspecified, which is usually the case in real data sets, the MTJM-GG consistently outperforms other models. The GG family of distribution facilitates estimating a model with improved fit over the MTJM-gamma, standard Weibull, or Log-Normal distributions.

Keywords: marginalized two-part model, zero-inflated, right-skewed, semi-continuous, generalized gamma

Procedia PDF Downloads 160

Authors: Brady Manescau, Ilyas Sellami, Khaled Chetehouna, Charles De Izarra, Rachid Nait-Said, Fati Zidani

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During a fire in an oil and gas refinery, several thermal accidents can occur and cause serious damage to people and environment. Among these accidents, the BLEVE (Boiling Liquid Expanding Vapor Explosion) is most observed and remains a major concern for risk decision-makers. It corresponds to a violent vaporization of explosive nature following the rupture of a vessel containing a liquid at a temperature significantly higher than its normal boiling point at atmospheric pressure. Their effects on the environment generally appear in three ways: blast overpressure, radiation from the fireball if the liquid involved is flammable and fragment hazards. In order to estimate the potential damage that would be caused by such an explosion, risk decision-makers often use quantitative risk analysis (QRA). This analysis is a rigorous and advanced approach that requires a reliable data in order to obtain a good estimate and control of risks. However, in most cases, the data used in QRA are obtained from the empirical correlations. These empirical correlations generally overestimate BLEVE effects because they are based on simplifications and do not take into account real parameters like the geometry effect. Considering that these risk analyses are based on an assessment of BLEVE effects on human life and plant equipment, more precise and reliable data should be provided. From this point of view, the CFD modeling of BLEVE effects appears as a solution to the empirical law limitations. In this context, the main objective is to develop a numerical tool in order to predict BLEVE thermal effects using the CFD code FDS version 6. Simulations are carried out with a mesh size of 1 m. The fireball source is modeled as a vertical release of hot fuel in a short time. The modeling of fireball dynamics is based on a single step combustion using an EDC model coupled with the default LES turbulence model. Fireball characteristics (diameter, height, heat flux and lifetime) issued from the large scale BAM experiment are used to demonstrate the ability of FDS to simulate the various steps of the BLEVE phenomenon from ignition up to total burnout. The influence of release parameters such as the injection rate and the radiative fraction on the fireball heat flux is also presented. Predictions are very encouraging and show good agreement in comparison with BAM experiment data. In addition, a numerical study is carried out on an operational propane accumulator in an Algerian gas processing plant of SONATRACH company located in the Hassi R’Mel Gas Field (the largest gas field in Algeria).

Keywords: BLEVE effects, CFD, FDS, fireball, LES, QRA

Procedia PDF Downloads 172

Authors: Aqsa Jamil, Tamura Hiroshi, Katsuchi Hiroshi, Wang Jiaqi

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The yield point represents the upper limit of forces which can be applied to a specimen without causing any permanent deformation. After yielding, the behavior of the specimen suddenly changes, including the possibility of cracking or buckling. So, the accumulation of damage or type of fracture changes depending on this condition. As it is difficult to accurately detect yield points of the several stress concentration points in structural steel specimens, an effort has been made in this research work to develop a convenient technique using thermography (temperature-based detection) during tensile tests for the precise detection of yield point initiation. To verify the applicability of thermography camera, tests were conducted under different loading conditions and measuring the deformation by installing various strain gauges and monitoring the surface temperature with the help of a thermography camera. The yield point of specimens was estimated with the help of temperature dip, which occurs due to the thermoelastic effect during the plastic deformation. The scattering of the data has been checked by performing a repeatability analysis. The effects of temperature imperfection and light source have been checked by carrying out the tests at daytime as well as midnight and by calculating the signal to noise ratio (SNR) of the noised data from the infrared thermography camera, it can be concluded that the camera is independent of testing time and the presence of a visible light source. Furthermore, a fully coupled thermal-stress analysis has been performed by using Abaqus/Standard exact implementation technique to validate the temperature profiles obtained from the thermography camera and to check the feasibility of numerical simulation for the prediction of results extracted with the help of the thermographic technique.

Keywords: signal to noise ratio, thermoelastic effect, thermography, yield point

Procedia PDF Downloads 87

Authors: Stanislavs Gendelis, Maris Sinka, Andris Jakovics

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Insulation materials made from natural sources have become more popular for the ecologisation of buildings, meaning wide use of such renewable materials. Such natural materials replace synthetic products which consume a large quantity of energy. The most common and the cheapest natural materials in Latvia are cellulose-based (wood and agricultural plants). The ecological aspects of such materials are well known, but experimental data about physical properties remains lacking. In this study, six different samples of wood wool panels and a mixture of hemp shives and lime (hempcrete) are analysed. Thermal conductivity and heat capacity measurements were carried out for wood wool and cement panels using the calibrated hot plate device. Water vapor permeability was tested for hempcrete material by using the gravimetric dry cup method. Studied wood wool panels are eco-friendly and harmless material, which is widely used in the interior design of public and residential buildings, where noise absorption and sound insulation is of importance. They are also suitable for high humidity facilities (e.g., swimming pools). The difference in panels was the width of used wood wool, which is linked to their density. The results of measured thermal conductivity are in a wide range, showing the worsening of properties with the increasing of the wool width (for the least dense 0.066, for the densest 0.091 W/(m·K)). Comparison with mineral insulation materials shows that thermal conductivity for such materials are 2-3 times higher and are comparable to plywood and fibreboard. Measured heat capacity was in a narrower range; here, the dependence on the wool width was not so strong due to the fact that heat capacity value is related to mass, not volume. The resulting heat capacity is a combination of two main components. A comparison of results for different panels allows to select the most suitable sample for a specific application because the dependencies of the thermal insulation and heat capacity properties on the wool width are not the same. Hempcrete is a much denser material compared to conventional thermal insulating materials. Therefore, its use helps to reinforce the structural capacity of the constructional framework, at the same time, it is lightweight. By altering the proportions of the ingredients, hempcrete can be produced as a structural, thermal, or moisture absorbent component. The water absorption and water vapor permeability are the most important properties of these materials. Information about absorption can be found in the literature, but there are no data about water vapor transmission properties. Water vapor permeability was tested for a sample of locally made hempcrete using different air humidity values to evaluate the possible difference. The results show only the slight influence of the air humidity on the water vapor permeability value. The absolute ‘sd value’ measured is similar to mineral wool and wood fiberboard, meaning that due to very low resistance, water vapor passes easily through the material. At the same time, other properties – structural and thermal of the hempcrete is totally different. As a result, an experimentally-based knowledge of thermal and water vapor transmission properties for cellulose-based materials was significantly improved.

Keywords: heat capacity, hemp concrete, thermal conductivity, water vapor transmission, wood wool

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Authors: Jing Jiang, Wenhuan XU, Lei Zhang, Shiyi Zhang, Tongli Wang

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Climate data quality significantly affects the reliability of ecological modeling. In the Asia Pacific (AP) region, low-quality climate data hinders ecological modeling. ClimateAP, a software developed in 2017, generates high-quality climate data for the AP region, benefiting researchers in forestry and agriculture. However, its adoption remains limited. This study aims to confirm the validity of biologically relevant variable data generated by ClimateAP during the normal climate period through comparison with the currently available gridded data. Climate data from 2,366 weather stations were used to evaluate the prediction accuracy of ClimateAP in comparison with the commonly used gridded data from WorldClim1.4. Univariate regressions were applied to 48 monthly biologically relevant variables, and the relationship between the observational data and the predictions made by ClimateAP and WorldClim was evaluated using Adjusted R-Squared and Root Mean Squared Error (RMSE). Locations were categorized into mountainous and flat landforms, considering elevation, slope, ruggedness, and Topographic Position Index. Univariate regressions were then applied to all biologically relevant variables for each landform category. Random Forest (RF) models were implemented for the climatic niche modeling of Cunninghamia lanceolata. A comparative analysis of the prediction accuracies of RF models constructed with distinct climate data sources was conducted to evaluate their relative effectiveness. Biologically relevant variables were obtained from three unpublished Chinese meteorological datasets. ClimateAPv3.0 and WorldClim predictions were obtained from weather station coordinates and WorldClim1.4 rasters, respectively, for the normal climate period of 1961-1990. Occurrence data for Cunninghamia lanceolata came from integrated biodiversity databases with 3,745 unique points. ClimateAP explains a minimum of 94.74%, 97.77%, 96.89%, and 94.40% of monthly maximum, minimum, average temperature, and precipitation variances, respectively. It outperforms WorldClim in 37 biologically relevant variables with lower RMSE values. ClimateAP achieves higher R-squared values for the 12 monthly minimum temperature variables and consistently higher Adjusted R-squared values across all landforms for precipitation. ClimateAP's temperature data yields lower Adjusted R-squared values than gridded data in high-elevation, rugged, and mountainous areas but achieves higher values in mid-slope drainages, plains, open slopes, and upper slopes. Using ClimateAP improves the prediction accuracy of tree occurrence from 77.90% to 82.77%. The biologically relevant climate data produced by ClimateAP is validated based on evaluations using observations from weather stations. The use of ClimateAP leads to an improvement in data quality, especially in non-mountainous regions. The results also suggest that using biologically relevant variables generated by ClimateAP can slightly enhance climatic niche modeling for tree species, offering a better understanding of tree species adaptation and resilience compared to using gridded data.

Keywords: climate data validation, data quality, Asia pacific climate, climatic niche modeling, random forest models, tree species

Procedia PDF Downloads 52

Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

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Authors: M. T. Alodat

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In this paper, we propose a mechanism for skewing any symmetric distribution. The new distribution is called the deflation-inflation distribution (DID). We discuss some statistical properties of the DID such moments, stochastic representation, log-concavity. Also we fit the distribution to real data and we compare it to normal distribution and Azzlaini's skew normal distribution. Numerical results show that the DID fits the the tree ring data better than the other two distributions.

Keywords: normal distribution, moments, Fisher information, symmetric distributions

Procedia PDF Downloads 643

Authors: Najoua El Hajjaji El Idrissi, Necip Gokhan Kasapoglu

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Polarimetric Synthetic Aperture Radar-based imaging is a powerful technique used for earth observation and classification of surfaces. Forest evolution has been one of the vital areas of attention for the remote sensing experts. The information about forest areas can be achieved by remote sensing, whether by using active radars or optical instruments. However, due to several weather constraints, such as cloud cover, limited information can be recovered using optical data and for that reason, Polarimetric Synthetic Aperture Radar (PolSAR) is used as a powerful tool for forestry inventory. In this [14paper, we applied support vector machine (SVM) and Mahalanobis distance to the fully polarimetric AIRSAR P, L, C-bands data from the Nezer forest areas, the classification is based in the separation of different tree ages. The classification results were evaluated and the results show that the SVM performs better than the Mahalanobis distance and SVM achieves approximately 75% accuracy. This result proves that SVM classification can be used as a useful method to evaluate fully polarimetric SAR data with sufficient value of accuracy.

Keywords: classification, synthetic aperture radar, SAR polarimetry, support vector machine, mahalanobis distance

Procedia PDF Downloads 116

Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

Procedia PDF Downloads 159

Authors: Sinan Alsaadi, Mustafa Merdan

Abstract:

Communication and access systems can be made with many methods in today’s world. These systems are such standards as Wifi, Wimax, Bluetooth, GPS and GPRS. Devices which use these standards also use system resources excessively in direct proportion to their transmission speed. However, large-scale data communication is not always needed. In such cases, a technology which will use system resources as little as possible and support smart network topologies has been needed in order to enable the transmissions of such small packet data and provide the control for this kind of devices. IEEE issued 802.15.4 standard upon this necessity and enabled the production of Zigbee protocol which takes these standards as its basis and devices which support this protocol. In our project, this communication protocol was preferred. The aim of this study is to provide the immediate data transmission of our robot from the field within the scope of the project. In addition, making the communication with the robot through Zigbee Protocol has also been aimed. While sitting on the computer, obtaining the desired data from the region where the robot is located has been taken as the basis. Arduino Uno R3 microcontroller which provides the control mechanism, 1298 shield as the motor driver.

Keywords: ZigBee, wireless network, remote monitoring, smart home, agricultural industry

Procedia PDF Downloads 262

Authors: Muhammad Bilal Shaikh

Abstract:

Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: Linda Ana Maria Ungureanu

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This article details the manner in which European law establishes the protection and ownership rights over works created in off-world environments or in relation to space exploration. In this sense, the analysis is focused on identifying the legal treatment applicable to creative works based on the provisions regulated under the International Space Treaties, on one side, and the International IP Treaties and subsequent EU legislation, on the other side, with a special interest on ESA Rules and Regulations. Furthermore, the article analyses the manner in which ESA regulates the ownership regime applicable for creative works, taking into account the relationship existing between the inventor/creator and ESA and the environment in which the creative work was developed. Moreover, the article sets a series of de lege ferenda proposals for the regulation of intellectual property matters in the context of space exploration, the main purpose being to identify legal measures and steps that need to be taken in order to ensure that creative activities are fostered and understood as a significant catalyst for encouraging space exploration.

Keywords: intellectual property law, ESA guidelines, international IP treaties, EU legislation

Procedia PDF Downloads 168

Authors: Newroz Nooralddin Abdulrazaq, Thuraya Mahmood Qaradaghi

Abstract:

The McEliece cryptosystem is an asymmetric type of cryptography based on error correction code. The classical McEliece used irreducible binary Goppa code which considered unbreakable until now especially with parameter [1024, 524, and 101], but it is suffering from large public key matrix which leads to be difficult to be used practically. In this work Irreducible and Separable Goppa codes have been introduced. The Irreducible and Separable Goppa codes used are with flexible parameters and dynamic error vectors. A Comparison between Separable and Irreducible Goppa code in McEliece Cryptosystem has been done. For encryption stage, to get better result for comparison, two types of testing have been chosen; in the first one the random message is constant while the parameters of Goppa code have been changed. But for the second test, the parameters of Goppa code are constant (m=8 and t=10) while the random message have been changed. The results show that the time needed to calculate parity check matrix in separable are higher than the one for irreducible McEliece cryptosystem, which is considered expected results due to calculate extra parity check matrix in decryption process for g2(z) in separable type, and the time needed to execute error locator in decryption stage in separable type is better than the time needed to calculate it in irreducible type. The proposed implementation has been done by Visual studio C#.

Keywords: McEliece cryptosystem, Goppa code, separable, irreducible

Procedia PDF Downloads 255

Authors: İrem Bulut, Mustafa Z. Söyük, Ertuğrul Yalçın, Simge Şişman-Bal

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Eyewitness testimony is one of the most frequently used methods in criminal cases for the determination of crime and perpetrator. In the literature, the number of studies about the reliability of eyewitness testimony is increasing. The study aims to reveal the factors that affect the short-term and long-term visual memory performance of the participants in the event of an accident. In this context, the effect of the emotional content of the accident and the sounds during the accident on visual memory performance was investigated with eye-tracking. According to the results, the presence of visual and auditory stimuli with emotional content during the accident decreases the participants' both short-term and long-term recall performance. Moreover, the data obtained from the eye monitoring device showed that the participants had difficulty in answering even the questions they focused on at the time of the accident.

Keywords: eye tracking, eyewitness testimony, long-term recall, short-term recall, visual memory

Procedia PDF Downloads 142

Authors: Ziad. Sh. Al Sarraf

Abstract:

Ultrasonic metal welding has been the subject of ongoing research and development, most recently concentrating on metal joining in miniature devices, for example to allow solder-free wire bonding. As well as at the small scale, there are also opportunities to research the joining of thicker sheet metals and to widen the range of similar and dissimilar materials that can be successfully joined using this technology. This study presents the design, characterisation and test of a lateral-drive ultrasonic metal spot welding device. The ultrasonic metal spot welding horn is modelled using finite element analysis (FEA) and its vibration behaviour is characterised experimentally to ensure ultrasonic energy is delivered effectively to the weld coupon. The welding stack and fixtures are then designed and mounted on a test machine to allow a series of experiments to be conducted for various welding and ultrasonic parameters. Weld strength is subsequently analysed using tensile-shear tests. The results show how the weld strength is particularly sensitive to the combination of clamping force and ultrasonic vibration amplitude of the welding tip, but there are optimal combinations of these and also limits that must be clearly identified.

Keywords: ultrasonic welding, vibration amplitude, welding force, weld strength

Procedia PDF Downloads 348

Authors: Paul Rowley, Adam Thirkill, Nick Doylend, Philip Leicester, Becky Gough

Abstract:

The dynamic performance of all energy generation technologies is impacted to varying degrees by the stochastic properties of the wider system within which the generation technology is located. This stochasticity can include the varying nature of ambient renewable energy resources such as wind or solar radiation, or unpredicted changes in energy demand which impact upon the operational behaviour of thermal generation technologies. An understanding of these stochastic impacts are especially important in contexts such as highly distributed (or embedded) generation, where an understanding of issues affecting the individual or aggregated performance of high numbers of relatively small generators is especially important, such as in ESCO projects. Probabilistic evaluation of monitored or simulated performance data is one technique which can provide an insight into the dynamic performance characteristics of generating systems, both in a prognostic sense (such as the prediction of future performance at the project’s design stage) as well as in a diagnostic sense (such as in the real-time analysis of underperforming systems). In this work, we describe the development, application and outcomes of a new approach to the acquisition of datasets suitable for use in the subsequent performance and impact analysis (including the use of Bayesian approaches) for a number of distributed generation technologies. The application of the approach is illustrated using a number of case studies involving domestic and small commercial scale photovoltaic, solar thermal and natural gas boiler installations, and the results as presented show that the methodology offers significant advantages in terms of plant efficiency prediction or diagnosis, along with allied environmental and social impacts such as greenhouse gas emission reduction or fuel affordability.

Keywords: renewable energy, dynamic performance simulation, Bayesian analysis, distributed generation

Procedia PDF Downloads 477

Authors: Abasali Masoumi, Amir Gholamian Bozorgi

Abstract:

To have the best quality in wood products such as tabletops and inlay-woods, applying two principles are required: aesthetic and protection against the destructive agent. Artists spent a lot of time creating a masterwork project and also for better demonstrating beautiful appearance and preserving it for hundred years. So they need good material and appropriate method to finish it. As usual, wood painters use polyester or epoxy resins. These finishes need a special skill to use and then give a fantastic paint film and clearness. If we let resins dry in exposure to environmental agents such as unstable temperature, dust and etc., no doubt it becomes cloudy, crack, blister and much wood dust and air bubbles in it. We have designed a special wood coating booth (IR-Patent No: 70429) for wood-coating resins (polyester and epoxy), and this booth provides an adjustable space to control factors that is necessary to have a good finish in the end. Anti-bubble painting booth has the ability to remove bubbles from resin, precludes the cracking process and causes the resin to be the best. With this booth drying time of resin is reduced from 24 hours to 6 hours by fixing the optimum temperature, and it is very good for saving time. This booth is environment-friendly and never lets the poisonous vapors and other VOC (Volatile organic components) enter to workplace atmosphere because they are very harmful to humans.

Keywords: wood coating, epoxy resin, polyester resin, wood finishes

Procedia PDF Downloads 205

Authors: M. H. Brandtner-Hafner

Abstract:

Adhesives have established themselves as an innovative joining technology in the wood industry. The strengths of adhesive bonding lie in the realization of lightweight designs, the avoidance of material weakening, and the joining of different types of materials. There is now a number of ways to positively influence the properties of bonded joints. One way is to add fiber fillers. This leads to an improvement in adhesion, structural integrity, and fracture toughness. In this study, the effectiveness of fiber-modified adhesives for bonding wooden joints is reviewed. A series of experimental tests were performed using the fracture analytical GF-principle to study the adhesive bonding safety and performance of the wood-adhesive interface. Two different construction adhesives based on epoxy and PUR were modified with different fiber materials and applied to bond wooden joints. The results show that bonding efficiency by adding fibrous materials to the bonding matrix leads to significant improvements in structural material properties.

Keywords: fiber-modified adhesives, bonding safety, wood-adhesive interfaces, fracture analysis

Procedia PDF Downloads 81

Authors: Naci Büyükkaracığan

Abstract:

Today, the need for water sources is swiftly increasing due to population growth. At the same time, it is known that some regions will face with shortage of water and drought because of the global warming and climate change. In this context, evaluation and analysis of hydrological data such as the observed trends, drought and flood prediction of short term flow has great deal of importance. The most accurate selection probability distribution is important to describe the low flow statistics for the studies related to drought analysis. As in many basins In Turkey, Gediz River basin will be affected enough by the drought and will decrease the amount of used water. The aim of this study is to derive appropriate probability distributions for frequency analysis of annual minimum flows at 6 gauging stations of the Gediz Basin. After applying 10 different probability distributions, six different parameter estimation methods and 3 fitness test, the Pearson 3 distribution and general extreme values distributions were found to give optimal results.

Keywords: Gediz Basin, goodness-of-fit tests, minimum flows, probability distribution

Procedia PDF Downloads 261

Authors: S. K. Ashiquer Rahman

Abstract:

Recently, oil has become more influential in almost every economic sector as a key material. As can be seen from the news, when there are some changes in an oil price or OPEC announces a new strategy, its effect spreads to every part of the economy directly and indirectly. That’s a reason why people always observe the oil price and try to forecast the changes of it. The most important factor affecting the price is its supply which is determined by the number of wildcats drilled. Therefore, a study about the number of wellheads and other economic variables may give us some understanding of the mechanism indicated by the amount of oil supplies. In this paper, we will consider a relationship between the number of wellheads and three key factors: the price of the wellhead, domestic output, and GNP constant dollars. We also add trend variables in the models because the consumption of oil varies from time to time. Moreover, this paper will use an econometrics method to estimate parameters in the model, apply some tests to verify the result we acquire, and then conclude the model.

Keywords: price, domestic output, GNP, trend variable, wildcat activity

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Authors: Nooshin Salari, Viliam Makis

Abstract:

In this paper, we propose a condition-based maintenance policy for multi-unit systems considering the existence of economic dependency among units. We consider a system composed of N identical units, where each unit deteriorates independently. Deterioration process of each unit is modeled as a three-state continuous time homogeneous Markov chain with two working states and a failure state. The average production rate of units varies in different working states and demand rate of the system is constant. Units are inspected at equidistant time epochs, and decision regarding performing maintenance is determined by the number of units in the failure state. If the total number of units in the failure state exceeds a critical level, maintenance is initiated, where units in failed state are replaced correctively and deteriorated state units are maintained preventively. Our objective is to determine the optimal number of failed units to initiate maintenance minimizing the long run expected average cost per unit time. The problem is formulated and solved in the semi-Markov decision process (SMDP) framework. A numerical example is developed to demonstrate the proposed policy and the comparison with the corrective maintenance policy is presented.

Keywords: reliability, maintenance optimization, semi-Markov decision process, production

Procedia PDF Downloads 139

Authors: Shyh Ming Chern

Abstract:

Cubic equations of state (EoS), popular due to their simple mathematical form, ease of use, semi-theoretical nature and, reasonable accuracy are normally fitted to vapor-liquid equilibrium P-v-T data. As a result, They often show poor accuracy in the region near and above the critical point. In this study, the performance of the renowned Peng-Robinson (PR) and Patel-Teja (PT) EoS’s around the critical area has been examined against the P-v-T data of water. Both of them display large deviations at critical point. For instance, PR-EoS exhibits discrepancies as high as 47% for the specific volume, 28% for the enthalpy departure and 43% for the entropy departure at critical point. It is shown that incorporating P-v-T data of the supercritical region into the retuning of a cubic EoS can improve its performance above the critical point dramatically. Adopting a retuned acentric factor of 0.5491 instead of its genuine value of 0.344 for water in PR-EoS and a new F of 0.8854 instead of its original value of 0.6898 for water in PT-EoS reduces the discrepancies to about one third or less.

Keywords: equation of state, EoS, supercritical water, SCW

Procedia PDF Downloads 513

Authors: Rajesh Bansal, Amit Raj Sharma, Vakil Singh

Abstract:

The success and predictability of titanium implants for long durations are well established and there has been a tremendous increase in the popularity of implants among patients as well as clinicians over the last four decades. However, sometimes complications arise, which lead to the loss of the implant as well as the prosthesis. Fracture of dental implants is rare; however, at times, implants or abutment screws fracture and lead to many problems for the clinician and the patient. Possible causes of implant fracture include improper design, overload, fatigue and corrosion. Six retrieved fractured dental implants, with varying diameters and designs, were collected from time to time to examine by scanning electron microscope (SEM) to characterize fracture behavior and assess the mechanism of fracture. In this investigation, it was observed that fracture of the five dental implants occurred due to fatigue crack initiation and propagation from the thread roots.

Keywords: titanium, dental, implant, fracture, failure

Procedia PDF Downloads 66

Authors: Ching Louis Liu, Edmund Kazmierczak, Tim Miller

Abstract:

Safety analysis for multi-agent systems is complicated by the, potentially nonlinear, interactions between agents. This paper proposes a method for analyzing the safety of multi-agent systems by explicitly focusing on interactions and the accident data of systems that are similar in structure and function to the system being analyzed. The method creates a Bayesian network using the accident data from similar systems. A feature of our method is that the events in accident data are labeled with HAZOP guide words. Our method uses an Ontology to abstract away from the details of a multi-agent implementation. Using the ontology, our methods then constructs an “Interaction Map,” a graphical representation of the patterns of interactions between agents and other artifacts. Interaction maps combined with statistical data from accidents and the HAZOP classifications of events can be converted into a Bayesian Network. Bayesian networks allow designers to explore “what it” scenarios and make design trade-offs that maintain safety. We show how to use the Bayesian networks, and the interaction maps to improve multi-agent system designs.

Keywords: multi-agent system, safety analysis, safety model, integration map

Procedia PDF Downloads 399