Search results for: noise filters

Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

Procedia PDF Downloads 151

Authors: Watcharapong Yaowarat, Thanee Kaewthummanukul, Waruntorn Jongrungrotsakul

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Noise-induced hearing loss, the most significant occupational and safety problem among the working population, can be effectively prevented through hearing protection devices (HPDs) use. This study aimed to examine whether the following factors, perceived benefits, perceived barriers, perceived self-efficacy, and interpersonal and situational influences about using hearing protection could predict HPD use among 132 qualified workers in production lines at Kaolin Mineral Dressing factories, Uttaradit and Lampang provinces. Data collection was undertaken from August to September 2020 according to the interview form developed by Yaruang et al. (2010), which was assured by a panel of experts and its reliability value was at an acceptable level. Data analysis was performed using logistic regression analysis. The results revealed that only the situational factor of using hearing protection could predict HPD use, which accounted for 21.80 percent of the total variance for HPD use. It was also found that the study sample who had a score for the situational factors on using hearing protection greater than or equal to the median was 4.16 times more likely to use HPDs than those who had lower median scores. (OR = 4.16, p < .05). The results, thus, indicate that organization policies addressing worker health along with enhancing a supportive environment for HPD use, in particular, the provision of various HPDs, are of great importance. Therefore, occupational health nurses and related health teams should enhance workers’ use of HPDs effectively through knowledge dissemination by adopting strategies appropriate to the workplace context leading to an achievement of worker health policy focusing on work safety.

Keywords: predicting factors, hearing protection device, factors predicting hearing protection device use, kaolin mineral dressing factories

Procedia PDF Downloads 111

Authors: B. L. Ho, L. Shi, D. F. Wang, V. C. T. Mok

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The brain’s functional connectivity while temporally non-stationary does express consistency at a macro spatial level. The study of stable resting state connectivity patterns hence provides opportunities for identification of diseases if such stability is severely perturbed. A mathematical model replicating the brain’s spatial connections will be useful for understanding brain’s representative geometry and complements the empirical model where it falls short. Empirical computations tend to involve large matrices and become infeasible with fine parcellation. However, the proposed analytical model has no such computational problems. To improve replicability, 92 subject data are obtained from two open sources. The proposed methodology, inspired by financial theory, uses multivariate regression to find relationships of every cortical region of interest (ROI) with some pre-identified hubs. These hubs acted as representatives for the entire cortical surface. A variance-covariance framework of all ROIs is then built based on these relationships to link up all the ROIs. The result is a high level of match between model and empirical correlations in the range of 0.59 to 0.66 after adjusting for sample size; an increase of almost forty percent. More significantly, the model framework provides an intuitive way to delineate between systemic drivers and idiosyncratic noise while reducing dimensions by more than 30 folds, hence, providing a way to conduct attribution analysis. Due to its analytical nature and simple structure, the model is useful as a standalone toolkit for network dependency analysis or as a module for other mathematical models.

Keywords: functional magnetic resonance imaging, multivariate regression, network hubs, resting state functional connectivity

Procedia PDF Downloads 130

Authors: Md. Moniruzzaman, Thirayoot Limanond

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Being the most visible indicator, the transport system of a city points out how developed the city is. Dhaka megacity holds a mixed composition of motorized and non-motorized modes of transport and the number of vehicle figure is escalating over times. And this obviously poses associated environmental costs like air pollution, noise etc. which is degrading the quality of life in the city. Eventually sustainable transport or more importantly green transport from environmental point of view has become a prime choice to the transport professionals in order to cope up the crisis. Currently the city authority is planning to execute such sustainable transport systems that could serve the pressing demand of the present and meet the future needs effectively. This study focuses on the selection and evaluation of green transportation systems among potential alternatives on a priority basis. In this paper, Fuzzy TOPSIS - a multi-criteria decision method is presented to find out the most prioritized alternative. In the first step, Twenty-one individual specific criteria for sustainability assessment are selected. In the following step, experts provide linguistic ratings to the potential alternatives with respect to the selected criteria. The approach is used to generate aggregate scores for sustainability assessment and selection of the best alternative. In the third step, a sensitivity analysis is performed to understand the influence of criteria weights on the decision making process. The key strength of fuzzy TOPSIS approach is its practical applicability having a generation of good quality solution even under uncertainty.

Keywords: green transport, multi-criteria decision approach, urban transportation system, sustainability assessment, fuzzy theory, uncertainty

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Authors: Hong Geng, Zaiyu Fan

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With the acceleration of urbanization and motorization in China, the scale of cities and the travel distance of residents are constantly expanding, and the number of cars is continuously increasing, so the urban traffic problem is more and more serious. Traffic congestion, environmental pollution, energy consumption, travel safety and direct interference between traffic and other urban activities are increasingly prominent problems brought about by motorized development. This not only has a serious impact on the lives of the residents but also has a major impact on the healthy development of the city. The paper found that, in order to solve the development of motorization, a number of problems will arise; urban planning and traffic planning and design in residential planning often take into account the development of motorized traffic but neglects the demand for street life. This kind of planning has resulted in the destruction of the traditional communication space of the residential area, the pollution of noise and exhaust gas, and the potential safety risks of the residential area, which has disturbed the previously quiet and comfortable life of the residential area, resulting in the inconvenience of residents' life and the loss of street vitality. Based on these facts, this paper takes a residential area in Wuhan as the research object, through the actual investigation and research, from the perspective of micro-transformation analysis, combined with the concept of traffic micro-reconstruction governance. And research puts forward the residential traffic optimization strategies such as strengthening the interaction and connection between the residential area and the urban street system, street traffic classification and organization.

Keywords: micro-transformation, residential traffic, residents demand, traffic microcirculation

Procedia PDF Downloads 98

Authors: Madikeri Rohit

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As energy efficiency is the key concern in many aircraft manufacturing companies regenerative braking is a technique using which energy lost due to friction while braking can be regained. In the operation of an aircraft, significant energy is lost during deceleration or braking which occurs during its landing phase. This problem can be overcome using Regenerative Breaking System (RBS) in landing gear. The major problem faced is regarding the batteries and the overall efficiency gained in competence with the added weight. As the amount of energy required to store is huge we need batteries with high capacity for storage. Another obstacle by using high capacity batteries is the added weight which undermines the efficiency obtained using RBS. An approach to this problem is to either use the obtained energy immediately without storage or to store in other forms such as mechanical, pneumatic and hydraulic. Problem faced with mechanical systems is the weight of the flywheel needed to obtain required efficiency. Pneumatic and hydraulic systems are a better option at present. Using hydraulic systems for storing energy is efficient as it integrates into the overall hydraulic system present in the aircraft. Another obstacle is faced with the redundancy of this system. Conventional braking must be used along with RBS in order to provide redundancy. Major benefits obtained using RBS is with the help of the energy obtained during landing which can be used of engine less taxing. This reduces fuel consumption as well as noise and air pollution. Another added benefit of using RBS is to provide electrical supply to lighting systems, cabin pressurization system and can be used for emergency power supply in case of electric failure. This paper discusses about using RBS in landing gear, problems, prospects and new techniques being pursued to improve RBS.

Keywords: regenerative braking, types of energy conversion, landing gear, energy storage

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Authors: Hrishikesh Purandare, Ashwini Pethe

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Research from developed countries has demonstrated that the built environment can have a significant effect on children’s cognitive and socio-emotional development. A majority of the studies on the relationship between the built environment and the well-being of children have been conducted in North America and Western Europe, though most of the world’s children live in the global South. Millions of children living in urban slums in India confront issues associated with poor living conditions and lack of access to basic services. It is a well-known fact that slums are places of extreme poverty, substandard housing, overcrowding, and poor sanitation. These challenges faced by children living in slums can have a significant impact on their physical, psychological, and social development. Despite the magnitude of the problem, the area of research, particularly on the impact of the built environment of slums on children and adolescent well-being, has been understudied in India. Only a few studies in the global South have investigated the impact of the built environment on children’s well-being. Apart from issues of the limited access to health and education of these children, the perception of children regarding the built environment which they inhabit is rarely addressed. A sample of 120 children living in the slums of Pune city between the ages 7 and 16 participated in this study, which employed a concurrent embedded approach of mixed method research. Questionnaires were administered to obtain quantitative data that included attributes of crowding, noise, privacy, territoriality and housing quality in the built environment. The qualitative analysis of children’s sketches highlighted aspects of the built environment with which they associated themselves the most. The study sought to examine the perception of the deprived children living in the urban slums in the city of Pune (India) towards their built environment.

Keywords: physical environment, poverty, underprivileged children, urban Indian slums

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Authors: Maydariana Ayuningtyas, Bambang Riyanto, Akhiruddin Maddu

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Industrial waste of crustacean shells, such as shrimp and crab, has been considered as one of the major issues contributing to environmental pollution. The waste processing mechanisms to form new, practical substances with added value have been developed. Chitosan, a derived matter from chitin, which is obtained from crab and shrimp shells, performs prodigiously in broad range applications. A chitosan composite-based diaphragm is a new inspiration in fiber optic acoustic sensor advancement. Elastic modulus, dynamic response, and sensitivity to acoustic wave of chitosan-based composite film contribute great potentials of organic-based sound-detecting material. The objective of this research was to develop chitosan diaphragm application in fiber optic microphone system. The formulation was conducted by blending 5% polyvinyl alcohol (PVA) solution with dissolved chitosan at 0%, 1% and 2% in 1:1 ratio, respectively. Composite diaphragms were characterized for the morphological and mechanical properties to predict the desired acoustic sensor sensitivity. The composite with 2% chitosan indicated optimum performance with 242.55 µm thickness, 67.9% relative humidity, and 29-76% light transmittance. The Young’s modulus of 2%-chitosan composite material was 4.89×104 N/m2, which generated the voltage amplitude of 0.013V and performed sensitivity of 3.28 mV/Pa at 1 kHz. Based on the results above, chitosan from crustacean shell waste can be considered as a viable alternative material for fiber optic acoustic sensor sensing pad development. Further, the research in chitosan utilisation is proposed as novel optical microphone development in anthropogenic noise controlling effort for environmental and biodiversity conservation.

Keywords: acoustic sensor, chitosan, composite, crab shell, diaphragm, waste utilisation

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Authors: Venkata Madhusudana Rao Kapavarapu

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The Intermediate Slug Catcher (ISC) facility was built to process nominally 234 MSCFD of export gas from the booster station on a day-to-day basis and to receive liquid slugs up to 1600 m³ (10,000 BBLS) in volume when the incoming 24” gas pipelines are pigged following upsets or production of non-dew-pointed gas from gathering centers. The maximum slug sizes expected are 812 m³ (5100 BBLS) in winter and 542 m³ (3400 BBLS) in summer after operating for a month or more at 100 MMSCFD of wet gas, being 60 MMSCFD of treated gas from the booster station, combined with 40 MMSCFD of untreated gas from gathering center. The water content is approximately 60% but may be higher if the line is not pigged for an extended period, owing to the relative volatility of the condensate compared to water. In addition to its primary function as a slug catcher, the ISC facility will receive pigged liquids from the upstream and downstream segments of the 14” condensate pipeline, returned liquids from the AGRP, pigged through the 8” pipeline, and blown-down fluids from the 14” condensate pipeline prior to maintenance. These fluids will be received in the condensate flash vessel or the condensate separator, depending on the specific operation, for the separation of water and condensate and settlement of solids scraped from the pipelines. Condensate meeting the colour and 200 ppm water specifications will be dispatched to the AGRP through the 14” pipeline, while off-spec material will be returned to BS-171 via the existing 10” condensate pipeline. When they are not in operation, the existing 24” export gas pipeline and the 10” condensate pipeline will be maintained under export gas pressure, ready for operation. The gas manifold area contains the interconnecting piping and valves needed to align the slug catcher with either of the 24” export gas pipelines from the booster station and to direct the gas to the downstream segment of either of these pipelines. The manifold enables the slug catcher to be bypassed if it needs to be maintained or if through-pigging of the gas pipelines is to be performed. All gas, whether bypassing the slug catcher or returning to the gas pipelines from it, passes through black powder filters to reduce the level of particulates in the stream. These items are connected to the closed drain vessel to drain the liquid collected. Condensate from the booster station is transported to AGRP through 14” condensate pipeline. The existing 10” condensate pipeline will be used as a standby and for utility functions such as returning condensate from AGRP to the ISC or booster station or for transporting off-spec fluids from the ISC back to booster station. The manifold contains block valves that allow the two condensate export lines to be segmented at the ISC, thus facilitating bi-directional flow independently in the upstream and downstream segments, which ensures complete pipeline integrity and facility integrity. Pipeline failures will be attended to with the latest technologies by remote techno plug techniques, and repair activities will be carried out as needed. Pipeline integrity will be evaluated with ili pigging to estimate the pipeline conditions.

Keywords: integrity, oil & gas, innovation, new technology

Procedia PDF Downloads 52

Authors: Yu Zhang, Ye Tian, Fang Ye Yixuan Kang

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The capacity of conventional cellular networks has reached its upper bound and it can be well handled by introducing femtocells with low-cost and easy-to-deploy. Spectrum interference issue becomes more critical in peace with the value-added multimedia services growing up increasingly in two-tier cellular networks. Spectrum allocation is one of effective methods in interference mitigation technology. This paper proposes a game-theory-based on OFDMA downlink spectrum allocation aiming at reducing co-channel interference in two-tier femtocell networks. The framework is formulated as a non-cooperative game, wherein the femto base stations are players and frequency channels available are strategies. The scheme takes full account of competitive behavior and fairness among stations. In addition, the utility function reflects the interference from the standpoint of channels essentially. This work focuses on co-channel interference and puts forward a negative logarithm interference function on distance weight ratio aiming at suppressing co-channel interference in the same layer network. This scenario is more suitable for actual network deployment and the system possesses high robustness. According to the proposed mechanism, interference exists only when players employ the same channel for data communication. This paper focuses on implementing spectrum allocation in a distributed fashion. Numerical results show that signal to interference and noise ratio can be obviously improved through the spectrum allocation scheme and the users quality of service in downlink can be satisfied. Besides, the average spectrum efficiency in cellular network can be significantly promoted as simulations results shown.

Keywords: femtocell networks, game theory, interference mitigation, spectrum allocation

Procedia PDF Downloads 131

Authors: Veruska C. Dutra, Mary L. G. S. Senna, Afonso R. Aquino

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Tourists bring many benefits to a local community, encouraging it to be involved in that activity; however, it may also have detrimental effects like garbage, noise, violence, external culture and the damaging of the natural environment among others, which may promote community dissatisfaction. The contact between the tourist and the local community is a concern, especially when the community is located near protected areas. In this case, the community must know the tourist destination well, so it can collaborate in the tourism development without harming the environment. In this context, the present article aims to demonstrate the results of a research study conducted as part of a doctorate program in Sciences from the University of Sao Paulo, Brazil. It had as an objective to elaborate a methodology proposal to measure the local community satisfaction indicator, with applicability on a case study in the Mateiros community located in the surrounding area of the Parque Estadual do Jalapão –PEJ conservation unit in the state of Tocantins, Brazil. This is a study of an interdisciplinary nature that had the deductive method as its guide. The indicator result is going to be presented in this study. It pointed out as negative factors: there is no involvement between the local community and the tourism sector, and there is also dissatisfaction with regard to the town’s basic services. The study showed as positive the local community knowledge about the various attractions in the surrounding area and that the group recognizes the importance of the tourism for the town and life. Concerning the methodology that was used, the results showed that it can collaborate in seeking actions of improvement and involvement of the community in the planning and development of the local tourism. It comes out as an efficient analysis tool, thus enabling the perceiving of the local community point of view.

Keywords: satisfaction indicator, tourism, community, Jalapão

Procedia PDF Downloads 310

Authors: Subhajit Das, Nirjhar Dhang

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Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data.

Keywords: damage detection, finite element model updating, modal assurance criteria, structural health monitoring, teaching learning based optimization

Procedia PDF Downloads 192

Authors: Tugba Bayoglu

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Aerodynamic parameter estimation is very crucial in missile design phase, since accurate high fidelity aerodynamic model is required for designing high performance and robust control system, developing high fidelity flight simulations and verification of computational and wind tunnel test results. However, in literature, there is not enough missile aerodynamic parameter identification study for three main reasons: (1) most air to air missiles cannot fly with constant speed, (2) missile flight test number and flight duration are much less than that of fixed wing aircraft, (3) variation of the missile aerodynamic parameters with respect to Mach number is higher than that of fixed wing aircraft. In addition to these challenges, identification of aerodynamic parameters for high wind angles by using classical estimation techniques brings another difficulty in the estimation process. The reason for this, most of the estimation techniques require employing polynomials or splines to model the behavior of the aerodynamics. However, for the missiles with a large variation of aerodynamic parameters with respect to flight variables, the order of the proposed model increases, which brings computational burden and complexity. Therefore, in this study, it is aimed to solve nonlinear aerodynamic parameter identification problem for a supersonic air to air missile by using Artificial Neural Networks. The method proposed will be tested by using simulated data which will be generated with a six degree of freedom missile model, involving a nonlinear aerodynamic database. The data will be corrupted by adding noise to the measurement model. Then, by using the flight variables and measurements, the parameters will be estimated. Finally, the prediction accuracy will be investigated.

Keywords: air to air missile, artificial neural networks, open loop simulation, parameter identification

Procedia PDF Downloads 250

Authors: Nipa Debnath, Harinarayan Das, Takahiko Kawaguchi, Naonori Sakamoto, Kazuo Shinozaki, Hisao Suzuki, Naoki Wakiya

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Nowadays spinel ferrite magnetic thin films have drawn considerable attention due to their interesting magnetic and electrical properties with enhanced chemical and thermal stability. Spinel ferrite magnetic films can be implemented in magnetic data storage, sensors, and spin filters or microwave devices. It is well established that the structural, magnetic and transport properties of the magnetic thin films are dependent on microstructure. Spinodal decomposition (SD) is a phase separation process, whereby a material system is spontaneously separated into two phases with distinct compositions. The periodic microstructure is the characteristic feature of SD. Thus, SD can be exploited to control the microstructure at the nanoscale level. In bulk spinel ferrites having general formula, MₓFe₃₋ₓ O₄ (M= Co, Mn, Ni, Zn), phase separation via SD has been reported only for cobalt ferrite (CFO); however, long time post-annealing is required to occur the spinodal decomposition. We have found that SD occurs in CoF thin film without using any post-deposition annealing process if we apply magnetic field during thin film growth. Dynamic Aurora pulsed laser deposition (PLD) is a specially designed PLD system through which in-situ magnetic field (up to 2000 G) can be applied during thin film growth. The in-situ magnetic field suppresses the recombination of ions in the plume. In addition, the peak’s intensity of the ions in the spectra of the plume also increases when magnetic field is applied to the plume. As a result, ions with high kinetic energy strike into the substrate. Thus, ion-impingement occurred under magnetic field during thin film growth. The driving force of SD is the ion-impingement towards the substrates that is induced by in-situ magnetic field. In this study, we report about the occurrence of phase separation through SD and evolution of microstructure after phase separation in spinel ferrite thin films. The surface morphology of the phase separated films show checkerboard like domain structure. The cross-sectional microstructure of the phase separated films reveal columnar type phase separation. Herein, the decomposition wave propagates in lateral direction which has been confirmed from the lateral composition modulations in spinodally decomposed films. Large magnetic anisotropy has been found in spinodally decomposed nickel ferrite (NFO) thin films. This approach approves that magnetic field is also an important thermodynamic parameter to induce phase separation by the enhancement of up-hill diffusion in thin films. This thin film deposition technique could be a more efficient alternative for the fabrication of self-organized phase separated thin films and employed in controlling of the microstructure at nanoscale level.

Keywords: Dynamic Aurora PLD, magnetic anisotropy, spinodal decomposition, spinel ferrite thin film

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Authors: Gurukiran Veerapur, Nytik Birudavolu, Seetharam U. N., Chandravva Hebbi, R. Praneeth Reddy

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This work presents a robust approach for the recognition of characters in Telugu and Kannada, two South Indian scripts with structural similarities in characters. To recognize the characters exhaustive datasets are required, but there are only a few publicly available datasets. As a result, we decided to create a dataset for one language (source language),train the model with it, and then test it with the target language.Telugu is the target language in this work, whereas Kannada is the source language. The suggested method makes use of Canny edge features to increase character identification accuracy on pictures with noise and different lighting. A dataset of 45,150 images containing printed Kannada characters was created. The Nudi software was used to automatically generate printed Kannada characters with different writing styles and variations. Manual labelling was employed to ensure the accuracy of the character labels. The deep learning models like CNN (Convolutional Neural Network) and Visual Attention neural network (VAN) are used to experiment with the dataset. A Visual Attention neural network (VAN) architecture was adopted, incorporating additional channels for Canny edge features as the results obtained were good with this approach. The model's accuracy on the combined Telugu and Kannada test dataset was an outstanding 97.3%. Performance was better with Canny edge characteristics applied than with a model that solely used the original grayscale images. The accuracy of the model was found to be 80.11% for Telugu characters and 98.01% for Kannada words when it was tested with these languages. This model, which makes use of cutting-edge machine learning techniques, shows excellent accuracy when identifying and categorizing characters from these scripts.

Keywords: base characters, modifiers, guninthalu, aksharas, vattakshara, VAN

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

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Authors: Amir Abbas Fatemi, Zahra Tabrizian, Kabir Sadeghi

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To find the location and severity of damage that occurs in a structure, characteristics changes in dynamic and static can be used. The non-destructive techniques are more common, economic, and reliable to detect the global or local damages in structures. This paper presents a non-destructive method in structural damage detection and assessment using GA and static data. Thus, a set of static forces is applied to some of degrees of freedom and the static responses (displacements) are measured at another set of DOFs. An analytical model of the truss structure is developed based on the available specification and the properties derived from static data. The damages in structure produce changes to its stiffness so this method used to determine damage based on change in the structural stiffness parameter. Changes in the static response which structural damage caused choose to produce some simultaneous equations. Genetic Algorithms are powerful tools for solving large optimization problems. Optimization is considered to minimize objective function involve difference between the static load vector of damaged and healthy structure. Several scenarios defined for damage detection (single scenario and multiple scenarios). The static damage identification methods have many advantages, but some difficulties still exist. So it is important to achieve the best damage identification and if the best result is obtained it means that the method is Reliable. This strategy is applied to a plane truss. This method is used for a plane truss. Numerical results demonstrate the ability of this method in detecting damage in given structures. Also figures show damage detections in multiple damage scenarios have really efficient answer. Even existence of noise in the measurements doesn’t reduce the accuracy of damage detections method in these structures.

Keywords: damage detection, finite element method, static data, non-destructive, genetic algorithm

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Authors: Patrícia T. Souza, Marina Ansolin, Eduardo A. C. Batista, Antonio J. A. Meirelles, Matthieu Tubino

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Lipid oxidation is a major cause of the deterioration of the quality of the biodiesel, because the waste generated damages the engines. Among the main undesirable effects are the increase of viscosity and acidity, leading to the formation of insoluble gums and sediments which cause the blockage of fuel filters. The auto-oxidation is defined as the spontaneous reaction of atmospheric oxygen with lipids. Unsaturated fatty acids are usually the components affected by such reactions. They are present as free fatty acids, fatty esters and glycerides. To determine the oxidative stability of biodiesels, through the induction period, IP, the Rancimat method is used, which allows continuous monitoring of the induced oxidation process of the samples. During the oxidation of the lipids, volatile organic acids are produced as byproducts, in addition, other byproducts, including alcohols and carbonyl compounds, may be further oxidized to carboxylic acids. By the methodology developed in this work using ion chromatography, IC, analyzing the water contained in the conductimetric vessel, were quantified organic anions of carboxylic acids in samples subjected to oxidation induced by Rancimat. The optimized chromatographic conditions were: eluent water:acetone (80:20 v/v) with 0.5 mM sulfuric acid; flow rate 0.4 mL min-1; injection volume 20 µL; eluent suppressor 20 mM LiCl; analytical curve from 1 to 400 ppm. The samples studied were methyl biodiesel from soybean oil and unsaturated fatty acids standards: oleic, linoleic and linolenic. The induced oxidation kinetics curves were constructed by analyzing the water contained in the conductimetric vessels which were removed, each one, from the Rancimat apparatus at prefixed intervals of time. About 3 g of sample were used under the conditions of 110 °C and air flow rate of 10 L h-1. The water of each conductimetric Rancimat measuring vessel, where the volatile compounds were collected, was filtered through a 0.45 µm filter and analyzed by IC. Through the kinetic data of the formation of the organic anions of carboxylic acids, the formation rates of the same were calculated. The observed order of the rates of formation of the anions was: formate >>> acetate > hexanoate > valerate for the oleic acid; formate > hexanoate > acetate > valerate for the linoleic acid; formate >>> valerate > acetate > propionate > butyrate for the linolenic acid. It is possible to suppose that propionate and butyrate are obtained mainly from linolenic acid and that hexanoate is originated from oleic and linoleic acid. For the methyl biodiesel the order of formation of anions was: formate >>> acetate > valerate > hexanoate > propionate. According to the total rate of formation these anions produced during the induced degradation of the fatty acids can be assigned the order of reactivity: linolenic acid > linoleic acid >>> oleic acid.

Keywords: anions of carboxylic acids, biodiesel, ion chromatography, oxidation

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Authors: Davies Obaromi, Qin Yongsong, James Ndege

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To interpolate scattered or regularly distributed data, there are imprecise or exact methods. However, there are some of these methods that could be used for interpolating data in a regular grid and others in an irregular grid. In spatial epidemiology, it is important to examine how a disease prevalence rates are distributed in space, and how they relate with each other within a defined distance and direction. In this study, for the geographic and graphic representation of the disease prevalence, linear and biharmonic spline methods were implemented in MATLAB, and used to identify, localize and compare for smoothing in the distribution patterns of tuberculosis (TB) in Eastern Cape Province. The aim of this study is to produce a more “smooth” graphical disease map for TB prevalence patterns by a 3-D curve fitting techniques, especially the biharmonic splines that can suppress noise easily, by seeking a least-squares fit rather than exact interpolation. The datasets are represented generally as a 3D or XYZ triplets, where X and Y are the spatial coordinates and Z is the variable of interest and in this case, TB counts in the province. This smoothing spline is a method of fitting a smooth curve to a set of noisy observations using a spline function, and it has also become the conventional method for its high precision, simplicity and flexibility. Surface and contour plots are produced for the TB prevalence at the provincial level for 2012 – 2015. From the results, the general outlook of all the fittings showed a systematic pattern in the distribution of TB cases in the province and this is consistent with some spatial statistical analyses carried out in the province. This new method is rarely used in disease mapping applications, but it has a superior advantage to be assessed at subjective locations rather than only on a rectangular grid as seen in most traditional GIS methods of geospatial analyses.

Keywords: linear, biharmonic splines, tuberculosis, South Africa

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Authors: Si-Geun Choi, Hoon-Jae Park, Jung-Woo Cho, Jin-Ho Lim, Jin-Young Park, Joo-Young Oh, Jae-Il Jeong Seock-Sam Kim, Young Tae Cho, Chan Gyu Kim, Jong-Hyoung Kim

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The tilting pad bearing is a kind of the fluid film bearing and it can contribute to the high speed and the high load performance compared to other bearings including the rolling element bearing. Furthermore, the tilting bearing has many advantages such as high stability at high-speed performance, long life, high damping, high impact resistance and low noise. Therefore, it mostly used in mid to large size turbomachines, despite the high price disadvantage. Recently, manufacture and process employing laser techniques advancing at a fast-growing rate in mechanical industry, the dissimilar metal weld process employing laser techniques is actively studied. Moreover, also, Industry fields try to apply for welding the white metal and the back metal using laser cladding method for high durability. Furthermore, it has followed that laser cladding method has a lot better bond strength, toughness, anti-abrasion and environment-friendly than centrifugal casting method through preceding research. Therefore, the laser cladding method has a lot better quality, cost reduction, eco-friendliness and permanence of technology than the centrifugal casting method or the gravity casting method. In this study, we compare the mechanical properties of different bearing materials by evaluating the behavior of laser cladding layer with various materials (i.e. SS400, SCM440, S20C) under the same parameters. Furthermore, we analyze the porosity of various tilting pad bearing materials which white metal treated on samples. SEM, EDS analysis and hardness tests of three materials are shown to understand the mechanical properties and tribological behavior. W/D ratio, surface roughness results with various materials are performed in this study.

Keywords: laser cladding, tilting pad bearing, white metal, mechanical properties

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Authors: Sofia Papadimitriou

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INTRODUCTION: The differential diagnosis between acute viral and bacterial infections is an important cost-effectiveness parameter at the stage of the treatment process in order to achieve the maximum benefits in therapeutic intervention by combining the minimum cost to ensure the proper use of antibiotics.The discovery of sensitive and robust molecular diagnostic tests in response to the role of the host in infections has enhanced the accurate diagnosis and differentiation of infections. METHOD: The study used a sample of six independent blood samples (total=756) which are associated with human proteins-proteins, each of which at the transcription stage expresses a different response in the host network between viral and bacterial infections.Τhe individual blood samples are subjected to a sequence of computer filters that identify a gene panel corresponding to an autonomous diagnostic score. The data set and the correspondence of the gene panel to the diagnostic patents a new Bangalore -Viral Bacterial (BL-VB). FINDING: We use a biomarker based on the blood of 10 genes(Panel-VB) that are an important prognostic value for the detection of viruses from bacterial infections with a weighted average AUROC of 0.97(95% CL:0.96-0.99) in eleven independent samples (sets n=898). We discovered a base with a patient score (VB 10 ) according to the table, which is a significant diagnostic value with a weighted average of AUROC 0.94(95% CL: 0.91-0.98) in 2996 patient samples from 56 public sets of data from 19 different countries. We also studied VB 10 in a new cohort of South India (BL-VB,n=56) and found 97% accuracy in confirmed cases of viral and bacterial infections. We found that VB 10 (a)accurately identifies the type of infection even in unspecified cases negative to the culture (b) shows its clinical condition recovery and (c) applies to all age groups, covering a wide range of acute bacterial and viral infectious, including non-specific pathogens. We applied our VB 10 rating to publicly available COVID 19 data and found that our rating diagnosed viral infection in patient samples. RESULTS: Τhe results of the study showed the diagnostic power of the biomarker VB 10 as a diagnostic test for the accurate diagnosis of acute infections in recovery conditions. We look forward to helping you make clinical decisions about prescribing antibiotics and integrating them into your policies management of antibiotic stewardship efforts. CONCLUSIONS: Overall, we are developing a new property of the RNA-based biomarker and a new blood test to differentiate between viral and bacterial infections to assist a physician in designing the optimal treatment regimen to contribute to the proper use of antibiotics and reduce the burden on antimicrobial resistance, AMR.

Keywords: acute infections, antimicrobial resistance, biomarker, blood transcriptome, systems biology, classifier diagnostic score

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Authors: Sujata Kulkarni, Udhav Bhosle, Vijaykumar T.

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Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.

Keywords: Carl STA/LTA, features extraction, real time, dataset, machine learning, seismic detection

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Authors: N. Benkaci, E. Oubaiche, J.-L. Chatelain, R. Bensalem, K. Abbes

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The ambient vibration H/V technique is widely used nowadays in microzonation studies, because of its easy field handling and its low cost, compared to other geophysical methods. However, in presence of complex geology or lateral heterogeneity evidenced by more than one peak frequency in the H/V curve, it is difficult to interpret the results, especially when soil information is lacking. In this work, we focus on the construction site of the Baraki 40000=place stadium, located in the north-east side of the Mitidja basin (Algeria), to identify the seismic wave amplification zones. H/V curve analysis leads to the observation of spatial and time variability of the H/V frequency peaks. The spatial variability allows dividing the studied area into three main zones: (1) one with a predominant frequency around 1,5 Hz showing an important amplification level, (2) the second exhibits two peaks at 1,5 Hz and in the 4 Hz – 10 Hz range, and (3) the third zone is characterized by a plateau between 2 Hz and 3 Hz. These H/V curve categories reveal a consequent lateral heterogeneity dividing the stadium site roughly in the middle. Furthermore, a continuous ambient vibration recording during several weeks allows showing that the first peak at 1,5 Hz in the second zone, completely disappears between 2 am and 4 am, and reaching its maximum amplitude around 12 am. Consequently, the anthropogenic noise source generating these important variations could be the Algiers Rocade Sud highway, located in the maximum amplification azimuth direction of the H/V curves. This work points out that the H/V method is an important tool to perform nano-zonation studies prior to geotechnical and geophysical investigations, and that, in some cases, the H/V technique fails to reveal the resonance frequency in the absence of strong anthropogenic source.

Keywords: ambient vibrations, amplification, fundamental frequency, lateral heterogeneity, site effect

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Authors: S. Ebadzadsahraei, H. Kazemian

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The main objective of this research was to measure particulate matter (PM₂.₅) and Volatile Organic Compound (VOCs) as two classes of air pollutants, at Prince George (PG) neighborhood in warm and cold seasons. To fulfill this objective, analytical protocols were developed for accurate sampling and measurement of the targeted air pollutants. PM₂.₅ samples were analyzed for their chemical composition (i.e., toxic trace elements) in order to assess their potential source of emission. The City of Prince George, widely known as the capital of northern British Columbia (BC), Canada, has been dealing with air pollution challenges for a long time. The city has several local industries including pulp mills, a refinery, and a couple of asphalt plants that are the primary contributors of industrial VOCs. In this research project, which is the first study of this kind in this region it measures physical and chemical properties of particulate air pollutants (PM₂.₅) at the city neighborhood. Furthermore, this study quantifies the percentage of VOCs at the city air samples. One of the outcomes of this project is updated data about PM₂.₅ and VOCs inventory in the selected neighborhoods. For examining PM₂.₅ chemical composition, an elemental analysis methodology was developed to measure major trace elements including but not limited to mercury and lead. The toxicity of inhaled particulates depends on both their physical and chemical properties; thus, an understanding of aerosol properties is essential for the evaluation of such hazards, and the treatment of such respiratory and other related diseases. Mixed cellulose ester (MCE) filters were selected for this research as a suitable filter for PM₂.₅ air sampling. Chemical analyses were conducted using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for elemental analysis. VOCs measurement of the air samples was performed using a Gas Chromatography-Flame Ionization Detector (GC-FID) and Gas Chromatography-Mass Spectrometry (GC-MS) allowing for quantitative measurement of VOC molecules in sub-ppb levels. In this study, sorbent tube (Anasorb CSC, Coconut Charcoal), 6 x 70-mm size, 2 sections, 50/100 mg sorbent, 20/40 mesh was used for VOCs air sampling followed by using solvent extraction and solid-phase micro extraction (SPME) techniques to prepare samples for measuring by a GC-MS/FID instrument. Air sampling for both PM₂.₅ and VOC were conducted in summer and winter seasons for comparison. Average concentrations of PM₂.₅ are very different between wildfire and daily samples. At wildfire time average of concentration is 83.0 μg/m³ and daily samples are 23.7 μg/m³. Also, higher concentrations of iron, nickel and manganese found at all samples and mercury element is found in some samples. It is able to stay too high doses negative effects.

Keywords: air pollutants, chemical analysis, particulate matter (PM₂.₅), volatile organic compound, VOCs

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Authors: Seyedamir Makinejadsanij

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One of the most important factors in the production of quality steel is to know the exact weight of steel in the steelmaking area. In this study, a calculation method is presented to estimate the exact weight of the melt as well as the objects transported by the overhead crane. Iran Alloy Steel Company's steelmaking area has three 90-ton cranes, which are responsible for transferring the ladles and ladle caps between 34 areas in the melt shop. Each crane is equipped with a Disomat Tersus weighing system that calculates and displays real-time weight. The moving object has a variable weight due to swinging, and the weighing system has an error of about +-5%. This means that when the object is moving by a crane, which weighs about 80 tons, the device (Disomat Tersus system) calculates about 4 tons more or 4 tons less, and this is the biggest problem in calculating a real weight. The k-means algorithm is an unsupervised clustering method that was used here. The best result was obtained by considering 3 centers. Compared to the normal average(one) or two, four, five, and six centers, the best answer is with 3 centers, which is logically due to the elimination of noise above and below the real weight. Every day, the standard weight is moved with working cranes to test and calibrate cranes. The results are shown that the accuracy is about 40 kilos per 60 tons (standard weight). As a result, with this method, the accuracy of moving weight is calculated as 99.95%. K-means is used to calculate the exact mean of objects. The stopping criterion of the algorithm is also the number of 1000 repetitions or not moving the points between the clusters. As a result of the implementation of this system, the crane operator does not stop while moving objects and continues his activity regardless of weight calculations. Also, production speed increased, and human error decreased.

Keywords: k-means, overhead crane, melt weight, weight estimation, swing problem

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Authors: Yuekun Chen, Yousef Sardahi, Salam Hajjar, Christopher Greer

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This paper presents a multi-objective optimal design of a cascade control system for an underactuated mechanical system. Cascade control structures usually include two control algorithms (inner and outer). To design such a control system properly, the following conflicting objectives should be considered at the same time: 1) the inner closed-loop control must be faster than the outer one, 2) the inner loop should fast reject any disturbance and prevent it from propagating to the outer loop, 3) the controlled system should be insensitive to measurement noise, and 4) the controlled system should be driven by optimal energy. Such a control problem can be formulated as a multi-objective optimization problem such that the optimal trade-offs among these design goals are found. To authors best knowledge, such a problem has not been studied in multi-objective settings so far. In this work, an underactuated mechanical system consisting of a rotary servo motor and a ball and beam is used for the computer simulations, the setup parameters of the inner and outer control systems are tuned by NSGA-II (Non-dominated Sorting Genetic Algorithm), and the dominancy concept is used to find the optimal design points. The solution of this problem is not a single optimal cascade control, but rather a set of optimal cascade controllers (called Pareto set) which represent the optimal trade-offs among the selected design criteria. The function evaluation of the Pareto set is called the Pareto front. The solution set is introduced to the decision-maker who can choose any point to implement. The simulation results in terms of Pareto front and time responses to external signals show the competing nature among the design objectives. The presented study may become the basis for multi-objective optimal design of multi-loop control systems.

Keywords: cascade control, multi-Loop control systems, multiobjective optimization, optimal control

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Authors: Hussain Sajid, Jung-Hun Shin, Kum-Won Cho

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Image super-resolution is the most common computer vision problem with many important applications. Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory requirements of GAN-based SR (mainly generators) lead to performance degradation and increased energy consumption, making it difficult to implement it onto resource-constricted devices. To relieve such a problem, In this paper, we introduce an optimized and highly efficient architecture for SR-GAN (generator) model by utilizing model compression techniques such as Knowledge Distillation and pruning, which work together to reduce the storage requirement of the model also increase in their performance. Our method begins with distilling the knowledge from a large pre-trained model to a lightweight model using different loss functions. Then, iterative weight pruning is applied to the distilled model to remove less significant weights based on their magnitude, resulting in a sparser network. Knowledge Distillation reduces the model size by 40%; pruning then reduces it further by 18%. To accelerate the learning process, we employ the Horovod framework for distributed training on a cluster of 2 nodes, each with 8 GPUs, resulting in improved training performance and faster convergence. Experimental results on various benchmarks demonstrate that the proposed compressed model significantly outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image quality for x4 super-resolution tasks.

Keywords: single-image super-resolution, generative adversarial networks, knowledge distillation, pruning

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Authors: Z. Karahaliloğlu, C. Hacker, M. Demirbilek, G. Seide, E. B. Denkbaş, T. Gries

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Currently, textile industry has played an important role in world’s economy, especially in developing countries. Dyes and pigments used in textile industry are significant pollutants. Most of theirs are azo dyes that have chromophore (-N=N-) in their structure. There are many methods for removal of the dyes from wastewater such as chemical coagulation, flocculation, precipitation and ozonation. But these methods have numerous disadvantages and alternative methods are needed for wastewater decolorization. Titanium-mediated photodegradation has been used generally due to non-toxic, insoluble, inexpensive, and highly reactive properties of titanium dioxide semiconductor (TiO2). Melt electrospinning is an attractive manufacturing process for thin fiber production through electrospinning from PP (Polyprophylene). PP fibers have been widely used in the filtration due to theirs unique properties such as hydrophobicity, good mechanical strength, chemical resistance and low-cost production. In this study, we aimed to investigate the effect of titanium nanoparticle localization and amine modification on the dye degradation. The applicability of the prepared chemical activated composite and pristine fabrics for a novel treatment of dyeing wastewater were evaluated.In this study, a photocatalyzer material was prepared from nTi (titanium dioxide nanoparticles) and PP by a melt-electrospinning technique. The electrospinning parameters of pristine PP and PP/nTi nanocomposite fabrics were optimized. Before functionalization with nTi, the surface of fabrics was activated by a technique using glutaraldehyde (GA) and polyethyleneimine to promote the dye degredation. Pristine PP and PP/nTi nanocomposite melt-electrospun fabrics were characterized using scanning electron microscopy (SEM) and X-Ray Photon Spectroscopy (XPS). Methyl orange (MO) was used as a model compound for the decolorization experiments. Photocatalytic performance of nTi-loaded pristine and nanocomposite melt-electrospun filters was investigated by varying initial dye concentration 10, 20, 40 mg/L). nTi-PP composite fabrics were successfully processed into a uniform, fibrous network of beadless fibers with diameters of 800±0.4 nm. The process parameters were determined as a voltage of 30 kV, a working distance of 5 cm, a temperature of the thermocouple and hotcoil of 260–300 ºC and a flow rate of 0.07 mL/h. SEM results indicated that TiO2 nanoparticles were deposited uniformly on the nanofibers and XPS results confirmed the presence of titanium nanoparticles and generation of amine groups after modification. According to photocatalytic decolarization test results, nTi-loaded GA-treated pristine or nTi-PP nanocomposite fabric filtern have superior properties, especially over 90% decolorization efficiency at GA-treated pristine and nTi-PP composite PP fabrics. In this work, as a photocatalyzer for wastewater treatment, surface functionalized with nTi melt-electrospun fabrics from PP were prepared. Results showed melt-electrospun nTi-loaded GA-tretaed composite or pristine PP fabrics have a great potential for use as a photocatalytic filter to decolorization of wastewater and thus, requires further investigation.

Keywords: titanium oxide nanoparticles, polyprophylene, melt-electrospinning

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Authors: J. Alvarez, J. Pimienta, R. Sarmiento

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Diode lasers working in free mode present different shifting and broadening determined by external factors such as temperature, current or mechanical vibrations, and they are not more useful in applications such as spectroscopy, metrology, and cooling of atoms, among others. Different configurations can reduce the spectral width of a laser; one of the most effective is to extend the optical resonator of the laser diode and use optical feedback either with the help of a partially reflective mirror or with a diffraction grating; this latter configuration is not only allowed to reduce the spectral width of the laser line but also to coarsely adjust its working wavelength, within a wide range typically ~ 10nm by slightly varying the angle of the diffraction grating. Two settings are commonly used for this purpose, the Littrow configuration and the Littmann Metcalf. In this paper, we present the design, construction, and characterization of a compact extended laser cavity in Littrow configuration. The designed cavity is compact and was machined on an aluminum block using computer numerical control (CNC); it has a mass of only 380 g. The design was tested on laser diodes with different wavelengths, 650nm, 780nm, and 795 nm, but can be equally efficient at other wavelengths. This report details the results obtained from the extended cavity working at a wavelength of 780 nm, with an output power of around 35mW and a line width of less than 1Mhz. The cavity was used to observe the spectrum of the corresponding Rubidium D2 line. By modulating the current and with the help of phase detection techniques, a dispersion signal with an excellent signal-to-noise ratio was generated that allowed the stabilization of the laser to a transition of the hyperfine structure of Rubidium with an integral proportional controller (PI) circuit made with precision operational amplifiers.

Keywords: Littrow, Littman-Metcalf, line width, laser stabilization, hyperfine structure

Procedia PDF Downloads 196